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Cooperative Hybrid Transmit Beamforming in Cell-free mmWave MIMO Networks
Department of Electrical Engineering, Indian Institute of Technology, Kanpur, India.ORCID iD: 0000-0002-2052-8254
Department of Electrical Engineering, Indian Institute of Technology, Kanpur, India.ORCID iD: 0000-0002-5793-6040
Linköping University, Department of Science and Technology, Physics, Electronics and Mathematics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-8145-7392
Department of Electrical Engineering, Indian Institute of Technology, Kanpur, India.ORCID iD: 0000-0003-1594-5181
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2023 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 72, no 5, p. 6023-6038Article in journal (Refereed) Published
Abstract [en]

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 for jointly designing the RF and baseband precoders/combiners for the various scenarios considered. Furthermore, we also conceive the uplink counterpart of our HTBF scheme, which is based on maximizing the signal-to-interference-plus noise ratio (SINR) of each individual user. Finally, the efficacy of the proposed schemes is characterized by our extensive simulation results in terms of cancelling the interuser/intergroup interference, which improves the spectral efficiency.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023. Vol. 72, no 5, p. 6023-6038
Keywords [en]
mmWave, hybrid beamforming, multi-cell multi-user, broadcast, and multicast, Bayesian learning
National Category
Communication Systems Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-191071DOI: 10.1109/tvt.2022.3229832ISI: 000991849700038Scopus ID: 2-s2.0-85147271306OAI: oai:DiVA.org:liu-191071DiVA, id: diva2:1727493
Note

Funding: Qualcomm Innovation Fellowship; Arun Kumar Chair Professorship; Engineering and Physical Sciences Research Council [EP/W016605/1, EP/P003990/1]; European Research Councils Advanced Fellow Grant QuantCom [789028]

Available from: 2023-01-16 Created: 2023-01-16 Last updated: 2024-03-26Bibliographically approved

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Venkategowda, Naveen

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Jafri, MeesamSrivastava, SurajVenkategowda, NaveenJagannatham, Aditya K.Hanzo, Lajos
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