Coalitional game based resource allocation in D2D-enabled V2V communication

The joint resource block(RB)allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle(V2V)links in the device-to-device(D2D)-enabled V2V communication system,where one feasible cellular user(FCU)can share its RB with multiple V2V pairs.The problem is first formulated as a nonconvex mixed-integer nonlinear programming(MINLP)problem with constraint of the maximum interference power in the FCU links.Using the game theory,two coalition formation algorithms are proposed to accomplish V2V link partitioning and FCU selection,where the transferable utility functions are introduced to minimize the interference among the V2V links and the FCU links for the optimal RB allocation.The successive convex approximation(SCA)is used to transform the original problem into a convex one and the Lagrangian dual method is further applied to obtain the optimal transmit power of the V2V links.Finally,numerical results demonstrate the efficiency of the proposed resource allocation algorithm in terms of the system sum-rate.

Joint Task Scheduling, Resource Allocation, and UAV Trajectory under Clustering for FANETs

This paper establishes a new layered flying ad hoc networks(FANETs) system of mobile edge computing(MEC) supported by multiple UAVs,where the first layer of user UAVs can perform tasks such as area coverage, and the second layer of MEC UAVs are deployed as flying MEC sever for user UAVs with computing-intensive tasks. In this system, we first divide the user UAVs into multiple clusters, and transmit the tasks of the cluster members(CMs) within a cluster to its cluster head(CH). Then, we need to determine whether each CH’ tasks are executed locally or offloaded to one of the MEC UAVs for remote execution(i.e., task scheduling), and how much resources should be allocated to each CH(i.e., resource allocation), as well as the trajectories of all MEC UAVs.We formulate an optimization problem with the aim of minimizing the overall energy consumption of all user UAVs, under the constraints of task completion deadline and computing resource, which is a mixed integer non-convex problem and hard to solve. We propose an iterative algorithm by applying block coordinate descent methods. To be specific, the task scheduling between CH UAVs and MEC UAVs, computing resource allocation, and MEC UAV trajectory are alternately optimized in each iteration. For the joint task scheduling and computing resource allocation subproblem and MEC UAV trajectory subproblem, we employ branch and bound method and continuous convex approximation technique to solve them,respectively. Extensive simulation results validate the superiority of our proposed approach to several benchmarks.

Energy Efficiency Optimization for D2D Communications Based on SCA and GP Method

In this paper, we propose an energy-efficient power control scheme for device-to-device(D2D) communications underlaying cellular networks, where multiple D2D pairs reuse the same resource blocks allocated to one cellular user. Taking the maximum allowed transmit power and the minimum data rate requirement into consideration, we formulate the energy efficiency maximization problem as a non-concave fractional programming(FP) problem and then develop a two-loop iterative algorithm to solve it. In the outer loop, we adopt Dinkelbach method to equivalently transform the FP problem into a series of parametric subtractive-form problems, and in the inner loop we solve the parametric subtractive problems based on successive convex approximation and geometric programming method to obtain the solutions satisfying the KarushKuhn-Tucker conditions. Simulation results demonstrate the validity and efficiency of the proposed scheme, and illustrate the impact of different parameters on system performance.

Beamforming design for RIS-aided amplify-and-forward relay networks

The use of a reconfigurable intelligent surface(RIS)in the enhancement of the rate performance is considered to involve the limitation of the RIS being a passive reflector.To address this issue,we propose a RIS-aided amplify-and-forward(AF)relay network in this paper.By jointly optimizing the beamforming matrix at AF relay and the phase-shift matrices at RIS,two schemes are put forward to address a maximizing signal-to-noise ratio(SNR)problem.First,aiming at achieving a high rate,a high-performance alternating optimization(AO)method based on Charnes–Cooper transformation and semidefinite programming(CCT-SDP)is proposed,where the optimization problem is decomposed into three subproblems solved using CCT-SDP,and rank-one solutions can be recovered using Gaussian randomization.However,the optimization variables in the CCT-SDP method are matrices,leading to extremely high complexity.To reduce the complexity,a low-complexity AO scheme based on Dinkelbachs transformation and successive convex approximation(DT-SCA)is proposed,where the variables are represented in vector form,and the three decoupling subproblems are solved using DT-SCA.Simulation results verify that compared to three benchmarks(i.e.,a RIS-assisted AF relay network with random phase,an AF relay network without RIS,and a RIS-aided network without AF relay),the proposed CCT-SDP and DT-SCA schemes can harvest better rate performance.Furthermore,it is revealed that the rate of the low-complexity DT-SCA method is close to that of the CCT-SDP method.

Proactive optimization of transmission power and 3D trajectory in UAV-assisted relay systems with mobile ground users

In this paper,an Unmanned Aerial Vehicle(UAV)-assisted relay communication system is studied,where a UAV is served as a flying relay to maintain a communication link between a mobile source node and a remote destination node.Specifically,an average outage probability minimization problem is formulated firstly,with the constraints on the transmission power of the source node,the maximum energy consumption budget,the transmission power,the speed and acceleration of the flying UAV relay.Next,the closed-form of outage probability is derived,under the hybrid line-of-sight and non-line-of-sight probability channel model.To deal with the formulated nonconvex optimization,a long-term proactive optimization mechanism is developed.In particular,firstly,an approximation for line-of-sight probability and a reformulation of the primal problem are given,respectively.Then,the reformulated problem is transformed into two subproblems:one is the transmission power optimization with given UAV’s trajectory and the other is the trajectory optimization with given transmission power allocation.Next,two subproblems are tackled via tailoring primal–dual subgradient method and successive convex approximation,respectively.Furthermore,a proactive optimization algorithm is proposed to jointly optimize the transmission power allocation and the three-dimensional trajectory.Finally,simulation results demonstrate the performance of the proposed algorithm under various parameter configurations.

Deep Q-Network Based Dynamic Trajectory Design for UAV-Aided Emergency Communications

In this paper,an unmanned aerial vehicle(UAV)-aided wireless emergence communication system is studied,where a UAV is deployed to support ground user equipments(UEs)for emergence communications.We aim to maximize the number of the UEs served,the fairness,and the overall uplink data rate via optimizing the trajectory of UAV and the transmission power of UEs.We propose a deep Q-network(DQN)based algorithm,which involves the well-known deep neural network(DNN)and Q-learning,to solve the UAV trajectory prob-lem.Then,based on the optimized UAV trajectory,we further propose a successive convex approximation(SCA)based algorithm to tackle the power control problem for each UE.Numerical simulations demonstrate that the proposed DQN based algorithm can achieve considerable performance gain over the existing benchmark algorithms in terms of fairness,the number of UEs served and overall uplink data rate via optimizing UAV’s trajectory and power optimization.