Deep Learning Based Pilot Design for Multi-user Distributed Massive MIMO Systems

Abstract

This letter proposes a deep learning based pilot design scheme to minimize the sum mean square error (MSE) of channel estimation for multi-user distributed massive multiple-input multiple-output (MIMO) systems. The pilot signal of each user is expressed as a weighted superposition of orthonormal pilot sequence basis, where the power assigned to each pilot sequence is the corresponding weight. A multi-layer fully connected deep neural network (DNN) is designed to optimize the power allocated to each pilot sequence to minimize the sum MSE, which takes the channel large-scale fading coefficients as input and outputs the pilot power allocation vector. The loss function of the DNN is defined as the sum MSE, and we leverage the unsupervised learning strategy to train the DNN. Simulation results show that the proposed scheme achieves better sum MSE performance than other methods with low complexity.

4 Pages, 4 figures, accepted by IEEE Wireless Communications Letters

References in corpus (5)

• A Coordinated Approach to Channel Estimation in Large-scale Multiple-antenna Systems
• Pilot Reuse for Massive MIMO Transmission over Spatially Correlated Rayleigh Fading Channels
• Learning the MMSE Channel Estimator
• An Iterative BP-CNN Architecture for Channel Decoding
• Towards Optimal Power Control via Ensembling Deep Neural Networks

Cited by in corpus (6)

• AI based Service Management for 6G Green Communications
• Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming
• Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks
• Channel Charting Aided Pilot Reuse for Massive MIMO Systems with Spatially Correlated Channels
• A Survey on Deep-Learning based Techniques for Modeling and Estimation of MassiveMIMO Channels
• Pilot Contamination in Massive MIMO Systems: Challenges and Future Prospects