In-Memory Computing Enabled Deep MIMO Detection to Support Ultra-Low-Latency Communications

TL;DR

This paper presents a novel in-memory computing architecture for deep MIMO detection in 6G networks, significantly reducing latency and improving robustness by integrating memristor-based hardware with neural network algorithms.

Contribution

It introduces the deep IM-MIMO detector architecture that combines in-memory computing with neural networks, optimizing for ultra-low latency and robustness in 6G MIMO systems.

Findings

Achieves nanosecond-scale matrix-vector multiplication for MIMO detection.

Demonstrates robustness against memristor programming noise.

Quantifies detection accuracy under various noise conditions.

Abstract

The development of sixth-generation (6G) mobile networks imposes unprecedented latency and reliability demands on multiple-input multiple-output (MIMO) communication systems, a key enabler of high-speed radio access. Recently, deep unfolding-based detectors, which map iterative algorithms onto neural network architectures, have emerged as a promising approach, combining the strengths of model-driven and data-driven methods to achieve high detection accuracy with relatively low complexity. However, algorithmic innovation alone is insufficient; software-hardware co-design is essential to meet the extreme latency requirements of 6G (i.e., 0.1 milliseconds). This motivates us to propose leveraging in-memory computing, which is an analog computing technology that integrates memory and computation within memristor circuits, to perform the intensive matrix-vector multiplication (MVM) operations inherent in deep MIMO detection at the nanosecond scale. Specifically, we introduce a novel architecture, called the deep in-memory MIMO (IM-MIMO) detector, characterized by two key features. First, each of its cascaded computational blocks is decomposed into channel-dependent and channel-independent neural network modules. Such a design minimizes the latency of memristor reprogramming in response to channel variations, which significantly exceeds computation time. Second, we develop a customized detector-training method that exploits prior knowledge of memristor-value statistics to enhance robustness against programming noise. Furthermore, we conduct a comprehensive analysis of the IM-MIMO detector's performance, evaluating detection accuracy, processing latency, and hardware complexity. Our study quantifies detection error as a function of various factors, including channel noise, memristor programming noise, and neural network size.