Abstract
In cellular communications, recently Multi In Multi Out (MIMO) and Massive MIMO research is getting attention for the need of high data rates in Long Term Evolution Advanced(LTE-A) and 5G Communications. In MIMO baseband signal processing at physical layer, both the channel estimation and the detection algorithms play a crucial role. In this paper it is discussed the estimation algorithms least square (LS) and minimum mean square error (MMSE) and the channel detection algorithms Zero Forcing (ZF) and MMSE. Currently none of the channel estimation algorithms of LTE-A offers twin advantages of low battery consumption and very low latency, which is a key requirement of 5G. It is expected the massive MIMO with 128 or more antennas will be a norm at 5G base stations. To achieve the ultra-low latency, the matrix computations for massive MIMO are the very big bottleneck in realizing the channel estimation and massive MIMO detection algorithms. For the optimization of the 5G Massive MIMO channel estimation and detection algorithms, the prerequisite is massive complex matrix inversion speed. In this paper, a parallel processing based coding scheme is proposed by using Gauss-Jordan elimination kernel algorithm on a single instruction multiple data (SIMD) stream vector processor to realize a complex matrix inverse with optimum speed which is the need of 5G channel estimation and detection.
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