Performance Optimization of Distributed Primal-Dual Algorithms over Wireless Networks
Performance Optimization of Distributed Primal-Dual Algorithms over Wireless Networks
复制标题
DOI：
10.1109/icc42927.2021.9500853
复制
发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Zhaohui Yang;Mingzhe Chen;Kai‐Kit Wong;W. Saad;H. Poor;Shuguang Cui
中科院分区：
文献类型：
--
作者：
Zhaohui Yang;Mingzhe Chen;Kai‐Kit Wong;W. Saad;H. Poor;Shuguang Cui
在本文中，实现分布式原始对偶算法在现实的无线网络进行了研究。在所考虑的模型中，用户和一个基站（BS）合作执行一个分布式的原始-对偶算法控制和优化无线网络。特别地，每个用户必须本地更新原始变量和对偶变量，并将更新的原始变量发送到BS。BS聚合接收到的原始变量，并将聚合的变量广播给所有用户。由于所有的原始和对偶变量以及聚合变量都是通过无线链路传输的，不完美的无线链路将影响分布式原始-对偶算法所获得的解。因此，有必要研究传输差错等无线因素对分布式原对偶算法实现的影响，以及如何优化无线网络性能，以提高分布式原对偶算法实现的解。为了解决这些挑战，原始对偶算法的收敛速度首先推导出一个封闭的形式，同时考虑无线因素，如数据传输错误的影响。基于推导的收敛速度，设计了最优发射功率和资源块分配方案，以最小化目标解与分布式原对偶算法解之间的差距。仿真结果表明，在不考虑无线传输缺陷的情况下，与分布式原对偶算法相比，所提分布式原对偶算法可将目标与解之间的差距减小52%.
In this paper, the implementation of a distributed primal-dual algorithm over realistic wireless networks is investigated. In the considered model, the users and one base station (BS) cooperatively perform a distributed primal-dual algorithm for controlling and optimizing wireless networks. In particular, each user must locally update the primal and dual variables and send the updated primal variables to the BS. The BS aggregates the received primal variables and broadcasts the aggregated variables to all users. Since all of the primal and dual variables as well as aggregated variables are transmitted over wireless links, the imperfect wireless links will affect the solution achieved by the distributed primal-dual algorithm. Therefore, it is necessary to study how wireless factors such as transmission errors affect the implementation of the distributed primal-dual algorithm and how to optimize wireless network performance to improve the solution achieved by the distributed primal-dual algorithm. To address these challenges, the convergence rate of the primal-dual algorithm is first derived in a closed form while considering the impact of wireless factors such as data transmission errors. Based on the derived convergence rate, the optimal transmit power and resource block allocation schemes are designed to minimize the gap between the target solution and the solution achieved by the distributed primal-dual algorithm. Simulation results show that the proposed distributed primal-dual algorithm can reduce the gap between the target and obtained solution by up to 52% compared to the distributed primal-dual algorithm without considering imperfect wireless transmission.