# Highly Parallel Singular Value Decomposition for Low-Latency MIMO Processing

arXiv: https://arxiv.org/abs/2509.18799 · HTML: https://mlsanity.com/paper/2509.18799/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G](https://mlsanity.com/paper/2601.12375/) (2026)

## Abstract (arXiv)

Singular value decomposition (SVD) is widely used in wireless systems, including multiple-input multiple-output (MIMO) processing and dimension reduction in distributed MIMO (D-MIMO). However, the iterative nature of decomposition methods results in increased execution time as system size grows, posing challenges for real-time and low-latency applications. To address this, we analyze the latency of state-of-art SVD methods, and highlight the efficiency of a 4-step highly parallel method based on Gram matrix tridiagonalization. Furthermore, we develop a time complexity (processing latency) analysis framework with hardware profiling, allowing scalable and realistic evaluation without full implementation. The numerical results demonstrate the superior time efficiency of the selected parallel method, particularly in massive MIMO scenarios.