This article presents a multi-intelligent reflecting surface (IRS)- and multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system for 5G/6G networks. In the studied system, multiple UAVs are integrated for providing services to large-scale user equipment (UEs) with the help of multiple IRSs. This article aims to minimize the overall cost including energy consumption, completion time, and maintenance cost of UAVs by jointly optimizing the trajectories of UAVs and phase shifts of IRSs. When solving this problem, one has to count in mind the deployment of stop points (SPs) of UAVs, and consider the association among UEs, and UAVs (i.e., which UE will send data to which UAV at which SP), the order of SPs, and the phase shifts of IRSs. Therefore, traditional optimization techniques may not solve the above-mentioned problem in an efficient way. To tackle the above-mentioned problem, this article proposes an algorithm called TPaPBA that consists of four phases. The first phase optimizes the SPs’ deployment via using a differential evolution algorithm having variable population size. As a result, all the SPs of UAVs can be obtained. Then, the second phase optimizes the association among UEs, SPs, and UAVs. Specifically, TPaPBA first adopts a clustering algorithm to optimize the SPs-UAVs association, and then a close criterion is introduced to optimize UEs-SPs association. Subsequently, third phase adopts a low-complexity greedy algorithm to optimize the order of SPs for UAVs. Finally, the phase shifts of IRSs are optimized to enhance the data rate between UEs and UAVs. The simulation results of TPaPBA on ten instances having UEs ranging from 100 to 1000, reveals that TPaPBA has significantly improved the system performance contribution and outperforms other approaches in terms of reducing the overall cost of UAVs. 

Multi-IRS and Multi-UAV-Assisted MEC System for 5G/6G Networks: Efficient Joint Trajectory Optimization and Passive Beamforming Framework

In this paper, a reconfigurable intelligent surfaces (RISs)-aided millimeter wave (mmWave) uplink (UL) rate-splitting multiple access (RSMA) system is investigated which targets to achieve better rate performance and enhanced coverage capability for multiple users. The considered UL RSMA model splits the rate for each user by dividing their message into multiple parts and hence exploits all the necessary degrees of freedom to achieve maximum capacity region and high user fairness. In particular, we focus on the sum-rate maximization for considered UL RSMA system subject to joint optimization of power allocation to the UL users and beamforming design, i.e., active receive beamforming at the base-station (BS) and passive beamforming at multiple RISs. To efficiently mitigate high inter-node interference in multi-user scenario, we first provided a low-complex user pairing scheme based on k-means clustering and then develop an effective low-cost alternating optimization framework to solve the joint optimization problem sub-optimally by decoupling the problem into different sub-problems of power allocation and beamforming design. Specifically, the sub-problems of power allocation and beamforming design are solved using successive convex approximation, Riemannian manifold and fractional programming techniques. Later, the unified solution based on block coordinate descent (BCD) algorithm is proposed. Extensive numerical simulations validate that the user-clustering effectively significantly improves the performance gain and the considered RSMA system outperforms the conventional multiple schemes in terms rate and user-fairness. Also, the exploitation of spatial correlation among each RIS elements i.e., non-diagonal phase-matrices at each RIS achieve better performance that conventional diagonal phase-matrices setting. 

https://www.techrxiv.org/articles/preprint/Rate-splitting_Multiple_Access_and_Dynamic_User_Clustering_for_Sum-Rate_Maximization_in_Multiple_RISs-aided_Uplink_mmWave_System/21220724/1/files/37634978.pdf

Rate-Splitting Multiple Access and Dynamic User Clustering for Sum-Rate Maximization in Multiple RISs-Aided Uplink mmWave System

In this paper, a reconfigurable intelligent surfaces (RISs)-aided millimeter wave (mmWave) uplink (UL) rate-splitting multiple access (RSMA) system is investigated which targets to achieve better rate performance and enhanced coverage capability for multiple users. The considered UL RSMA model splits the rate for each user by dividing their message into multiple parts and hence exploits all the necessary degrees of freedom to achieve maximum capacity region and high user fairness. In particular, we focus on the sum-rate maximization for considered UL RSMA system subject to joint optimization of power allocation to the UL users and beamforming design, i.e., active receive beamforming at the base-station (BS) and passive beamforming at multiple RISs. To efficiently mitigate high inter-node interference in multi-user scenario, we first provided a low-complex user pairing scheme based on k-means clustering and then develop an effective low-cost alternating optimization framework to solve the joint optimization problem sub-optimally by decoupling the problem into different sub-problems of power allocation and beamforming design. Specifically, the sub-problems of power allocation and beamforming design are solved using successive convex approximation, Riemannian manifold and fractional programming techniques. Later, the unified solution based on block coordinate descent (BCD) algorithm is proposed. Extensive numerical simulations validate that the user-clustering effectively significantly improves the performance gain and the considered RSMA system outperforms the conventional multiple schemes in terms rate and user-fairness. Also, the exploitation of spatial correlation among each RIS elements i.e., non-diagonal phase-matrices at each RIS achieve better performance that conventional diagonal phase-matrices setting.

