Online Meta-Learning Channel Autoencoder for Dynamic End-to-end Physical Layer Optimization

TL;DR

Problem: Channel autoencoders for end-to-end physical layer design struggle to adapt to dynamic wireless channels with limited pilot data in online operation. Approach: The authors introduce OML-CAE, an online meta-learning CAE that uses a MAML-style inner/outer loop and a task buffer to rapidly adapt to new fading channels with only a few pilots. Contributions: online meta-training framework, formalization for online dynamic fading channels, and extensive experiments showing SER gains and substantially reduced pilot requirements relative to conventional CAEs and QPSK+MLE. Significance: The method enables practical, data-efficient, real-time CAE deployment for wireless systems by reducing pilot overhead and improving adaptability.

Abstract

Channel Autoencoders (CAEs) have shown significant potential in optimizing the physical layer of a wireless communication system for a specific channel through joint end-to-end training. However, the practical implementation of CAEs faces several challenges, particularly in realistic and dynamic scenarios. Channels in communication systems are dynamic and change with time. Still, most proposed CAE designs assume stationary scenarios, meaning they are trained and tested for only one channel realization without regard for the dynamic nature of wireless communication systems. Moreover, conventional CAEs are designed based on the assumption of having access to a large number of pilot signals, which act as training samples in the context of CAEs. However, in real-world applications, it is not feasible for a CAE operating in real-time to acquire large amounts of training samples for each new channel realization. Hence, the CAE has to be deployable in few-shot learning scenarios where only limited training samples are available. Furthermore, most proposed conventional CAEs lack fast adaptability to new channel realizations, which becomes more pronounced when dealing with a limited number of pilots. To address these challenges, this paper proposes the Online Meta Learning channel AE (OML-CAE) framework for few-shot CAE scenarios with dynamic channels. The OML-CAE framework enhances adaptability to varying channel conditions in an online manner, allowing for dynamic adjustments in response to evolving communication scenarios. Moreover, it can adapt to new channel conditions using only a few pilots, drastically increasing pilot efficiency and making the CAE design feasible in realistic scenarios.

Figures (7)

• Figure 1: Block diagram of the general CAE design.
• Figure 2: Learned constellation (left) and received symbols (right) under two different fading channels.
• Figure 3: Overview of the OML-CAE framework. Steps related to the pilot and data are shown with dashed and solid lines respectively. At sequence i, with the fading channel realization i The encoder and the decoder start with initial parameters $\theta_{i}enc$ and $\theta_{i}dec$. In steps 1 and 2, the pilot is encoded and transmitted through the channel. In step 3, the transmitted pilot signals are decoded, and a loss is calculated based on the few labeled samples available in the pilot. In step 4, adaptation (inner loop) is performed and task-specific optimized parameters for the encoder ($\theta'_ienc$) and the decoder ($\theta'_idec$) are obtained. After the CAE is adapted for the given channel, the data is transmitted and decoded through steps 5, 6, and 7. No updates are done in these steps and the output only determines the model's accuracy for the given fading channel. In step 8, the received pilot is added to the task buffer. In the first sequence, the task buffer is empty and the task buffer is sequentially filled as the model observes new channel realizations. In steps 9 and 10, meta-training is performed using all the pilots stored in the task buffer. Through the meta-training, the initial parameters for the encoder ($\theta_{i+1}enc$) and the decoder ($\theta_{i+1}dec$) are updated which will be used in the next sequence when the fading channel changes.
• Figure 4: SER of the compared CAE methods in 1 to 5 shots. Subfigures (a) and (b) represent SNR=5 and SNR=10 respectively. Number of bits=4, Channel uses=2. For QPSK+MLE, each two bits are transmitted through different channels using QPKS and an error occurs if any of the 4 bits is demodulated incorrectly.
• Figure 5: SER of the compared CAE methods in 1 to 5 shots. Subfigures (a) and (b) represent SNR=5 and SNR=10 respectively. Number of bits=6, Channel uses=3. For QPSK+MLE, each two bits are transmitted through different channels using QPKS and an error occurs if any of the 6 bits is demodulated incorrectly.