Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2611.00910

Denoising Multiuser Interference Enabled Iterative AI Receiver

Xiangzhao Qin·Sha Hu·Jing Qian·Hao Wang
View original source ↗Read source document ↗Export metadata ↓

Abstract

When interfering users transmit finite-alphabet symbols, the aggregate of multiuser interference and thermal noise in a multiple-input multiple-output (MIMO) receiver is generally non-Gaussian. Its covariance alone does not specify the likelihood required for Bayesian detection. Conventional minimum mean-square error interference rejection combining (MMSE-IRC) is optimal among linear estimators with known second-order statistics, but it does not generally attain the Bayesian MMSE estimate in this setting. In this article, we propose an iterative artificial intelligence (AI) receiver that jointly refines interference cancellation, channel estimation, and desired-user detection. At each iteration, an interference denoising network reconstructs the received interference from the residual observation, covariance information, and uncertainty in the desired-user soft decisions. After cancellation, the preceding soft decisions support successive interference cancellation among desired-user streams and yield least squares (LS) channel estimates at data positions. A channel estimation network refines these estimates using the preceding channel estimate. Finally, MMSE-IRC preprocessing provides an effective channel matrix and soft symbol estimates to a Transformer-ResNet-PIC detector, which produces symbol probabilities for the next iteration. We also relate these structured detector inputs to the likelihood gradient of a relaxed Bayesian detection problem. The resulting receiver uses soft decisions to couple the three processing stages across iterations. Link-level simulations show that denoising multiuser interference enables the proposed iterative AI receiver to approach the attainable theoretical performance bound. The proposed receiver substantially outperforms the conventional quasi-ML receiver based on LMMSE channel estimation and MMSE-IRC preprocessing.

Explore related subjects

eess.SP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xiangzhao Qin, Sha Hu, Jing Qian, Hao Wang. 2026-10-31. Denoising Multiuser Interference Enabled Iterative AI Receiver. https://arxiv.org/abs/2611.00910

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

SpellerSSL: Self-Supervised Learning with P300 Aggregation for Speller BCIs

Electroencephalogram (EEG)-based P300 speller brain-computer interfaces (BCIs) face three main challenges: low signal-to-noise ratio (SNR), poor generalization, and time-consuming calibration. We propose SpellerSSL, a framework that combines self-supervised learning (SSL) with P300 aggregation to address these issues. First, we introduce an aggregation strategy to enhance SNR. Second, to achieve generalization in training, we employ a customized 1D U-Net backbone and pre-train the model on both cross-domain and in-domain EEG data. The pretrained model is subsequently fine-tuned with a lightweight ERP-Head classifier for P300 detection, which adapts the learned representations to subject-specific data. Our evaluations on calibration time demonstrate that combining the aggregation strategy with SSL significantly reduces the calibration burden per subject and improves robustness across subjects. Experimental results show that SSL learns effective EEG representations in both in-domain and cross-domain, with in-domain achieving a state-of-the-art character recognition rate of 94% with only 7 repetitions and the highest information transfer rate (ITR) of 21.86 bits/min on the public II-B dataset. Moreover, in-domain SSL with P300 aggregation reduces the required calibration size by 60% while maintaining a comparable character recognition rate. To the best of our knowledge, this is the first study to apply SSL to P300 spellers, highlighting its potential to improve both efficiency and generalization in speller BCIs and paving the way toward an EEG foundation model for P300 speller BCIs. The code is available at: https://github.com/Emotiv/SpellerSSL-NeurIPS2025

eess.SP↗
arXiv2026

Outage Probability Analysis of MRC-Based Fluid Antenna Systems under Rician Fading

This paper investigates a fluid antenna system (FAS) where a single-antenna transmitter communicates with a receiver equipped with a fluid antenna (FA) over Rician fading channels. Two channel models are considered to incorporate the correlation among the ports into the fading gains, namely: i) the widely used physical reference port model, implemented by considering the first physical port as the reference port; and ii) the virtual reference port model, considering a common port correlation coefficient and implemented by assuming the presence of a virtual reference port. Assuming that multiple ports among the M available FA ports can be activated, the receiver selects the best K with the highest instantaneous signal-to-noise ratio (SNR) and combines the received signals at the selected ports using maximum ratio combining. For both port models, the statistics of the post-combining SNR are derived using a characteristic function approach, which allows to analyze the outage probabilities (OPs) of the FAS. Additional closed-form lower bounds on the OPs and the asymptotic OPs at high SNR are derived, revealing the diversity order of the FAS to be M. Numerical results validate the analytical framework and demonstrate the interplay of key system parameters on the performance of the considered MRC-based FAS. Specifically, the performance difference between the two reference port models becomes increasingly evident as M, K, and the average SNR of the FAS increase.

eess.SP↗
arXiv2026

The Symmetric Location Problem: a Song of Efficiency and Robustness

The aim of this Lecture Note is to introduce the Signal Processing (SP) community to a powerful yet still under-utilised tool: the semiparametric statistics. In short, the semiparametric framework allows us to estimate or perform hypothesis testing on a finite-dimensional parameter $\boldsymbolθ \in Θ\subseteq \mathbb{R}^p$ in the presence of an \textit{infinite-dimensional nuisance parameter} $g \in \mathcal{S}$ (i.e. a function), such as the density of the noise. Clearly, this framework is general enough to include almost every SP application. Remarkably, as the title suggests drawing on George R. R. Martin's famous book series, the greatest advantage of semiparametric statistics over parametric and non-parametric ones lies in the fact that it is able to reconcile two seemingly dichotomous concepts: statistical efficiency and distributional robustness. To explain exactly what this means, in this Lecture Note we will focus our attention on the famous and fundamental symmetric location problem.

eess.SP↗