A Dynamic Task Scheduling Strategy for Multi-Access Edge Computing in IRS-Aided Vehicular Networks

ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

A game incentive mechanism for energy efficient federated learning in computing power networks

Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.

Mobile Network Operators (MNO) can reduce their Capital and Operational Expenditure (CAPEX) and (OPEX) with the help of tower sharing approach by utilizing the physical infrastructure equipped by a third party tower provider to expand their network coverage. Moreover, Computing Resource Providers (CRP) are also setting up their micro-datacenters at tower stations to provide the Multi-access Edge Computing (MEC) services by cooperating with tower providers. Since both the communication and computing services contribute to the task offloading in MEC, the resource allocation has become a challenging problem. In this paper, we formulate the joint uplink, downlink, and computing resources allocation problem in which the objectives of both MNOs and CRP are to minimize their OPEX. The task offloading is modeled as a network of queues where the end-to-end latency is calculated based on the performance of the queue network. Then, the formulated problem is transformed into a Generalized Nash Equilibrium Problem (GNEP) to capture the conflicting interests in the resource allocation among MNOs and CRP. To solve the formulated GNEP efficiently, two decentralized algorithms are proposed by introducing the penalty parameters to the coupling constraints. In addition, the convergence and performance of the algorithms on different parameters are analyzed.

Sparse code multiple access (SCMA) is a kind of code-domain non-orthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multi-access edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. The integration of these two technologies can efficiently improve the computation service. In this paper, we propose a novel optimization scheme in the SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. To minimize the total overhead of devices, the communication and computation resources allocation, as well as computation offloading are jointly considered. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed to maximize the uplink transmit rate. Moreover, we put forward CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and computation offloading can achieve a good compromise between time delay and energy consumption for IoT devices.

Multi-access edge computing (MEC) plays a key role in fifth-generation (5G) networks in bringing cloud functionalities at the edge of the radio access network, in close proximity to mobile users. In this paper we focus on mobile-edge computation offloading, a way to transfer heavy demanding, and latency-critical applications from mobile handsets to close-located MEC servers, in order to reduce latency and/or energy consumption. Our goal is to provide an optimal strategy to associate mobile users to access points (AP) and MEC hosts, while contextually optimizing the allocation of radio and computational resources to each user, with the objective of minimizing the overall user transmit power under latency constraints incorporating both communication and computation times. The overall problem is a mixed-binary problem. To overcome its inherent computational complexity, we propose two alternative strategies: i) a method based on successive convex approximation (SCA) techniques, proven to converge to local optimal solutions; ii) an approach hinging on matching theory, based on formulating the assignment problem as a matching game.

The use of intelligent reflecting surfaces (IRS) in next‐generation mobile networks is currently a hot topic. At the same time, next‐generation mobile networks are going airborne as heavily discussed in this book. Since the base stations and users both are meant to be mobile in this setting, this chapter explores the possibility of using IRS in airborne networks (ANs) for localization of users and base stations. Positioning is an important aspect in present and future wireless networks, where it augments the network operations and assists in multiple localization‐based applications. This chapter outlines the use of IRS in AN in different settings and gives an overview of potential positioning using pilot symbols in beyond‐5G (B5G) mobile networks employing IRSs.

The deployment of fifth-generation (5G) and beyond 5G (B5G) networks is the ambitious objective of modern research on future mobile networks that are evolving to support computation-intensive and communication-sensitive applications. Such applications (e.g., autonomous vehicles, industrial automation, and remote surgery) impose diverse quality-of-service (QoS) requirements on the network in terms of processing, latency, reliability, and bandwidth, and will require ultra-reliable low-latency communication (URLLC), paving the way for multi-access edge computing (MEC). Our work considers a dynamic indoor B5G network in a robotic scenario where agents continuously need MEC services and migrate from one cell to another to perform their tasks in an ultra-dense cell environment. Assuming that every MEC service is a virtual machine (VM) to execute in one of the cells with the possibility of migrating the VM to another cell by paying some cost, we formalize the joint problem of (1) placing/migrating the VMs to respect their end-to-end communication latency requirements and (2) allocating their computation and communication bandwidth as a mixed-integer linear program (MILP). An MILP solver is then used to find the optimal VM placements/migrations and bandwidth allocations over a time horizon.Keywords5GMECMILPURLLCReal-time

Internet of Things (IoT) expects to incorporate massive machine-type (MCT) devices, such as vehicles, sensors, and wearable devices, which brings a large number of application tasks that need to be processed. Additionally, data collected from various devices needs to be executed and processed in a timely, reliable, and efficient manner. In this paper, we introduce the multi-access edge computing (MEC) service into the IoT system such that it can assist the computation offloading and resource allocation for different application tasks. Further, we define a total cost function as the weighted sum of task delay and energy consumption and propose a novel deep reinforcement learning (DRL)-based decision-making algorithm to cooperatively optimize the task offloading and resource allocation. More specifically, we transform the initial problem into a convex optimization problem with the aid of our proposed atom action generation technique (2AGT) and adaptive aggregation parameter update strategy (2APUS). Additionally, the DRL agent will be retrained periodically to adapt to different network environments. Finally, numerical simulation results validate that the proposed algorithm is nearly close to optimal performance compared with some cutting-edge schemes.

