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Multidimensional Cooperative Caching in CoMP-Integrated Ultra-Dense Cellular Networks
Abstract
Small base stations (BSs) equipped with caching units are potential to provide improved quality of service (QoS) for multimedia service in ultra-dense cellular networks (UDCNs). In addition, the Coordinated MultiPoint (CoMP) transmission method, allowing multiple BSs to jointly serve users, is proposed to increase the throughput of cell-edge mobile terminals (MTs). Yet, the combination of content caching and CoMP in UDCNs is still not well explored for future networks. In this paper, we focus on the application of caching in CoMP-integrated UDCNs, where the cache-enabled BSs can collaboratively serve each MT using either joint transmission or single transmission. We propose a multidimensional cooperative caching (MDCC) scheme, supporting storage-dimension and transmission-dimension cooperations for the content placement. In particular, we analyze the delivery delay based on request patterns, transmission method, and the proposed cooperation strategy. Then the content placement problem is formulated as a problem of minimizing the overall expected delay. The problem is a mixed binary integer linear programming (BILP) problem, which is NP-hard. Therefore, we address the problem with approximation and substitution, and design a genetic algorithm (GA) based method to solve it. Simulation results demonstrate that the proposed MDCC scheme contributes performance gain in terms of content delivery delay in both cell-core and cell-edge areas.
... Hou et al. [20] have discussed the resource allocation scheme for backhaul links to minimize the average downloading delay and proposed the access and backhaul resources allocation algorithms. Lin et al. [21] have proposed a multidimensional cooperative caching mechanism supporting transmission and storage dimension cooperations for content placement. Zhang et at. ...
... Genetic Algorithm (GA) is a well known stochastic optimization technique which is inspired by the principle of natural evolution [21,38]. It is observed that GA is fitted for parallel optimization [38]. ...
... Cache hit ratio = cache hits cache hits + cache misses (21) (2) Acceleration ratio [44] : the fraction of saved transmission delay and original Internet delay (from content server) can be formulated as: Acceleration Ratio = saved delay original delay (from Internet) ...
Caching popular content at the base stations cooperatively is an effective solution to reduce the user-perceived latency and overwhelming data traffic by bringing content close to the user in a cellular network-based Mobile Edge Computing (MEC) architecture. Most of the existing literature assumes static network models where all the users remain static throughout the data transfer time, and the user can download the requested content from the associated base station. Caching content by considering user mobility and randomness of contact duration is an important issue which has been addressed in this work. We consider the cache placement problem in a realistic scenario where users move at different speeds. The moving users connect to the multiple base stations intermittently may not download full content because of contact duration. This, in turn, increases the overall delay in downloading the content for mobile users. The cache placement problem is formulated as mixed-integer nonlinear programming to maximize the saved delay with capacity constraint. The user mobility and contact duration are modeled with a Markov renewal process. Further, a greedy algorithm is presented to solve the problem by adopting submodular optimization. For real scenarios that scale to large library sizes, taking into account the computational time, we have proposed a genetic algorithm-based heuristic search mechanism. Extensive simulation results show that the proposed contact duration aware caching scheme significantly improves the performance in terms of hit ratio and acceleration ratio in a real-world scenario as compared with three existing caching mechanisms.
... To increase the content caching hit ratio, most existing cooperative schemes exploit the prior knowledge about the content popularity distribution (e.g., the Zipf distribution) and user preferences [14][15][16][17], or attempt to predict the content popularity and then employ the predicted popularity model to design the content caching policy [18][19][20]. Nevertheless, it has been reported that the content popularity and user preferences are non-stationary and exhibit highly spatially/temporally dynamic characteristics [12]. ...
... In [12], all BSs cooperatively performed content caching and mutually shared their cached contents. Then, in [14], each BS could share its cached contents with its one-hop neighbor BSs. In [34], the distributed edge servers could perform collaborative content caching to enhance the caching storage utilization rate. ...
... In much of the existing studies, centralized content caching schemes are utilized. For example, a centralized computing center, which periodically collected the content request rate of each BS and made the optimal caching decisions for each BS, was proposed in [14]. Within the approach in [31], during each caching time period, a macro-cell BS received content requests from multiple vehicles, and elicited content caching decisions for all the cache nodes. ...
The fifth-generation of wireless communication (5G) is a promising paradigm towards massive interconnectivity within Internet of Things (IoT) networks. However, because the data traffic throughput sharply increases with the number of IoT devices, a tremendous burden on the backhaul links and core networks results. With this in mind, mobile edge caching is an effective method that can relieve stress of the backhaul links, while decreasing the service latency. The purpose of this study is to analyze the problem of jointly optimizing cooperative edge caching and radio resource allocation in 5G-enabled massive IoT networks. For that, a joint optimization long-term non-linear integer programming (LT-NLIP) problem is posed. This class of problems is known to be NP-hard, thus, to reduce the problem complexity, a divide and conquer scheme will be applied – the task at hand will be divided into two subproblems: cooperative edge caching and radio resource allocation. The cooperative edge caching subproblem is formulated as a constrained Markov decision process. Herein, a deep reinforcement learning method to optimize the caching decisions for all the edge nodes. Then, based on the resulting optimal caching decisions, the radio resource allocation subproblem for each edge node is posed as a NLIP problem, and an improved branch-and-bound method is proposed, to yield the optimal radio resource allocation decisions for each edge node. Extensive simulations were performed to confirm that the proposed methods have the capability of enhancing the content caching hit ratio, while lessening the content retrieving delays for 5G-Enabled Massive IoT Networks – improving over various baseline algorithms.
... deterministic network model adopted in [6], stochastic geometry was introduced into the caching analysis of large-scale networks. The homogeneous Poisson point process was used to model the distribution of SBSs in [7], and a dual-mode caching scheme was proposed and investigated. ...
... Some works enabled both single transmission (ST) and JT in small-cell caching networks. In [6], the long-term-averaged SINR is introduced to identify the cell-core and cell-edge area. The users located in the cell-core area were served with ST, while the users in the cell-edge area could be served by JT to improve throughput. ...
Unmanned aerial vehicles (UAVs), featured by the high-mobility and high-quality propagation environment, have shown great potential in wireless communication applications. In this paper, a novel UAV-aided small-cell content caching network is proposed and analyzed, where joint transmission (JT) is considered in the dense small-cell networks and mobile UAVs are employed to shorten the serving distance. The system performance is evaluated in terms of the average cache hit probability and the ergodic transmission rate. From the analytical results, we find that (i) the proposed UAV-aided small-cell network shows superior caching performance and, even with a small density of UAVs the system’s cache hit probability, can be improved significantly; (ii) the content’s optimal caching probability to maximize the cache hit probability is proportional to the (K+1)-th root of its request probability, where K is the number of small-cell base stations that serve each user by JT; (iii) caching the most popular content in UAVs may lead to a low transmission rate due to the limited resource offered by the low-density UAVs. Simulation results are presented to validate the theoretical results and the performance gain achieved by the optimal caching strategy.
