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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/9E7283FC-A2E0-4A5E-8F4E-79762381F904 | ||
| Year | 2022 | ||
| Type of Item | Diploma Work | ||
| License |
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| Bibliographic Citation | Pavlos-Stylianos Neonakis, "Estimation of sparse, in the angle domain, Massive MIMO channels", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2022 https://doi.org/10.26233/heallink.tuc.91411 | ||
| Appears in Collections |
One of the technologies that makes 5G different from previous generations of wireless communications systems is Massive MIMO. When we refer to Massive MIMO, we mean that there are many antennas (of the order of hundreds) at the Base Station.Major benefits of Massive MIMO systems are the increased energy efficiency, through directional beamforming, and the increased channel capacity. In order to attain these advantages, it is crucial to have channel knowledge at the transmitter, which, in this case, requires large training overhead, due to the large number of channel coefficients.In this Diploma thesis, we exploit the channel sparsity in the angle domain and study Massive MIMO channel estimation methods with low training overhead. First, we present an approach where channel estimation is done by the minimization of a weighted l1 norm, with weights equal to 0 and 1, using prior knowledge about the positions of the nonzero elements in the angular domain. Next, we propose an alternative, where we use different weights. We simulate these methods and we test their behavior in many case studies.
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