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1. A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback

   Zhenyu Liu, Mason del ( January 2021 , IEEE transactions on wireless communications)

   null (Ed.)

   Channel state information (CSI) plays a vital role in scheduling and capacity-approaching transmission optimization of massive MIMO communication systems. In frequency division duplex (FDD) MIMO systems, forward link CSI reconstruction at transmitter relies on CSI feedback from receiving nodes and must carefully weigh the tradeoff between reconstruction accuracy and feedback bandwidth. Recent application of recurrent neural networks (RNN) has demonstrated promising results of massive MIMO CSI feedback compression. However, the cost of computation and memory associated with RNN deep learning remains high. In this work, we exploit channel temporal coherence to improve learning accuracy and feedback efficiency. Leveraging a Markovian model, we develop a deep convolutional neural network (CNN)-based framework called MarkovNet to efficiently encode CSI feedback to improve accuracy and efficiency. We explore important physical insights including spherical normalization of input data and deep learning network optimizations in feedback compression. We demonstrate that MarkovNet provides a substantial performance improvement and computational complexity reduction over the RNN-based work.We demonstrate MarkovNet’s performance under different MIMO configurations and for a range of feedback intervals and rates. CSI recovery with MarkovNet outperforms RNN-based CSI estimation with only a fraction of computational cost. 

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2. Exploiting Partial FDD Reciprocity for Beam Based Pilot Precoding and CSI Feedback in Deep Learning

   https://doi.org/10.1109/TWC.2023.3289929  

   Lin, Yu-Chien ; Lee, Ta-Sung ; Ding, Zhi ( May 2023 , IEEE Transactions on Wireless Communications)

   Massive MIMO systems can achieve high spectrum and energy efficiency in downlink (DL) based on accurate estimate of channel state information (CSI). Existing works have developed learning-based DL CSI estimation that lowers uplink feedback overhead. One often overlooked problem is the limited number of DL pilots available for CSI estimation. One proposed solution leverages temporal CSI coherence by utilizing past CSI estimates and only sending CSI-reference symbols (CSIRS) for partial arrays to preserve CSI recovery performance. Exploiting CSI correlations, FDD channel reciprocity is helpful to base stations with direct access to uplink CSI. In this work, we propose a new learning-based feedback architecture and a reconfigurable CSI-RS placement scheme to reduce DL CSI training overhead and to improve encoding efficiency of CSI feedback. Our results demonstrate superior performance in both indoor and outdoor scenarios by the proposed framework for CSI recovery at substantial reduction of computation power and storage requirements at UEs. 

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3. Deep Learning for Partial MIMO CSI Feedback by Exploiting Channel Temporal Correlation

   https://doi.org/10.1109/IEEECONF53345.2021.9723211  

   Lin, Yu-Chien ; Lee, Ta-Sung ; Ding, Zhi ( October 2021 , 55th Asilomar Conference on Signals, Systems, and Computers)

   Accurate estimation of DL CSI is required to achievehigh spectrum and energy efficiency in massive MIMO systems.Previous works have developed learning-based CSI feedbackframework within FDD systems for efficient CSI encoding andrecovery with demonstrated benefits. However, downlink pilotsfor CSI estimation by receiving terminals may occupy excessivelylarge number of resource elements for massive number ofantennas and compromise spectrum efficiency. To overcome thisproblem, we propose a new learning-based feedback architecturefor efficient encoding of partial CSI feedback of interleavednon-overlapped antenna subarrays by exploiting CSI temporalcorrelation. For ease of encoding, we further design an IFFTapproach to decouple partial CSI of antenna subarrays andto preserve partial CSI sparsity. Our results show superiorperformance in indoor/outdoor scenarios by the proposed modelfor CSI recovery at significantly reduced computation power andstorage needs. 

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4. Physics-Inspired Deep Learning Anti-Aliasing Framework in Efficient Channel State Feedback

   Lin, Yu-Chien Lin ; Xin, Yan ; Lee, Ta-Sung ; Zhang, Charlie Jianzhong ; Ding, Zhi ( August 2024 , IEEE transactions on wireless communications)

   Acquiring downlink channel state information (CSI) at the base station is vital for optimizing performance in massive Multiple input multiple output (MIMO) Frequency-Division Duplexing (FDD) systems. While deep learning architectures have been successful in facilitating UE-side CSI feedback and gNB side recovery, the undersampling issue prior to CSI feedback is often overlooked. This issue, which arises from low-density pilot placement in current standards, results in significant aliasing effects in outdoor channels and consequently limits CSI recovery performance. To this end, this work introduces a new CSI upsampling framework at the gNB as a post-processing solution to address the gaps caused by undersampling. Leveraging the physical principles of discrete Fourier transform shifting theorem and multipath reciprocity, our framework effectively uses uplink CSI to mitigate aliasing effects. We further develop a learning based method that integrates the proposed algorithm with the Iterative Shrinkage-Thresholding Algorithm Net (ISTA-Net) architecture, enhancing our approach for non-uniform sampling recovery. Our numerical results show that both our rule-based and deep learning methods significantly outperform traditional interpolation techniques and current state-of-the-art approaches in terms of performance. 

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5. Learning-Based MIMO Channel Estimation under Practical Pilot Sparsity and Feedback Compression

   https://doi.org/10.1109/TWC.2022.3202750  

   Del Rosario, Mason ; Ding, Zhi ( February 2023 , IEEE transactions on wireless communications)

   Wireless links using massive MIMO transceivers are vital for next generation wireless communications networks. Precoding in Massive MIMO transmission requires accurate downlink channel state information (CSI). Many recent works have effectively applied deep learning (DL) to jointly train UE-side compression networks for delay domain CSI and a BS-side decoding scheme. Vitally, these works assume that the full delay domain CSI is available at the UE, but in reality, the UE must estimate the delay domain based on a limited number of frequency domain pilots. In this work, we propose a linear pilot-to-delay estimator (P2DE) that acquires the truncated delay CSI via sparse frequency pilots. We show the accuracy of the P2DE under frequency downsampling, and we demonstrate the P2DE’s efficacy when utilized with existing CSI estimation networks. Additionally, we propose to use trainable compressed sensing (CS) networks in a differential encoding network for time-varying CSI estimation, and we propose a new network, MarkovNet-ISTA-ENet (MN-IE), which combines a CS network for initial CSI estimation and multiple autoencoders to estimate the error terms. We demonstrate that MN-IE has better asymptotic performance than networks comprised of only one type of network. 

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