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Details of Grant 

EPSRC Reference: EP/P000673/1
Title: Low-complexity processing for mm-Wave massive MIMO
Principal Investigator: Matthaiou, Dr M
Other Investigators:
Fusco, Professor V
Researcher Co-Investigators:
Project Partners:
Huawei Technologies Co Limited (Global) University of Utah
Department: Sch of Electronics, Elec Eng & Comp Sci
Organisation: Queen's University of Belfast
Scheme: Standard Research
Starts: 01 March 2017 Ends: 31 August 2020 Value (£): 713,111
EPSRC Research Topic Classifications:
Digital Signal Processing RF & Microwave Technology
EPSRC Industrial Sector Classifications:
Communications
Related Grants:
EP/P000703/1
Panel History:
Panel DatePanel NameOutcome
10 Jun 2016 EPSRC ICT Prioritisation Panel - Jun 2016 Announced
Summary on Grant Application Form
There are more than five billion wirelessly connected mobile devices in service today, most of which are handheld terminals or mobile-broadband devices such as computers and tablets. By 2020, mobile communications data traffic is expected to increase 1,000-fold, by which time there will be an estimated 50 billion Internet-capable devices. This transition will present a formidable challenge. Improving the energy efficiency (EE) of existing telecommunication networks is not just a necessary contribution towards the fight against global warming, but with the inevitable increases in the price of energy, it is becoming also a financial imperative. Future technologies (e.g. 5G) on which these devices will operate will require dramatically higher data rates and will consume far more power, and as a consequence increase their environmental footprint. To mitigate this, significant network densification, that is increasing the number of antennas per unit area, seems inevitable. To this end, a novel technological paradigm, known as massive MIMO, considers the deployment of hundreds of low-power antennas on the base station (BS) site to provide enhanced performance, reduced energy consumption, and better reliability.

At the same time, the spectrum scarcity in the RF bands has stimulated a lot of research effort into mm-wave frequencies (30 to 300GHz). These frequencies offer numerous advantages: massive bandwidth/data rates, reduced RF interference, narrow beamwidths. The combination of the above technologies gives rise to mm-Wave massive MIMO, which is considered by many experts as the 'next big thing in wireless'. This paradigm shift avails of the vast available bandwidth at mm-frequencies, smaller form factors than designs implemented at current frequencies, reduced RF interference, channel orthogonality, and large beamforming/multiplexing gains. Yet, the practical design of mm-Wave massive MIMO faces many fundamental challenges, in respect of total energy consumption, circuitry cost, digital signal processing among others. In the context of the project, we envision a mm-wave massive MIMO topology performing a fraction of processing in the baseband (digital) and the remaining fraction in the RF band (analogue), with a reduced number of RF chains, to effectively address most of these challenges. In addition, by deploying low-resolution (coarse) analog-to-digital converters (ADCs), we can substantially reduce the power dissipation of mm-Wave massive MIMO transceivers.

This visionary project will investigate the realisable potential of hybrid processing and 1-bit ADC quantisation. The specific project goals will be to: (a) find the optimal balance between analogue and digital processing for future MIMO configurations in order to maximise the end-to-end EE and experimentally validate the proposed solution, and; (b) investigate the realisable potential of 1-bit ADC quantisation and the channel estimation/resource allocation challenges it induces.

By bringing together a world leading research team with expertise in communications engineering, signal processing, microwave engineering and antenna theory, and with the technical support of the biggest telecom equipment manufacturer in the world, Huawei Technologies Ltd, we will devise scalable low-complexity, low-power solutions suitable for the new generation of BS. We will investigate the algorithms and hardware that will optimise the performance of future BS to precisely meet performance and QoS targets, allied to minimum energy consumption. The application of the project results will contribute to the reduction of the ICT sector's contribution to global warming, through reduced power consumption and improved EE of future BSs. It will also influence many dynamic economical sectors within the UK: telecom equipment manufacturing, telecom operators, positioning systems, surveillance sector, smart cities, e-health, military equipment and automotive companies.
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