Joint UAV Deployment and Resource Allocation in THz-Assisted MEC-Enabled Integrated Space-Air-Ground Networks

Multi-access edge computing (MEC)-enabled integrated space-air-ground (SAG) networks have drawn much attention recently, as they can provide communication and computing services to wireless devices in areas that lack terrestrial base stations (TBSs). Leveraging the ample bandwidth in the terahertz (THz) spectrum, in this paper, we propose MEC-enabled integrated SAG networks with collaboration among unmanned aerial vehicles (UAVs). We then formulate the problem of minimizing the energy consumption of devices and UAVs in the proposed MEC-enabled integrated SAG networks by optimizing tasks offloading decisions, THz sub-bands assignment, transmit power control, and UAVs deployment. The formulated problem is a mixed-integer nonlinear programming (MILP) problem with a non-convex structure, which is challenging to solve. We thus propose a block coordinate descent (BCD) approach to decompose the problem into four sub-problems: 1) device task offloading decision problem, 2) THz sub-band assignment and power control problem, 3) UAV deployment problem, and 4) UAV task offloading decision problem. We then propose to use a matching game, concave-convex procedure (CCP) method, successive convex approximation (SCA), and block successive upper-bound minimization (BSUM) approaches for solving the individual subproblems. Finally, extensive simulations are performed to demonstrate the effectiveness of our proposed algorithm.

Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning

The development of 6G/B5G wireless networks, which have requirements that go beyond current 5G networks, is gaining interest from academia and industry. However, to increase 6G/B5G network quality, conventional cellular networks that rely on terrestrial base stations are constrained geographically and economically. Meanwhile, NOMA allows multiple users to share the same resources, which improves the spectral efficiency of the system and has the advantage of supporting a larger number of users. Additionally, by intelligently manipulating the phase and amplitude of both the reflected and transmitted signals, STAR-RISs can achieve improved coverage, increased spectral efficiency, and enhanced communication reliability. However, STAR-RISs must simultaneously optimize the amplitude and phase shift corresponding to reflection and transmission, which makes the existing terrestrial networks more complicated and is considered a major challenging issue. Motivated by the above, we study the joint user pairing for NOMA and beamforming design of Multi-STAR-RISs in an indoor environment. Then, we formulate the optimization problem with the objective of maximizing the total throughput of MUs by jointly optimizing the decoding order, user pairing, active beamforming, and passive beamforming. However, the formulated problem is a MINLP. To address this challenge, we first introduce the decoding order for NOMA networks. Next, we decompose the original problem into two subproblems, namely: 1) MU pairing and 2) Beamforming optimization under the optimal decoding order. For the first subproblem, we employ correlation-based K-means clustering to solve the user pairing problem. Then, to jointly deal with beamforming vector optimizations, we propose MAPPO, which can make quick decisions in the given environment owing to its low complexity.

SpaceRIS: LEO Satellite Coverage Maximization in 6G Sub-THz Networks by MAPPO DRL and Whale Optimization

Satellite systems face a significant challenge in effectively utilizing limited communication resources to meet the demands of ground network traffic, characterized by asymmetrical spatial distribution and time-varying characteristics. Moreover, the coverage range and signal transmission distance of low Earth orbit (LEO) satellites are restricted by notable propagation attenuation, molecular absorption, and space losses in sub-terahertz (THz) frequencies. This paper introduces a novel approach to maximize LEO satellite coverage by leveraging reconfigurable intelligent surfaces (RISs) within 6G sub-THz networks. The optimization objectives encompass enhancing the end-to-end data rate, optimizing satellite-remote user equipment (RUE) associations, data packet routing within satellite constellations, RIS phase shift, and ground base station (GBS) transmit power (i.e., active beamforming). The formulated joint optimization problem poses significant challenges owing to its time-varying environment, non-convex characteristics, and NP-hard complexity. To address these challenges, we propose a block coordinate descent (BCD) algorithm that integrates balanced K-means clustering, multi-agent proximal policy optimization (MAPPO) deep reinforcement learning (DRL), and whale optimization (WOA) techniques. The performance of the proposed approach is demonstrated through comprehensive simulation results, exhibiting its superiority over existing baseline methods in the literature.

