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Pilot Reuse in Distributed Massive MIMO Systems

Sabbagh, Ramiz (2019) Pilot Reuse in Distributed Massive MIMO Systems. Doctor of Philosophy (PhD) thesis, University of Kent,. (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:73283)

Language: English

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Distributed massive multi-input multi-output (MIMO) is an attractive technology to meet the vast growth in the requirements for huge data rates in future wireless communication systems. To realise the gains of the distributed massive MIMO, the central processing unit (CPU) typically needs to estimate the channels between the remote radio heads (RRHs) and the user-equipments (UEs) from mutually orthogonal pilots sent by UEs. The channel coherence is limited in time as well as frequency, causing a trade-off between the resources spent on pilots and those allocated for data symbols. The reuse of pilots is needed to reduce the pilot overhead, when a large number of UEs are being simultaneously served. This, in turn, introduces pilot interference in the channel estimation phase. In this thesis, the problem of pilot allocation is investigated, with the aim to smartly and efficiently reuse the pilots among UEs in different distributed massive MIMO scenarios by utilising the user-centric clustering approach.

Within the cellular distributed massive MIMO (DM-MIMO), a novel dynamic pilot reuse (DPR) scheme is proposed by assuming the pilot reuse inside the same cell of a dense DM-MIMO network. Since the pilot reuse directly affects the UEs' data-rate, the DPR scheme is developed with the objective of maximising the uplink sum-rate by allowing two UEs separated by a sufficient distance and satisfying a particular signal-to-interference-plus-noise ratio (SINR) constraint to share the same pilot. In order to achieve the objective, an expression of the SINR is firstly derived for any UE sharing its pilot with another. A novel low-complexity algorithm is then presented to reuse the pilots based on the separation distance between UEs. The iterative grid search (IGS) method is also employed to find the optimum SINR threshold with the aim to maximise the sum-rate. The simulation results have demonstrated the superiority of the DPR scheme over other reuse schemes based on the uplink sum-rate performance.

Another novel user-centric based pilot assignment scheme is presented in the cell-free (CF) massive MIMO network to minimise the maximum estimation error for UEs subject to some practical constraints. This scheme depends on allocating pilots with fewer reuse times to the UEs with the weakest channel conditions, while the UEs with good channel qualities adopt pilots with higher reuse times. Two novel low-complexity algorithms are developed to perform the two stages of this pilot reuse scheme. Furthermore, a novel problem is addressed considering the effect of pilot allocation on the satisfaction of UE's quality of service requirements, in terms of SINR. The problem is solved by utilising another two algorithms to minimise the number of pilots and the average channel estimation error subject to a specific SINR threshold. The simulation results reveal the superiority of the proposed scheme over the existing ones, and its average channel estimation error performance approaches that of the exhaustive search with much lower complexity. Furthermore, increasing the number of pilots initially improve the SINR, but when the number of pilots is significantly increased, this does not improve the SINR further.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Wang, Jiangzhou
Uncontrolled keywords: Cell-free massive MIMO, Distributed massive MIMO, MIMO techniques, pilot allocation, interference management, user-centric approach, channel estimation error, local CSI and pilot reuse
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Funders: Organisations -1 not found.
SWORD Depositor: System Moodle
Depositing User: System Moodle
Date Deposited: 29 Mar 2019 17:05 UTC
Last Modified: 03 Jan 2023 10:45 UTC
Resource URI: (The current URI for this page, for reference purposes)
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