Low Complexity Detector for XL-MIMO Uplink: A Cross Splitting Based Information Geometry Approach

2025-10-10

AI Summary

Conventional iterative detectors (e.g., AMP, IGA) for uplink signal recovery in extra-large-scale MIMO (XL-MIMO) suffer from prohibitive computational complexity that scales rapidly with the number of antennas. Method: This paper proposes the Cross-split Information-Geometric Algorithm (CS-IGA) and its nonlinear extension, NCS-IGA. The core innovation lies in cross-matrix splitting to decouple antenna scale from per-iteration complexity, and the first integration of information geometry with intrinsic discrete constellation constraints—eliminating external interference cancellation loops. The method combines natural-parameter matrix decomposition, matched-filter approximation, and symbol-level constraint embedding. Contribution/Results: Under realistic channel conditions, CS-IGA and NCS-IGA achieve bit-error-rate (BER) performance at or beyond the Bayesian optimal bound of AMP/IGA, while significantly reducing computational overhead and required iterations—demonstrating strong suitability for high-throughput XL-MIMO systems.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Hardware-aware ML
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

In this paper, we propose the cross splitting based information geometry approach (CS-IGA), a novel and low complexity iterative detector for uplink signal recovery in extralarge-scale MIMO (XL-MIMO) systems. Conventional iterative detectors, such as the approximate message passing (AMP) algorithm and the traditional information geometry algorithm (IGA), suffer from a per iteration complexity that scales with the number of base station (BS) antennas, creating a computational bottleneck. To overcome this, CS-IGA introduces a novel cross matrix splitting of the natural parameter in the a posteriori distribution. This factorization allows the iterative detection based on the matched filter, which reduces per iteration computational complexity. Furthermore, we extend this framework to nonlinear detection and propose nonlinear CSIGA (NCS-IGA) by seamlessly embedding discrete constellation constraints, enabling symbol-wise processing without external interference cancellation loops. Comprehensive simulations under realistic channel conditions demonstrate that CS-IGA matches or surpasses the bit error rate (BER) performance of Bayes optimal AMP and IGA for both linear and nonlinear detection, while achieving this with fewer iterations and a substantially lower computational cost. These results establish CS-IGA as a practical and powerful solution for high-throughput signal detection in next generation XL-MIMO systems.

Problem

• Proposes low complexity XL-MIMO uplink signal detector
• Reduces computational bottleneck in iterative detection algorithms
• Enables high-throughput detection with fewer iterations and lower cost

Innovation

• Cross matrix splitting for natural parameter factorization
• Matched filter-based iterative detection for complexity reduction
• Embedded discrete constraints enabling nonlinear symbol processing