Performance Optimization of Distributed Primal-Dual Algorithms over Wireless Networks

Abstract

In this paper, the problem of convergence rate optimization for distributed primal-dual algorithm over wireless communications is investigated. In the considered model, each user locally updates the primal and dual variables, which are uploaded to the base station (BS). The BS aggregates the data from the users and broadcast the aggregated value to all users. This resource allocation problem is formulated as an optimization problem whose goal is to minimize the gap between the optimal value and the obtained value after a fixed number of iterations in distributed primal-dual algorithm. To solve this problem, the convergence rate is obtained in closed form for the primal-dual algorithm with considering the impact of wireless factors. Based on this convergence rate, the optimal condition for the power control and resource block allocation is obtained. An iterative algorithm with low complexity is proposed to solve this joint power control and resource block allocation problem. Simulation results show that the proposed algorithm can achieve better compared to baseline methods.