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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. 

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. 

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.