Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee

• URL: http://arxiv.org/abs/2106.10426v1
• Date: Sat, 19 Jun 2021 05:23:05 GMT
• Title: Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee
• Authors: Yandong Shi, Hayoung Choi, Yuanming Shi, Yong Zhou
• Abstract summary: Massive access is a critical design challenge of Internet of Things (IoT) networks. We consider the grant-free uplink transmission of an IoT network with a multiple-antenna base station (BS) and a large number of single-antenna IoT devices. We propose a novel algorithm unrolling framework based on the deep neural network to simultaneously achieve low computational complexity and high robustness.
• Score: 30.86806523281873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Massive access is a critical design challenge of Internet of Things (IoT) networks. In this paper, we consider the grant-free uplink transmission of an IoT network with a multiple-antenna base station (BS) and a large number of single-antenna IoT devices. Taking into account the sporadic nature of IoT devices, we formulate the joint activity detection and channel estimation (JADCE) problem as a group-sparse matrix estimation problem. This problem can be solved by applying the existing compressed sensing techniques, which however either suffer from high computational complexities or lack of algorithm robustness. To this end, we propose a novel algorithm unrolling framework based on the deep neural network to simultaneously achieve low computational complexity and high robustness for solving the JADCE problem. Specifically, we map the original iterative shrinkage thresholding algorithm (ISTA) into an unrolled recurrent neural network (RNN), thereby improving the convergence rate and computational efficiency through end-to-end training. Moreover, the proposed algorithm unrolling approach inherits the structure and domain knowledge of the ISTA, thereby maintaining the algorithm robustness, which can handle non-Gaussian preamble sequence matrix in massive access. With rigorous theoretical analysis, we further simplify the unrolled network structure by reducing the redundant training parameters. Furthermore, we prove that the simplified unrolled deep neural network structures enjoy a linear convergence rate. Extensive simulations based on various preamble signatures show that the proposed unrolled networks outperform the existing methods in terms of the convergence rate, robustness and estimation accuracy.

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