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 commu...

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Published inIEEE transactions on vehicular technology Vol. 71; no. 3; pp. 3281 - 3286
Main Authors Wang, Yalin, Chen, Xihan, Cai, Yunlong, Hanzo, Lajos
Format Journal Article
LanguageEnglish
Published New York IEEE 01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Alternating optimization
Analog to digital converters
Antennas
Array signal processing
Beamforming
Decoupling
fractional programming
Hybrid power systems
Hybrid systems
imperfect CSI
massive multiple-input multiple-output (MIMO)
Mathematical programming
MIMO communication
Optimization
Quantization (signal)
Radio frequency
reconfigurable intelligent surface (RIS)
Resource allocation
robust beamforming design
Robust design
Transforms
Uplink
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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 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 classic 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.
AbstractList 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 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 classic 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.
Author Chen, Xihan
Wang, Yalin
Hanzo, Lajos
Cai, Yunlong
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SubjectTerms Algorithms
Alternating optimization
Analog to digital converters
Antennas
Array signal processing
Beamforming
Decoupling
fractional programming
Hybrid power systems
Hybrid systems
imperfect CSI
massive multiple-input multiple-output (MIMO)
Mathematical programming
MIMO communication
Optimization
Quantization (signal)
Radio frequency
reconfigurable intelligent surface (RIS)
Resource allocation
robust beamforming design
Robust design
Transforms
Uplink
Title RIS-Aided Hybrid Massive MIMO Systems Relying on Adaptive-Resolution ADCs: Robust Beamforming Design and Resource Allocation
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