Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth

TL;DR

This paper introduces a deep learning method with adaptive depth for sparse linear inverse problems in communication systems, allowing the network to dynamically determine the number of layers needed for each task, improving efficiency.

Contribution

It proposes a novel end-to-end trainable neural network with a halting score that adaptively adjusts network depth based on task complexity, addressing limitations of fixed-depth models.

Findings

Enhanced efficiency in sparse signal recovery tasks

Effective application to massive MTC and MIMO channel estimation

Outperforms fixed-depth DL methods in experiments

Abstract

Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications. In recent years, deep learning (DL) based approaches have attracted interests of researchers to solve the sparse linear inverse problem by unfolding iterative algorithms as neural networks. Typically, research concerning DL assume a fixed number of network layers. However, it ignores a key character in traditional iterative algorithms, where the number of iterations required for convergence changes with varying sparsity levels. By investigating on the projected gradient descent, we unveil the drawbacks of the existing DL methods with fixed depth. Then we propose an end-to-end trainable DL architecture, which involves an extra halting score at each layer. Therefore, the proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase. We conduct experiments using both synthetic data and applications including random access in massive MTC and massive MIMO channel estimation, and the results demonstrate the improved efficiency for the proposed approach.