Reducing downlink reference signal overhead for CSI acquisition in massive MIMO systems

Abstract

The number of antennas used in massive multiple input multiple output (MIMO) systems is expected to increase significantly to meet requirements of future radio access networks (RANs). In legacy 5G New Radio, scaling the number of antennas imposes a proportional increase in overhead associated with the acquisition of channel state information (CSI) through downlink (DL) transmission of reference signals (CSI-RS). This thesis investigates methods to reduce the overhead of CSI-RS transmission using a twofold approach: optimising pilot placement for sparse sounding of the reference signals, and reconstructing the full channel information from these sparse measurements at the user equipment (UE). First, the sparse sampling of CSI-RS is formulated as a submodular optimisation problem, and a cost function is presented based on the frame potential of the DL channel estimated by the UE. A greedy algorithm for solving the sparse pilot placement problem is proposed and evaluated for a simulated 3rd Generation Partnership Project (3GPP) MIMO urban microcell environment with a uniform planar array (UPA), achieving near-optimal pilot placement for subsets of antenna ports. The second part of the thesis investigates the recovery of the full channel information from the sparsely sounded CSI-RS using an artificial neural network (ANN). A physics-informed U-Net architecture is developed, that leverages the sparse angular representation of the DL channel to recover the full-rank channel. The ANN is trained on a large dataset of simulated noiseless DL channels for the same 3GPP environment and for several different spatial pilot configurations and muting levels. The results of the experiments show that the ANN model can achieve a reconstruction accuracy comparable to basis pursuit denoising (BPDN), while outperforming BPDN in computational efficiency. In addition, the choice of spatial antenna port muting pattern has a noticeable impact on the reconstruction performance of both methods in the considered scenario, with the found near-optimal sampling patterns gives the closest spectral similarity to the full-rank channel in terms of the Itakura-Saito distance. The combined approach of optimising sparse pilot placement and using a neural network for reconstruction demonstrates the potential of AI functionality for CSI-RS overhead reduction and for improving the performance of massive MIMO systems in upcoming 6G networks and beyond.