Spatially Sparse Precoding in Millimeter Wave MIMO Systems

# Re-implementation Spatially Sparse Precoding in Millimeter Wave MIMO Systems. MATLAB 2019a is used, no additional toolbox is needed to employ. ## Introduction Millimeter wave (mmWave) communication systems have attracted significant interest regarding meeting the capacity requirements of the future 5G network. The mmWave systems have frequency ranges in between 30 and 300 GHz where a total of around 250 GHz bandwidths are available. They experience orders-of-magnitude more pathloss than the microwave signals. Fortunately, the small wavelengths of mmWave frequencies enable large numbers of antenna elements to be deployed in the same form factor thereby providing high spatial processing gains. Beamforming with multiple data streams, known as precoding, can be used to further improve mmWave spectral efficiency. Focus on RF processing is increase due to high-cost and complexity concerns. The solution on hardware contraints projected onto the feasible set of precoders and combiners in both sides. In this paper, we consider large antenna arrays and designing such systems that comply with practical constraints. We formulate the problem as a sparse reconstruction problem and used well-known matching pursuit algorithms to find a solution that is close to optimal unconstrained solution. We present performance in moderate and large antenna arrays, different angle spread and cluster environments. We compare our findings with beam steering solution and optimal unconstrained solution. ## Figures This repo produces Fig.3, Fig.4 and Fig.6 of the paper. ``` O. E. Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi and R. W. Heath, "Spatially Sparse Precoding in Millimeter Wave MIMO Systems," in IEEE Transactions on Wireless Communications, vol. 13, no. 3, pp. 1499-1513, March 2014, doi: 10.1109/TWC.2014.011714.130846. ```