# Distributionally Robust Receive Combining

arXiv: https://arxiv.org/abs/2401.12345 · HTML: https://mlsanity.com/paper/2401.12345/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2024.

## Built on by (most cited first, 9 of 9)

- [Assessing Judging Bias in Large Reasoning Models: An Empirical Study](https://mlsanity.com/paper/2504.09946/) (2025)
- [DriveAgent: Multi-Agent Structured Reasoning with LLM and Multimodal Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2505.02123/) (2025)
- [Quadratic Gating Mixture of Experts: Statistical Insights into Self-Attention](https://mlsanity.com/paper/2410.11222/) (2024)
- [Can we generate portable representations for clinical time series data using LLMs?](https://mlsanity.com/paper/2603.23987/) (2026)
- [Think Beyond Size: Adaptive Prompting for More Effective Reasoning](https://mlsanity.com/paper/2410.08130/) (2024)
- [Shaping Explanations: Semantic Reward Modeling with Encoder-Only Transformers for GRPO](https://mlsanity.com/paper/2509.13081/) (2025)
- [TSFormer: A Robust Framework for Efficient UHD Image Restoration](https://mlsanity.com/paper/2411.10951/) (2024)
- [Making Bias Non-Predictive: Training Robust LLM Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2602.01528/) (2026)
- [Robust Processing and Learning: Principles, Methods, and Wireless Applications](https://mlsanity.com/paper/2602.09848/) (2026)

## Abstract (arXiv)

This article investigates signal estimation in wireless transmission (i.e., receive combining) from the perspective of statistical machine learning, where the transmit signals may be from an integrated sensing and communication system; that is, 1) signals may be not only discrete constellation points but also arbitrary complex values; 2) signals may be spatially correlated. Particular attention is paid to handling various uncertainties such as the uncertainty of the transmit signal covariance, the uncertainty of the channel matrix, the uncertainty of the channel noise covariance, the existence of channel impulse noises, the non-ideality of the power amplifiers, and the limited sample size of pilots. To proceed, a distributionally robust receive combining framework that is insensitive to the above uncertainties is proposed, which reveals that channel estimation is not a necessary operation. For optimal linear estimation, the proposed framework includes several existing combiners as special cases such as diagonal loading and eigenvalue thresholding. For optimal nonlinear estimation, estimators are limited in reproducing kernel Hilbert spaces and neural network function spaces, and corresponding uncertainty-aware solutions (e.g., kernelized diagonal loading) are derived. In addition, we prove that the ridge and kernel ridge regression methods in machine learning are distributionally robust against diagonal perturbation in feature covariance.