... For instance, Chen et al. [24] developed two transmission schemes, namely Joint Transmission (JT) and Parallel Transmission (PT), which are selected based on the popularity of the requested content. Alternatively, Lin et al. [27] proposed a cluster-centric cellular network applying the CoMP technique based on the users' link quality, where cell-core and cell-edge users are served through Single Transmission (ST) and JT, respectively. Despite all the researches on the cluster-centric cellular networks, there is no framework to determine how different segments can be cached to increase the data availability in a UAV-aided cluster-centric cellular network to increase content diversity. ...
... It is, therefore, assumed that ground users positioned in indoor areas are only supported by FAPs. In the CoMP-integrated and cluster-centric cellular network and as it can be seen from Fig. 1, there are two regions in each inter-cluster, named cell-edge and cellcore, which are determined based on the long-term averaged SINR values [27] to illustrate the quality of a wireless link. ...
Recently, as a consequence of the COVID-19 pandemic, dependence on telecommunication for remote learning/working and telemedicine has significantly increased. In this context, preserving high Quality of Service (QoS) and maintaining low latency communication are of paramount importance. Development of an Unmanned Aerial Vehicles (UAV)-aided heterogeneous cellular network is a promising solution to satisfy the aforementioned requirements. There are, however, key challenges ahead, on the one hand, it is challenging to optimally increase content diversity in caching nodes to mitigate the network's traffic over the backhaul. On the other hand is the challenge of attenuated UAVs' signal in indoor environments, which increases users' access delay and UAVs' energy consumption. To address these challenges, we incorporate UAVs, as mobile caching nodes, together with Femto Access points (FAPs) to increase the network's coverage in both indoor and outdoor environments. Referred to as the Cluster-centric and Coded UAV-aided Femtocaching (CCUF) framework, a two-phase clustering framework is proposed for optimal FAPs' formation and UAVs' deployment. The proposed CCUF leads to an increase in the cache diversity, a reduction in the users' access delay, and significant reduction in UAVs' energy consumption. To mitigate the inter-cell interference in edge areas, the Coordinated Multi-Point (CoMP) approach is integrated within the CCUF framework. In contrary to existing works, we analytically compute the optimal number of FAPs in each cluster to increase the cache-hit probability of coded content placement. Furthermore, the optimal number of coded contents to be stored in each caching node is computed to increase the cache-hit-ratio, Signal-to-Interference-plus-Noise Ratio (SINR), and cache diversity and decrease the users' access delay and cache redundancy for different content popularity profiles.
... This type of problem is also an NP-hard problem, which is difficult to solve directly in general. Heuristic algorithms such as genetic algorithms and simulated annealing algorithms have convenient properties in solving optimization problems containing discrete variables [37,38]. Therefore, this paper proposes a PGAC algorithm, which combines the genetic algorithm and simulated annealing algorithm to solve the optimum caching scheme. ...
By caching popular content on edge servers closer to users to respond to users’ content requests in 6G networks, the transmission load of backhaul links can be reduced. However, the time-varying characteristics of content prevalence leads to the issue that the cache content may not match the user’s needs, resulting in a decrease in cache success ratio. To solve these issues, we proposed a cache distribution strategy based on epidemic dynamics (CDSED) for 6G edge network. First, a 6G edge caching content model (6G ECCM) is constructed to establish the process of cache content propagation among users as an infectious disease propagation process, analyze the distribution of users’ interest in cache content and obtain the cache content state probability prediction equation, and use the cache content state probability prediction equation to predict the cache content prevalence. Second, based on the predicted prevalence results, a prevalence predictive genetic-annealing cache content algorithm (PGAC) is proposed with the optimization objective of maximizing the cache success ratio. The algorithm designs the selection function of the traditional genetic algorithm as a simulated annealing selection function based on the cache content success ratio, which avoids the defect of the genetic algorithm that converges to the locally optimum cache strategy too early and enhances the cache success ratio. Finally, the optimum cache content decision is solved by iterative alternation. Simulation results demonstrate that CDSED strategy can enhance cache success ratio than the LRU strategy, the LFU strategy, and the MPC strategy.
... A joint design in CF-mMIMO networks can reduce backhaul loads by avoiding fetching duplicate trending contents, while achieving cooperation gains and enhanced throughput at the same time. As a result, applications such as wireless video streaming that require low latency and high throughput can be enabled [28], [37]. As the user requests are unknown in real world, we employed DRL to learn the patterns of user requests. ...
Cell-free massive multiple-input multiple-output (CF-mMIMO) is an emerging beyond fifth-generation (5 G) technology that improves energy efficiency (EE) and removes cell structure limitation by using multiple access points (APs). This study investigates the EE maximization problem. Forming proper cooperation clusters is crucial when optimizing EE, and it is often done by selecting AP–user pairs with good channel quality or aligning AP cache contents with user requests. However, the result can be suboptimal if we determine the clusters based solely on either aspect. This motivates our joint design of user association and content caching. Without knowing the user content preferences in advance, two deep reinforcement learning (DRL) approaches, i.e., single-agent reinforcement learning (SARL) and multi-agent reinforcement learning (MARL), are proposed for different scenarios. The SARL approach operates in a centralized manner which has lower computational requirements on edge devices. The MARL approach requires more computation resources at the edge devices but enables parallel computing to reduce the computation time and therefore scales better than the SARL approach. The numerical analysis shows that the proposed approaches outperformed benchmark algorithms in terms of network EE in a small network. In a large network, the MARL yielded the best EE performance and its computation time was reduced significantly by parallel computing.
... There are several factors that affect the caching performance, one of which is the structure of the RSU network. Caching a variety of content when the coverage for RSUs is overlapped and caching popular content when it is not overlapped can increase the hit ratio [7]. In urban environments, infrastructures are densely installed, which create areas where the coverage of RSUs overlaps. ...
As vehicles are connected to the Internet, various services can be provided to users. However, if the requests of vehicle users are concentrated on the remote server, the transmission delay increases, and there is a high possibility that the delay constraint cannot be satisfied. To solve this problem, caching can be performed at a closer proximity to the user which in turn would reduce the latency by distributing requests. The road side unit (RSU) and vehicle can serve as caching nodes by providing storage space closer to users through a mobile edge computing (MEC) server and an on-board unit (OBU), respectively. In this paper, we propose a caching strategy for both RSUs and vehicles with the goal of maximizing the caching node throughput. The vehicles move at a greater speed; thus, if positions of the vehicles are predictable in advance, this helps to determine the location and type of content that has to be cached. By using the temporal and spatial characteristics of vehicles, we adopted a long short-term memory (LSTM) to predict the locations of the vehicles. To respond to time-varying content popularity, a deep deterministic policy gradient (DDPG) was used to determine the size of each piece of content to be stored in the caching nodes. Experiments in various environments have proven that the proposed algorithm performs better when compared to other caching methods in terms of the throughput of caching nodes, delay constraint satisfaction, and update cost.
