Scenario-Adaptive MU-MIMO OFDM Semantic Communication With Asymmetric Neural Network

TL;DR

This paper introduces a scenario-adaptive MU-MIMO semantic communication framework with asymmetric neural networks, improving robustness and efficiency in 6G downlink systems by dynamically adjusting to channel conditions.

Contribution

It proposes a novel asymmetric neural network architecture with scenario-aware encoding and pilot-guided decoding for MU-MIMO SemCom, addressing interference and fading challenges.

Findings

Significant performance gains over DJSCC and traditional schemes in PSNR and accuracy

Effective mitigation of multi-user interference and fading effects

Enhanced low-SNR robustness with low latency and computational cost

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 with a novel pilot-guided attention mechanism is employed to implicitly perform channel equalization and feature calibration using reference pilot symbols. Extensive simulation results over 3GPP channel models demonstrate that the proposed framework significantly outperforms DJSCC and traditional Separate Source-Channel Coding (SSCC) schemes in terms of Peak Signal-to-Noise Ratio (PSNR) and classification accuracy, particularly in low-SNR regimes, while maintaining low latency and computational cost on edge devices.