5G cellular networks for sub-6GHz have become a reality and are rolling out worldwide. Yet, mmWave band (FR2) operation, a necessity for future high-capacity networks, has not been widely adopted by major carriers mainly due to limited communication ranges, which are measured in ‘street blocks’, rather than hundreds or thousands of metres that are typical for the sub-6GHz networks. This renders the existing technology relevant only for hotspots like stadiums and airports, far from the preferred nationwide deployment. This constraint is the result of huge pathloss suffered in mmWave frequencies. For example, the pathloss endured by 28 GHz electromagnetic (EM) free-space wave reaches 81 dB at mere 10 metres, i.e. only a seven billionth of total energy received. This is 177 times lower than at 2.1 GHz, where higher RF power levels are significantly easier to achieve.
Massive arrays are commonly branded as ‘companion’ to mmWave communications, as they enjoy compact form factors and are capable of creating high-gain beams, which, theoretically, are more than sufficient to compensate huge channel pathloss. In practice the cost to harness the full multiplexing (or beamforming) gain in massive arrays is prohibitive, due to; a) high cost of mmWave radio frequency (RF) chains of which hundreds are needed, b) high energy consumption associated with RF chains (e.g. power amplifiers of poor efficiency) and the digital processing, and, more critically, c) high transmission latency as the result of estimation and processing/inversion of channel matrix of large dimension.
The current solution thus relies on the ‘simplified’ massive MIMO architecture, i.e., hybrid MIMO that employs an analogue beamforming network to reduce the dimension of channel matrix observed in the digital baseband [1, 2]. It can effectively reduce the number of required RF chains as well as the other costs mentioned above. On the other hand, in the hybrid MIMO the channel matrix conversion from ‘Physical’ propagation domain to ‘Digital Baseband’ domain inevitably brings a performance drop due to the high insertion loss of analogue beamforming networks and reduced ranks of channel matrices. In other words, the ‘Baseband Wireless Channel’ in hybrid MIMO is inferior to the ‘Physical Wireless Channel’ exploited by full-dimension MIMO. Hybrid-coding (namely two-stage analogue & digital channel coding) algorithms have been developed to minimise the performance drop against the full-dimension Massive MIMO. Yet they are limited only in specific propagation environments, and the lack of a generic architecture & adaptive algorithms renders this ‘simplified’ massive MIMO underperforming (commonly losing SNR as high as 10 dB) in constantly changing channel environments.
In this PhD project, we aim to investigate the interaction among various RF modules, including antennas (arrays), power amplifiers, beamforming networks, as well as digital predistortion and precoding, so that the entire RF chain can be co-designed and optimised for various wireless communication scenarios. We will study the practical RF devices, and their impact on the link and network performance. The imperfection of RF devices, e.g. mismatching, coupling, non-linearity, etc., will be considered. At the end of the PhD project, it is expected a framework of RF-informed generic highly integrated mmWave transceivers will be established.
How to Apply
1. Important Information before you Apply
When applying through the Heriot-Watt on-line system please ensure you provide the following information:
(a) in ‘Study Option’
You will need to select ‘Edinburgh’ and ‘Postgraduate Research’. ‘Programme’ presents you with a drop-down menu. Choose Chemistry PhD, Physics PhD, Chemical Engineering PhD, Mechanical Engineering PhD, Bio-science & Bio-Engineering PhD or Electrical PhD as appropriate and select September 2022 for study option (this can be updated at a later date if required)
(b) in ‘Research Project Information’
You will be provided with a free text box for details of your research project. Enter Title and Reference number of the project for which you are applying and also enter the potential supervisor’s name.
This information will greatly assist us in tracking your application.
Please note that once you have submitted your application, it will not be considered until you have uploaded your CV and transcripts.