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BRAINS: Joint Bandwidth-Relay Allocation in Multi-Homing Cooperative D2D Networks
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Abstract
Cooperative device to device (CD2D) communication has been considered to be a solution to capacity shortage problem. Combining multi-homing and CD2D techniques together can potentially improve network performance. We propose a novel multi-homing CD2D (MH-CD2D) network, in which multiple homing mobile devices (MMDs) act as relays for the cooperative communications of ordinary mobile devices (OMDs). We formulate such joint bandwidth-relay allocation problem as a two-stage game, in order to deal with two challenges: how to motivate MMDs to lease spare bandwidths and help OMDs to choose appropriate MMD relays. In the first stage, we use a non-cooperative game to model the competition between MMDs in terms of shared bandwidth and price. In the second stage, we model the behavior of OMDs selecting MMDs by an evolutionary game. We prove that there exists Nash equilibrium in the game and propose a distributed incentive scheme named IMES to solve the joint bandwidth-relay allocation problem. Extensive simulation results show that the equilibrium can be achieved and the best response price of one MMD increases with the other's best price in the Stackelberg game. The utility of MMDs increases with the number of OMDs in each OMD group at the evolutionary equilibrium. The proposed algorithms are able to reduce average service delay by more than 25% in comparison to the randomized scheme which is frequently used in existing works. On average, IMES outperforms existing scheme by about 20.37% in terms of utility of MMDs.
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To meet the increasing demand of wireless broadband applications in 4G/beyond 4G cellular networks, D2D communication can serve as a candidate paradigm to improve spectrum efficiency. By reusing the spectrum of cellular users, two D2D users can form a direct data link without routing base stations and core networks; thus, the spectral efficiency can be improved. Further, when the cooperation between cellular users and D2D users is enabled, a win-win situation can be achieved to make all users better off. Thus motivated, we propose a cooperative D2D communication framework in this article, which introduces the cooperative relay technique to conventional underlay/overlay D2D communications. Adaptive mode selection and spectrum allocation schemes are also presented to ensure better performance for both cellular and D2D users. Extensive numerical results show the effectiveness of the proposed framework for a variety of scenarios.
Millimeter-wave communication is a promising technology for future 5G cellular networks to provide very high data rate (multi-gigabits-per-second) for mobile devices. Enabling D2D communications over directional mmWave networks is of critical importance to efficiently use the large bandwidth to increase network capacity. In this article, the propagation features of mmWave communication and the associated impacts on 5G cellular networks are discussed. We introduce an mmWave+4G system architecture with TDMA-based MAC structure as a candidate for 5G cellular networks. We propose an effective resource sharing scheme by allowing non-interfering D2D links to operate concurrently. We also discuss neighbor discovery for frequent handoffs in 5G cellular networks.
Data traffic in cellular networks has dramatically surged in recent years due to the booming growth of various mobile applications. It is hence crucial to increase network capacity to accommodate new applications and services. In this article, we propose a promising concept of cooperative device-to-device communication to improve resource utilization in cellular networks. Based on a novel fine-grained resource allocation scheme, we study the problem of maximizing the minimum rate among multiple wireless links by jointly considering relay assignment, transmission scheduling, and channel allocation. Simulation results show that our proposed solutions can significantly increase resource utilization in cellular networks.
Device-to-Device (D2D) communication underlaying cellular networks enhances system capacity through using spectrum resources of idle cellular users, which at the same time can act as relays to form cooperative D2D transmissions. On the other hand, network coding increases the efficiency of relay cooperation.
In this letter, we investigate the random linear network coding aided D2D communications, by addressing the problem of joint resource allocation and relay selection among multiple idle cellular users and D2D pairs.
By formulating it as a binary integer non-linear programming problem, we obtain the optimal solution by introducing the concept of D2D cluster. Extensive simulations demonstrate that our proposed scheme increases the system sum rate by about 50\% on average, while guaranteeing the minimum required rate of D2D pair.
This paper studies radio resource allocation for mobile terminals (MTs) in a heterogeneous wireless access medium. Unlike the existing solutions in literature, we consider the simultaneous presence of both single-network and multi-homing services in the networking environment. In single-network services, an MT is assigned to the best wireless access network available at its location. On the other hand, in multi-homing services, an MT utilizes all available wireless access networks simultaneously. The objective of the radio resource allocation is of twofold: to determine the optimal assignment of MTs with single-network service to the available wireless access networks, and to find the corresponding optimal bandwidth allocation to the MTs with single-network and multi-homing services. We develop a sub-optimal decentralized implementation of the radio resource allocation, which relies on network cooperation to perform the allocation in a dynamic environment in an efficient manner. The MT plays an active role in the resource allocation operation, whether by selecting the best available wireless network for single-network services or by determining the required bandwidth share from each available network for multi-homing services. Simulation results are presented to demonstrate the performance of the proposed algorithm.
