Open AccessSuccessive convex approximation for rate maximisation in cooperative multiple‐input–multiple‐output‐orthogonal frequency‐division multiplexing systems
Author(s) -
Hsu Chihyu,
Yeoh Phee Lep,
Krongold Brian Scott
Publication year - 2015
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.2014.1136
Subject(s) - mathematical optimization , beamforming , computer science , telecommunications link , orthogonal frequency division multiplexing , base station , transmitter power output , multiplexing , transmission (telecommunications) , constraint (computer aided design) , channel (broadcasting) , power (physics) , algorithm , convex optimization , mathematics , telecommunications , regular polygon , transmitter , geometry , physics , quantum mechanics
In this study, the authors propose a continuous rate and power allocation algorithm for multiuser downlink multiple‐input–multiple‐output orthogonal frequency‐division multiplexing systems with coordinated multi‐point transmission. The optimisation problem is formulated as a weighted sum‐rate maximisation problem subject to per‐antenna power constraints across multiple cooperating base stations (BSs). The practical consideration of the per‐antenna power constraint limits the average transmit antenna power which indirectly controls the inherent issue of high peak powers in OFDM. The proposed algorithm employs a successive convex approximation (SCA) technique to dynamically allocate powers to multiple co‐channel user terminals. They provide a convexity proof of the transformed optimisation problem and they show that the proposed algorithm converges to a unique solution. They compare the proposed SCA algorithm with two alternative approaches: (i) iterative waterfilling (IWF) and (ii) zero‐forcing beamforming (ZFB) with semi‐orthogonal user selection under both per‐antenna and per‐BS power constraint scenarios. Their simulation results highlight that the proposed SCA algorithm outperforms the existing IWF and ZFB in noise‐limited environments under both power constraint scenarios.