RIS-aided hybrid massive MIMO systems relying on adaptive-resolution ADCs: robust beamforming design and resource allocation
RIS-aided hybrid massive MIMO systems relying on adaptive-resolution ADCs: robust beamforming design and resource allocation
The large-scale multiple-input multiple-output (MIMO) uplink is investigated in the presence of channel-induced uncertainty, where variable-resolution analog-to-digital converters (ADCs) are used at the base station (BS) and a reconfigurable intelligent surface (RIS) is employed for supporting communications between the single-antenna users and the multi-antenna BS. We formally maximize the system throughput by jointly optimizing the ADC's resolution, the transmit power, the passive reflection coefficients of the RIS and of the hybrid combiner of the BS subject to practical constraints under statistical cascaded channel state information (CSI) error model. The robust nonconvex optimization problem is firstly decoupled via the Lagrangian dual transform and fractional programming method, followed by a powerful decoupling-based alternating maximization (D-AltMax) algorithm to solve this challenging problem. Our simulation results reveal the supremacy of our proposed algorithm over the benchmark schemes by quantifying the improved system throughput of this robust design.
Array signal processing, Hybrid power systems, Massive multiple-input multiple-output (MI-MO), Optimization, Quantization (signal), Radio frequency, Transforms, Uplink, alternating optimization, fractional programming, imperfect CSI, reconfigurable intelligent surface (RIS), robust beamforming design
Wang, Yalin
861aa937-a944-48ad-bb91-d51c43239e98
Chen, Xihan
eaeb3d54-a9b0-4b85-9845-1c9879cfb9ae
Cai, Yunlong
c872ab27-3cdd-4115-8536-9cd1662f5c78
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
20 December 2021
Wang, Yalin
861aa937-a944-48ad-bb91-d51c43239e98
Chen, Xihan
eaeb3d54-a9b0-4b85-9845-1c9879cfb9ae
Cai, Yunlong
c872ab27-3cdd-4115-8536-9cd1662f5c78
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Wang, Yalin, Chen, Xihan, Cai, Yunlong and Hanzo, Lajos
(2021)
RIS-aided hybrid massive MIMO systems relying on adaptive-resolution ADCs: robust beamforming design and resource allocation.
IEEE Transactions on Vehicular Technology.
(doi:10.1109/TVT.2021.3136676).
Abstract
The large-scale multiple-input multiple-output (MIMO) uplink is investigated in the presence of channel-induced uncertainty, where variable-resolution analog-to-digital converters (ADCs) are used at the base station (BS) and a reconfigurable intelligent surface (RIS) is employed for supporting communications between the single-antenna users and the multi-antenna BS. We formally maximize the system throughput by jointly optimizing the ADC's resolution, the transmit power, the passive reflection coefficients of the RIS and of the hybrid combiner of the BS subject to practical constraints under statistical cascaded channel state information (CSI) error model. The robust nonconvex optimization problem is firstly decoupled via the Lagrangian dual transform and fractional programming method, followed by a powerful decoupling-based alternating maximization (D-AltMax) algorithm to solve this challenging problem. Our simulation results reveal the supremacy of our proposed algorithm over the benchmark schemes by quantifying the improved system throughput of this robust design.
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More information
Accepted/In Press date: December 2021
Published date: 20 December 2021
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Publisher Copyright:
IEEE
Keywords:
Array signal processing, Hybrid power systems, Massive multiple-input multiple-output (MI-MO), Optimization, Quantization (signal), Radio frequency, Transforms, Uplink, alternating optimization, fractional programming, imperfect CSI, reconfigurable intelligent surface (RIS), robust beamforming design
Identifiers
Local EPrints ID: 453203
URI: http://eprints.soton.ac.uk/id/eprint/453203
ISSN: 0018-9545
PURE UUID: 1ce20926-b387-48d8-be1a-f567132d2b72
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Date deposited: 10 Jan 2022 18:37
Last modified: 18 Mar 2024 02:36
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Contributors
Author:
Yalin Wang
Author:
Xihan Chen
Author:
Yunlong Cai
Author:
Lajos Hanzo
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