Multi-Agent Distributed Beamforming With Improper Gaussian Signaling for MIMO Interference Broadcast Channels

For rate optimization in interference limited networks, improper Gaussian signaling has shown its ability to outperform conventional proper Gaussian signaling. In this paper, we study a weighted sum-rate maximization problem with improper Gaussian signaling for the multiple-input multiple-output int...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2019-01, Vol.18 (1), p.136-151
Main Authors: Lin, Jhe-Yi, Chang, Ronald Y., Lee, Chia-Han, Tsao, Hen-Wai, Su, Hsuan-Jung
Format: Article
Language:English
Subjects:
Algorithms
alternating direction method of multipliers (ADMM)
Array signal processing
Beamforming
cloud radio access network (C-RAN)
Computer simulation
Convergence
Covariance matrices
Covariance matrix
distributed beamforming
Gaussian beams (optics)
improper signaling
Interference
MIMO communication
multi-agent optimization
Multiagent systems
Multiple-input multiple-output interference broadcast channel (MIMO-IBC)
Optimization
Signaling
Wireless communication
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Summary:For rate optimization in interference limited networks, improper Gaussian signaling has shown its ability to outperform conventional proper Gaussian signaling. In this paper, we study a weighted sum-rate maximization problem with improper Gaussian signaling for the multiple-input multiple-output interference broadcast channel. To solve this nonconvex and NP-hard problem, we propose an effective separate covariance and pseudo-covariance matrices optimization algorithm. In the covariance optimization, a weighted minimum mean square error algorithm is adopted, and in the pseudo-covariance optimization, an alternating optimization (AO) algorithm is proposed, which guarantees convergence to a stationary solution and ensures a sum-rate improvement over proper Gaussian signaling. An alternating direction method of multipliers-based multi-agent distributed algorithm is proposed to solve an AO subproblem with the globally optimal solution in a parallel and scalable fashion. The proposed scheme exhibits favorable convergence, optimality, and complexity properties for future large-scale networks. The simulation results demonstrate the superior sum-rate performance of the proposed algorithm as compared with the existing schemes with proper as well as improper Gaussian signaling under various network configurations.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2018.2878030