Open AccessMulti‐pair massive MIMO relay networks: power scaling laws and user scheduling strategy
Author(s) -
Liang Xuesong,
Jin Shi,
Wong KaiKit,
Hong Tao,
Zhu Hongbo
Publication year - 2017
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2016.1487
Subject(s) - relay , ergodic theory , mimo , computer science , scheduling (production processes) , beamforming , mathematical optimization , transmitter power output , topology (electrical circuits) , algorithm , power (physics) , mathematics , telecommunications , channel (broadcasting) , quantum mechanics , combinatorics , mathematical analysis , physics , transmitter
This study studies a multi‐pair massive multiple‐input multiple‐output (MIMO) relaying network, where multiple pairs of users are served by a single relay station with a large number of antennas, and the amplify‐and‐forward protocol and zero‐forcing (ZF) beamforming are used at the relay. The authors investigate the ergodic achievable rates for the users and obtain tight approximations in closed form for finite number of antennas. The rate performance and power efficiency are studied based on the analytical results for asymptotic scenarios, and the effect of scaling factors of transmit powers for users and relay are discussed. The closed‐form expressions enable us to determine the optimal user scheduling which maximizes the ergodic sum‐rate for the selected pairs. A simplified user scheduling algorithm is proposed which greatly reduces the average complexity of the optimal use pair search without any rate loss. Moreover, the complexity reduction for the proposed algorithm increases nonlinearly with the increase of the number of user pairs, which indicates that the simplified scheduling algorithm has notable advantages when the number of users is increased. The tightness for the analytical approximations and the superiority of the proposed algorithm are verified by Monte‐Carlo simulation results.