Due to their limited computation capabilities and battery life, Internet of Things (IoT) networks face significant challenges in executing delay-sensitive and computation-intensive mobile applications and services. Therefore, the Unmanned Aerial Vehicle (UAV) mobile edge computing (MEC) paradigm offers low latency communication, computation, and storage capabilities, which makes it an attractive way to mitigate these limitations by offloading them. Nevertheless, the majority of the offloading schemes let IoT devices send their intensive tasks to the connected edge server, which predictably limits the performance gain due to overload. Therefore, in this paper, besides integrating task offloading and load balancing, we study the resource allocation problem for multi-tier UAV-aided MEC systems. First, an efficient load-balancing algorithm is designed for optimizing the load among ground MEC servers through the handover process as well as hovering UAVs over the crowded areas which are still loaded due to the fixed location of the ground base stations server (GBSs). Moreover, we formulate the joint task offloading, load balancing, and resource allocation as an integer problem to minimize the system cost. Furthermore, an efficient task offloading algorithm based on deep reinforcement learning techniques is proposed to derive the offloading solution. Finally, the experimental results show that the proposed approach not only has a fast convergence performance but also has a significantly lower system cost when compared to the benchmark approaches.

/pdf/joint-task-offloading-resource-allocation-and-load-balancing-1w4puhfh.pdf

Joint Task Offloading, Resource Allocation, and Load-Balancing Optimization in Multi-UAV-Aided MEC Systems

Due to their passive nature and thus low energy consumption, intelligent reflecting surfaces (IRSs) have shown promise as means of extending coverage as a proxy for connection reliability.The relative locations of the base station (BS), IRS, and user equipment (UE) determine the extent of the coverage that IRS provides which demonstrates the importance of IRS placement problem.More specifically, locations, which determine whether BS-IRS and IRS-UE line of sight (LOS) links exist, and surface orientation, which determines whether the BS and UE are within the field of view (FoV) of the surface, play crucial roles in the quality of provided coverage.Moreover, another challenge is high computational complexity, since IRS placement problem is a combinatorial optimization, and is NP-hard.Identifying the orientation of the surface and LOS channel as two crucial factors, we propose an efficient IRS placement algorithm that takes these two characteristics into account in order to maximize the network coverage.We prove the submodularity of the objective function which establishes near-optimal performance bounds for the algorithm.Simulation results demonstrate the performance of the proposed algorithm in a real environment. 

Near-Optimal LOS and Orientation Aware Intelligent Reflecting Surface Placement

Using intelligent reflecting surfaces (IRSs) is a promising approach to enhance the performance of wireless communication systems. In this letter, the placement optimization of multi-IRSs is investigated in multi-IRS-aided wireless communication systems, with the aim of minimizing the number of IRSs subject to the average achievable data rate. Then, an adaptive differential evolution algorithm is developed to jointly optimize the number, locations, and phase shift coefficients of IRSs, in which a novel strategy is devised to adaptively select the mutation operator for each individual. Compared with other algorithms, the proposed algorithm performs well in reducing the number of IRSs while satisfying the average achievable data rate.

/pdf/placement-optimization-for-multi-irs-aided-wireless-18y3svm3.pdf

Placement Optimization for Multi-IRS-Aided Wireless Communications: An Adaptive Differential Evolution Algorithm

Abstract Cloud computing has emerged as a promising paradigm for meeting the growing resource demands of Internet of Things (IoT) devices. Meanwhile, with the popularity of mobile aerial base stations, Unmanned Aerial Vehicle (UAV) assisted cloud computing is essential for providing diversified service at areas without available infrastructure. However, it is difficult to meet the requirements of a number of IoT devices which distribute a large area through one single UAV cloud server, and thus multi-clouds have been applied in large-scale IoT environments. Due to the limited battery capacity of UAV, hybrid energy supply has been considered as an effective approach. How to allocate the computing resources and offload the tasks to the UAV-assisted clouds is a challenging task. In this paper, we study the trade-off between the energy consumption and system performance in a UAV-assisted multi-clouds system. Considering the transmission and execution cost, a dynamic optimization problem with the objective of minimizing the power consumption of UAVs with the constraint of queue stability is formulated, which is further decomposed into three sub-problems using stochastic optimization techniques. A collaborative task offloading and resources allocation algorithm (CTORAA) based on artificial intelligent (AI) technique is proposed to jointly determine task offloading and energy harvesting. We provide corresponding mathematical analysis showing that CTORAA can reach the arbitrary profit-stability trade-off. Finally, we conduct simulation experiments to validate the efficacy of our algorithm. 

/pdf/collaborative-task-offloading-and-resource-allocation-with-1m09chg9.pdf

Collaborative task offloading and resource allocation with hybrid energy supply for UAV-assisted multi-clouds

The allocation and control of network resources play an important role in improving users’ satisfaction with network services and optimizing the overall performance of the communication system. Game theory, as a branch of economics, provides an interdisciplinary technique for efficiently allocating wireless resources, such as channels, bandwidth, and spectrum level for mobile devices. This paper reviews the applications of game theory in mobile systems in recent years. Based on the different game models, categorized by cooperative game, non-cooperative game and their combination, this paper summarizes the most effective resource allocation methods for typical mobile communication scenarios, such as cognitive radio, cloud computing environment, device-to-device communications and cooperative relay networks.

A Review of Game Theoretical Resource Allocation Methods in Wireless Communications

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Placement Optimization for Multi-IRS-Aided Wireless Communications: An Adaptive Differential Evolution Algorithm

Collaborative task offloading and resource allocation with hybrid energy supply for UAV-assisted multi-clouds

A Review of Game Theoretical Resource Allocation Methods in Wireless Communications