Main supervisor profile and contact: Dr. Marco di Renzo <marco.di.renzo@gmail.com>

Institution: Laboratory of Signals and Systems (CNRS UMR 8506), CentraleSupelec, Paris-Saclay

Description of the job: Wired communications are the status quo in industry because of their high level of reliability and stable latency. The downside of using cables is the expensive, bulky, and inflexible deployment: an ensemble of robots cannot be freely manoeuvred in a warehouse. Wireless solutions provide much higher flexibility but are prone to channel fading, shadowing, interference, and disturbances from industrial machines. However, contemporary wireless networks are designed based on the postulate that only the transmitters and the receivers can be optimized for improving the network performance. The propagation environment that lies in between them (physical objects like walls, buildings, furniture, ceilings, floors, etc.) are out of control of the communication engineers. This approach to design and optimize wireless networks has three fundamental limitations: (i) the ultimate performance of wireless networks may not have been reached yet - By optimizing the transmitter, the receiver, and the environment, the performance of wireless networks may be further improved; (ii) in the industry Internet of Things, some devices are unlikely to be equipped with multiple antennas - Having the opportunity of customizing and controlling the environment may open new opportunities for network optimization; (iii) The radio waves are used inefficiently, since when reflected or refracted by an object, for example, the energy is scattered towards unwanted directions, thus reducing the efficiency of utilization of the emitted power - Equipping wireless networks with the functionalities of customizing the radio environment (i.e. controlling the propagation of radio waves and programming environmental objects) besides the capability of optimizing the end-points of multi-antenna distributed communication links, and optimizing the resulting wireless communications with the aid of machine learning computational techniques, constitute a paradigm-shift vision affecting the physical layer and the medium access control. The propagation environment within a given scenario has a unique structure that could be controlled using reconfigurable metasurfaces (RMS), complementing multi-antenna technologies at the transmitter and receiver. RMSs are an emerging technology that can alter the wavefront of a radio wave that impinges upon them by controlling an external stimulus and shape complex propagation environments, such as industrial environments. However, their fundamental performance limits and optimized design and operation are not known yet. In this context, the use of model-based methods and machine learning based methods (data based) constitute a promising solution for enhancing the performance in practical propagation environments.

Mission: 1) To conduct research on an emerging approach for optimizing networks jointly combining mismatched model-based and small-size data driven (machine learning based) methods using tools of transfer learning and deep unfolding. 2) To jointly combine stochastic geometry and optimization methods in order to develop optimal resource allocation and scheduling algorithms bringing full potential to IRE-based deployments for industry Internet of Things. 3) To leverage the theory of point processes and Benders decomposition to solve general mixed-integer non-linear optimization scheduling and resource allocation problems with performance guarantee. 4) Develop a new innovative approach to system optimization based on jointly combining models and data. 5) Full characterization of performance limits and guidelines for their optimal design and operation in realistic industry Internet of Things. 6) Publications in flagship conferences and leading journals. 7) Integration in the NEC system simulator and patent filing.

Main functions: 1) Undertake postgraduate research in support of the agreed doctoral research project. 2) Work closely with the academic supervisors to ensure the compatibility of the individual project with the overall goals of 5GSmartFact. 3) Present and publish research in both academic and non-academic audiences. 4) Attend and participate to academic and non-academic conferences, events and seminars. 5) Attend and participate to all training events and supervisory meetings. 6) Be seconded for 18 months to network (industrial) partners as necessary to fulfil the grant obligations. 7) Prepare progress reports and similar documents on research for funding bodies, as required. 8) Contribute to the delivery and management of the wider programme, including attending and participating in programme committee meetings. 9) Actively contribute to the public engagement and outreach activities as described in the grant agreement. As job descriptions cannot be exhaustive, the ESR may be required to undertake other duties, which are broadly in line with the above duties and responsibilities.

Secondments: The selected candidate will take part in one secondment of 18 months in NEC (Heidelberg, Germany) (supervised by Dr. Vincenzo Sciancalepore and Dr. Xavier Costa-Pérez).

Doctoral programme: The ESR will be enroled in the doctoral programme of Université Paris-Saclay.

Requirements of the candidate

• Education level: Master’s degree.

• Degree/speciality: Electronic or electrical engineering, mathematics, electromagnetics, or a physical sciences subject.

• Language skills: Excellent written and verbal communication, including presentation skills. Highly proficient English language skills.

• Research experience: Will be valued.

• Other skills: 1) Excellent mathematical skills and background, 2) High proficiency in Matlab, Mathematica, Maple, R, or similar programming software. 3) Solid background on wireless communications (antennas, propagation, stochastic geometry is a plus). 4) Excellent written and verbal communication, including presentation skills. 5) Excellent organisational skills, attention to detail and the ability to meet deadlines. 6) Ability to think logically, create solutions and make informed decisions. 7) Willingness to work collaboratively in a research environment. 8) A strong commitment to his/her own continuous professional development. 9) Willingness to travel and work across Europe.

Apply: Fill in your job application form. Send also your application to the application material to the CNRS recruitment website. Informal enquires for further information about the positions can be send to Dr. Marco Di Renzo (marco.di.renzo@gmail.com). Application deadline for this position is 10 July 2021. You may download the information of this particular offer in pdf format through this link.