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[2602.13557] Scenario-Adaptive MU-MIMO OFDM Semantic Communication With Asymmetric Neural Network
Summary
This paper presents a scenario-adaptive framework for MU-MIMO OFDM semantic communication, addressing challenges in multi-user environments by utilizing an asymmetric neural network architecture.
Why It Matters
As 6G networks evolve, semantic communication is crucial for efficient data transmission. This research addresses significant challenges like multi-user interference and frequency-selective fading, providing a solution that enhances performance in low-SNR conditions, which is vital for future communication systems.
Key Takeaways
- Proposes a novel framework for semantic communication in MU-MIMO OFDM systems.
- Introduces a scenario-aware semantic encoder to optimize feature extraction based on channel conditions.
- Demonstrates significant performance improvements over existing coding schemes in low-SNR scenarios.
- Utilizes a lightweight decoder with a pilot-guided attention mechanism for enhanced channel equalization.
- Shows potential for low latency and computational efficiency on edge devices.
Computer Science > Machine Learning arXiv:2602.13557 (cs) [Submitted on 14 Feb 2026] Title:Scenario-Adaptive MU-MIMO OFDM Semantic Communication With Asymmetric Neural Network Authors:Chongyang Li, Tianqian Zhang, Shouyin Liu View a PDF of the paper titled Scenario-Adaptive MU-MIMO OFDM Semantic Communication With Asymmetric Neural Network, by Chongyang Li and Tianqian Zhang and Shouyin Liu View PDF HTML (experimental) Abstract:Semantic Communication (SemCom) has emerged as a promising paradigm for 6G networks, aiming to extract and transmit task-relevant information rather than minimizing bit errors. However, applying SemCom to realistic downlink Multi-User Multi-Input Multi-Output (MU-MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems remains challenging due to severe Multi-User Interference (MUI) and frequency-selective fading. Existing Deep Joint Source-Channel Coding (DJSCC) schemes, primarily designed for point-to-point links, suffer from performance saturation in multi-user scenarios. To address these issues, we propose a scenario-adaptive MU-MIMO SemCom framework featuring an asymmetric architecture tailored for downlink transmission. At the transmitter, we introduce a scenario-aware semantic encoder that dynamically adjusts feature extraction based on Channel State Information (CSI) and Signal-to-Noise Ratio (SNR), followed by a neural precoding network designed to mitigate MUI in the semantic domain. At the receiver, a lightweight decoder equipped wi...
