dc.contributor.author | Ruan, H | |
dc.contributor.author | Xiao, P | |
dc.contributor.author | Xiao, L | |
dc.contributor.author | Kelly, J | |
dc.date.accessioned | 2021-02-18T13:44:56Z | |
dc.date.available | 2021-02-18T13:44:56Z | |
dc.date.issued | 2021-01-01 | |
dc.identifier.issn | 0090-6778 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/70407 | |
dc.description.abstract | IEEE This paper proposes a joint iterative optimization based hybrid beamforming technique for massive MU-MIMO systems. The proposed technique jointly and iteratively optimizes the transmitter precoders and combiners, aiming to approach the global optimum solution for the system sum-rate maximization problem. The proposed technique develops an adaptive algorithm exploiting the stochastic gradients (SG) of the local beamformers and provides low-complexity closed-form solutions. Furthermore, an efficient adaptive scheme is developed based on the proposed adaptive algorithm and the closed-form solutions. The proposed algorithm requires the signal-to-interference-plus-noise ratio (SINR) feedback from each user and a limited size transition vector to be exchanged between the transmitter and receivers at each step to update beamformers locally. Analytic result shows that the proposed adaptive algorithm achieves low-complexity when the array size is large and is able to converge within a small number of iterations. Simulation result shows that the proposed technique is able to achieve superior performance comparing to the existing state-of-art techniques. In addition, the knowledge of instantaneous channel state information (CSI) is not required as the channels are also adaptively estimated with each coherence time which is a practical assumption since the CSI is usually unavailable or have time-varying nature in real-time applications. | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | IEEE Transactions on Communications | |
dc.title | Joint Iterative Optimization Based Low-Complexity Adaptive Hybrid Beamforming for Massive MU-MIMO Systems | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
dc.identifier.doi | 10.1109/TCOMM.2021.3053021 | |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | Millimeter-wave Antennas and Components for Future Mobile Broadband Networks (MILLIBAN)::Engineering and Physical Sciences Research Council | en_US |
qmul.funder | Millimeter-wave Antennas and Components for Future Mobile Broadband Networks (MILLIBAN)::Engineering and Physical Sciences Research Council | en_US |