Massive MIMO systems are shown to be a promising technology for next generations of wireless communication networks. The realization of the attractive merits
promised by massive MIMO systems requires advanced linear precoding and receiving
techniques in order to mitigate the interference in downlink and uplink transmissions.
This work considers the precoder and receiver design in massive MIMO systems.
We first consider the design of the linear precoder and receiver that maximize the
minimum signaltointerferenceplusnoise ratio (SINR) subject to a given power constraint. The analysis is carried out under the asymptotic regime in which the number
of the BS antennas and that of the users grow large with a bounded ratio. This
allows us to leverage tools from random matrix theory in order to approximate the
parameters of the optimal linear precoder and receiver by their deterministic approximations. Such a result is of valuable practical interest, as it provides a handier way to
implement the optimal precoder and receiver. To reduce further the complexity, we
propose to apply the truncated polynomial expansion (TPE) concept on a peruser
basis to approximate the inverse of large matrices that appear on the expressions of
4
the optimal linear transceivers. Using tools from random matrix theory, we determine
deterministic approximations of the SINR and the transmit power in the asymptotic
regime. Then, the optimal peruser weight coefficients that solve the maxmin SINR
problem are derived. The simulation results show that the proposed precoder and
receiver provide very close to optimal performance while reducing significantly the
computational complexity.
As a second part of this work, the TPE technique in a peruser basis is applied
to the optimal linear precoding that minimizes the transmit power while satisfying
a set of target SINR constraints. Due to the emerging research field of green cellular networks, such a problem is receiving increasing interest nowadays. Closed form
expressions of the optimal parameters of the proposed low complexity precoding for
power minimization are derived. Numerical results show that the proposed power
minimization precoding approximates well the performance of the optimal linear precoding while being more practical for implementation.
Date of Award  May 2016 

Original language  English (US) 

Awarding Institution   Computer, Electrical and Mathematical Science and Engineering


Supervisor  MohamedSlim Alouini (Supervisor) 

 Massive MIMO
 Linear precoding
 linear receiver
 Asymptotic analysis
 Random matrix theory
 low complexity