Cooperative Hybrid Transmit Beamforming in Cell-free mmWave MIMO Networks
Meesam Jafri, Suraj Srivastava, Naveen K. D. Venkategowda, Aditya K., Jagannatham, Lajos Hanzo

TL;DR
This paper introduces novel hybrid beamforming strategies for cooperative cell-free mmWave MIMO networks, enhancing interference mitigation and spectral efficiency through optimal and Bayesian learning-based designs.
Contribution
It proposes new hybrid transmit beamformers for broadcast, unicast, and multicast scenarios, including a Bayesian learning approach for joint RF and baseband precoding, and analyzes their interference mitigation capabilities.
Findings
Effective interference cancellation improves spectral efficiency.
Proposed schemes support a large number of users.
Bayesian learning-based design enhances precoding performance.
Abstract
Hybrid precoders and combiners are designed for cooperative cell-free multi-user millimeter wave (mmWave) multiple-input multiple-output (MIMO) cellular networks for low complexity interference mitigation. Initially, we derive an optimal hybrid transmit beamformer (HTBF) for a broadcast scenario considering both total and per access point (AP) power constraints. Next, an optimal successive hybrid beamformer technique is proposed for unicast and multicast scenarios which relies on the optimal minimum variance distortionless response (MVDR). We demonstrate that it mitigates both the interuser and intergroup interference, while successively ensuring orthogonality to the previously scheduled users/user groups. Furthermore, it is shown theoretically that the proposed schemes are capable of supporting a large number of users. Subsequently, a Bayesian learning (BL) based method is conceived…
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Taxonomy
TopicsMillimeter-Wave Propagation and Modeling · Advanced MIMO Systems Optimization · Microwave Engineering and Waveguides
