This project will focus on the energy efficiency of Unmanned Aerial Vehicles (UAVs)-aided wireless communication networks.
Unmanned aerial vehicles (UAVs) have evolved rapidly in the past decade and are expected to play a significant role in our daily lives in the future. Although UAVs were initially developed and used by military, the recent development in technology as well as the reduced production cost have made the use of UAVs more attractive for a wide range of civil and industrial applications such as cargo distribution, video streaming, communication networks, etc. As far as wireless communication networks are concerned, UAVs can be utilized to provide reliable and low-cost wireless connectivity for many practical scenarios. More specifically, using UAVs as aerial base stations (BSs) promises several advantages over the conventional BS-based wireless networks. For instance, UAV-based wireless networks not only can allow line-of-sight communications with ground users but can also provide additional capacity, thanks to their flexibility, which can deliver coverage to some hard-to-reach areas. Another attractive feature of UAVs is their ability to act as flying relays allowing the establishment of more reliable communication links over longer distances. The aforementioned advantages, along with others, qualify UAV-aided wireless communications to be a major component in future wireless systems including the forthcoming 5G and beyond. However, a reliable and efficient realisation of such networks faces significant challenges, the most notable of which is perhaps the limited battery capacity of the UAVs. Therefore, energy efficiency of such networks is a detrimental factor which needs to be considered to prevent any potential power depletion and hence link disruption. In this respect, the aim of this project is to develop advanced signal processing algorithms and design new protocols to maximize the energy efficiency of UAV-based wireless communication networks. Different practical scenarios will be investigated. A mixture of simulation-based modelling and mathematical analysis will be undertaken to assess the performance of the developed algorithms and protocols.
- Strong mathematical, analytical and programming skills, e.g., MATLAB.
- Strong background in communication theory and digital signal processing.
- IELTS 6.5 (or equivalent) with no element below 6.0.
- Basic knowledge of machine leaning tools is a plus.
- A demonstrable ability or potential to publish in IEEE journals/conferences
This opportunity is open to UK, EU and Overseas applicants with the option to study full-time, part-time, and by distance learning.
Informal enquiries can be made to:
Dr Khaled Rabie K.Rabie@mmu.ac.uk
The quickest and most efficient way to apply for this course is to apply online. This way, you can also track your application at each stage of the process.
Please quote the reference: SciEng-KR-2019-UAVWCN1.
Please complete the additional Postgraduate Research Degree Supplementary Information document and upload it to the Student documents section of your online application. This collects important information about your research application and there may be delays if you do not submit this document.
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Once the university is satisfied with the following, we will send you an offer letter informing you that you have been offered a place of study:
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31 August 2020