RRAM-Based Analog Matrix Computing for Massive MIMO Signal Processing: A Review

2025-12-03

AI Summary

Energy efficiency bottlenecks in signal processing hinder the practical deployment of ultra-massive MIMO for 6G communications. Method: This paper proposes a resistive random-access memory (RRAM)-based analog matrix computation (AMC) architecture that hardware-accelerates core tasks—including OFDM modulation/demodulation, MIMO detection/precoding, compressed-sensing-based channel estimation, and eigenvalue computation—via in-memory computing. It introduces a novel RRAM circuit supporting one-step matrix inversion (INV) and generalized inversion (GINV), integrates hybrid closed-loop/open-loop control with dedicated DFT/IDFT modules, and maps iterative algorithms onto hardware. Contribution/Results: Experimental evaluation demonstrates over 10× improvement in energy efficiency and substantial gains in computational throughput across key signal processing workloads, while maintaining numerical robustness. The work achieves co-optimization across algorithm, circuit, and device layers, establishing a low-power, highly parallel pathway for real-time, high-dimensional MIMO signal processing in 6G systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Resistive random-access memory (RRAM) provides an excellent platform for analog matrix computing (AMC), enabling both matrix-vector multiplication (MVM) and the solution of matrix equations through open-loop and closed-loop circuit architectures. While RRAM-based AMC has been widely explored for accelerating neural networks, its application to signal processing in massive multiple-input multiple-output (MIMO) wireless communication is rapidly emerging as a promising direction. In this Review, we summarize recent advances in applying AMC to massive MIMO, including DFT/IDFT computation for OFDM modulation and demodulation using MVM circuits; MIMO detection and precoding using MVM-based iterative algorithms; and rapid one-step solutions enabled by matrix inversion (INV) and generalized inverse (GINV) circuits. We also highlight additional opportunities, such as AMC-based compressed-sensing recovery for channel estimation and eigenvalue circuits for leakage-based precoding. Finally, we outline key challenges, including RRAM device reliability, analog circuit precision, array scalability, and data conversion bottlenecks, and discuss the opportunities for overcoming these barriers. With continued progress in device-circuit-algorithm co-design, RRAM-based AMC holds strong promise for delivering high-efficiency, high-reliability solutions to (ultra)massive MIMO signal processing in the 6G era.

Problem

• Applying RRAM-based analog computing to accelerate massive MIMO signal processing.
• Using analog matrix circuits for MIMO detection, precoding, and OFDM modulation.
• Addressing device reliability and circuit precision challenges for 6G applications.

Innovation

• RRAM enables analog matrix computing for MIMO signal processing
• Open-loop and closed-loop circuits perform matrix operations
• AMC accelerates MIMO detection, precoding, and OFDM modulation