Dependency Tasks Offloading and Communication Resource Allocation in Collaborative UAVs Networks: A Meta-Heuristic Approach

In recent years, unmanned aerial vehicles (UAVs) assisted mobile edge computing systems have been exploited by researchers as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. However, it remains challenging for the standalone MEC-enabled UAVs in order to meet the computation requirement of numerous mobile users due to the limited computation capacity of their onboard servers and battery lives. Therefore, we propose a collaborative scheme among UAVs so that UAVs can share the workload with idle UAVs. Moreover, current task offloading strategies frequently overlook task topology, which may result in poor performance or even system failure. To address the problem, we consider offloading tasks consisting of a set of sub-tasks, and each sub-task has dependencies on other sub-tasks, which is practical in the real world. Sub-tasks with dependencies need to wait for the resulting signal from preceding sub-tasks before being executed. This mechanism has serious effects on the offloading strategy. Then, we formulate an optimization problem to minimize the average latency experienced by users by jointly controlling the offloading decision for dependent tasks and allocating the communication resources of UAVs. The formulated problem appears to be NP-hard and cannot be solved in polynomial time. Therefore, we divide the problem into two sub-problems: the offloading decision problem and the communication resource allocation problem. Then a meta-heuristic method is proposed to find the sub-optimal solution of the task offloading problem, while the communication resource allocation problem is solved by using convex optimization. Finally, we perform substantial simulation experiments, and the result shows that the proposed offloading technique effectively minimizes the average latency of users, compared with other benchmark schemes.

3TO: THz-Enabled Throughput and Trajectory Optimization of UAVs in 6G Networks by Proximal Policy Optimization Deep Reinforcement Learning

Next-generation networks need to meet ubiquitous and high data-rate demand. Therefore, this paper considers the throughput and trajectory optimization of terahertz (THz)-enabled unmanned aerial vehicles (UAVs) in the sixth-generation (6G) communication networks. In the considered scenario, multiple UAVs must provide on-demand terabits per second (TB/s) services to an urban area along with existing terrestrial networks. However, THz-empowered UAVs pose some new constraints, e.g., dynamic THz-channel conditions for ground users (GUs) association and UAV trajectory optimization to fulfill GU's throughput demands. Thus, a framework is proposed to address these challenges, where a joint UAVs-GUs association, transmit power, and the trajectory optimization problem is studied. The formulated problem is mixed-integer non-linear programming (MINLP), which is NP-hard to solve. Consequently, an iterative algorithm is proposed to solve three sub-problems iteratively, i.e., UAVs-GUs association, transmit power, and trajectory optimization. Simulation results demonstrate that the proposed algorithm increased the throughput by up to 10%, 68.9%, and 69.1% respectively compared to baseline algorithms.

Trajectory Optimization and Phase-Shift Design in IRS Assisted UAV Network for High Speed Trains

The recent trend towards the high-speed transportation system has spurred the development of high-speed trains (HSTs). However, enabling HST users with seamless wireless connectivity using the roadside units (RSUs) is extremely challenging, mostly due to the lack of line of sight link. To address this issue, we propose a novel framework that uses intelligent reflecting surfaces (IRS)-enabled unmanned aerial vehicles (UAVs) to provide line of sight communication to HST users. First, we formulate the optimization problem where the objective is to maximize the minimum achievable rate of HSTs by jointly optimizing the trajectory of UAV and the phase-shift of IRS. Due to the non-convex nature of the formulated problem, it is decomposed into two subproblems: IRS phase-shift problem and UAV trajectory optimization problem. Next, a Binary Integer Linear Programming (BILP) and a Soft Actor-Critic (SAC) are constructed in order to solve our decomposed problems. Finally, comprehensive numerical results are provided in order to show the effectiveness of our proposed framework.

A Business Model for Resource Sharing in Cell-Free UAVs-Assisted Wireless Networks

Unmanned aerial vehicles (UAVs) are widely deployed to enhance the wireless network capacity and to provide communication services to mobile users beyond the infrastructure coverage. Recently, with the help of a promising technology called network virtualization, multiple service providers (SPs) can share the infrastructures and wireless resources owned by the mobile network operators (MNOs). Then, they provide specific services to their mobile users using the resources obtained from MNOs. However, wireless resource sharing among SPs is challenging as each SP wants to maximize their utility/profit selfishly while satisfying the QoS requirement of their mobile users. Therefore, in this paper, we propose joint user association and wireless resource sharing problem in the cell-free UAVs-assisted wireless networks with the objective of maximizing the total network utility of the SPs while ensuring QoS constraints of their mobile users and the resource constraints of the UAVs deployed by MNOs. To solve the proposed mixed-integer non-convex problem, we decompose the proposed problem into two subproblems: users association, and resource sharing problems. Then, a two-sided matching algorithm is deployed in order to solve users association problem. We further deploy the whale optimization and Lagrangian relaxation algorithms to solve the resource sharing problem. Finally, extensive numerical results are provided in order to show the effectiveness of our proposed algorithm.