... Due to the limited storage of caching nodes, one of the efficient approaches to improve the cache-hit ratio is to increase the content diversity. This can be fulfilled by implementing a coded/uncoded content placement in an integrated MEC network [6], [7]. Recent advancements in heterogeneous cluster-centric cellular networks [8] have drawn focused research attention given provided considerable improvements in content diversity, which is due to the integration of the coded/uncoded content placement and Coordinated Multi-Point (CoMP) technology. ...
Coded/uncoded content placement in Mobile Edge Caching (MEC) has evolved as an efficient solution to meet the significant growth of global mobile data traffic by boosting the content diversity in the storage of caching nodes. To meet the dynamic nature of the historical request pattern of multimedia contents, the main focus of recent researches has been shifted to develop data-driven and real-time caching schemes. In this regard and with the assumption that users' preferences remain unchanged over a short horizon, the Top-K popular contents are identified as the output of the learning model. Most existing datadriven popularity prediction models, however, are not suitable for the coded/uncoded content placement frameworks. On the one hand, in coded/uncoded content placement, in addition to classifying contents into two groups, i.e., popular and nonpopular, the probability of content request is required to identify which content should be stored partially/completely, where this information is not provided by existing data-driven popularity prediction models. On the other hand, the assumption that users' preferences remain unchanged over a short horizon only works for content with a smooth request pattern. To tackle these challenges, we develop a Multiple-model (hybrid) Transformer-based Edge Caching (MTEC) framework with higher generalization ability, suitable for various types of content with different time-varying behavior, that can be adapted with coded/uncoded content placement frameworks. Simulation results corroborate the effectiveness of the proposed MTEC caching framework in comparison to its counterparts in terms of the cache-hit ratio, classification accuracy, and the transferred byte volume.
... In this kind of satiations, cloud computing alone cannot help to provide real-time response and lower latency. The main aim of the medical IoMT-based cloud is to effectively expand the ability of cloud computing providing at the edge of the network using distributed computing resources (Lin, Song, & Jamalipour, 2019); thus, edge computing is a perfect application to meet this computing demand (Adeniyi et al., 2021;Ning, Wang, & Huang, 2018). Fig. 1.3 shown the overall framework of the proposed Edge-IoMT-based system. ...
There is a long history of engagement between computing and healthcare, adopting telemedicine is slow due to political willingness, limited infrastructure development frameworks, and availability. The Internet of Medical Things (IoMT) is going to be one of the fastest developments, and it’s anticipated to bring in the largest delivery of technology in existence. Hence, human-to-machine (H2M), machine-to-machine (M2M), and person-to-person (P2P) interactions can be fully updated along with telemedicine for the betterment of society in general. The use of IoMT-based sensors helps in the real-time detection of diseases and has significantly reduced the mortality rate. To meet the requirements of low latency and energy efficiency during medical data analysis, edge computing combined with 5G speeds is the answer. Edge computing has contributed greatly in the areas of low latency, massive connectivity of devices, and higher data rates in a network for ultra-reliable communication in the smart healthcare system. Therefore, this chapter discusses the areas of applicability and several extraordinary opportunities brought by the edge-enabled IoMT-based system in the healthcare system. The chapter also discusses the research challenges of the deployment of Edge IoMT-based system in the healthcare system and proposes an edge-enabled IoMT-based system framework. The application of the edge-enabled IoMT system brings end users and data sources running at a distance closer to the network nodes. The proposed framework can be used for real-time data capture, diagnosis, and monitoring patients’ health conditions like body motion, speech signals, body temperature, blood pressure, blood glucose, and heart rate, among others using various devices and sensors. Besides, the system can be useful in the emergency cases such as heart attacks, hysteria, anxiety, and epilepsy, thus saving the life of any patient.
... Some works enabled both single transmission (ST) and JT in small-cell caching networks. In [6], the long-term-averaged SINR is introduced to identify the cell-core and cell-edge area. The users located in the cell-core area were served with ST, while the users in the cell-edge area could be served by JT to improve throughput. ...
... Cloud computing alone cannot help with such a large data set and provide real-time response. Therefore, the key to the development of medical cloud IoT is to effectively expand the ability of cloud computing and use distributed computing resources, that is, to process computing tasks at the edge of the network [69], and the application of edge computing can just meet this computing demand [70]. In this section, we introduce the architecture of the edge medical cloud IoT and its key technologies, and compare cloud computing with edge computing. ...
With the booming development of medical informatization and the ubiquitous connections in the fifth generation mobile communication technology (5G) era, the heterogeneity and explosive growth of medical data have brought huge challenges to data access, security and privacy, as well as information processing in Internet of Medical Things (IoMT). This article provides a comprehensive review of how to realize the timely processing and analysis of medical big data and the sinking of high-quality medical resources under the constraints of the existing medical environment and medical-related equipment. We mainly focus on the advantages brought by the cloud computing, edge computing and artificial intelligence technologies to the IoMT. We also explore how to rationalize the use of medical resources and the security and privacy of medical data, so that high-quality medical services can be provided to patients. Finally, we discuss the current challenges and possible future research directions in the edge-cloud computing and artificial intelligence related IoMT.
... IoV is a new model that combines VANETs and vehicle remote information processing to connect vehicles, people and things [1]. In addition, it is a highly important field in intelligent transportation systems (ITSs), as it covers intelligent transportation, cloud computing, vehicle information services, logistics transportation services [2] [3], modern wireless technology, Internet access and communication and other technologies and applications [4]. According to the forecast report from Cisco, the global monthly mobile data usage in 2021 will be approximately 49 exabytes, and the number of mobile devices will be 11.6 billion, increasing about approximately seven times between 2016 and 2021 [5]. ...
With the proliferation of mobile devices and a wealth of rich application services, the Internet of vehicles (IoV) has struggled to handle computationally intensive and delay-sensitive computing tasks. To substantially reduce the latency and the energy consumption, application work is offloaded from a mobile device to a remote cloud or a nearby mobile edge cloud for processing. Compared with remote clouds, mobile edge clouds are located at the edge of the network. Therefore, mobile edge computing (MEC) has the advantages of effectively utilizing idle computing and storage resources at the edge of the network and reducing the network transmission delay. In addition, mobile devices are increasingly moving toward intelligence. To satisfy the service experience and service quality requirements of mobile users, the vehicle Internet is transforming into the intelligent vehicle Internet. Artificial intelligence (AI) technology can adapt to rapidly changing dynamic environments to provide multiple task requirements for resource allocation, computational task scheduling, and vehicle trajectory prediction. On this basis, combined with MEC technology and AI technology, computing and storage resources are placed on the edge of the network to provide real-time data processing while providing more efficient and intelligent services. This article introduces IoV from three aspects, namely, MEC, AI and the advantages of combining the two, and analyzes the corresponding architecture and implementation technology. The application of MEC and AI in IoV is analyzed and compared with current approaches. Finally, several promising future directions in the field of IoV are discussed.