Cellular networks are in a major transition from a carefully planned set of large tower-mounted base-stations (BSs) to an irregular deployment of heterogeneous infrastructure elements that often additionally includes micro, pico, and femtocells, as well as distributed antennas. In this paper, we develop a tractable, flexible, and accurate model for a downlink heterogeneous cellular network (HCN) consisting of K tiers of randomly located BSs, where each tier may differ in terms of average transmit power, supported data rate and BS density. Assuming a mobile user connects to the strongest candidate BS, the resulting Signal-to-Interference-plus-Noise-Ratio (SINR) is greater than 1 when in coverage, Rayleigh fading, we derive an expression for the probability of coverage (equivalently outage) over the entire network under both open and closed access, which assumes a strikingly simple closed-form in the high SINR regime and is accurate down to -4 dB even under weaker assumptions. For external validation, we compare against an actual LTE network (for tier 1) with the other K-1 tiers being modeled as independent Poisson Point Processes. In this case as well, our model is accurate to within 1-2 dB. We also derive the average rate achieved by a randomly located mobile and the average load on each tier of BSs. One interesting observation for interference-limited open access networks is that at a given sinr, adding more tiers and/or BSs neither increases nor decreases the probability of coverage or outage when all the tiers have the same target-SINR.
Multimedia social networks have been introduced as a new technology to enrich people's lives through enhanced multimedia distribution. On the other hand, a media cloud system can perform multimedia processing and storage, and provide heterogeneous multimedia services. However, the challenges still remain for end users (e.g., mobile devices and PCs) to receive multimedia streaming from the cloud system with satisfied quality-of-service (QoS). To address these challenges, an efficient multimedia distribution approach taking advantage of live-streaming social networks is innovated in this paper to deliver the media services from the cloud to both desktop and wireless end users. Our approach allows bandwidth limited mobile users to acquire live multimedia streaming from desktop users, directly based on their social relationships rather than from the cloud. When a number of mobile users compete for limited bandwidth access with the desktop users, a bandwidth allocation problem must be solved to meet all users' QoS requirements in the live-streaming social network. We formulate the problem as a two-stage Stackelberg game, in which both desktop users and mobile users target at maximizing their utilities. In our study, a noncooperative game is used to model the competition among the desktop users in terms of shared bandwidth and price in the first stage of the game. The second stage of the game models the behavior of a mobile user selecting the desktop users by an evolutionary game. In addition, a case study is conducted following the general Stackelberg game formulation, where the existence of a unique Nash equilibrium is proved. Based on our game modeling, we design protocols for both desktop and mobile users and evaluate them with numerical examples.
In this paper, a round-robin-based relay protocol dubbed round-robin relaying with source selection protocol (R3SSP) is proposed to achieve full cooperative diversity in multisource cooperative communication networks. In R3SSP, all the sources transmit their individual information in turn. The relays then forward the messages of some specific sources in a fixed order according to the limited feedback information. Compared with traditional relay selection-based protocols, R3SSP is based on round-robin relaying, thus avoiding relay selection and requiring no specific channel state information feedback. R3SSP can therefore be implemented with lower complexity. Furthermore, the exact and asymptotic expressions of the outage probability are derived. The diversity-multiplexing tradeoff (DMT) performance is also analyzed. Theoretical analysis shows that R3SSP achieves full cooperative diversity and provides better DMT performance than relay selection-based protocols in a system where the number of sources is higher than that of the relays. Based on the DMT analysis, we further propose an adaptive relay activation scheme that is capable of achieving higher DMT by dynamically selecting the number of relays to be activated in the entire network. Simulation results also verify the validity and superiority of R3SSP.
In this paper, we investigate the scheduling scheme to combine cooperative
diversity (CD) and multiuser diversity (MUD) in multiuser cooperative networks
under the time resource allocation (TRA) framework in which the whole
transmission is divided into two phases: the broadcast phase and the relay
phase. The broadcast phase is for direct transmission whereas the relay phase
is for relay transmission. Based on this TRA framework, a user selection based
low complexity relay protocol (US-LCRP) is proposed to combine CD and MUD. In
each time slot (TS) of the broadcast phase, a "best" user is selected for
transmission in order to obtain MUD. In the relay phase, the relays forward the
messages of some specific users in a fixed order and then invoke the limited
feedback information to achieve CD. We demonstrate that the
diversity-multiplexing tradeoff (DMT) of the US-LCRP is superior to that of the
existing schemes, where more TSs are allocated for direct transmission in order
to jointly exploit CD and MUD. Our analytical and numerical results show that
the US-LCRP constitutes a more efficient resource utilization approach than the
existing schemes. Additionally, the US-LCRP can be implemented with low
complexity because only the direct links' channel state information (CSI) is
estimated during the whole transmission.
