Antenna Selection in Massive MIMO Based on Greedy Algorithms

As wireless services proliferate, the demand for available spectrum also grows. As a result, spectral efficiency is still an issue being addressed by many researchers aiming at improving the quality of service to a growing number of users. Massive multiple-input multiple-output (MIMO) has been presented as an attractive technology for the next wireless systems since it can alleviate the expected spectral shortage. Nevertheless, such a technique requires a dedicated chain of radio frequency (RF) components for each antenna element which result in high costs at base station (BS) side. To reduce the number of RF chains, we propose several transmit antenna selection schemes aiming at minimizing the mean square reception error and also reducing the transmission power which is one of the main contributions of our work. The proposed strategies are inspired by the matching pursuit technique and its quantized version, named matching pursuit with generalized bit planes. The presented results show that reliable reception can be accomplished with low computationally intensive algorithms for antenna selection.