Kernel-based parallel multi-user detector for massive-MIMO. (January 2018) - Mitra, Rangeet; Bhatia, Vimal
Highlights: A novel algorithm for massive MIMO detection was proposed to deal with power amplifier nonlinearity. The algorithm did not require knowledge of the PA characteristic and is learnt from the data using RKHS techniques. The large dimensional problem is split into sub-problems which are solved in parallel. Finally the fused consensus is used for final detection. This work considers a massive MIMO system with ISI and transmit power-amplifier nonlinearity (omni-prevalent impairments in millimetre wave communication proposed for 5G and beyond standards) and proposes a scalable detector to mitigate these impairment without explicitly knowing the exact characteristics of these impairments. Graphical abstract: Abstract: One of the proposed solutions to meet the ever growing demand for data rates in 5G communication systems is to use large number of antennas (100–1000) in a massive multiple input multiple output (MIMO) communication system. Performing multi-user (MU) detection over massive-MIMO systems presents many challenges, prime among them being: large-dimensionality of the received dataset which can increase the computational complexity of traditional algorithms, and susceptibility to device impairments like power amplifier (PA) nonlinearity. Due to these factors, detection of the users' symbols over uplink-MU massive-MIMO systems in a fast and computationally efficient way is an open problem. In this work, a reproducing kernel Hilbert space (RKHS) based block symbol detector is proposed for uplink-MU massive-MIMO systems that works on decomposed blocks of the observations, and selectively decides the use of an incoming observation, thereby rendering the detector to be computationally tractable, and robust to PA-nonlinearity encountered in uplink-MU massive-MIMO. Simulations have been carried out in this work that demonstrate superior performance of the proposed approach as compared to batch/iterative least squares based algorithms.