Cognitive radios hold tremendous promise for increasing spectral efficiency in wireless systems. This paper surveys the fundamental capacity limits and associated transmission techniques for different wireless network design paradigms based on this promising technology. These paradigms are unified by the definition of a cognitive radio as an intelligent wireless communication device that exploits side information about its environment to improve spectrum utilization. This side information typically comprises knowledge about the activity, channels, codebooks, and/or messages of other nodes with which the cognitive node shares the spectrum. Based on the nature of the available side information as well as a priori rules about spectrum usage, cognitive radio systems seek to underlay, overlay, or interweave the cognitive radios' signals with the transmissions of noncognitive nodes. We provide a comprehensive summary of the known capacity characterizations in terms of upper and lower bounds for each of these three approaches. The increase in system degrees of freedom obtained through cognitive radios is also illuminated. This information-theoretic survey provides guidelines for the spectral efficiency gains possible through cognitive radios, as well as practical design ideas to mitigate the coexistence challenges in today's crowded spectrum.
Power optimization techniques are becoming increasingly important in wireless system design since battery technology has not kept up with the demand of mobile devices. They are also critical to interference management in wireless systems because interference usually results from both aggressive spectral reuse and high power transmission and severely limits system performance. In this paper, we develop an energy-efficient power optimization scheme for interference-limited wireless communications. We consider both circuit and transmission powers and focus on energy efficiency over throughput. We first investigate a non-cooperative game for energy-efficient power optimization in frequency-selective channels and reveal the conditions of the existence and uniqueness of the equilibrium for this game. Most importantly, we discover a sufficient condition for generic multi-channel power control to have a unique equilibrium in frequency-selective channels. Then we study the tradeoff between energy efficiency and spectral efficiency and show by simulation results that the proposed scheme improves both energy efficiency and spectral efficiency in an interference-limited multi-cell cellular network.
Recently, cooperative communications, in the form of keeping each node with a single antenna and having a node exploit a relay node's antenna, is shown to be a promising approach to achieve spatial di- versity. Under this communication paradigm, the choice of relay node plays a significant role in the overall system performance. In this paper, we study the relay node assignment problem in a net- work environment, where multiple source-destination pairs com- pete for the same pool of relay nodes in the network. The main contribution of this paper is the development of a polynomial time algorithm to solve this problem. A key idea in this algorithm is a "linear marking" mechanism, which is able to offer a linear com- plexity for each iteration. We give a formal proof of optimality for this algorithm. We also show several attractive properties associ- ated with this algorithm.
On one hand, cooperative communication has been gaining more and more popularity since it has great potential to increase the capacity of wireless networks. On the other hand, the applications of cooperative communication technology are rarely seen in reality, even in some scenarios where the demands for bandwidth-hungry applications have pushed the system designers to develop innovative network solutions. A main obstacle lying between the potential capability of channel capacity improvement and the wide adoption of cooperative communication is the lack of incentives for the participating wireless nodes to serve as relay nodes. Hence, in this paper, we design TASC, an auction scheme for the cooperative communications, where wireless node can trade relay services. TASC makes an important contribution of maintaining truthfulness while fulfilling other design objectives. We show analytically that TASC is truthful and has polynomial time complexity. Extensive experiments show that TASC can achieve multiple economic properties without significant performance degradation compared with pure relay assignment algorithms.
In this paper, we exploit a novel setting for Cognitive Radio (CR) networks to enable multiple operators to involve secondary users (SUs) as cooperative relays for their primary users. In return, SUs get an opportunity to access spare channels for their own data transmission. Initially, we assume that the CR network supports payment transfer. Then, we formulate the system as a transferable utility coalitional game. We show that there is an operating point that maximizes the sum utility over all operators and SUs while providing each player a share such that no subset of operators and SUs has an incentive to break away from the grand coalition. Such operating points exist when the solution set of the game, the core, is nonempty. Subsequently, we examine an interesting scenario where there is no payment mechanism in the network. This scenario can be investigated by using a nontransferable utility coalitional game model. We show that there exists a joint action to make the core nonempty. A general method with exponential computational complexity to get such a joint action is discussed. Then, we relate the core of this game to a competitive equilibrium of an exchange economy setting under special situations. As a result, several available efficient centralized or distributed algorithms in economics can be employed to compute a member in the core. In a nutshell, this paper constitutes the design of new coalition based dynamics that could be used in future CR networks.
A major contribution of biology to competitive decision making is the development of the discipline of evolutionary games. Its ESS (evolutionary stable strategy) equilibrium concept, well adapted to large populations of players, describes robustness against deviations of a whole fraction of the population (in contrast to Nash equilibrium that requires robustness against a single user's deviation). The second appealing element of evolutionary games, the replicator dynamics, describes the evolution of strategies in time. Under suitable conditions, this (and some other) dynamics, converge to an ESS. In this paper we study the effect of slow time scale delays on the convergence of various dynamics. We apply this to an evolutionary game describing competition between mobile terminals over the access to a common channel.
