Robust MIMO Precoding on Real-World Measured Channels
It is well known that multi-input multi-output (MIMO) wireless communication systemsthat employ precoding techniques are capable of meeting the high expectations of modernand future wireless communication standards. In order to fully utilize these techniques, thecommunication system typically requires information of the channel, commonly referred toas channel state information (CSI). In practice, the CSI at the transmitter (CSIT) is oftennot perfect which addresses the need for robust precoding designs, that can mitigate theeffects of precoding with imperfect CSIT. By modeling the imperfect CSIT as deterministic,it can be assumed that the estimated channel, as represented by the CSIT, belongs to aconvex uncertainty set. With this approach, the problem of finding a robust precoding designcan be formulated as a convex maximin problem, where the solution optimizes the systemperformance for the worst channel that belongs to the uncertainty set. How the uncertaintyset is modeled impacts the performance of the communication system, which calls for theevaluation of several robust precoding designs. While different characteristics of the convexuncertainty sets has been evaluated for MIMO flat-fading channels represented by i.i.d. zero-mean, unit variance Gaussian elements, it is of interest to apply the theory of worst-caserobust precoding designs on real-world measured MIMO channels.More concisely, this project investigates MIMO precoding designs with deterministic im-perfect CSIT for real-world measured channels that utilizes orthogonal frequency divisionmultiplexing (OFDM) schemes. The worst-case received signal-to-noise ratio (SNR) will bepresented as a result of using MIMO precoding designs on real-world channels, and the effectof the choice of model parameters and characteristics of the chosen uncertainty set will bevisualized and discussed. Furthermore, orthogonal space-time block code (OSTBC) transmis-sion designs will be employed to measure the worst case symbol error rate (SER) as a tool toevaluate the system performance in different scenarios. The results will be compared to thatwhen the channel is composed of i.i.d. zero-mean, unit variance Gaussian elements and forthe case when the channel is based on the Kronecker model.The results indicate that a further analysis of how the Kronecker model behaves in termsof capacity is required in order to draw accurate conclusions regarding the implementation ofrobust precoding strategies when each pair of antennas are correlated. Also, it is essential todevelop a framework that offers methods on how to accurately model the uncertainty set sothat it can represent errors that originates from both quantization errors, estimation errorsand outdated estimates.
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