The past decade has seen an enormous increase in the number of connected wireless devices, and currently there are billions of devices that are connected and managed by wireless networks. At the same time, the applications that are running on these devices have also developed significantly and became more data rate insatiable. As the number of wireless devices and the demand for a high data rate will always increase, in addition to the growing concern about the energy consumption of wireless communication systems, the future wireless communication systems will have to meet three main requirements. These three requirements are: i) being able to achieve high throughput; ii) serving a large number of users simultaneously; and iii) being energy efficient (less energy consumption). Massive multiple-input multiple-output (MIMO) technology can satisfy the aforementioned requirements; and thus, it is a promising candidate technology for the next generations of wireless communication systems. Massive MIMO technology simply refers to the idea of utilizing a large number of antennas at the base station (BS) to serve a large number of users simultaneously using the same time-frequency resources. The hypothesis behind using a massive number of antennas at the BS is that as the number of antennas increases, the channels become favourable. In other words, the channel vectors between the users and their serving BS become (nearly) pairwisely orthogonal as the number of BS antennas increases. This in turn enables the use of linear processing at the BS to achieve near optimal performance. Moreover, a huge throughput and energy efficiency can be attained due to users multiplexing and array gain. In this thesis, we investigate the performance of massive MIMO systems under different scenarios. Firstly, we investigate the performance of a single-cell multi-user massive MIMO system, in which the channel vectors for the different users are assumed to be correlated. In this aspect, we propose two algorithms for users grouping that aim to improve the system performance. Afterwards, the problem of pilot contamination in multi-cell massive MIMO systems is discussed. Based on this discussion, we propose a pilot allocation algorithm that maximizes the minimum achievable rate in a target cell. Following that, we consider two different scenarios for pilot sequences allocation in multi-cell massive MIMO systems. Lower bounds on the achievable rates are derived for two linear detectors, and the performance under different system settings is analysed and discussed for both scenarios. Finally, two algorithms for pilot sequences allocation are proposed. The first algorithm takes advantage of the multiplicity of pilot sequences over the number of users to improve the achievable rate of edge cell users. While the second algorithm aims to mitigate the negative impact of pilot contamination by utilizing more system resources for the channel estimation process to reduce the inter-cell interference.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:764669 |
| Date | January 2018 |
| Creators | Alkhaled, Makram Hashim Mahmood |
| Contributors | Alsusa, Emad |
| Publisher | University of Manchester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.research.manchester.ac.uk/portal/en/theses/performance-enhancement-of-massive-mimo-systems-under-channel-correlation-and-pilot-contamination(05802cd8-8265-40a0-a9b6-9fe8ab5cfde2).html |
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