In the underlay cognitive radio networks, this paper defines the joint channel and power allocation problem, which aims to optimise the max-total and max-min throughputs of secondary users (SUs), with the constraints of interference on primary receivers. For the max-total problem, we formulate the problem as a bipartite matching and derive a maximum weighted matching-based sum throughput maximisation algorithm (STMA) to solve this problem. For the max-min problem, on the basis of the optimal relay assignment (ORA) algorithm, we derive a polynomial time optimal channel assignment algorithm (OCAA) to iteratively assign channels to each SU pair under the power constraint. Simulation results demonstrate the effectiveness of our algorithms when compared with random method.
This brief examines current research on cooperative device-to-device (D2D) communication as an enhanced offloading technology to improve the performance of cognitive radio cellular networks. By providing an extensive review of recent advances in D2D communication, the authors demonstrate that the quality of D2D links significantly affects offloading performance in cellular networks, which motivates the design of cooperative D2D communication. After presenting the architecture of cooperative D2D communication, the challenges of capacity maximization and energy efficiency are addressed by optimizing relay assignment, power control and resource allocation. Furthermore, cooperative D2D communication is enhanced by network coding technology, and then is extended for broadcast sessions. Along with detailed problem formulation and hardness analysis, fast algorithms are developed by exploiting problem-specific characteristics such that they can be applied in practice.
This paper considers a wireless powered communication network (WPCN) with group cooperation, where two communication groups cooperate with each other via wireless power transfer and time sharing to fulfill their expected information delivering and achieve "win-win" collaboration. To explore the system performance limits, we formulate optimization problems to respectively maximize the weighted sum-rate and minimize the total consumed power. The time assignment, beamforming vector and power allocation are jointly optimized under available power and quality of service requirement constraints of both groups. For the WSR-maximization, both fixed and flexible power scenarios are investigated. As all problems are non-convex and have no known solution methods, we solve them by using proper variable substitutions and the semi-definite relaxation. We theoretically prove that our proposed solution method guarantees the global optimum for each problem. Numerical results are presented to show the system performance behaviors, which provide some useful insights for future WPCN design. It shows that in such a group cooperation-aware WPCN, optimal time assignment has the greatest effect on the system performance than other factors.
A simple diamond half-duplex relay network composed of a source, two decode-and-forward half-duplex relays, and a destination is considered, where a direct link between the source and the destination does not exist. For this network, we study the case of buffer-aided relays, where the relays are equipped with buffers. Each relay can receive data from the source, store it in the buffer, and forward it to the destination, when the channel conditions are advantageous. Thereby, buffering enables adaptive scheduling of the transmissions and receptions over time, which allows the network to exploit the diversity offered by the fading channels. For the considered half-duplex network, four transmission modes are defined based on whether the relay nodes receive or transmit. In this paper, we derive the locally optimal scheduling of the transmission modes over time and investigate the achievable average rate, when the relays are affected by inter-relay interference. Since the proposed buffer-aided transmission policies introduce unbounded delay, we provide a sub-optimal buffer-aided transmission policy with limited delay. Moreover, for inter-relay interference cancellation, we consider two coding schemes with different complexities. In the first scheme, we employ dirty paper coding, which entails a high complexity, whereas in the second scheme, we adopt a low-complexity technique based on successive interference cancellation at the receiving relay nodes and optimal power allocation at the transmitting nodes. Our numerical results show that the proposed protocols, with and without delay constraints, outperform existing protocols for the considered network from the literature.
Spectrum auction is an emerging economic scheme to stimulate both primary spectrum operators (POs) and secondary users (SUs) to be involved in spectrum sharing. Previous spectrum auction works mostly assume each PO can only have one type spectrum or each SU can only buy homogeneous spectrum bands from the same PO. However, in a ubiquitous network scenario, each PO possesses heterogeneous spectrum resources such as WiFi, 3G and each SU may request different types of spectrum bands from the same PO. Existing auction schemes cannot be used to effectively solve the problem. Therefore, the authors come out with a lightweight combinatorial double auction to tackle this challenge. Since spectrum combinatorial double auction problem is NP-hard, the authors develop a general greedy algorithm G-Greedy to solve the problem. Inspired by the recent group-buying discounts, they also invent an enhanced scheme E-Greedy to further optimise total utility. They theoretically prove the economy properties of the proposed schemes such as individual rationality, budget balance and truthfulness. Simulation results show that both of the two algorithms can yield higher utilities and are effective.
Thanks to the convergence of pervasive mobile communications and fast-growing online social networking, mobile social networking is penetrating into our everyday life. Aiming to develop a systematic understanding of mobile social networks, in this paper we exploit social ties in human social networks to enhance cooperative device-to-device (D2D) communications. Specifically, as handheld devices are carried by human beings, we leverage two key social phenomena, namely social trust and social reciprocity, to promote efficient cooperation among devices. With this insight, we develop a coalitional game-theoretic framework to devise social-tie-based cooperation strategies for D2D communications. We also develop a network-assisted relay selection mechanism to implement the coalitional game solution, and show that the mechanism is immune to group deviations, individually rational, truthful, and computationally efficient. We evaluate the performance of the mechanism by using real social data traces. Simulation results corroborate that the proposed mechanism can achieve significant performance gain over the case without D2D cooperation.
