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

Sammanfattning

The number of antennas used in massive multiple input multiple output (MIMO) systemsis 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 proportionalincrease in overhead associated with the acquisition of channel state information (CSI)through downlink (DL) transmission of reference signals (CSI-RS). This thesis investigatesmethods to reduce the overhead of CSI-RS transmission using a twofold approach:optimising pilot placement for sparse sounding of the reference signals, and reconstructingthe full channel information from these sparse measurements at the user equipment (UE).First, the sparse sampling of CSI-RS is formulated as a submodular optimisationproblem, and a cost function is presented based on the frame potential of the DL channelestimated by the UE. A greedy algorithm for solving the sparse pilot placement problem isproposed and evaluated for a simulated 3rd Generation Partnership Project (3GPP) MIMOurban microcell environment with a uniform planar array (UPA), achieving near-optimalpilot placement for subsets of antenna ports.The second part of the thesis investigates the recovery of the full channel informationfrom the sparsely sounded CSI-RS using an artificial neural network (ANN). Aphysics-informed U-Net architecture is developed, that leverages the sparse angularrepresentation of the DL channel to recover the full-rank channel. The ANN is trained ona large dataset of simulated noiseless DL channels for the same 3GPP environment andfor several different spatial pilot configurations and muting levels.The results of the experiments show that the ANN model can achieve a reconstructionaccuracy comparable to basis pursuit denoising (BPDN), while outperforming BPDNin computational efficiency. In addition, the choice of spatial antenna port mutingpattern has a noticeable impact on the reconstruction performance of both methods inthe considered scenario, with the found near-optimal sampling patterns gives the closestspectral similarity to the full-rank channel in terms of the Itakura-Saito distance. Thecombined approach of optimising sparse pilot placement and using a neural networkfor reconstruction demonstrates the potential of AI functionality for CSI-RS overheadreduction and for improving the performance of massive MIMO systems in upcoming 6Gnetworks and beyond.