Model-free Training of End-to-end Communication Systems (1812.05929v3)

Abstract: The idea of end-to-end learning of communication systems through neural network-based autoencoders has the shortcoming that it requires a differentiable channel model. We present in this paper a novel learning algorithm which alleviates this problem. The algorithm enables training of communication systems with an unknown channel model or with non-differentiable components. It iterates between training of the receiver using the true gradient, and training of the transmitter using an approximation of the gradient. We show that this approach works as well as model-based training for a variety of channels and tasks. Moreover, we demonstrate the algorithm's practical viability through hardware implementation on software-defined radios where it achieves state-of-the-art performance over a coaxial cable and wireless channel.

• The paper introduces a model-free learning method that trains communication systems without requiring differentiable channel models.
• It employs an alternating algorithm using precise gradients for the receiver and approximated gradients for the transmitter.
• Extensive experiments demonstrate that the approach matches or outperforms traditional model-based schemes on AWGN and Rayleigh channels.

Model-Free Training of End-to-End Communication Systems

The paper "Model-free Training of End-to-end Communication Systems" by Fayçal Ait Aoudia and Jakob Hoydis addresses a significant limitation in the field of end-to-end learning for communication systems. The prevalent approach utilizes neural network-based autoencoders, which conventionally mandates a differentiable channel model, thereby imposing constraints on practical implementation. The authors propose an innovative learning algorithm that overcomes this bottleneck, facilitating training of communication systems despite the presence of unknown channel models or non-differentiable components.

To circumvent the reliance on channel model differentiability, the proposed method employs an alternating algorithm: training the receiver using the precise gradient and the transmitter using an approximated gradient. This is achieved by relaxing the transmitter's output to a random variable representation. The approach thereby enables training from purely observational data, without necessitating knowledge of the channel model.

The efficacy of this model-free training approach is evidenced by extensive experimentation across various channel types, including AWGN and Rayleigh block-fading channels. Notably, the performance metrics indicate that model-free training mirrors the efficacy of model-based training. In practical implementations utilizing software-defined radios (SDRs), the system displays state-of-the-art performance both over coaxial cables and wireless media.

Numerical Results and Claims

The authors provide comprehensive numerical results that underpin the legitimacy of their approach. They demonstrate:

• The model-free learning method achieves identical block error rate (BLER) performance to model-aware schemes on both AWGN and Rayleigh block-fading channels.
• On the AWGN channel, the trained system surpasses quaternary phase-shift keying (QPSK) and closely approaches Agrell's sphere-packing optimality.
• For Rayleigh block-fading channels, it comprehensively outperforms both QPSK and Agrell solutions.

Implications and Future Directions

The implications of this research are profound, offering both practical and theoretical advancements. Practically, the ability to train communication systems without channel model knowledge unshackles development from the constraints of differentiable channel modeling, fostering more robust systems adaptable to real-world conditions. Theoretically, it posits RL-inspired methodologies as formidable alternatives in optimizing communication systems, especially when direct gradient computation is infeasible.

Looking forward, the synergy between reinforcement learning (RL) strategies and the realms of communications holds promise for evolving sophisticated systems capable of addressing high-dimensional transmitters' output complexities. Future explorations could delve into the optimization of batch sizes to mediate estimator variance or explore channel emulators to enhance system generalization beyond static environments.

The experimental realizations reported signify the maiden instance of training a neural network autoencoder directly over empirical channels, marking a pivotal step in neural network training methodologies applied to communications. This breakthrough underlines the transformative potential of AI-driven approaches across a myriad of communication paradigms, warranting further explorations into scalable architectures and adaptive real-time training mechanics.