For improving Quality of Service in wireless networks, multi-homing techniques have been considered as a promising solution. To date, most of the conventional research efforts have been focusing on user side interests (e.g. improving throughput, minimizing packet loss). However, a service provider could have different interests (e.g. profit, energy efficiency). Nevertheless, to the best of our knowledge, there is no research concern that takes into account the interests of user and service provider side at the same time. In this paper, we first model utility functions of both sides considering the aforementioned issues. Then, based on these utility functions we address the joint pricing and load distribution problem of multi-homing in heterogeneous wireless networks. Here, the problem is formulated into a Stackelberg game. Then, we propose schemes to obtain an optimal solution; so that, both sides (i.e. service providers and users) are satisfied. Finally, we provide rigorous analysis varying different parameters (e.g. cost, rate allocation, energy efficiency) which can potentially affect to the game. In addition to that, we show the proposed schemes are well converged to equilibrium point.
The phenomenal growth of mobile data demand has brought about increasing scarcity in available radio spectrum. Meanwhile, mobile customers pay more attention to their own experience, especially in communication reliability and service continuity on the move. To address these issues, LTE-Unlicensed, or LTEU, is considered one of the latest groundbreaking innovations to provide high performance and seamless user experience under a unified radio technology by extending LTE to the readily available unlicensed spectrum. In this article, we offer a comprehensive overview of the LTEU technology from both operator and user perspectives, and examine its impact on the incumbent unlicensed systems. Specifically, we first introduce the implementation regulations, principles, and typical deployment scenarios of LTE-U. Potential benefits for both operators and users are then discussed. We further identify three key challenges in bringing LTE-U into reality together with related research directions. In particular, the most critical issue of LTE-U is coexistence with other unlicensed systems, such as widely deployed WiFi. The LTE/WiFi coexistence mechanisms are elaborated in time, frequency, and power aspects, respectively. Simulation results demonstrate that LTE-U can provide better user experience to LTE users while well protecting the incumbent WiFi users??? performance compared to two existing advanced technologies: cellular/WiFi interworking and licensed-only heterogeneous networks (Het-Nets).
Users and providers have different requirements and objectives in an investment market. Users will pay the lowest price possible with certain guaranteed levels of service at a minimum and providers would follow the strategy of achieving the highest return on their investment. Designing an optimal market-based resource allocation that considers the benefits for both the users and providers is a fundamental criterion of resource management in distributed systems, especially in cloud computing services. Most of the current market-based resource allocation models are biased in favor of the provider over the buyer in an unregulated trading environment. In this study, the problem was addressed by proposing a new market model called the Combinatorial Double Auction Resource Allocation (CDARA), which is applicable in cloud computing environments. The CDARA was prototyped and simulated using CloudSim, a Java-based simulator for simulating cloud computing environments, to evaluate its efficiency from an economic perspective. The results proved that the combinatorial double auction-based resource allocation model is an appropriate market-based model for cloud computing because it allows double-sided competition and bidding on an unrestricted number of items, which causes it to be economically efficient. Furthermore, the proposed model is incentive-compatible, which motivates the participants to reveal their true valuation during bidding.
In this paper, we focus on a mobile wireless network comprising a powerful communication center and a multitude of mobile users. We investigate the propagation of latency-constrained content in the wireless network characterized by heterogeneous (time-varying and user-dependent) wireless channel conditions, heterogeneous user mobility, and where communication could occur in a hybrid format (e.g., directly from the central controller or by exchange with other mobiles in a peer-to-peer manner). We show that exploiting double opportunities, i.e., both time-varying channel conditions and mobility, can result in substantial performance gains. We develop a class of double opportunistic multicast schedulers and prove their optimality in terms of both utility and fairness under heterogeneous channel conditions and user mobility. Extensive simulation results are provided to demonstrate that these algorithms can not only substantially boost the throughput of all users (e.g., by 50% to 150%), but also achieve different consideration of fairness among individual users and groups of users.
In this article, we discuss full duplex for heterogenous networks that accommodate the coexistence of device-to-device communications. The short link distance and lower transmit power of device-to-device communications make them excellent candidates to exploit full duplex inband transmission. By incorporating power allocation for self-interference cancellation based on antenna isolation, analog cancellation, and digital cancellation, full-duplex device-to-device, FD-D2D, nodes can potentially improve spectrum efficiency in HetNets. We provide a comprehensive overview on FD-D2D communications in Het-Nets. Additionally, we identify several challenges, provide potential solutions to interference mitigation based on power control, beamforming, and resource scheduling, and further discuss applications of FD in 5G networks.
