Lattice Reduction Assisted Likelihood Ascent Search Algorithm for Multiuser Detection in Massive MIMO System

Massive multiple input multiple output (MIMO) system achieves high spectral and energy efficiency by incorporating a large number of antennas at the transmitter and/or receivers. Multiuser detection is an important task that needs to be done at the receiver of the Massive MIMO system to mitigate multiuser interference. The classical Zero Forcing (ZF) detector suffers from high residual interference. By making channel matrix orthogonal, the Lattice Reduction (LR) techniques can be assisted for the ZF detector to minimize interference. On the other hand, the Likelihood Ascent Search (LAS) is a neighborhood search based low complexity detection algorithm that is used for massive MIMO systems. It takes the Zero Forcing (ZF) solution as initial vector and searches for a near-optimal solution by examining cost values of its neighborhood vectors. The performance of the LAS algorithm is mainly relying on an initial vector. So, this paper investigates the design of LAS algorithm with LR assisted ZF solution as an initial vector to improve performance over the classical ZFLAS detector. The proposed algorithm attains a more achievable trade-off between Bit Error Rate (BER) performance and exponential time detection complexity for large extended systems.

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