Deep Learning Based Fast Multiuser Detection for Massive Machine-Type Communication

Bai, Yanna; Ai, Bo; Chen, Wei

doi:10.48550/arxiv.1807.00967

Cited by 1 publication

(1 citation statement)

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“…In [33], a new block activation unit with the block size of six has been proposed to handle the block-sparse vector in wideband wireless communication systems. Inspired by it, we use two 7 × 7 convolutional layers in CsiNet+ to replace the first 3 × 3 convolution layer of CsiNet at the encoder.…”

Section: A Network Architecture For Channel Dimension Reductionmentioning

confidence: 99%

Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis

Guo

Wen

Jin

et al. 2020

IEEE Trans. Wireless Commun.

280

178

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Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel state information (CSI) at the base station (BS). Therefore, user equipment (UE) needs to keep on feeding CSI back to the BS, thereby consuming precious bandwidth resource. Large-scale antennas at the BS for massive MIMO seriously increase this overhead. In this paper, we propose a multiple-rate compressive sensing neural network framework to compress and quantize the CSI. This framework not only improves reconstruction accuracy but also decreases storage space at the UE, thus enhancing the system feasibility. Specifically, we establish two network design principles for CSI feedback, propose a new network architecture, CsiNet+, according to these principles, and develop a novel quantization framework and training strategy.Next, we further introduce two different variable-rate approaches, namely, SM-CsiNet+ and PM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%, respectively. Experimental results show that CsiNet+ outperforms the state-of-the-art network by a margin but only slightly increases the parameter number. We also investigate the compression and reconstruction mechanism behind deep learning-based CSI feedback methods via parameter visualization, which provides a guideline for subsequent research.

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Section: A Network Architecture For Channel Dimension Reductionmentioning

confidence: 99%