Device-to-device (D2D) communication has recently attracted much research attention because of its potential to increase the capacity of cellular networks. Most existing works aim to maximise the overall system throughput (system-centric), which ignores the actual traffic demands of D2D users. In this study, the authors consider user-centric relay assisted D2D communications where D2D users have different evaluations for the significance of every unit of increased data rate. By considering the traffic demands of D2D users, the authors propose a Vickrey-Clarke-Groves auction based relay allocation mechanism (ARM) in which every D2D user submits a bid to the basestation (BS). The submitted bids indicate D2D users' valuation on every unit of the increased data rate. The BS then allocates relays to D2D users by maximising the social welfare of D2D users while maintaining a predefined data rate requirement for cellular users. A payment scheme to charge D2D users for using relays is designed, and the authors show that the auction is truthful. The authors also extend the results to a general case and provide a general ARM accordingly. Extensive simulation results are provided to demonstrate the performance of the proposed mechanisms.
To satisfy the ever increasing wireless service demand, it is effective to form a converged network by utilizing interworking mechanisms, such that the resources of heterogeneous wireless networks can be allocated in a coordinated and efficient manner. Despite the potential advantages of a converged network, its performance needs further improvement, especially at cell edges and rural areas where only one network is available. In this article, we investigate how to leverage device-to-device, D2D, communication to further improve the performance of a converged network which consists of an LTE-A cellular network and IEEE 802.11n WLANs. Three main technical challenges that complicate resource allocation are identified: allocation of resources capturing diverse radio access technologies of the networks, selection of users' communication modes for multiple networks to maximize hop and reuse gains, and interference management. To address these challenges, we propose a resource allocation scheme that performs mode selection, allocation of WLAN resources, and allocation of LTE-A network resources in three different timescales. The resource allocation scheme is semi-distributedly implemented in the underlying converged D2D communication network, and the achievable performance improvements are demonstrated via simulation results.
Cooperative networking is a promising technology to meet the rapidly growing demand of mobile data traffic. To stimulate effective and trustworthy user cooperation, we leverage the knowledge of the social tie structure among mobile users and develop a social trust based cooperative D2D relaying framework, which takes into account both physical distances and social distances among users. Based on (finite-horizon) optimal stopping theory, we derive the optimal social aware relay selection strategy, which strikes a balance between performance gain and relay probing cost. We further show that the optimal stopping policy for social aware relay selection exhibits a stage-dependent threshold structure that has a monotonically non-increasing property. Numerical results demonstrate that the proposed mechanism can yield significant throughput gain over the direct transmission scheme.
Recent studies on mobility-assisted schemes for routing and topology control and on mobility-induced link dynamics have presented significant findings on the properties of a pair of nodes (e.g., the intermeeting time and link life time) or a group of nodes (e.g., network connectivity and partitions). In contrast to the study on the properties of a set of nodes rather than individuals, many works share a common ground with respect to node mobility, i.e., independent mobility in multihop wireless networks. Nonetheless, in vehicular ad hoc networks (VANETs), mobile devices installed on vehicles or held by humans are not isolated; however, they are dependent on each other. For example, the speed of a vehicle is influenced by its close-by vehicles, and vehicles on the same road move at similar speeds. Therefore, the gap between our understanding of the impact of independent mobility and our interest in the properties of correlated mobility in VANETs, along with the real systems altogether, declare an interesting question. How can we measure the internode mobility correlation, such as to uncover the node groups and network components, and explore their impact on link dynamics and network connectivity? Bearing this question in mind, we first examine several traces and find that node mobility exhibits spatial locality and temporal locality correlations, which are closely related to node grouping. To study the properties of these groups on the fly, we introduce a new metric, i.e., dual-locality ratio (DLR), which quantifies mobility correlation of nodes. In light of taking spatial and temporal locality dimensions into account, the DLR can be used to effectively identify stable user groups, which in turn can be used for network performance enhancement.
Device-to-device (D2D) communications allow direct communications between nodes without transmitting data via the base stations in cellular systems, which could bring significant performance improvement. Since most applications are delay-sensitive, it is very important to consider delay performance in addition to physical layer throughput for D2D communications. To improve delay performance it is necessary to dynamically control the radio resource in a cross-layer way according to both the channel fading information and the queue length information. The former allows an observation of good transmission opportunity and the latter provides the urgency of data flows. However, the resource control with delay constraints involves stochastic optimization, which is very challenging. In this article we first summarize various approaches to solve the delay-aware resource allocation problems for D2D communications. We propose a low complexity practical solution by exploiting the interference filtering property of CSMA-like MAC protocols in the D2D system. Based on the solution structure, we further discuss the implementation issues based on LTE-Advanced systems and evaluate the associated performance and complexity. Finally we discuss the choice of MAC parameters for the overall D2D system performance.
