Title: Forward-Forward Autoencoder Architectures for Energy-Efficient Wireless Communications Show Chinese title

Title: 用于高效无线通信的前向-前向自编码器架构

Abstract: 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. Show Chinese abstract

Abstract: 深度学习在通信系统领域的应用近年来已成为一个日益受到关注的研究领域。 前向-前向（FF）学习是反向传播（BP）算法的一种高效替代方法，反向传播算法通常是用于神经网络的训练过程。 在其多个优点中，FF学习不需要通信信道是可微的，也不依赖于偏导数的全局可用性，从而允许实现节能的方案。 在本工作中，我们使用FF算法设计端到端学习的自编码器，并对加性高斯白噪声和瑞利块衰落信道中的性能进行数值评估。 我们展示了在联合编码和调制的情况下，以及在应用固定不可微调制阶段的场景下，其与BP训练系统的竞争力。 此外，我们进一步探讨了FF网络的设计原则、训练收敛行为，并与基于BP的方法相比，展示了显著的内存和处理时间节省。