In a level-5 autonomous driving system, the autonomous driving vehicles (AVs) are expected to sense the surroundings via analyzing a large amount of data captured by a variety of onboard sensors in near-real-time. As a result, enormous computing costs will be introduced to the AVs for processing the tasks with the deployed machine learning (ML) model, while the inference accuracy may not be guaranteed. In this context, the advent of edge intelligence (EI) and sixth-generation (6G) wireless networking are expected to pave the way to more reliable and safer autonomous driving by providing multi-access edge computing (MEC) together with ML to AVs in close proximity. To realize this goal, we propose a two-tier EI-empowered autonomous driving framework. In the autonomous-vehicles tier, the autonomous vehicles are deployed with the shallow layers by splitting the trained deep neural network model. In the edge-intelligence tier, an edge server is implemented with the remaining layers (also deep layers) and an appropriately trained multi-task learning (MTL) model. In particular, obtaining the optimal offloading strategy (including the binary offloading decision and the computational resources allocation) can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved via MTL in near-real-time with high accuracy. On another note, an edge-vehicle joint inference is proposed through neural network segmentation to achieve efficient online inference with data privacy-preserving and less communication delay. Experiments demonstrate the effectiveness of the proposed framework, and open research topics are finally listed.

The global connected cars market is growing rapidly. Novel services will be offered to vehicles, many of them requiring low-latency and high-reliability networking solutions. The Cloud Radio Access Network (C-RAN) paradigm, thanks to the centralization and virtualization of baseband functions, offers numerous advantages in terms of costs and mobile radio performance. C-RAN can be deployed in conjunction with a Multi-access Edge Computing (MEC) infrastructure, bringing services close to vehicles supporting time-critical applications. However, a massive deployment of computational resources at the edge may be costly, especially when reliability requirements demand deployment of redundant resources. In this context, cost optimization based on integer linear programming may result in being too complex when the number of involved nodes is more than a few tens. This paper proposes a scalable approach for C-RAN and MEC computational resource deployment with protection against single-edge node failure. A two-step hybrid model is proposed to alleviate the computational complexity of the integer programming model when edge computing resources are located in physical nodes. Results show the effectiveness of the proposed hybrid strategy in finding optimal or near-optimal solutions with different network sizes and with affordable computational effort.

With the development of intelligent transportation systems (ITS), computation-intensive and latency-sensitive applications are flourishing, posing significant challenges to resource-constrained task vehicles (TVEs). Multi-access edge computing (MEC) is recognized as a paradigm that addresses these issues by deploying hybrid servers at the edge and seamlessly integrating computing capabilities. Additionally, flexible unmanned aerial vehicles (UAVs) serve as relays to overcome the problem of non-line-of-sight (NLoS) propagation in vehicle-to-vehicle (V2V) communications. In this paper, we propose a UAV-assisted heterogeneous multi-server computation offloading (HMSCO) scheme. Specifically, our optimization objective to minimize the cost, measured by a weighted sum of delay and energy consumption, under the constraints of reliability requirements, tolerable delay, and computing resource limits, among others. Since the problem is non-convex, it is further decomposed into two sub-problems. First, a game-based binary offloading decision (BOD) is employed to determine whether to offload based on the parameters of computing tasks and networks. Then, a multi-agent enhanced dueling double deep Q-network (ED3QN) with centralized training and distributed execution is introduced to optimize server offloading decision and resource allocation. Simulation results demonstrate the good convergence and robustness of the proposed algorithm in a highly dynamic vehicular environment.

In order to create a connection in a specific remote area of a skip zone where an optical fiber cable or wireline solution is difficult to build, free space optics (FSO) is essential. For 5G and future transmission networks, FSO connection has been selected as a potential option for secure, low-latency, license-free spectrum and less costly wireless backhaul connectivity. The main obstructions to FSO transmission are adverse weather conditions as well as the need for a direct line of sight (LoS) from Source to Destination. An innovative approach termed intelligent reflecting surface (IRS) based FSO transmission is a most reliable and efficient interesting approach, similar to first-last mile access connection, to relax the line of sight condition of FSO transmission. In this study, IRS-assisted radio frequency (RF), as well as optical IRS-assisted FSO connections are modeled. The FSO link via the IRS module achieves 10–6 value of symbol error rate (SER) at 30 dB SNR while the FSO link without IRS module achieves only 10–3 and 10–2 values of SER at the same SNR for the FSO link is modeled with Gamma-gamma and generalised Malaga (M) distributions, respectively. The SER value is enhanced up to a factor of 10–8 at 20 dB SNR in case of multiple optical-IRS-based FSO connections by increasing additional reflecting elements module (N).

Fairness optimization in IRS-assisted MEC computational offloading

Vehicular networks are the key cornerstone of Intelligent Transportation Systems (ITS). With the growth of the technical revolution in 5G the next-generation networks, it is expected to meet various future communication requirements of Intelligent ITS. However, no single technology of 5G can efficiently accommodate the broad range of requirements of vehicular networks. However, the aggregation of various communication technologies with the 5G vehicular network could help to achieve ambitious goals like efficient network management, centralized view, and computation. The need for provisioning efficient network control, management, and high resource utilization in vehicular networks motivates the hierarchical 5G Next-generation vehicular networks. Blockchain has emerged as a viable solution to offer decentralization, transparency, and immutability among the stakeholders and manage trust in networking platforms. Motivated by the preceding discussion, in this chapter, we discuss the integration of software-defined networking (SDN), cloud computing over 5G mobile communication in vehicular networks, which is the key requirement of new hierarchical 5G-SDVNs. Then, programmable, efficient, and controllable network architecture is introduced for 5G-SDVN to achieve sustainable network development. Moreover, the centralization and flexibility provided by SDN and 5G communication technologies help to meet customer demand for various ITSs and vehicular network applications like bandwidth, high speed, and ubiquity, that can be employed in next-generation ITS. Finally, we present the SDN and cloud-enabled hierarchical future 5G-vehicular network architecture, its applications, issues, and challenges based on recent advances in technology and research.KeywordsBlockchainSoftware defined network5GIntelligent transport system