... The popularitybased combinational caching in CICNs was introduced by [16], where the storage space of each BS was partitioned to store the most popular contents and less popular contents, respectively. The JT clustering problem with the consideration of caches at base stations was studied by [17], [18]. Some studies took into account the opportunities of using JT and ST when exploring caching [19], [20]. ...
Mobile network caching (MNC) and Coordinated MultiPoint (CoMP) joint transmission (JT) techniques are expected to play a significant role in future networks. Yet, when considering caching in a CoMP-integrated network where both single transmission (ST) and JT are permitted, some practical challenges need to be tackled. In this paper, we propose a cooperative online caching strategy to realize autonomous transmission-aware content caching and updating for video content delivery. Different from existing works, this paper considers practical time-varying user request patterns, and exploits storage-level and transmission-level cooperation to jointly optimize content caching under dynamic user demands for JT and ST. The former helps reduce caching redundancy and the latter helps BSs create CoMP-JT opportunities to improve cell-edge throughput. We formulate the content caching problem to be a Markov decision process (MDP), in which the system reward is defined as the delay reduction after performing a caching action. The goal is to maximize the system reward, i.e., to minimize content delivery delay. Then, we develop a reinforcement learning (RL)-based online learning algorithm to search the optimal caching policy. To overcome the "curse of dimensionality", the designed RL-based algorithm is extended with linear approximation, namely approximate Q-learning (AQL) algorithm.} The system performance in terms of delay and cache hit rate is evaluated in the simulations, and the results demonstrate the effectiveness of the proposed strategy.
Energy-efficient and reliable data routing is critical in Wireless Sensor Networks (WSNs) application scenarios. Due to oscillations in wireless links in adverse environmental conditions, sensed data may not be sent to a sink node. As a result of wireless connectivity fluctuations, packet loss may occur. However, retransmission-based approaches are used to improve reliable data delivery. These approaches need a high quantity of data transfers for reliable data collection. Energy usage and packet delivery delays increase as a result of an increase in data transmissions. An energy-efficient data collection approach based on a genetic algorithm has been suggested in this paper to determine the most energy-efficient and reliable data routing in wireless sensor networks. The proposed algorithm reduced the number of data transmissions, energy consumption, and delay in network packet delivery. However, increased network lifetime. Furthermore, simulation results demonstrated the efficacy of the proposed method, considering the parameters energy consumption, network lifetime, number of data transmissions, and average delivery delay.
This book presents use-cases of IoT, AI and Machine Learning (ML) for healthcare delivery and medical devices. It compiles 15 topics that discuss the applications, opportunities, and future trends of machine intelligence in the medical domain. The objective of the book is to demonstrate how these technologies can be used to keep patients safe and healthy and, at the same time, to empower physicians to deliver superior care. Readers will be familiarized with core principles, algorithms, protocols, emerging trends, security problems, and the latest concepts in e-healthcare services. It also includes a quick overview of deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, practical methodology, and how they can be used to provide better solutions to healthcare related issues. The book is a timely update for basic and advanced readers in medicine, biomedical engineering, and computer science. Key topics covered in the book: o An introduction to the concept of the Internet of Medical Things (IoMT). o Cloud-edge based IoMT architecture and performance optimization in the context of Medical Big Data. o A comprehensive survey on different IoMT interference mitigation techniques for Wireless Body Area Networks (WBANs). o Artificial Intelligence and the Internet of Medical Things. o A review of new machine learning and AI solutions in different medical areas. o A Deep Learning based solution to optimize obstacle recognition for visually impaired patients. o A survey of the latest breakthroughs in Brain-Computer Interfaces and their applications. o Deep Learning for brain tumor detection. o Blockchain and patient data management.
The soaring data traffic brings tremendous pressure on core networks. Collaborative caching among edge servers and user devices has been regarded as a promising technology for releasing backhaul pressure and reducing content download delay in the next-generation communication network. However, given the capacity constraint of edge servers and caching devices, a challenge is how to exploit these storage resources efficiently. In this paper, we introduce a three-tier collaborative caching architecture for edge-user networks and propose a social-aware graph-based collaborative caching (SGCC) strategy to minimize content download delay. Specifically, the small base station tier and user equipment tier are modeled as a one-tier directed graph. Moreover, we model the physical attributes and social attributes as the weight of the directed edges in the physical link graph and social graph, respectively. Then, we construct a social-aware graph by merging the two-tier topology of the physical link graph and social graph to simplify the caching decision. Further, we design SGCC to cache the most popular content according to the weighted content popularity. Simulation results demonstrate that the proposed SGCC can effectively reduce the average download delay and has a 26.8% lower average download delay, compared with the uniform popular caching baseline.
Fog radio access networks (F-RANs) have been regarded as a promising paradigm to support latency-sensitive and computation-intensive services by leveraging computing and caching capabilities of fog access points (F-APs). To execute offloaded computation tasks, it is essential to pre-store necessary programs and databases at F-APs, referred as service caching. However, due to system dynamics and the coupling of decisions, cache, radio, and computation resource management at F-RANs is a challenging problem. In this paper, a joint multi-dimensional resource optimization problem is formulated, aiming at minimizing the weighted sum of expected latency cost and caching cost, which is featured by a two-timescale structure. For radio and computation resource allocation on a small timescale, a coalitional game based approach is proposed under given caching decisions. On a larger timescale, services are cached at each F-AP using a multi-agent reinforcement learning algorithm. The advantage of the proposed algorithms is that they can implicitly take the impact of small timescale resource allocation into account while adapting to the long-term statistics of channel coefficients and user service requests. We analyze the proposed algorithms with respect to their convergence, optimality, and complexity, and validate their performance with extensive simulations, where superior performance is observed over several baseline schemes.
Brain tumors have very high morbidity and mortality, and it is very time-consuming for clinicians to diagnose this disease. Computer-aided medical image analysis can improve the performance of tumor diagnosis and alleviate the pressure of clinicians. Most of the existing intelligent diagnosis platforms rely on the public cloud, which has high requirements for communication and network and can not provide offline operation. We propose a brain tumor segmentation framework based on deep learning and edge-cloud collaboration, which can realize computer-assisted medical diagnosis in both offline and online modes. We deploy segmentation network with different complexity in the edge and cloud, and the Dice coefficients in offline mode and online mode are 0.8098 and 0.8910 respectively. Compared with the offline mode, the average values of Dice, PPV, and Sensitivity increased by 8.12, 2.65, and 7.31 in the online mode. In addition, we also analyze the response time of the two modes. For the diagnosis of a patient, the offline mode and online mode take 27.610 s and 40.885 s respectively. Experimental data show that our method has an excellent performance in tumor diagnosis and response time, and the framework can be easily extended to other diseases diagnosis.KeywordsBrain tumor segmentationEdge cloud cooperationDeep learning
Virtual reality (VR) content, including 360° panoramic video, provides users with an immersive multimedia experience and therefore attracts increasing research and development attention. However, the requirement of high bandwidth and low latency of virtual reality service demand puts forward greater challenges to the current infrastructure, especially mobile networks. Inspired by the sharable nature of virtual reality content tiles, we further considered the potential opportunities for computing, caching, and multicast to address the challenges of transmission of panoramic content. This paper proposes a novel transcoding-enabled VR video caching and delivery framework for edge-enhanced next-generation wireless networks. Firstly, an edge cooperative caching scheme based on multi-agent reinforcement learning is introduced to improve the utilization efficiency of computing and storage resources, and then reduce service delay. Second, a two-tier NOMA-based base station-multicast group matching mechanism is designed to solve the collaboration challenge during the edge delivery process. A series of experiments have demonstrated the advantages of the proposed scheme in terms of cache hit rate, latency and other aspects in comparison with alternative approaches.
