用前向-前向算法训练自编码器，提升无线通信能效
Forward-Forward Autoencoder Architectures for Energy-Efficient Wireless Communications
- 采用前向-前向学习替代反向传播，无需梯度全局可用
- 在高斯噪声与瑞利衰落信道下性能媲美传统反向传播方法
- 显著降低内存占用与计算时间，适合低功耗部署
深度学习在通信系统中的应用近年来备受关注。前向-前向（Forward-Forward, FF）学习是反向传播（Backpropagation, BP）的一种高效替代方案，其优势在于无需信道可微分，也不依赖全局梯度信息，因而具备能耗更低的潜力。本文设计了基于FF算法的端到端学习型自编码器，并在加性高斯白噪声（AWGN）和瑞利块衰落信道上进行数值评估。结果表明，在联合编码调制场景以及固定不可微调制阶段下，其性能可与BP训练系统相媲美。此外，本文还深入分析了FF网络的设计原则、训练收敛特性，并展示了相比BP方法显著的内存与处理时间节省。
原文摘要 · Abstract (English)
The application of deep learning to the area of communications systems has been a growing field of interest in recent years. Forward-forward (FF) learning is an efficient alternative to the backpropagation (BP) algorithm, which is the typically used training procedure for neural networks. Among its several advantages, FF learning does not require the communication channel to be differentiable and does not rely on the global availability of partial derivatives, allowing for an energy-efficient implementation. In this work, we design end-to-end learned autoencoders using the FF algorithm and numerically evaluate their performance for the additive white Gaussian noise and Rayleigh block fading channels. We demonstrate their competitiveness with BP-trained systems in the case of joint coding and modulation, and in a scenario where a fixed, non-differentiable modulation stage is applied. Moreover, we provide further insights into the design principles of the FF network, its training convergence behavior, and significant memory and processing time savings compared to BP-based approaches.
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