Uplink Signal Detection For Large-Scale MIMO-ISAC Systems

2026-04-21

AI Summary

This work addresses the uplink signal detection challenge in large-scale MIMO-ISAC systems by formulating it as a mixed-integer least squares (MILS) problem. It proposes a novel projected neighborhood search alternating direction method of multipliers (P-NS-ADMM) algorithm, which uniquely integrates neighborhood search with ADMM, and further develops an iterative variant, I-NS-ADMM, to reduce computational complexity. Theoretical analysis demonstrates that P-NS-ADMM achieves the same diversity order as maximum-likelihood detection, while the proposed flexible iterative mechanism significantly enhances sensing signal estimation accuracy. Simulation results show that the proposed approach substantially outperforms existing methods in terms of both bit error rate (BER) and normalized mean square error (NMSE), with I-NS-ADMM offering a considerable reduction in computational overhead.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Neuro-Symbolic Learning
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Next-generation wireless communication systems are unifying large-scale multiple-input multiple-output (MIMO) and integrated sensing and communication (ISAC) to enhance sensing and communication performance. In this paper, the signal detection problem for MIMO-ISAC systems is modeled as a mixed-integer least squares (MILS) problem. To solve it efficiently, we propose a projection-based neighborhood search-aided alternating direction method of multipliers (P-NS-ADMM) detection scheme. By theoretical analysis, we demonstrate that P-NS-ADMM achieves the same received diversity order as maximum likelihood (ML) detection. For further complexity reduction, an iteration-based NS-ADMM (I-NS-ADMM) is proposed to remove the complex projection operation. Complexity analysis shows its complexity advantage compared with P-NS-ADMM. Moreover, to better estimate the sensing signals for I-NS-ADMM, a flexible mechanism of ADMM iterations is given. Finally, simulations demonstrate the proposed NS-aided ADMM detection schemes have significant performance advantages in terms of both BER and NMSE.

Problem

• MIMO-ISAC
• signal detection
• uplink
• large-scale MIMO
• integrated sensing and communication

Innovation

• MIMO-ISAC
• mixed-integer least squares
• ADMM
• neighborhood search
• signal detection