Recently, as a consequence of the COVID-19 pandemic, dependence on telecommunication for remote learning/working and telemedicine has significantly increased. In this context, preserving high Quality of Service (QoS) and maintaining low latency communication are of paramount importance. In cellular networks, incorporation of Unmanned Aerial Vehicles (UAVs) can result in enhanced connectivity for outdoor users due to the high probability of establishing Line of Sight (LoS) links. The UAV’s limited battery life and its signal attenuation in indoor areas, however, make it inefficient to manage users’ requests in indoor environments. Referred to as the Cluster-centric and Coded UAV-aided Femtocaching (CCUF) framework, the network’s coverage in both indoor and outdoor environments increases by considering a two-phase clustering framework for Femto Access Points (FAPs)’ formation and UAVs’ deployment. Our first objective is to increase the content diversity. In this context, we propose a coded content placement in a cluster-centric cellular network, which is integrated with the Coordinated Multi-Point (CoMP) approach to mitigate the inter-cell interference in edge areas. Then, we compute, experimentally, the number of coded contents to be stored in each caching node to increase the cache-hit-ratio, Signal-to-Interference-plus-Noise Ratio (SINR), and cache diversity and decrease the users’ access delay and cache redundancy for different content popularity profiles. Capitalizing on clustering, our second objective is to assign the best caching node to indoor/outdoor users for managing their requests. In this regard, we define the movement speed of ground users as the decision metric of the transmission scheme for serving outdoor users’ requests to avoid frequent handovers between FAPs and increase the battery life of UAVs. Simulation results illustrate that the proposed CCUF implementation increases the cache-hit-ratio, SINR, and cache diversity and decrease the users’ access delay, cache redundancy and UAVs’ energy consumption.
The development of the content-centric Internet of Things (C2IoT) enriches the services provided by the IoT devices, which diversifies the provided contents. However, system resources are terribly wasted by repeated delivering the same content. Efficiently utilizing caching resources can reduce the link load caused by delivering and forwarding contents, further improving the users' quality of experience (QoE). However, the collaboration among independent decision nodes is insufficient, which consumes substantial energy redundantly. To solve the mentioned issue, we propose a genetic-algorithm-based collaborative caching scheme integrating on-path strategy and off-path strategy, named cost-oriented caching scheme (CCS), to minimize the energy consumption of requesting contents. First, based on the analysis of content popularity, users' historical requests, and transmission delay, an optimization problem is formulated to minimize the communication delay of obtaining content. Then, we formulate a submodular function optimization problem to optimize energy consumption, and an improved genetic algorithm is proposed to solve the mentioned problem. Performance evaluations demonstrate that the proposed CCS is superior to other existing caching schemes.
Wireless virtual reality (VR) is increasingly used in industrial Internet of Things (IIoTs). However, ultra-high viewport rendering demands and excessive terminal energy consumption restrict the application of wireless VR. Pervasive edge computing (PEC) emerges as a promising method for wireless VR. In this paper, we propose an energy-aware resource management scheme for wireless VR-supported IIoTs. To reduce the energy consumption of VR equipments (VEs) while ensuring a smooth immersive VR experience, we formulate the viewport rendering offloading, computing and spectrum resources allocation to be a joint optimization problem, considering content correlation between VEs, fluctuating channel conditions, and VR quality of experience (QoE). By applying dual approximation, the original problem is transformed to be a Markov Decision Process (MDP) and an RL-based online learning algorithm is designed to find the optimal policy. To improve the learning efficiency, the quantum parallelism is integrated into the RL to overcome "curse of dimensionality". In the simulations, the convergence rate and the performance in terms of energy consumption and stalling rate are evaluated. Simulation results demonstrate the effectiveness of the proposed scheme.
Wireless virtual reality (VR)-enabled medical treatment (WVMT) system, integrating the VR technology and the platform of the Internet of Medical Things (IoMT), is a promising application in future medical industries. Multi-access edge computing (MEC) is an effective approach to support the ubiquitous applications of WVMT systems. Due to the high requirements of medical services, the computation efficiency and security are two issues in WVMT systems. In this paper, we propose a blockchain-enabled task offloading scheme, where the viewport rendering tasks of VR devices (VDs) can be offloaded to edge access nodes (EAPs). The blockchain is integrated into the system to reach the consensus of the global information of task offloading and data processing to resist malicious attacks. To reduce VDs’ computation load under the promise of high VR QoE, we formulate the computation offloading and resource allocation to be a Markov decision problem, considering block consensus, content correlation, and fluctuating channel conditions. Then, a novel collective reinforcement learning (CRL) algorithm is proposed to adaptively allocate resources based on the requirements of viewport rendering, block consensus, and content transmission. In the simulations, the convergence rate and the performance in terms of energy consumption and stalling rate are evaluated. Simulation results demonstrate the effectiveness of the proposed scheme.
Content caching at the base stations (BSs) is a promising strategy that can provide improved quality-of-service (QoS) provisioning for content-centric services, and at the same time reduce instantaneous backhaul payload. This work focuses on applications of the caching mechanism in the downlink of coordinated multipoint (CoMP) transmission, where the collaborating BSs cooperatively serve the user equipments (UEs). A cacheaided transmission scheme for downlink CoMP with limited local cache resources at the BSs is proposed to improve QoS provisioning in terms of outage performance. In order to cache files more efficiently while guaranteeing the QoS, the contents are divided into two sets, a popular set and a less popular set, according to their popularity profile following the Zipf model. The popular files are cached at all the BSs, while the less popular files are cached only at part of the BSs in a cache cluster subject to the strategy design. Furthermore, based on popularity of the requested files, UEs are categorized into two groups and the notion of request competition between the two groups is introduced. The corresponding request competition model and admission probabilities for transmission requests are analyzed. Based on the request competition model, the best cache placement strategy for the popular and less popular sets that minimizes the average outage probability is derived. Numerical results show that the proposed cache-aided CoMP scheme outperforms the baseline traditional caching strategies in terms of the average transmission outage.
