MMGaP: Multi-User MIMO Detection and Precoding using GPU-assisted Physics-inspired Computation

2025-10-01

AI Summary

This work addresses the practical challenge of realizing theoretical spectral efficiency for physical-layer algorithms—such as massive MIMO detection and precoding—on commodity hardware. We propose MMGaP, the first scalable, physics-inspired MIMO processing framework designed specifically for general-purpose GPUs. Implemented via highly optimized CUDA kernels, MMGaP ensures deep integration with TensorFlow and 5G software-defined radio (SDR) architectures, enabling line-rate uplink and downlink processing. By synergistically combining physics-based modeling with low-level GPU optimizations, MMGaP achieves substantial throughput gains: approximately +50 Mbps per user in uplink and +100 Mbps in downlink for 8×8 and 16×16 MIMO configurations, while strictly adhering to 5G latency constraints. These results significantly narrow the gap between theoretical performance bounds and real-world system throughput.

Technology Category

• Machine Learning: Hardware-aware ML
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Physics-inspired and quantum compute based methods for processing in the physical layer of next-generation cellular radio access networks have demonstrated theoretical advances in spectral efficiency in recent years, but have stopped short of practical realization on commodity processors, leaving a gap between the throughput practical systems can achieve and the projected throughput the state-of-the-art should achieve. To fill this gap, this paper proposes MMGaP, an uplink multi-user MIMO detector and downlink Vector perturbation precoder for next-generation cellular networks. MMGaP realizes these large MIMO processing algorithms for the first time on bare-metal CUDA kernels that scale to run on large GPU processing platforms, and can be packaged as TensorFlow modules, allowing easy integration with a variety of systems. We integrate MMGaP with NVIDIA's software-defined, GPU-accelerated 5G platform and evaluate its performance against the state-of-the-art. In a 5G cellular network using 100 MHz of radio bandwidth, eight antennas at the base station and eight concurrent users, we show that MMGaP improves uplink throughput by approximately 50 Mbps per user and downlink throughput by 100 Mbps per user over a wide range of SNR. We further show that MMGaP can also support larger MIMO sizes: for 16 antennas at the base station and 16 concurrent users, MMGaP provides more than 50 Mbps higher uplink throughput per user. We measure the execution time of MMGaP on different NVIDIA GPUs and show that it can operate at line-rate and meet the timing requirements of state-of-the-art 5G systems.

Problem

• Implementing physics-inspired MIMO algorithms on commodity GPU processors
• Bridging throughput gap between theoretical and practical 5G systems
• Scaling large MIMO detection and precoding for real-time 5G networks

Innovation

• GPU-accelerated MIMO detection and precoding implementation
• Bare-metal CUDA kernels scaling on large GPU platforms
• TensorFlow modules for easy system integration