| Περίληψη: | The increasing demand for higher data rates and for more connected devices has
led to the adoption of Massive MIMO Technologies for transmission in wireless
channels. Having a high number of transmit and receive antennas is the basic
feature of Massive MIMO networks and this is what differentiates them from conventional
MIMO systems where users communicate using base stations with a
small number of antennas. Massive MIMO technology has many benefits such as
high link reliability, high throughput rate, high spectral efficiency and therefore it
plays a crucial role at 5G networks. However due to the large number of antennas,
Massive MIMO systems are known for their cost and for their high computational
complexity. The complexity of optimum detectors such as MAP and ML increases
fast with the number of antennas, which makes their implementation and application
in networks practically impossible. Expectation Propagation (EP) algorithm
provides a good bit error rate (BER) performance similar to linear algorithms when
≫ , where linear algorithms behave as the optimals. Also, EP seems to
maintain this good behavior with low BERs compared to linear detectors, even
when the number of transmission antennas is about the same as the number of
receiving antennas. Moreover, the complexity of EP algorithm is polynomial in
contrast with the complexity of MAP and ML detectors complexity, which rises
exponentially with the number of transmit and receive antennas.
EP is an iterative algorithm that attempts to minimize the Kullback Leibler
divergence
and approach the probability density function used for the MAP criterion
via Gaussian distributions. Specifically, in the iterative section, an attempt is made
to approximate the average value of the distribution, that we are interested in, because
the average, or otherwise, the expected value of the distribution will be the
estimate for the symbol that was sent. In this thesis, an implementation of an EP in a Massive MIMO network is presented. This thesis focuses on uplink communication
where users transmit data to the base station. Furthermore a comparison
between EP and other detectors, proposed in the literature, is presented based on
BER performance using MATLAB simulations. Various methods of calculating
the inverse of a matrix are used in this thesis considering the computational complexity,
the latency and the required word length for hardware implementation.
Implementation results of these methods are also evaluated. Finally, some hardware
implementations of EP targeting FPGA designs are recommended.
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