THESIS
2016
xii, 45 pages : illustrations ; 30 cm
Abstract
As a promising technique to meet the dramatic growing demand for both high throughput
and uniform coverage in the fifth generation (5G) wireless networks, massive multiple-input
multiple-output (MIMO) systems have attracted significant attention in recent years. Accurate
channel state information (CSI) is critical for the signal processing tasks in such systems.
However, in massive MIMO systems, conventional uplink training methods to obtain
CSI will incur prohibitively high training overhead, which is proportional to the number of
MUs. Thus, innovative training techniques are necessary to improve the overall performance
of multiuser massive MIMO systems.
In this thesis, we first propose a non-orthogonal pilot design based on the mean square
error (MSE) minimization approach. I...[
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As a promising technique to meet the dramatic growing demand for both high throughput
and uniform coverage in the fifth generation (5G) wireless networks, massive multiple-input
multiple-output (MIMO) systems have attracted significant attention in recent years. Accurate
channel state information (CSI) is critical for the signal processing tasks in such systems.
However, in massive MIMO systems, conventional uplink training methods to obtain
CSI will incur prohibitively high training overhead, which is proportional to the number of
MUs. Thus, innovative training techniques are necessary to improve the overall performance
of multiuser massive MIMO systems.
In this thesis, we first propose a non-orthogonal pilot design based on the mean square
error (MSE) minimization approach. In this method, the pilot length can be smaller than
the number of MUs, i.e., the pilots are correlated, which significantly reduces the training
overhead. We establish the relationship between the pilot correlation coefficients and the
MSE, and then minimize the MSE by optimizing the pilot correlation coefficients. Numerical
results show that the proposed non-orthogonal pilot design enjoys significant throughput
improvement compared to the conventional methods. Moreover, when the MU density increases,
the proposed training method obtains higher throughput gain.
Then, we propose a selective uplink training method for massive MIMO systems, where
in each channel block only part of the MUs will send uplink pilots for channel training, and
the channel states of the remaining MUs are predicted from the estimates in previous blocks,
taking advantage of the channels’ temporal correlation. We propose an efficient algorithm
to dynamically select the MUs to be trained within each block and determine the optimal
uplink training length. Simulation results show that the proposed training method provides
significant throughput gains compared to the existing methods, with much lower estimation
complexity. It is observed that the throughput gain becomes higher as the MU density increases.
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