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Power-Efficient Distributed Beamforming for Full-Duplex MIMO Relaying Networks.
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Power-Efficient Distributed Beamforming for Full-Duplex MIMO Relaying Networks.

Xiaofei Xu, Xiang Chen, Ming Zhao, Shidong Zhou, Chong-Yung ChiJing Wang
IEEE Xplore Digital Library IEEE Transactions on Vehicular Technology, (99), 頁.1
2016

摘要

Distributed beamforming;interference suppression;power efficiency;MIMO;full-duplex relays
Multiple-Input Multiple-Output (MIMO) full-duplexrelaying (FDR) has been considered as an efficient techniqueto provide coverage to users where their direct links from thebase station (BS) are too weak for reliable signal reception.However, when multiple MIMO full-duplex relays are deployedin a network, the signal reception quality relies on the effectivesuppression of multiple types of interference. In this paper,the distributed beamforming is studied for the MIMO FDRnetwork, by formulating a power minimization problem witha non-strict convex objective function (total transmit power ofboth the BS and all the relays in the network) under individualuser rate constraints. We come up with two iterative distributedbeamforming algorithms, Algorithm 1 for relays equipped withsingle receive antenna and Algorithm 2 with multiple receiveantennas. The former can yield a global optimal solution of thepower minimization problem, while the latter can only yielda local optimal solution due to conservative successive convexapproximations performed at each iteration, and a rigorousanalysis on the upper bounds of step sizes is also proposedto guarantee their convergence. The proposed two algorithmsonly require local information exchange between relays, andhence are scalable for different network sizes and topologies.An “early termination” strategy in the operation of the proposedtwo algorithms is also presented to acquire an acceptable transmitpower solution with less computation time consumption, and thussuitable for realistic applications. Finally, some simulation resultsare provided to demonstrate that the proposed two algorithmsperform well and significantly better than the existing state-ofthe-artscheme reported in [22].

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