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.

Channel estimation and channel tracking for correlated block-fading channels in massive MIMO systems

This paper presents a channel estimation and tracking method for correlated block-fading channels in massive MIMO wireless cellular systems. In order to conserve resources, the proposed algorithm requires the uplink pilot signal only once, at the start of communication. By utilizing the temporal correlation between consecutive Resource Blocks （RBs） and the error correction capability of turbo codes, the channel matrix in subsequent RBs is estimated at the Base Station （BS） itself using the uplink data of current the RB and the estimated channel matrix of previous the RB. Compared to existing blind estimation methods, the proposed method places fewer limitations on the system settings such as the number of BS antennas, the number of users, and the number of coherent channel usage compared to existing blind estimation methods. Simulation results show that the proposed algorithm provides better performance for a moderate RB size, a high-order of QAM scheme, and a smaller ratio of the number of BS antennas and mobile terminals （N/K）. For a reasonably small N/K （order of 10）, the proposed scheme achieves a lower symbol error probability than the conventional pilot-based estimation approach.

Secure Federated Learning over Wireless Communication Networks with Model Compression

Although federated learning(FL)has become very popular recently,it is vulnerable to gradient leakage attacks.Recent studies have shown that attackers can reconstruct clients’private data from shared models or gradients.Many existing works focus on adding privacy protection mechanisms to prevent user privacy leakages,such as differential privacy(DP)and homomorphic encryption.These defenses may cause an increase in computation and communication costs or degrade the performance of FL.Besides,they do not consider the impact of wireless network resources on the FL training process.Herein,we propose weight compression,a defense method to prevent gradient leakage attacks for FL over wireless networks.The gradient compression matrix is determined by the user’s location and channel conditions.We also add Gaussian noise to the compressed gradients to strengthen the defense.This joint learning of wireless resource allocation and weight compression matrix is formulated as an optimization problem with the objective of minimizing the FL loss function.To find the solution,we first analyze the convergence rate of FL and quantify the effect of the weight matrix on FL convergence.Then,we seek the optimal resource block(RB)allocation by exhaustive search or ant colony optimization(ACO)and then use the CVX toolbox to obtain the optimal weight matrix to minimize the optimization function.The simulation results show that the optimized RB can accelerate the convergence of FL.

Resource allocation scheme based on game theory in heterogeneous networks

As an effective solution for indoor coverage and service offioading from the conventional cellular networks, femtocells have attracted a lot of attention in recent years. This study investigates the resource block （RB） and power allocation in heterogeneous networks （HetNets）. Specifically, the concern here is to maximize the signal to interference-plus-noise ratio （SINR） of macrocell and energy efficiency of femtocell while providing the finite interference. In this paper, the system model is divided to two layers, in which the macro base station and clusters constitute the first layer network; femtocells in cluster make up the second layer network. Because of the different model structures, different game theories are used in different layers. Stackelberg game is used in the first layer, and non-cooperation game is used in the second layer. Meanwhile RB and power levels stand for the actions that are associated with each player in the game. The problem of resource allocation is formulated as a mixed integer programming problem. In order to minimize the complexity of the proposed algorithm, the resource allocation task is decomposed into two sub problems： a RB allocation and a power allocation. The result is compared with the traditional methods, the analysis illustrates the proposed algorithm has a better performance regarding SINR and energy efficiency of the heterogeneous networks.