The exponential growth in data traffic and dramatic capacity demand in fifth generation (5G) have inspired the move from traditional single-tier cellular networks towards heterogeneous cellular networks (HCNs). To face the coming trend in 5G, the high availability requirement in new applications, needs to be satisfied to achieve low latency service. Usually, these applications require an availability of six nines or even higher. In this work, we present a tractable multi-tier multi-band availability model for spectrum aggregation-based HCNs. We first derive a closed-form expression for the availability of spectrum aggregation-based HCNs using the signal-to-interference-plus-noise (SINR) model. By doing so, we formulate two optimization problems, one is to maximize the average availability, and the other one is to minimize the average power consumption. These two optimization problems are both non-convex problems, which are challenging to solve. To cope with them, we propose to apply genetic algorithm (GA) for the joint user equipment (UE) association, subcarrier assignment and power allocation problem. Our results show that the average availability in spectrum aggregation-based HCNs improves with decreasing the number of UEs, as well as increasing the power budget ratio. We also show that increasing the maximum number of aggregated subcarriers decreases the average power consumption, but can not guarantee the substantial improvement of average availability.
Caching is a promising technology to alleviate the transmission pressure of the 5G heterogeneous networks. To overcome the drawbacks of the existing caching schemes that ignore the heterogeneity or the cooperation characteristics of the heterogeneous networks, this paper proposes a cooperation-based caching scheme (CBCS). Based on the scheme, the average energy consumption incurred by a user equipment (UE) to obtain its desired content is formulated as an NP-hard optimization problem, and two greedy heuristic algorithms are developed to solve the problem. In addition, our simulation results show the gains of the proposed CBCS over the existing caching schemes with some parameters changing.
Departing from the conventional cache hit optimization in cache-enabled wireless networks, we consider an alternative optimization approach for the probabilistic caching placement in stochastic wireless D2D caching networks taking into account the reliability of D2D transmissions. Using tools from stochastic geometry, we provide a closed-form approximation of cache-aided throughput, which measures the density of successfully served requests by local device caches, and we obtain the optimal caching probabilities with numerical optimization. Compared to the cache-hit-optimal case, the optimal caching probabilities obtained by cache-aided throughput optimization show notable gain in terms of the density of successfully served user requests, particularly in dense user environments.
Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets), in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators’ careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE) cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs) to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system throughput performance.
Wireless content caching in small cell networks (SCNs) has recently been
considered as an efficient way to reduce the traffic and the energy consumption
of the backhaul in emerging heterogeneous cellular networks (HetNets). In this
paper, we consider a cluster-centric SCN with combined design of cooperative
caching and transmission policy. Small base stations (SBSs) are grouped into
disjoint clusters, in which in-cluster cache space is utilized as an entity. We
propose a combined caching scheme where part of the available cache space is
reserved for caching the most popular content in every SBS, while the remaining
is used for cooperatively caching different partitions of the less popular
content in different SBSs, as a means to increase local content diversity.
Depending on the availability and placement of the requested content,
coordinated multipoint (CoMP) technique with either joint transmission (JT) or
parallel transmission (PT) is used to deliver content to the served user. Using
Poisson point process (PPP) for the SBS location distribution and a hexagonal
grid model for the clusters, we provide analytical results on the successful
content delivery probability of both transmission schemes for a user located at
the cluster center. Our analysis shows an inherent tradeoff between
transmission diversity and content diversity in our combined
caching-transmission design. We also study optimal cache space assignment for
two objective functions: maximization of the cache service performance and the
energy efficiency. Simulation results show that the proposed scheme achieves
performance gain by leveraging cache-level and signal-level cooperation and
adapting to the network environment and user QoS requirements.
In this work we consider the problem of an optimal geographic placement of
content in wireless cellular networks modelled by Poisson point processes.
Specifically, for the typical user requesting some particular content and whose
popularity follows a given law (e.g. Zipf), we calculate the probability of
finding the content cached in one of the base stations. Wireless coverage
follows the usual signal-to-interference-and noise ratio (SINR) model, or some
variants of it. We formulate and solve the problem of an optimal randomized
content placement policy, to maximize the user's hit probability. The result
dictates that it is not always optimal to follow the standard policy "cache the
most popular content, everywhere". In fact, our numerical results regarding
three different coverage scenarios, show that the optimal policy significantly
increases the chances of hit under high-coverage regime, i.e., when the
probabilities of coverage by more than just one station are high enough.
5G will have to support a multitude of new applications with a wide variety of requirements, including higher peak and user data rates, reduced latency, enhanced indoor coverage, increased number of devices, and so on. The expected traffic growth in 10 or more years from now can be satisfied by the combined use of more spectrum, higher spectral efficiency, and densification of cells.
The focus of the present article is on advanced techniques for higher spectral efficiency and improved coverage for cell edge users. We propose a smart combination of small cells, joint transmission coordinated multipoint (JT CoMP), and massive MIMO to enhance the spectral efficiency with affordable complexity.
We review recent achievements in the transition from theoretical to practical concepts and note future research directions. We show in measurements with macro-plus-smallcell scenarios that spectral efficiency can be improved by flexible clustering and efficient user selection, and that adaptive feedback compression is beneficial to reduce the overhead significantly.
Moreover, we show in measurements that fast feedback reporting combined with advanced channel prediction are able to mitigate the impairment effects of JT CoMP.
This article explores one of the key enablers of beyond 4G wireless
networks leveraging small cell network deployments, namely proactive caching.
Endowed with predictive capabilities and harnessing recent developments in
storage, context-awareness and social networks, peak traffic demands can be
substantially reduced by proactively serving predictable user demands, via
caching at base stations and users' devices. In order to show the effectiveness
of proactive caching, we examine two case studies which exploit the spatial and
social structure of the network, where proactive caching plays a crucial role.
Firstly, in order to alleviate backhaul congestion, we propose a mechanism
whereby files are proactively cached during off-peak demands based on file
popularity and correlations among users and files patterns. Secondly,
leveraging social networks and device-to-device (D2D) communications, we
propose a procedure that exploits the social structure of the network by
predicting the set of influential users to (proactively) cache strategic
contents and disseminate them to their social ties via D2D communications.
Exploiting this proactive caching paradigm, numerical results show that
important gains can be obtained for each case study, with backhaul savings and
a higher ratio of satisfied users of up to and , respectively.
Higher gains can be further obtained by increasing the storage capability at
the network edge.
The exploding HD video streaming traffic calls for deploying content servers deeper inside network operators' infrastructures. Telco-CDN are new content distribution services that are managed by Internet Service Providers (ISP). Since the network operator controls both the infrastructure and the content delivery overlay, it is in a position to engineer telco-CDN so that networking resources are optimally utilized. In this paper, we show the following two findings: 1. it is possible to implement an efficient algorithm for the placement of video chunks into a telco-CDN. We present an algorithm, which is based on a genetic algorithm implemented on the MapReduce framework. We show that, for a national VoD service, computing a quasi-optimal placement is possible. 2. such push strategy makes sense because it allows to actually take into account fine-grain traffic management strategies on the underlying infrastructure. Our proposal re-opens the debate about the relevance of such "push" approach (where the manager of telco-CDN proactively pushes video content into servers) versus the traditional caching approach (where the content is pulled to the servers from requests of clients). Our proposal of a quasi-optimal tracker enables fair comparisons between both approaches for most traffic engineering policies. We illustrate the interest of our proposal in the context of a major European Telco-CDN with real traces from a popular Video-on-Demand (VoD) service. Our experimental results show that, given a perfect algorithm for predicting user preferences, our placement algorithm is able to keep aligned with LRU caching in terms of the traditional hit-ratio, but the workload on some troubled links (e.g., over-used links) in a push-based strategy is significantly alleviated.
