PROJECT TITLE :
Low-Complexity SSOR-Based Precoding for Massive MIMO Systems
With the rise of the number of base station (BS) antennas in huge multiple-input multiple-output (MIMO) systems, linear precoding schemes are ready to attain the close to-optimal performance, and so are more attractive than nonlinear precoding techniques. However, conventional linear precoding schemes like zero-forcing (ZF) precoding involve the matrix inversion of enormous size with high computational complexity, particularly in massive MIMO systems. To reduce the complexity, in this letter, we tend to propose a coffee-complexity linear precoding scheme primarily based on the symmetric successive over relaxation (SSOR) methodology. Moreover, we tend to propose a straightforward approach to approximate the optimal relaxation parameter of the SSOR-based precoding by exploiting the channel property of asymptotical orthogonality in huge MIMO systems. We tend to show that the proposed SSOR-based precoding can cut back the complexity of the classical ZF precoding by about one order of magnitude without performance loss, and it conjointly outperforms the recently proposed linear approximate precoding schemes in typical fading channels.
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