用新型神经网络直接从信号中预测比特可靠性，提升通信系统性能。
Novel Deep Neural OFDM Receiver Architectures for LLR Estimation
- 采用注意力机制与残差结构设计双模型，直接从IQ信号输出LLR
- 在不同信噪比下，误比特率和误块率均优于传统系统和现有神经接收机
- 适合通信系统优化、深度学习用于无线传输的科研与工程人员
近年来，神经接收机成为研究热点，可通过数据驱动方法如机器学习和深度学习直接解码接收信号。本文提出两种基于神经网络的正交频分复用（OFDM）接收机架构，实现信道估计、均衡并直接从接收的同相与正交相位（IQ）信号中预测对数似然比（LLR）。第一个网络为双注意力变换器（DAT），采用先进的变压器架构与注意力机制；第二个网络为残差双非局部注意力网络（RDNLA），采用并行残差结构与非局部注意力模块。在不同信噪比（SNR）条件下，对比了多种先进神经接收机架构的性能。仿真结果表明，DAT与RDNLA在误比特率（BER）和误块率（BLER）方面均优于传统通信系统及现有神经接收机模型。
原文摘要 · Abstract (English)
Neural receivers have recently become a popular topic, where the received signals can be directly decoded by data driven mechanisms such as machine learning and deep learning. In this paper, we propose two novel neural network based orthogonal frequency division multiplexing (OFDM) receivers performing channel estimation and equalization tasks and directly predicting log likelihood ratios (LLRs) from the received in phase and quadrature phase (IQ) signals. The first network, the Dual Attention Transformer (DAT), employs a state of the art (SOTA) transformer architecture with an attention mechanism. The second network, the Residual Dual Non Local Attention Network (RDNLA), utilizes a parallel residual architecture with a non local attention block. The bit error rate (BER) and block error rate (BLER) performance of various SOTA neural receiver architectures is compared with our proposed methods across different signal to noise ratio (SNR) levels. The simulation results show that DAT and RDNLA outperform both traditional communication systems and existing neural receiver models.
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