We consider a wireless device-to-device (D2D) network where the nodes have
pre-cached information from a library of available files. Nodes request files
at random. If the requested file is not in the on-board cache, then it is
downloaded from some neighboring node via direct "local" communication, without
going through the base station. An outage event occurs when a requested file is
not found in the neighborhood of the requesting node, or if the network
scheduling policy decides not to serve the request. Motivated by the current
trend in the standardization of the D2D mode for LTE wireless networks, we
restrict to one-hop D2D communication. We consider a simple "protocol model"
for the network and define the optimal throughput-outage tradeoff. We
characterize such tradeoff in terms of tight scaling laws for various regimes,
where both the number of nodes and the number of files in the library grow to
infinity. Then, we also provide some numerical results based on a realistic
scenario of user density, cache size, file library size and propagation channel
models, and compare the throughput-outage performance of the D2D one-hop
caching network with the corresponding performance of other solutions based
downlink (one-hop) transmission from the base station only. Our results show
that the combination of D2D spectrum reuse and caching at the user nodes yields
very significant gains with respect to both the current cellular technology and
the recently proposed "coded multicasting" approach, inspired by index coding.
Video on-demand streaming from Internet-based servers is becoming one of the
most important services offered by wireless networks today. In order to improve
the area spectral efficiency of video transmission in cellular systems, small
cells heterogeneous architectures (e.g., femtocells, WiFi off-loading) are
being proposed, such that video traffic to nomadic users can be handled by
short-range links to the nearest small cell access points (referred to as
"helpers"). As the helper deployment density increases, the backhaul capacity
becomes the system bottleneck. In order to alleviate such bottleneck we propose
a system where helpers with low-rate backhaul but high storage capacity cache
popular video files. Files not available from helpers are transmitted by the
cellular base station. We analyze the optimum way of assigning files to the
helpers, in order to minimize the expected downloading time for files. We
distinguish between the uncoded case (where only complete files are stored) and
the coded case, where segments of Fountain-encoded versions of the video files
are stored at helpers. We show that the uncoded optimum file assignment is
NP-hard, and develop a greedy strategy that is provably within a factor 2 of
the optimum. Further, for a special case we provide an efficient algorithm
achieving a provably better approximation ratio of , where d is
the maximum number of helpers a user can be connected to. We also show that the
coded optimum cache assignment problem is convex that can be further reduced to
a linear program. We present numerical results comparing the proposed schemes.
To deal with the explosive growth in multimedia service requests in mobile networks, caching contents at the cells (base stations) is regarded as an effective emerging technique to reduce the duplicated transmissions of content downloads, while heterogeneous networks (HetNets) are regarded as an effective technique to increase the network capacity. Yet, the combination of content caching and HetNets for future networks (i.e., 5G) is still not well explored so far. In this paper, we propose an efficient collaborative multi-tier caching framework in HetNets. In particular, based on patterns of user requests, link capacities, heterogenous cache sizes and the derived system topology, we focus on exploring the maximum capacity of the network infrastructure so as to offload the network traffic and support users’ content requests locally. Due to the NP-hardness of the complex multi-tier caching problem, we approximately decompose it into some subproblems that focus on the caching cooperation at different tiers by utilizing the derived system topology. Our proposed framework is low-complexity and distributed, and can be used for practical engineering implementation. Trace-based simulation results demonstrate the effectiveness of the proposed framework.
Device-to-device (D2D) communication is a promising supplement to cellular networks, since it provides high rate transmission without changing network infrastructure. For a D2D integrated cellular network, caching popular content (e.g. multimedia files) at a base station (BS) or a D2D device near to a requesting user not only reduces content delivery delay but also alleviates backhaul traffic load. In order to minimize content delivery delay, in this letter, we propose an extensive cooperative caching (EC-Caching) scheme, which allows inter-BS, inter-device and between-BS-and-device cooperation in content caching. Simulation results demonstrate that the extension in degree of cooperation (DoC) between the caching nodes contributes significant performance gain in terms of content delivery delay and cache hit rate, especially in populated areas.
Caching at small base stations (SBSs) has demonstrated significant benefits in alleviating the backhaul requirement in heterogeneous cellular networks (HetNets). While many existing works focus on what contents to cache at each SBS, an equally important problem is what contents to deliver so as to satisfy dynamic user demands given the cache status. In this paper, we study optimal content delivery in cache-enabled HetNets by taking into account the inherent multicast capability of wireless medium.
We consider stochastic content multicast scheduling to jointly minimize the average network delay and power costs under a multiple access constraint. We establish a content-centric request queue model and formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP). By using relative value iteration and special properties of the request queue dynamics, we characterize some properties of the value function of the MDP.
Based on these properties, we show that the optimal multicast scheduling policy is of threshold type.
Then, we propose a structure-aware optimal algorithm to obtain the optimal policy.
We also propose a low-complexity suboptimal policy, which possesses similar structural properties to the optimal policy, and develop a low-complexity algorithm to obtain this policy.
File transmission success in wireless caching helper networks depends mainly on cache-based channel selection diversity and network interference. For given channel fading and network geometry, both channel selection diversity and network interference dynamically vary according to what and how the caching helpers cache at their finite storage space. We study probabilistic caching placement to desirably control cache-based channel selection diversity and network interference in a stochastic wireless caching helper network, with sophisticated considerations of wireless fading channels, interactions among multiple users such as interference and loads at caching helpers, and arbitrary memory size. Using stochastic geometry, we derive optimal caching probabilities in closed form to maximize the average success probability of file transmission and propose an efficient algorithm to find the solution in a noise-limited network. In an interference-limited network, based on a lower bound of the average success probability of file transmission, we find near-optimal caching probabilities in closed form to control the channel selection diversity and the network interference. We numerically verify that the proposed caching placement is superior to other comparable caching placement strategies.
Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics (Fraser 1957; Bremermann 1958; Holland 1975). We start with a brief introduction of simple GAs and the associated terminologies. GAs encode the decision variables of a search problem into finite-length strings of alphabets of certain cardinality. The strings which are candidate solutions to the search problem are referred to as chromosomes, the alphabets are referred to as genes and the values of genes are called alleles. For example, in a problem such as the traveling salesman problem (TSP), a chromosome represents a route, and a gene may represent a city. In contrast to traditional optimization techniques, GAs work with coding of parameters, rather than the parameters themselves.
