RIS-Aided Hybrid Massive MIMO Systems Relying on Adaptive-Resolution ADCs: Robust Beamforming Design and Resource Allocation

Abstract

The large-scale multiple-input multiple-output (MI-MO) 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 multiantenna 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.

Energy Efficient Resource Allocation for Uplink RIS-Aided Millimeter-Wave Networks With NOMA

“…9, we evaluate the impact of imperfect CSI (I-CSI) on the EE performance of the RIS-aided mmWave-NOMA, where r 0,k =0. Following the statistical cascaded CSI estimation model in [29], [30], the cascaded channel matrix Hk and the channel vector g k can be respectively modeled as Hk = Ĥk + E k and g k = ĝk + ϵ k , where Ĥk and ĝk are the estimated channel matrix and vector, respectively, E k and ϵ k are the corresponding CSI error matrix and vector, and they obey the circularly symmetric complex Gaussian distribution.…”

“…vec(E k ) ∼ CN (0, σ 2 k I M N ), and ϵ k ∼ CN (0, ς 2 k I N ), where σ 2 k and ς 2 k are the variances of the corresponding CSI errors. According to [29], [30], σ 2 k = µ 2 k ||vec( Ĥk )|| 2 and ς 2 k = μ2 k ||ĝ k || 2 , where µ 2 k ∈ [0, 1) and μ2 k ∈ [0, 1) are the normalized CSI errors that measure the CSI uncertainty level, and it is assumed that the normalized CSI errors of the users are the same for the convenience [29], [30], i.e., µ 2 k = µ 2 , and μ2 k = μ2 . In simulation, different errors µ 2 = μ2 =0.01 2 , and µ 2 = μ2 =0.02 2 are considered, and for comparison, the EE performance under perfect CSI (P-CSI) is also provided.…”

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

“…For ultramassive MIMO in higher frequency bands, research is being carried out, focusing on integrated circuit design, channel characteristics, modulation techniques and so on [344], [407], [408]; 3) In addition to the traditional centralized active antenna array, ultra-massive MIMO is expected to take a more flexible and diverse approach for implementation. By using RIS (which will be introduced later), instead of traditional active antennas, network coverage, multi-user capacity, and signal strength can be significantly improved [409]- [411]; 4) The distributed ultra-massive antenna system can deploy a large number of distributed antennas over a wide geographical area to build cell-free network, which is conducive to achieving consistent user experience, obtaining high SE, and reducing the transmission energy consumption of the system [307], [308], [412]; 5) The introduction of AI for ultramassive MIMO technology helps to achieve intelligence in multiple aspects such as channel estimation, channel sounding, beam management, and user detection. How to meet real-time requirements and obtain training data needs to be addressed [413], [414]; 6) Ultra-massive MIMO is also expected to be combined with space-air-ground-sea integrated networks.…”

Performance Analysis and Bit Allocation of Cell-Free Massive MIMO Network With Variable-Resolution ADCs

“…Ahmed et al [17] presented an optimal energy-efficient bit allocation algorithm under a power constraint. Considering RIS-aided massive MIMO systems, Wang et al in [18] optimized the ADC's resolution, the transmit power, the passive reflection coefficients of the RIS and the hybrid combiner via the Lagrangian dual transform and FP method. Although it has been shown that adopting variable-resolution ADCs can improve system performance, these studies have not investigated the use of variable-resolution ADCs over cell-free networks.…”

“…In comparison with [19], our optimization problems are different and we optimize ADC bits using different criteria, i.e., normalized mean square error (NMSE) for channel estimation and theoretical sum achievable SEs for both MRC and MMSE. In contrast to [13]- [18], wherein RIS-aided and hybrid mmWave systems are considered, we pay attention to cell-free massive MIMO; accordingly, the corresponding theoretical analysis and bit optimization are different.…”