Signal processing in massive MIMO systems realized with low complexity hardware

Abstract: The global mobile data traffic, as well as the energy consumption, for networks is constantly increasing. For this reason, techniques which can provide higher spectral efficiency while being more energy efficient are needed. Massive Multiple-Input Multiple-Output (MIMO) is a technique which can bring these two together and will play an important role in future wireless networks. The most power consuming part is the base stations and for the complexity in the digital signal processing, the per antenna functions are dominating. More specifically, in an Orthogonal Frequency-Division Multiplexing (OFDM) based system, this includes the filter and the inverse fast Fourier transform (IFFT). In this thesis the possibilities of implementing the per antenna functions in low complexity hardware is investigated. Both low accuracy and scaling down the supply voltage to the integrated circuits, exploring the error resilience of the system, are considered. Due to this, a remarkable amount of energy can be saved. Results in this thesis show that for a certain communication system implemented with low accuracy and allowing errors to occur, 97% of the signal processing power can be saved at a Signal-to-Noise Ratio (SNR) degradation of only 2 dB. Concluding this work, massive MIMO can provide high spectral efficiency and implemented with low accuracy hardware it can still be error resilient and lead to higher energy efficiency.