Learn to Adapt to New Environments From Past Experience and Few Pilot Blocks

Abstract

In recent years, deep learning has been widely applied in communications and achieved remarkable performance improvement. Most of the existing works are based on data-driven deep learning, which requires a significant amount of training data for the communication model to adapt to new environments and results in huge computing resources for collecting data and retraining the model. In this paper, we will significantly reduce the required amount of training data for new environments by leveraging the learning experience from the known environments. Therefore, we introduce few-shot learning to enable the communication model to generalize to new environments, which is realized by an attention-based method. With the attention network embedded into the deep learningbased communication model, environments with different power delay profiles can be learnt together in the training process, which is called the learning experience. By exploiting the learning experience, the communication model only requires few pilot blocks to perform well in the new environment. Through an example of deep-learning-based channel estimation, we demonstrate that this novel design method achieves better performance than the existing data-driven approach designed for few-shot learning.

Effective Adaptation into New Environment with Few Shots: Applications to OFDM Receiver Design

“…for i ∈ N do Update u ℓ+1 i by (13). Update z ℓ+1 i by the last equation in (11) . end end Return u ℓ and v ℓ n .…”

“…FSL is an emerging paradigm that addresses these challenges by producing creative work on data, models, and algorithms. According to [8], FSL techniques can be divided into data augmentation, multimodal learning, meta-learning [9]- [11], and transfer learning (TL) [12]- [14]. One critical issue of FSL in wireless communications is that the data from different environments are heterogeneous and the samples of a new environment are small.…”