Abstract
The emerging large-scale/massive multi-input multioutput (MIMO) system combined with orthogonal frequency division multiplexing (OFDM) is considered a key technology for its advantage of improving the spectral efficiency. In this paper, we introduce an iterative detection algorithm for uplink large-scale multiuser MIMO-OFDM communication systems. We design a Main-Branch structure iterative turbo detector using the Approximate Message Passing algorithm simplified by linear approximation (AMP-LA) and using the Mean Square Error (MSE) criterion to calculate the correlation coefficients between main detector and branch detector for the given iteration. The complexity of our method is compared with other detection algorithms. The simulation results show that our scheme can achieve better performance than the conventional detection methods and have the acceptable complexity.
Highlights
Multi-input multioutput systems with multiple antennas employed at both the transmitter and receiver got a lot of attention due to their multiplexing and diversity capabilities which can offer much higher data rates and enhance the system capacity [1]
The large-scale multiuser multi-input multioutput (MIMO) technology combined with orthogonal frequency division multiplexing (OFDM) promises significant improvements in terms of spectral efficiency, link reliability, and coverage compared to conventional small-scale MIMO systems [3]
We proposed an iterative detection algorithm in order to defy intersymbol interference and improve the spectral efficiency for uplink large-scale MIMO communication systems
Summary
Multi-input multioutput systems with multiple antennas employed at both the transmitter and receiver got a lot of attention due to their multiplexing and diversity capabilities which can offer much higher data rates and enhance the system capacity [1]. Wu et al proposed a relatively low complexity iterative detection algorithm for large-scale multiuser MIMO-OFDM systems using Approximate Message Passing in [13], but the performance is lower than conventional MMSE detection algorithm when the number of iterations is small. Besides those algorithms derived from factor graph, iterative BP algorithm based on Markov random field (MRF) was investigated in [14]. With Xn excluded; NC(X; X, τ) ≜ (πτ)−1exp(−|X − X|2/τ) denotes the circular Gaussian pdf with mean Xand variance τ
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