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Intelligent Network Optimisation for Beyond 5G Networks Considering Packet Drop Rate
... As the groundwork for the next-generation sixth-generation (6G) wireless communications is being laid, initiatives such as those by the International Telecommunication Union (ITU) and the 3GPP standards community are driving towards significant enhancements. These include achieving enormous data transfer at terabit rates, implementing AI/ML-driven processes for network function automation, expanding cloud-native operations, and supporting ultra-low-latency tactile applications in a real-time manner within the edge [4,5]. ...
This paper presents an advanced framework for securing 6G communication by integrating deep learning and physical layer security (PLS). The proposed model incorporates multi-stage detection mechanisms to enhance security against various attacks on the 6G air interface. Deep neural networks and a hybrid model are employed for sequential learning to improve classification accuracy and handle complex data patterns. Additionally, spoofing, jamming, and eavesdropping attacks are simulated to refine detection mechanisms. An anomaly detection system is developed to identify unusual signal patterns indicating potential attacks. The results demonstrate that machine learning (ML) and hybrid models outperform conventional approaches, showing improvements of up to 85% in bit error rate (BER) and 24% in accuracy, especially under attack conditions. This research contributes to the advancement of secure 6G communication systems, offering details on effective defence mechanisms against physical layer attacks.
With the explosive growth of network traffic and the diversification of service demands, network slicing (NS) and dual connectivity (DC) are considered as promising technologies in wireless networks. In this paper, we propose a novel algorithm that solves the resource allocation problem of NS in heterogeneous networks with the assistance of DC, while satisfying the characteristic requirements of eMBB and URLLC services. Firstly, we model the scenario and formulate the optimization problem, which is proved as an NP-Hard problem. Secondly, due to the nonconvex and combinatorial nature, the dueling double deep Q-network with long short-term memory (LSTM-D3QN) is proposed to solve this problem, aiming to improve the overall network utility, while ensuring the quality of experience (QoE). Then, we analyze the complexity of the algorithm. Finally, the simulation results show that the proposed algorithm can maximize the total utility of the system, while guaranteeing the user QoE. Compared with LSTM-A2C and DQN, the proposed algorithm improves the long-term network utility by 2.6% and 7.2%, respectively. In addition, compared with the algorithm without DC under the conditions of no priority, eMBB priority and URLLC priority, the proposed algorithm improves the network utility by 4.2%, 2.1% and 4.1%, respectively.
The inherent limitations of the network keep on going to be revealed with the continuous deployment of cellular networks. The next generation 6G is motivated by these drawbacks to properly integrate important rate-hungry applications such as extended reality, wireless brain-computer interactions, autonomous vehicles, etc. Also, to support significant applications, 6G will handle large amounts of data transmission in smart cities with much lower latency. It combines many state-of-the-art trends and technology to provide higher data rates for ultra-reliable and low latency communications. By outlining the system requirements, potential trends, technologies, services, applications, and research progress, this paper comprehensively conceptualized the 6G cellular system. Open research issues and current research groups in their field of research are summarised to provide readers with the technology road-map and the potential challenges to consider in their 6G research.
This paper provides a survey of resource allocation for network slicing. We focus on two classes of existing solutions: (
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) reservation-based approaches, which allocate resources on a reservation basis, and (
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) share-based approaches, which allocate resources based on static overall shares associated to individual slices. We identify the requirements that a slice-based resource allocation mechanism should satisfy, and evaluate the performance of both approaches against these requirements. Our analysis reveals that reservation-based approaches provide a better level of isolation as well as stricter guarantees, by enabling tenants to explicitly reserve resources, but one must pay a price in terms of efficiency unless reservations can be updated very dynamically; in particular, efficiency falls below 50% when reservations are performed over long timescales. We provide further comparisons in terms of customizability, complexity, privacy and cost predictability, and discuss which approach might be more suitable depending on the network slices’ characteristics. We also describe the additional mechanisms required to implement the desired resource allocations while meeting the latency and reliability requirements of the different slice types, and outline some issues for future work.
