Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Active RIS-Aided Massive MIMO Uplink Systems with Low-Resolution ADCs

Zhangjie Peng, Zecheng Lu, Xue Liu, Cunhua Pan, Jiangzhou Wang

TL;DR

This work analyzes an active RIS-aided uplink massive MIMO system with low-resolution ADCs and MRC detection. It derives a closed-form, approximate sum achievable rate and proposes a genetic algorithm to optimize the RIS phase shifts, demonstrating that active RIS yields substantial gains over passive RIS under realistic power constraints. The results show that low-resolution ADCs (e.g., 4 bits) can closely approach high-resolution performance, offering a favorable trade-off between rate, power consumption, and hardware cost. Overall, the study provides analytical tools and optimization methods to leverage active RIS in next-generation uplink communications with reduced quantization requirements.

Abstract

This letter considers an active reconfigurable intelligent surface (RIS)-aided multi-user uplink massive multipleinput multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). The letter derives the closedform approximate expression for the sum achievable rate (AR), where the maximum ratio combination (MRC) processing and low-resolution ADCs are applied at the base station. The system performance is analyzed, and a genetic algorithm (GA)-based method is proposed to optimize the RIS's phase shifts for enhancing the system performance. Numerical results verify the accuracy of the derivations, and demonstrate that the active RIS has an evident performance gain over the passive RIS.

Active RIS-Aided Massive MIMO Uplink Systems with Low-Resolution ADCs

TL;DR

This work analyzes an active RIS-aided uplink massive MIMO system with low-resolution ADCs and MRC detection. It derives a closed-form, approximate sum achievable rate and proposes a genetic algorithm to optimize the RIS phase shifts, demonstrating that active RIS yields substantial gains over passive RIS under realistic power constraints. The results show that low-resolution ADCs (e.g., 4 bits) can closely approach high-resolution performance, offering a favorable trade-off between rate, power consumption, and hardware cost. Overall, the study provides analytical tools and optimization methods to leverage active RIS in next-generation uplink communications with reduced quantization requirements.

Abstract

This letter considers an active reconfigurable intelligent surface (RIS)-aided multi-user uplink massive multipleinput multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). The letter derives the closedform approximate expression for the sum achievable rate (AR), where the maximum ratio combination (MRC) processing and low-resolution ADCs are applied at the base station. The system performance is analyzed, and a genetic algorithm (GA)-based method is proposed to optimize the RIS's phase shifts for enhancing the system performance. Numerical results verify the accuracy of the derivations, and demonstrate that the active RIS has an evident performance gain over the passive RIS.
Paper Structure (8 sections, 3 theorems, 50 equations, 4 figures, 1 table, 1 algorithm)

This paper contains 8 sections, 3 theorems, 50 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. Signal Transmission Model
  5. Analysis Of Achievable Uplink Rate
  6. Phase Shifts Optimization
  7. Numerical Results
  8. Conclusion

Key Result

Theorem 1

For an active RIS-aided massive MIMO system with low-resolution ADCs and MRC processing, the uplink AR of the $k$-th user can be approximated as

Figures (4)

  • Figure 1: Active RIS-aided massive MIMO uplink system with low-resolution ADCs.
  • Figure 2: The sum AR versus the number of BS antennas $M$ and RIS reflecting elements $N$ with $b$ = 1.
  • Figure 3: The sum AR versus the total power $P_{\mathrm{T}}$ with $b$ = 1.
  • Figure 4: The sum AR versus the number of quantization bits with different $M$ and $N$.

Theorems & Definitions (3)

  • Theorem 1
  • Corollary 1
  • Corollary 2