IEEE 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET 2016)

202 / 2015-12-25 21:32:10

MMSE Detection for Massive MIMO Networks Based on Steepest Descent and LMS Algorithms

Massive MIMO networks, MMSE receiver, SD, LMS

Massive multiple-input-multiple-output (MIMO)
wireless communications refers to the idea equipping transmitters
with a very large number of antennas and has been shown to
potentially allow for orders of magnitude improvement in spectral
and energy efficiency using relatively simple (linear) processing.
A major impediment is the complexity at the receiver needed to
detect the transmitted data. To overcome the cubic computation
complexity due to the inverse calculation that occurs when using
linear receivers such as zero forcing (ZF) and minimum mean
squared error (MMSE) in massive MIMO networks. As a result,
methods for reducing the complexity of the MMSE receiver have
been of great interest in recent years. In this paper, we propose
an efficient low-complexity MMSE receiver structure for massive
MIMO networks based on the steepest descent (SD) and least
mean square (LMS) algorithms which can be devoid of the matrix
inverse operation. Further, we prove that the two algorithms are
both convergent when they are applied in the MMSE detection.
And which factors affect the rate of convergence is also analyzed.
Simulation results show that the proposed receiver with low complexity
is practical for massive MIMO networks.