Among the novelties introduced by 5G networks, the formalization of the 'network slice' as a resource allocation unit is an important one. In legacy networks, resources such as link bandwidth, spectrum, computing capacity are allocated independently of each other. In 5G environments, a network slice is meant to directly serve end-to-end services, or verticals: behind a network slice demand, a tenant expresses the need to access a precise service type, under a fully qualified set of computing and network requirements. The resource allocation decision encompasses, therefore, a combination of different resources. In this paper, we address the problem of fairly sharing multiple resources between slices, in the critical situation in which the network does not have enough resources to fully satisfy slice demands. We model the problem as a multi-resource allocation problem, proposing a versatile optimization framework based on the Ordered Weighted Average (OWA) operator, that takes into account different fairness approaches. We show how, adapting the OWA utility function, our framework can generalize classical single-resource allocation methods, existing multi-resource allocation solutions at the state of the art, and implement novel multi-resource allocation solutions. We compare analytically and by extensive simulations the different methods in terms of fairness and system efficiency.
End-to-end network slicing has been viewed as a key enabler for the next generation mobile network (5G), where a Slice Provider (SP) creates various network slices for Slice Customers (SCs) to accommodate diverse services. Due to resource isolation, effective resource allocation for coexisted multiple network slices, i.e. network slice dimensioning, is essential to maximize network resource efficiency. From the perspective of operators, both SP and SC pursue a profit-earning business model. However, the relationship between resource efficiency and profit maximization is not clear so far. In this paper, we study network slice dimensioning with resource pricing policy, by exploring this relationship. We first develop an optimization framework for network slice dimensioning, in which the Slice Customer's Problem (SCP) maximizes the SC's profit and the Slice Provider's Problem (SPP) maximizes net social welfare (resource efficiency). We find that maximization of net social welfare and SP's profit are two consistent objectives when resources are scarce; otherwise, there is a tradeoff. Based on this finding, we propose a low-complexity distributed algorithm to achieve near-optimal net social welfare with profit guarantee for SP/SCs. Simulations and numerical results verify the effectiveness of our proposed slice dimensioning strategy, which can help fully exploiting the capability of network slicing.
To support the wide range of 5G use cases in a cost-efficient way, network slicing has been considered a promising solution, which makes it possible to serve multiple customized and isolated network services on a common physical infrastructure. In this paper, we investigate the resource allocation problem of network slicing in multi-tenant networks where network resources can be used by low-priority tenants change dynamically due to the preemption of high-priority tenants. We formulate the problem as an energy-minimizing mathematical optimization problem considering practical constraints. Due to the dynamic characteristics of the problem, the complexity of the optimization problem is exceptionally high, making it impossible to solve the problem in real-time using traditional optimization approaches. With discovering the special structure of the problem, we propose a Dueling-Deep Q Network (DQN)-based algorithm to solve the problem efficiently. The experimental results show that the proposed algorithm outperforms compared algorithms in terms of total energy cost, runtime, and robustness.
Network slicing is designed to support a variety of emerging applications with diverse performance and flexibility requirements, by dividing the physical network into multiple logical networks. These applications along with a massive number of mobile phones produce large amounts of data, bringing tremendous challenges for network slicing performance. From another perspective, this huge amount of data also offers a new opportunity for the management of network slicing resources. Leveraging the knowledge and insights retrieved from the data, we develop a novel Machine Learning-based scheme for dynamic resource scheduling for networks slicing, aiming to achieve automatic and efficient resource optimisation and End-to-End (E2E) service reliability. However, it is difficult to obtain the user-related data, which is crucial to understand the user behaviour and requests, due to the privacy issue. Therefore, Deep Reinforcement Learning (DRL) is leveraged to extract knowledge from experience by interacting with the network and enable dynamic adjustment of the resources allocated to various slices in order to maximise the resource utilisation while guaranteeing the Quality-of-Service (QoS). The experiment results demonstrate that the proposed resource scheduling scheme can dynamically allocate resources for multiple slices and meet the corresponding QoS requirements.
Multi-resource allocation for network slicing under service level agreements
- Francesca Fossati
- Other