Applications are invited for a full PhD Scholarship starting January 2020 to undertake research on Ultra-reliable Low-latency Communications (URLLC). The successful applicant will be based in the School of Electronic Engineering and Computer Science (www.eecs.qmul.ac.uk), Queen Mary University of London, UK.
URLLC has been envisioned as one of three pillar use cases in 5G and beyond, which imposes stringent requirements for achieving extremely low latency and high reliability simultaneously. Some typical applications include vehicle-to-vehicle (V2V) communications, tactile internet, remote surgery, industrial automation, unmanned aerial vehicle (UAV) control communications, etc. However, the research on URLLC is still in its infancy due to its challenging requirements. To unlock URLLC, some promising research directions are identified as follows: 1) Resource management and/or channel coding under short packet transmission regime; 2) Cross-layer transmission design under stringent latency and reliability requirements (such as queue scheduling and access protocol design); 3) Machine learning based optimization methods to reduce the computational delay (such as deep neural networks, deep reinforcement learning), etc. The PhD will be based in the QMUL Communication Systems Research (CSR) Group ( http://csr.eecs.qmul.ac.uk/ ) with strong publication record and high international impact. The project will benefit from a recent purchase of several new GPU servers to support machine learning simulations.
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博士
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0
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开学时间：
春季
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申请截止时间：
10月13日
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背景偏好：All applicants should have a first-class honour degree or equivalent, or a MSc degree, in Electronic Engineering or Computer Science (or a related discipline). Applicants should have a good knowledge of English and ability to express themselves clearly in both written and spoken form. The successful candidate must be strongly motivated to undertake doctoral studies, must have demonstrated the ability to work independently and to perform critical analysis.
Applicants are expected to possess fundamental knowledge and skills in two or more of the following aspects:
1.Excellent knowledge of wireless communications and/or signal processing.
2.Prior experience in optimization theory such as convex optimization and control theory.
3.Research background in applying machine learning for wireless communication.
4.Publish international conference papers or IEEE journal papers.
5.Good mathematical and programming skills.
招生人：Dr. Cunhua Pan
招生邮箱：c.pan@qmul.ac.uk
招生网页：http://www.eecs.qmul.ac.uk/~cunhua/
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