A Real-Time Deep Learning OFDM Receiver

Stefan Brennsteiner, T. Arslan, J. Thompson, A. McCormick
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引用次数: 2

Abstract

Machine learning in the physical layer of communication systems holds the potential to improve performance and simplify design methodology. Many algorithms have been proposed; however, the model complexity is often unfeasible for real-time deployment. The real-time processing capability of these systems has not been proven yet. In this work, we propose a novel, less complex, fully connected neural network to perform channel estimation and signal detection in an orthogonal frequency division multiplexing system. The memory requirement, which is often the bottleneck for fully connected neural networks, is reduced by ≈ 27 times by applying known compression techniques in a three-step training process. Extensive experiments were performed for pruning and quantizing the weights of the neural network detector. Additionally, Huffman encoding was used on the weights to further reduce memory requirements. Based on this approach, we propose the first field-programmable gate array based, real-time capable neural network accelerator, specifically designed to accelerate the orthogonal frequency division multiplexing detector workload. The accelerator is synthesized for a Xilinx RFSoC field-programmable gate array, uses small-batch processing to increase throughput, efficiently supports branching neural networks, and implements superscalar Huffman decoders.
一种实时深度学习OFDM接收机
通信系统物理层中的机器学习具有提高性能和简化设计方法的潜力。已经提出了许多算法;然而,模型的复杂性往往不适合实时部署。这些系统的实时处理能力尚未得到证实。在这项工作中,我们提出了一种新颖的,不太复杂的,全连接的神经网络来执行正交频分复用系统中的信道估计和信号检测。内存需求通常是全连接神经网络的瓶颈,通过在三步训练过程中应用已知的压缩技术,内存需求降低了约27倍。进行了大量的实验来修剪和量化神经网络检测器的权重。此外,权重采用Huffman编码,进一步降低了对内存的需求。基于这种方法,我们提出了第一个基于现场可编程门阵列的实时神经网络加速器,专门设计用于加速正交频分复用检测器的工作负载。该加速器是为赛灵思RFSoC现场可编程门阵列合成的,采用小批量处理来提高吞吐量,有效地支持分支神经网络,并实现标量霍夫曼解码器。
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