In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular bandwidth and all communications take place through the base stations. In this article, we envision a two-tier cellular network that involves a macrocell tier (i.e., BS-to-device communications) and a device tier (i.e., device-to-device communications). Device terminal relaying makes it possible for devices in a network to function as transmission relays for each other and realize a massive ad hoc mesh network. This is obviously a dramatic departure from the conventional cellular architecture and brings unique technical challenges. In such a two-tier cellular system, since the user data is routed through other users?? devices, security must be maintained for privacy. To ensure minimal impact on the performance of existing macrocell BSs, the two-tier network needs to be designed with smart interference management strategies and appropriate resource allocation schemes. Furthermore, novel pricing models should be designed to tempt devices to participate in this type of communication. Our article provides an overview of these major challenges in two-tier networks and proposes some pricing schemes for different types of device relaying.
The amplify-and-forward (AF) cooperative communication scheme is modeled using the Stackelberg market framework, where a relay is willing to sell its resources, power, and bandwidth to multiple users to maximize its revenue. The relay determines the prices for relaying the users' information, depending on its available resources and the users' demands. Subsequently, each user maximizes its own utility function by determining the optimum power and bandwidth to buy from the relay. The utility function of the user is formulated as a joint concave function in power and bandwidth. The existence and uniqueness of the Nash equilibrium (NE) are investigated using the concavity of the utility function and the exact potential game associated with the proposed utility function. The NE solution can be obtained in a centralized manner, which requires full knowledge of all channel gains of all users, which may be difficult to obtain in practice. In this sense, a distributed algorithm can be applied to obtain power and bandwidth allocations with minimum information exchange between the relay and the users. Similarly, the optimum prices for the power and bandwidth can also be obtained in a distributed manner. The convergence of the algorithms is investigated using the Jacobian matrix at the NE. Numerical simulations are used to verify the validation of the proposed framework.
Machine-to-Machine communication recently becomes a popular issue in 4G Standardization Committee, such as IEEE 802.16p and LTE-A. In an OFDMA cellular network embedded with a M2M system, the energy consumption (EC) minimization problem is formulated with the consideration of transmission and circuit energy consumed. We propose joint massive access control and resource allocation (RA) schemes, which perform machine node grouping, coordinator selection, and coordinator RA, and also determine the proper number of groups under a 2-hop transmission protocol, to minimize total EC in both flat- and frequency-selective fading channel. Numerical results show that proposed schemes can achieve sub-optimal EC.
Enterprises currently employ Cloud services to improve the scal-ability of their services and resource providers strategically price resources to maximize their utilities. While Nash equilibrium is the dominant concept for studying such kind of interaction, evolu-tionary game theory seems more appropriate for modeling agents' strategic interactions as it relaxes many strong assumptions. This paper applies evolutionary dynamics to generate resource providers' evolutionary stable strategies. We present a sequential monte carlo approach for simulation of multi-population evolutionary dynamics in which each agent's strategy space is continuous. We use resam-pling and Gaussian smoothing to prevent degeneration of particle samples. Simulation results show that the proposed approach al-ways converges to evolutionary stable strategies. Our approach is general in that it can be used to generate agents' evolutionary stable strategies for other resource allocation games.
This paper considers the multiuser power control problem in Gaussian frequency-flat interference relay channels using a game-theoretic framework. While a lot of attention has been paid to Gaussian interference games, where sufficient conditions for the uniqueness of the Nash equilibrium (NE) have been established, these types of games have not been studied in the context of interference relay channels. We consider here Gaussian interference relay games (GIRGs), where instead of allocating the power budget across a set of sub-channels, each player aims to decide the optimal power control strategy across a set of hops. We show that the GIRG always possesses a unique NE for a two-player version of the game, irrespective of any channel realization or initial system parameters such as power budgets and noise power. Furthermore, we derive explicitly a sufficient condition under which the NE achieves Pareto-optimality. To facilitate decentralized implementation, we propose a distributed and asynchronous algorithm. We also prove that the proposed algorithm always converges to the unique NE from an arbitrary starting point. We then conclude that the distributed game-theoretic approach exhibits great potential in the context of interference relay channels and qualifies as a practically appealing candidate for power control.
Routing protocols for multihop wireless networks have traditionally used shortest path routing to obtain paths to destinations and do not consider traffic load or delay as an explicit factor in the choice of routes. We focus on static mesh networks and formally establish that if the number of sources is not too large, then it is possible to construct a perfect flow-avoiding routing, which can boost the throughput provided to each user over that of the shortest path routing by a factor of four when carrier sensing can be disabled or a factor of 3.2 otherwise. So motivated, we address the issue of designing a multipath, load adaptive routing protocol that is generally applicable even when there are more sources. We develop a protocol that adaptively equalizes the mean delay along all utilized routes from a source to destination and does not utilize any routes that have greater mean delay. This is the property satisfied by a system in Wardrop equilibrium. We also address the architectural challenges confronted in the software implementation of a multipath, delay-feedback-based, probabilistic routing algorithm. Our routing protocol is 1) completely distributed, 2) automatically load balances flows, 3) uses multiple paths whenever beneficial, 4) guarantees loop-free paths at every time instant even while the algorithm is suntil converging, and 5) amenable to clean implementation. An ns-2 simulation study indicates that the protocol is able to automatically route flows to "avoid" each other, consistently out-performing shortest path protocols in a variety of scenarios. The protocol has been implemented in user space with a small amount of forwarding mechanism in a modified Linux 2.4.20 kernel. Finally, we discuss a proof-of-concept measurement study of the implementation on a six node testbed.
