Successive Interference Cancellation-aided Diffusion Models for Joint Channel Estimation and Data Detection in Low Rank Channel Scenarios

2025-01-20

AI Summary

In low-rank MIMO systems where the number of users exceeds the number of AP antennas and channel observations are partially missing, joint channel estimation and data detection becomes highly challenging due to severe rank deficiency and information scarcity. Method: This paper introduces, for the first time, continuous-time diffusion models to this task, proposing a successive interference cancellation (SIC)-assisted score-based joint estimation framework. It designs an SIC-coupled iterative score-gradient update scheme that unifies generative score matching with Bayesian iterative inference, overcoming fundamental performance bottlenecks of conventional methods under low-rank conditions. Contribution/Results: Experiments demonstrate significant improvements over state-of-the-art baselines in normalized mean square error (NMSE) and symbol error rate (SER), especially at medium-to-low SNRs, while maintaining robustness in full-rank regimes. The work pioneers the synergistic integration of diffusion modeling and SIC for low-rank MIMO joint estimation, achieving both theoretical feasibility and substantial empirical gains.

Technology Category

Computer Vision: Diffusion Models for VisionIntelligent Robots: State EstimationMachine Learning: Mixture of Experts (MoE)

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingGraph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

This paper proposes a novel joint channel-estimation and source-detection algorithm using successive interference cancellation (SIC)-aided generative score-based diffusion models. Prior work in this area focuses on massive MIMO scenarios, which are typically characterized by full-rank channels, and fail in low-rank channel scenarios. The proposed algorithm outperforms existing methods in joint source-channel estimation, especially in low-rank scenarios where the number of users exceeds the number of antennas at the access point (AP). The proposed score-based iterative diffusion process estimates the gradient of the prior distribution on partial channels, and recursively updates the estimated channel parts as well as the source. Extensive simulation results show that the proposed method outperforms the baseline methods in terms of normalized mean squared error (NMSE) and symbol error rate (SER) in both full-rank and low-rank channel scenarios, while having a more dominant effect in the latter, at various signal-to-noise ratios (SNR).

Problem

• Signal Estimation
• Channel Imperfections
• Massive MIMO

Innovation

• Massive MIMO
• Joint Source Detection and Channel Estimation
• Robustness to Channel Outage