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Reference : UMR8506-STEDOU-013
Workplace : GIF SUR YVETTE
Date of publication : Wednesday, October 13, 2021
Type of Contract : FTC Scientist
Contract Period : 12 months
Expected date of employment : 1 December 2021
Proportion of work : Full time
Remuneration : between 2728€ and 3880€ brut based on experience
Desired level of education : PhD
Experience required : Indifferent
The recruited candidate will work under the European-funded project ARIADNE and will develop innovative research in the field of reconfigurable intelligent surfaces for future wireless networks.
The distinguishable feature of cellular networks lies in the users' mobility. The locations of the base stations (BSs) cannot, in general, be modified according to the user's locations. The mobility of the users throughout a location-static deployment of BSs renders the user distribution uneven throughout the network, which results in some BSs to be severely overloaded and some others to be under-utilized. This is a well-known issue in cellular networks and is tackled in different ways, among which load balancing methods [Singh2013] and the densification of BSs (ultra-dense networks). Network densification is certainly a promising approach but has its own limitations [Andrews2016], [Renzo2016]. It is known, e.g., that network densification increases the network power consumption as the number of BSs per square kilometre increases. This is exacerbated even more with the advent of the Internet of Things (IoT), where the circuit power consumption increases with the number of users per square kilometre [Renzo2018], [Renzo2018-2]. Ultra-dense network deployments, in addition, enhance the level of interference, which needs to be appropriately controlled in order to achieve good performance. Furthermore, each BS necessitates a backhaul connection, which may not always be available. Other solutions based on Massive Multiple-Input-Multiple-Output (MIMO) schemes could be employed, but they usually necessitate a large number of individually controllable radio transmitters and advanced signal processing algorithms [Mollen2017]. Without pretending to be exhaustive, other relevant solutions that are typically employed in wireless encompass retransmission methods that negatively impact the network spectral efficiency, the optimized deployment of specific network elements, e.g., relays, which increase the network power consumption as they are made of active elements (e.g., power amplifiers), and that either reduce the achievable link rate if operate in half-duplex mode or are subject to severe self-interference if operate in full-duplex mode [Lu2015], [Shojaeifard2017]. These limitations are exacerbated if we consider the operation in very high-frequency bands, where cellular networks need to be more densely deployed, and more antennas elements are needed in order to obtain sufficient beamforming gains. These issues are already well-known in the relatively low frequencies in the mmWave band [Heath2016], [Andrews2017]. The present project, on the other hand, targets the use frequencies higher than 100 GHz, which makes conventional technologies used for 5G (network densification and antenna densification) not suitable and not scalable. Radically different approaches are needed, which bring about the need for developing new communication-theoretic tools and methods for their modelling, analysis, and optimization. In the present project, we propose to use intelligent (possible artificial intelligent) and reconfigurable metasurfaces to enable future beyond 5G cellular networks to leverage and unveil the potential of the large amount of spectrum that is available in the D-band. In what follows we described our proposed approach and the need of developing a new communication-theoretic approach for its modelling, analysis, and optimization that challenges the current models that have been used today, and are based on Shannon communication-theoretic approach.
As discussed in the previous sections, to fulfill the challenging communication requirements of beyond 5G cellular networks, which are characterized by the lack of strong LOS links and by the intermittent nature of NLOS links due to the lack of sufficiently strong reflectors in many cases, we believe that it is not sufficient anymore to rely solely on wireless networks whose logical operation is software-controlled and optimized [5GPPP2017]. We believe, on the other hand, that the wireless environment itself needs to be turned into a software-reconfigurable entity [Liaskos2018], whose operation is optimized to enable uninterrupted connectivity. Our proposed approach is based on a using intelligent reconfigurable metasurfaces, even basic reflect-arrays, that are capable of shaping the waves in way that cannot be found in nature. The fundamental idea, as elaborated in the detail in the previous sections, is that the objects that are spatially scattered throughout the wireless networks are coated with electromagnetic material that allow them to modify the radio waves in order to create strong NLOS links when it would not be possible in current networks. Compared with current 5G cellular network deployments described in the previous section, our approach is radically different. The metasurfaces are made of low-cost passive elements that do not require any active power sources for transmission [Liaskos2018]. Their circuitries can be powered with energy harvesting modules as well [Abadal2017]. They do not apply any sophisticated signal processing algorithms (coding, decoding, etc.), but primarily rely on the programmability and reconfigurability of the metasurfaces and on their capability of modifying the radio waves impinging upon them [Liu2018]. They can operate in full-duplex mode without significant or any self-interference, and do not need any backhaul connections to operate. Even more importantly, the metasurfaces are deployed where the issue naturally arises: Where the objects, which, in current wireless networks, reflect, refract, distort, etc. the radio waves in undesirable and uncontrollable ways, are located. These functionalities are transparent to the users, as there is no need to change the hardware and software of the devices. Furthermore, the number of environmental objects can potentially exceed the number of antennas at the endpoint radios, which implies that the degrees of freedom for system optimization can potentially exceed that of current wireless network deployments [Welkie2017]. Despite the practical challenges of deploying robotic (terrestrial) base stations capable of autonomously moving throughout a given region [Claussen2006], [Claussen2009], The possibility to deploy mobile reconfigurable metasurfaces is, on the contrary, practically viable. The metasurfaces can be easily attached to and removed from objects (e.g., facades of buildings, indoor walls and ceilings, advertising displays), respectively, thus yielding a high flexibility for their deployment. The position of small-size metasurfaces on large-size objects, e.g., walls, can be adaptively optimized as an additional degree of freedom for system optimization. Thanks to their 2D structure, the metasurfaces can be mechanically displaced, e.g., along with a discrete set of possible locations (moving grid) on a given wall. This proposed network's vision, necessitates a new communication-theoretic model for modelling, analysis, and design.
Current wireless networks are modelled, analysed, and optimized based on the Shannon and Wiener models. According to Shannon [Shannon1948], the system model is given and is formulated in terms of transition probabilities (i.e., Pr(y/x). According to Wiener [Wiener1948], the system model is still given, but its output is feedback to the input, which is optimized by taking the output into account. For example, the channel state is sent from a receiver back to a transmitter for channel-aware beamforming. If the environmental objects are coated with intelligent reconfigurable surfaces that are capable of sensing the system's response to the radio waves and feed it back to the input, then these models cannot be applied anymore. Based on the sensed data, in fact, the input signal and the response of the environmental objects to the radio waves can now be jointly optimized. For example, the input signal is steered towards a given environmental object, which reflects, by employing some optimized phase shifts, it towards a given receiver that is, in turn, steered towards it. The emerging system model is new to communication theory, and its ultimate performance limits and algorithms for achieving them are unknown yet. The proposed system model, in particular, has the potential of creating new strong and powerful NLOS links thanks to the reconfigurability of the intelligent surfaces.
The activities will be the following:
- Physics-based modeling of reconfigurable intelligent surfaces
- Computation of fundamental performance limits
- Development of innovative model-based and data-driven algorithm
Strong knowledge of wireless communications and communication theory.
Strong knowledge of reconfigurable intelligent surfaces.
Strong knowledge of performance analysis and optimization for wireless networks.
Strong research and publication record.
The research activity will be conducted in the context of the European-funded project ARIADNE.
For details about this project, please see here:
Constraints and risks
Further information on the context can be found here:
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