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Upper-Band MmWave (200-300 GHz) Massive-MIMO Beamforming using Artificial Intelligence for 6G Heterogeneous Networks

Project Description

Massive multiple-input multiple-output (MIMO) systems have shown promising features to significantly increase spectral efficiency of cellular communication systems. Future wireless communications such as 6G requires implementation of such systems in extremely high frequency (EHF) range up to 300 GHz for various reasons such as shortage of available spectrum and low attenuation, within 1-2dB/km. Deployment of massive MIMO systems in EHF band, i.e., millimetre-wave (mmWave) will result in spectral efficiency as well as increased available bandwidth. Nevertheless, the severe attenuation of mmWave signals is a dominant obstacle, and is generally compensated via beamforming techniques that employ the benefit of large antenna arrays in massive MIMO structures often embedded in a tiny dimension in mmWave frequencies. Analog beamforming has shown extensive advantages compared to its traditional counterpart, digital beamforming. Dominant techniques proposed so far for analogue beamforming are often practically challenging. Down-to-earth confrontations are namely the necessity of possessing excellent channel state information (CSI) by the base station (BS) or search complexity. Reduction in search complexity can be achieved by performing high number of iterations between user and BS to exchange information; this leads to inevitable practical overhead. In our research, we plan to propose intelligent beamforming (IB) schemes in contrast. IB is achievable by deployment of artificial intelligence (AI) techniques through design structures that can margin novel context-awareness beamforming to drive 6G. Such implementation will aggregate the advantages of combined massive MIMO and mmWave systems, specifically when designed in upper band EHF such as 200-300GHz, and at the same time intelligently diminishes the complexity and overhead of existing schemes to make it a resourceful option for engineering 6G.

Please contact Professor Hamed Al-Raweshidy at or +44(0)1895 265771 for an informal discussion about the project

Funding Notes

Applications are invited for a full time PhD studentship funded by VIAVI Solution Company. The studentship is for a period of 3 years (36 months) starting 1st June 2019 or at a mutually agreed time after that. The successful applicant will receive an annual stipend of £20,000 plus payment of their annual full time home/EU tuition fees.

International applicants are welcome to apply but be required to self-fund the difference between the published full time international and home/EU tuition fee.

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