# DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection

arXiv: https://arxiv.org/abs/2002.03214 · HTML: https://mlsanity.com/paper/2002.03214/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2020.

## Built on by (most cited first, 24 of 24)

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- [Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM](https://mlsanity.com/paper/2309.14353/) (2023)
- [Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection](https://mlsanity.com/paper/2102.00178/) (2021)
- [Adversarial Neural Networks for Error Correcting Codes](https://mlsanity.com/paper/2112.11491/) (2021)
- [SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers](https://mlsanity.com/paper/2308.12591/) (2023)
- [Deep Learning in Wireless Communication Receiver: A Survey](https://mlsanity.com/paper/2501.17184/) (2025)
- [Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding](https://mlsanity.com/paper/2504.03690/) (2025)
- [Recovering AES Keys with a Deep Cold Boot Attack](https://mlsanity.com/paper/2106.04876/) (2021)
- [MetaSSD: Meta-Learned Self-Supervised Detection](https://mlsanity.com/paper/2205.15271/) (2022)
- [Transfer-based Adversarial Poisoning Attacks for Online (MIMO-)Deep Receviers](https://mlsanity.com/paper/2409.02430/) (2024)
- [Learning Successive Interference Cancellation for Low-Complexity Soft-Output MIMO Detection](https://mlsanity.com/paper/2601.16586/) (2026)
- [Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis](https://mlsanity.com/paper/2602.20178/) (2026)
- [Online Learning via Learned Latent Bayesian Tracking](https://mlsanity.com/paper/2609.31559/) (2026)
- [Geometric Inductive Biases for Semi-Supervised Equalization: The Constellation-Aware Transformer](https://mlsanity.com/paper/2609.33695/) (2026)

## Abstract (arXiv)

Digital receivers are required to recover the transmitted symbols from their observed channel output. In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging. A family of algorithms capable of reliably recovering multiple symbols is based on interference cancellation. However, these methods assume that the channel is linear, a model which does not reflect many relevant channels, as well as require accurate channel state information (CSI), which may not be available. In this work we propose a multiuser MIMO receiver which learns to jointly detect in a data-driven fashion, without assuming a specific channel model or requiring CSI. In particular, we propose a data-driven implementation of the iterative soft interference cancellation (SIC) algorithm which we refer to as DeepSIC. The resulting symbol detector is based on integrating dedicated machine-learning (ML) methods into the iterative SIC algorithm. DeepSIC learns to carry out joint detection from a limited set of training samples without requiring the channel to be linear and its parameters to be known. Our numerical evaluations demonstrate that for linear channels with full CSI, DeepSIC approaches the performance of iterative SIC, which is comparable to the optimal performance, and outperforms previously proposed ML-based MIMO receivers. Furthermore, in the presence of CSI uncertainty, DeepSIC significantly outperforms model-based approaches. Finally, we show that DeepSIC accurately detects symbols in non-linear channels, where conventional iterative SIC fails even when accurate CSI is available.