Hybrid LMMSE Transceiver Optimization for Distributed IoT Sensing Networks with Different Levels of Synchronization

Heng Liu, Shuai Wang, Shiqi Gong, Nan Zhao, Jianping An, Tony Q.S. Quek

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

In this paper, we investigate the analog-digital hybrid transceiver optimization for distributed internet of things (IoT) sensing networks consisting of a multi-antenna fusion center (FC) and several multi-antenna sensor nodes. Analog-digital hybrid transceiver is an economic way to realize tradeoffs between hardware cost and performance for multi-antenna communications. Under the nonconvex unit modulus constraints and transmit power constraint at each sensor, two synchronization schemes are considered for the hybrid linear minimum mean square error (LMMSE) transceiver optimization. Firstly, a centralized algorithm is proposed, in which the hybrid transceivers are computed at the FC. Based on the framework of alternating direction method of multipliers (ADMM), the unit modulus constraints can be satisfied by projecting the elements of analog transceivers onto the unit modulus circle. However, the centralized algorithm usually suffers from strict synchronous requirements and high communication overhead. In order to accommodate the inevitable computing and communication delays in distributed IoT sensing networks, an asynchronous distributed ADMM (AD-ADMM) algorithm is proposed. By using the aged information, the hybrid transceivers are computed at the sensors without the coordination of the FC. Thus the AD-ADMM algorithm can greatly reduce the computation overhead of the FC and improve the scalability of IoT sensing networks. Simulation results are presented to show that both the centralized ADMM and AD-ADMM algorithms perform closely to the fully digital counterpart.

原文English
期刊IEEE Internet of Things Journal
DOIs
出版狀態Accepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • 訊號處理
  • 資訊系統
  • 硬體和架構
  • 電腦科學應用
  • 電腦網路與通信

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