Time series analysis of indoor millimetre wave distributed antenna systems

This experimental mmWave DAS setup enables the time series data to be collected and analysed in this thesis.<br><br>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.