We consider the problem of spectrum trading with multiple licensed users (i.e., primary users) selling spectrum opportunities to multiple unlicensed users (i.e., secondary users). The secondary users can adapt the spectrum buying behavior (i.e., evolve) by observing the variations in price and quality of spectrum offered by the different primary users or primary service providers. The primary users or primary service providers can adjust their behavior in selling the spectrum opportunities to secondary users to achieve the highest utility. In this paper, we model the evolution and the dynamic behavior of secondary users using the theory of evolutionary game. An algorithm for the implementation of the evolution process of a secondary user is also presented. To model the competition among the primary users, a noncooperative game is formulated where the Nash equilibrium is considered as the solution (in terms of size of offered spectrum to the secondary users and spectrum price). For a primary user, an iterative algorithm for strategy adaptation to achieve the solution is presented. The proposed game-theoretic framework for modeling the interactions among multiple primary users (or service providers) and multiple secondary users is used to investigate network dynamics under different system parameter settings and under system perturbation.
Next-generation wireless networks will integrate multiple wireless access technologies to provide seamless mobility to mobile users with high-speed wireless connectivity. This will give rise to a heterogeneous wireless access environment where network selection becomes crucial for load balancing to avoid network congestion and performance degradation. We study the dynamics of network selection in a heterogeneous wireless network using the theory of evolutionary games. The competition among groups of users in different service areas to share the limited amount of bandwidth in the available wireless access networks is formulated as a dynamic evolutionary game, and the evolutionary equilibrium is considered to be the solution to this game. We present two algorithms, namely, population evolution and reinforcement-learning algorithms for network selection. Although the network-selection algorithm based on population evolution can reach the evolutionary equilibrium faster, it requires a centralized controller to gather, process, and broadcast information about the users in the corresponding service area. In contrast, with reinforcement learning, a user can gradually learn (by interacting with the service provider) and adapt the decision on network selection to reach evolutionary equilibrium without any interaction with other users. Performance of the dynamic evolutionary game-based network-selection algorithms is empirically investigated. The accuracy of the numerical results obtained from the game model is evaluated by using simulations.
In this paper, we model the various users in a wireless network (e.g., cognitive radio network) as a collection of selfish autonomous agents that strategically interact to acquire dynamically available spectrum opportunities. Our main focus is on developing solutions for wireless users to successfully compete with each other for the limited and time-varying spectrum opportunities, given experienced dynamics in the wireless network. To analyze the interactions among users given the environment disturbance, we propose a stochastic game framework for modeling how the competition among users for spectrum opportunities evolves over time. At each stage of the stochastic game, a central spectrum moderator (CSM) auctions the available resources, and the users strategically bid for the required resources. The joint bid actions affect the resource allocation and, hence, the rewards and future strategies of all users. Based on the observed resource allocations and corresponding rewards, we propose a best-response learning algorithm that can be deployed by wireless users to improve their bidding policy at each stage. The simulation results show that by deploying the proposed best-response learning algorithm, the wireless users can significantly improve their own bidding strategies and, hence, their performance in terms of both the application quality and the incurred cost for the used resources.
The performance in cooperative communication depends on careful resource allocation such as relay selection and power control, but the traditional centralized resource allocation requires precise measurements of channel state information (CSI). In this paper, we propose a distributed game-theoretical framework over multiuser cooperative communication networks to achieve optimal relay selection and power allocation without knowledge of CSI. A two-level Stackelberg game is employed to jointly consider the benefits of the source node and the relay nodes in which the source node is modeled as a buyer and the relay nodes are modeled as sellers, respectively. The proposed approach not only helps the source find the relays at relatively better locations and "buyrdquo an optimal amount of power from the relays, but also helps the competing relays maximize their own utilities by asking the optimal prices. The game is proved to converge to a unique optimal equilibrium. Moreover, the proposed resource allocation scheme with the distributed game can achieve comparable performance to that employing centralized schemes.
In this paper, we propose a new and rather simple grouped-subcarrier allocation algorithms with proportional fairness among users in downlink OFDM transmission. The proposed algorithm tries to minimize the required transmit power while satisfying the rate requirement and BER constraint of each user. Subcarrier and power allocation are performed in two steps. We are mainly focusing on the subcarrier allocation step. First subcarriers are grouped to reduce the computational complexity of the allocation algorithm. Then we employ proportional constraints in terms of rate ratios to assure each user to achieve the target data rate in advance. Simulation results show that this low complexity resource allocation scheme achieves better performance than the fixed frequency division approach and shows comparable performance compared to previously derived suboptimal resource distribution schemes. It is also shown that with rate constraints the capacity is distributed more fairly and rationally among users