Time series analysis of indoor millimetre wave distributed antenna systems

Abstract

Driven by the abundant availability of spectrum resources and the high data rate demands, millimetre wave (mmWave) technologies have emerged as one of the strongest contenders for the delivery of future wireless applications. Nonetheless, due to their shorter wavelengths when compared to traditional microwave transmissions, the wireless signals at these frequencies are extremely sensitive to the blocking and shadowing caused by obstacles within the local environment. This challenge is especially pronounced for indoor dense small cell deployments. One potential approach to mitigate signal deteriorations is the application of distributed antenna systems (DASs) upon which the research in this thesis is focused. In this work, a distributed mmWave measurement system operating at 60 GHz is designed in order to take the snapshots of the signal received simultaneously at multiple candidate access point locations. This experimental mmWave DAS setup enables the time series data to be collected and analysed in this thesis.

In more detail, this thesis first investigates the influence of elevation angle on near-body path gain at 60 GHz and then presents a fading characterisation for indoor mmWave DASs. This is followed by a study of the cross correlation coefficient (CCC) and channel power imbalance (CPI) using statistical time series tools. The potential improvement in signal reliability has also been empirically studied by considering different access point selection and signal combining approaches. With the aim of assisting radio resource management, using small-scale fading features extracted from the raw received signal time series, a machine learning (ML)-based system which combines supervised and unsupervised learning is proposed to recognize common UE use cases in indoor mmWave DASs. This thesis also presents an empirical analysis of the handover performance in indoor mmWave DASs. This is achieved by considering some conventional handover algorithms and then determining the optimal ranges for the associated handover parameters.

Date of Award	Jul 2021
Original language	English
Awarding Institution	Queen's University Belfast
Sponsors	Northern Ireland Department for the Economy
Supervisor	Simon Cotton (Supervisor) & Hien-Quoc Ngo (Supervisor)