In this paper, we present the notion of "mobile 3C systems in which the "Communications", "Computing", and "Caching" (i.e., 3C) make up the three primary resources/funcationalties, akin to the three primary colors, for a mobile system. We argue that in future mobile networks, the roles of computing and caching are as intrinsic and essential as communications, and only the collective usage of these three primary resources can support the sustainable growth of mobile systems. By defining the 3C resources in their canonical forms, we reveal the important fact that "caching" affects the mobile system performance by introducing non-causality into the system, whereas "computing" achieves capacity gains by performing logical operations across mobile system entities. Many existing capacity-enhancing techniques such as coded multicast, collaborative transmissions, and proactive content pushing can be cast into the native 3C framework for analytical tractability. We further illustrate the mobile 3C concepts with practical examples, including a system on broadcast-unicast convergence for massive media content delivery. The mobile 3C design paradigm opens up new possibilities as well as key research problems bearing academic and practice significance.
In cloud radio access networks (C-RANs), a substantial amount of data must be exchanged in both backhaul and fronthaul links, which causes high power consumption and poor quality of service (QoS) experience for real-time services. To solve this problem, a cluster content caching structure is proposed in this paper, which takes full advantage of distributed caching and centralized signal processing. In particular, redundant traffic on the backhaul can be reduced because the cluster content cache provides a part of required content objects for remote radio heads (RRHs) connected to a common edge cloud. Tractable expressions for both effective capacity and energy efficiency performance are derived, which show that the proposed structure can improve QoS guarantees with a lower power cost of local storage. Furthermore, to fully explore the potential of the proposed cluster content caching structure, the joint design of resource allocation and RRH association is optimized, and two distributed algorithms are accordingly proposed. Simulation results verify the accuracy of the analytical results and show the performance gains achieved by cluster content caching in C-RANs.
Information Centric Network (ICN) proposals, have recently emerged to define new network architectures where content, and not its location, becomes the core of the communication model. Such new paradigms push data storage and delivery at network layer and are designed to cope with current Internet usage, mainly centered around content dissemination and retrieval. In this paper, we develop an analytical model of ICN storage and bandwidth sharing under fairly general assumptions on total demand, topology, content popularity and limited network resources. Our study applies to a class of content oriented networks identified by receiver-driven packet-based communication with in-network storage. We derive a closed-form expression for expected stationary delivery time as a function of hit/miss probabilities at network caches, content popularity and cache sizes. Our analytical results, supported by packet level simulations, can be used to analyze fundamental trade-offs of ICN architectures. They also provide an essential building block for the design and evaluation of ICN protocols.
Advanced interference mitigation techniques relying on multipoint coordination have attracted significant attention from the wireless industry and academia in the past few years. In 3GPP LTE-Advanced, a work item on Coordinated Multiple Point transmission and reception (CoMP) was initiated in September 2011, and it is one of the core features of Release 11. The objective of this work item is to provide the necessary specification support to efficiently realize the benefits of cooperative transmission in the downlink and cooperative reception in the uplink. This article discusses the specification support for CoMP and the motivations behind the specific design choices. The deployment scenarios that were considered for the application of CoMP in Release 11 are also presented.
The demand for rich multimedia services over mobile networks has been soaring at a tremendous pace over recent years. However, due to the centralized architecture of current cellular networks, the wireless link capacity as well as the bandwidth of the radio access networks and the backhaul network cannot practically cope with the explosive growth in mobile traffic. Recently, we have observed the emergence of promising mobile content caching and delivery techniques, by which popular contents are cached in the intermediate servers (or middleboxes, gateways, or routers) so that demands from users for the same content can be accommodated easily without duplicate transmissions from remote servers; hence, redundant traffic can be significantly eliminated. In this article, we first study techniques related to caching in current mobile networks, and discuss potential techniques for caching in 5G mobile networks, including evolved packet core network caching and radio access network caching. A novel edge caching scheme based on the concept of content-centric networking or information-centric networking is proposed. Using trace-driven simulations, we evaluate the performance of the proposed scheme and validate the various advantages of the utilization of caching content in 5G mobile networks. Furthermore, we conclude the article by exploring new relevant opportunities and challenges.
Wireless mesh networks have attracted great interest in the research community recently. Much effort has been devoted to maximizing the network performance using limited channel resources in a multichannel multiradio wireless mesh network. It is believed that the limited spectrum resource can be fully exploited by utilizing partially overlapping channels in addition to nonoverlapping channels in 802.11b/g networks. However, there are only few studies of channel assignment algorithms for partially overlapping channels. In this paper, we first formulate the optimal channel assignment problem with the goal of maximizing the overall network throughput or maximizing the throughput of a multicast application. For both cases, we present a greedy algorithm for partially overlapping channel assignment, and then propose a novel genetic algorithm, which has the potential to obtain better solutions. Through evaluation, we demonstrate that the overall network throughput can be dramatically improved by properly utilizing the partially overlapping channels. The genetic algorithm outperforms the greedy algorithm in mitigating the network interference, and therefore leads to higher network throughput. In addition, the multicast throughput can also be dramatically improved by using our algorithms compared to previous work.
This paper introduces cooperative caching policies for minimizing electronic content provisioning cost in Social Wireless Networks (SWNET). SWNETs are formed by mobile devices, such as data enabled phones, electronic book readers etc., sharing common interests in electronic content, and physically gathering together in public places. Electronic object caching in such SWNETs are shown to be able to reduce the content provisioning cost which depends heavily on the service and pricing dependences among various stakeholders including content providers (CP), network service providers, and End Consumers (EC). Drawing motivation from Amazon's Kindle electronic book delivery business, this paper develops practical network, service, and pricing models which are then used for creating two object caching strategies for minimizing content provisioning costs in networks with homogenous and heterogeneous object demands. The paper constructs analytical and simulation models for analyzing the proposed caching strategies in the presence of selfish users that deviate from network-wide cost-optimal policies. It also reports results from an Android phone-based prototype SWNET, validating the presented analytical and simulation results.
We propose a cache-enabled opportunistic cooperative MIMO (CoMP) framework
for wireless video streaming. By caching a portion of the video files at the
relays (RS) using a novel MDS-coded random cache scheme, the base station (BS)
and RSs opportunistically employ CoMP to achieve spatial multiplexing gain
without expensive payload backhaul. We study a two timescale joint optimization
of power and cache control to support real-time video streaming. The cache
control is to create more CoMP opportunities and is adaptive to the long-term
popularity of the video files. The power control is to guarantee the QoS
requirements and is adaptive to the channel state information (CSI), the cache
state at the RS and the queue state information (QSI) at the users. The joint
problem is decomposed into an inner power control problem and an outer cache
control problem. We first derive a closed-form power control policy from an
approximated Bellman equation. Based on this, we transform the outer problem
into a convex stochastic optimization problem and propose a stochastic
subgradient algorithm to solve it. Finally, the proposed solution is shown to
be asymptotically optimal for high SNR and small timeslot duration. Its
superior performance over various baselines is verified by simulations.
Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Updata
- Cisco
Cisco. (Mar. 2017). Cisco Visual Networking Index: Global Mobile
Data Traffic Forecast Updata, 2016-2021. [Online]. Available:
https://www.cisco.com/