# Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access

arXiv: https://arxiv.org/abs/2205.00849 · HTML: https://mlsanity.com/paper/2205.00849/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 52 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 16 of 16)

- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [AI Benchmark: Running Deep Neural Networks on Android Smartphones](https://mlsanity.com/paper/1810.01109/) (2018)
- [Model Complexity of Deep Learning: A Survey](https://mlsanity.com/paper/2103.05127/) (2021)
- [Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions](https://mlsanity.com/paper/1904.09673/) (2019)
- [DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection](https://mlsanity.com/paper/2002.03214/) (2020)
- [Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems](https://mlsanity.com/paper/2103.08212/) (2021)
- [Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends](https://mlsanity.com/paper/2201.03192/) (2022)
- [Model-Based Machine Learning for Communications](https://mlsanity.com/paper/2101.04726/) (2021)
- [Rate-Splitting Unifying SDMA, OMA, NOMA, and Multicasting in MISO Broadcast Channel: A Simple Two-User Rate Analysis](https://mlsanity.com/paper/1906.04474/) (2019)
- [Beyond Dirty Paper Coding for Multi-Antenna Broadcast Channel with Partial CSIT: A Rate-Splitting Approach](https://mlsanity.com/paper/1912.05409/) (2019)
- [Rate Splitting for MIMO Wireless Networks: A Promising PHY-Layer Strategy for LTE Evolution](https://mlsanity.com/paper/1603.01217/) (2016)
- [Rate-Splitting Multiple Access for Multi-antenna Downlink Communication Systems: Spectral and Energy Efficiency Tradeoff](https://mlsanity.com/paper/2001.03206/) (2020)
- [DemodNet: Learning Soft Demodulation from Hard Information Using Convolutional Neural Network](https://mlsanity.com/paper/2011.11337/) (2020)
- [Rate Splitting for Multi-Antenna Downlink: Precoder Design and Practical Implementation](https://mlsanity.com/paper/2002.07225/) (2020)
- [Rate-Splitting Multiple Access: A New Frontier for the PHY Layer of 6G](https://mlsanity.com/paper/2006.01437/) (2020)

## Built on by (most cited first, 3 of 3)

- [Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO](https://mlsanity.com/paper/2209.08456/) (2022)
- [Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG](https://mlsanity.com/paper/2403.02651/) (2024)
- [Meta-Learning Based Optimization for Large Scale Wireless Systems](https://mlsanity.com/paper/2407.01823/) (2024)

## Abstract (arXiv)

Effective and adaptive interference management is required in next generation wireless communication systems. To address this challenge, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been intensively studied in recent years, albeit mostly under the assumption of perfect Channel State Information at the Receiver (CSIR) and ideal capacity-achieving modulation and coding schemes. To assess its practical performance, benefits, and limits under more realistic conditions, this work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods, which aims to unite the simple structure of the conventional SIC receiver and the robustness and model agnosticism of deep learning techniques. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS), and average training overhead. Also, a comparison with the SIC receiver, with perfect and imperfect CSIR, is given. Results reveal that the MBDL receiver outperforms by a significant margin the SIC receiver with imperfect CSIR, due to its ability to generate on demand non-linear symbol detection boundaries in a pure data-driven manner.