A novel low complexity downlink linear precoding algorithm for massive MIMO systems

The tremendous advancements in 4G and beyond 4G wireless standards requires a high demand for the increased data rate, high spectral efficiency and minimal power requirement. Massive MIMO is a key technology that can be used to attain all the above requirements at the cost of increased complexity. The performance of this massive MIMO system can be optimized by simple linear precoding techniques, the complexity of which lies on the inversion of large size matrix. In this paper, we propose a large scale low complexity matrix inversion algorithm which is highly suitable for parallel architecture. The proposed algorithm makes use of Chebyshev polynomial and Weyl’s inequality and named as Weyl’s Chebyshev acceleration (WCA) algorithm. This algorithm is further simplified by exploiting the diagonal dominance property of the positive definite Hermitian Gram matrix that is to be inverted. The specialty of this algorithm is, it is inner product free which makes this highly suitable for parallel computing environment and thus the algorithm becomes speedy. The performance of the proposed algorithm is evaluated in urban micro cell scenario and is proven to be more efficient in terms of BER performance and complexity. Also the proposed WCA based precoding algorithm achieves approximately 50% of complexity saving in the total flop count in comparison with the existing algorithms for the micro cell scenario. The BER performance reaches the near optimal results of ZF algorithm as SNR increases.