Wireless connectivity is a fundamental need of our society. It is forecasted that between 2020 and 2030 the global IP traffic will increase by 55% each year, eventually reaching 5,016 exabytes [1], with data rates scaling up to 1 Tb/s [2]. In addition, besides supporting very high data-rates, future wireless networks are expected to provide several other heterogeneous services, such as sensing, localization, low-latency and ultra-reliable communications. However, 5G networks, which are to be rolled out in 2020, are not designed to meet these requirements. In fact, as requirements become more stringent, fundamental limitations arise, imposed directly by the very nature of wireless operation. In particular, from 1G to 5G, every wireless generation has been designed following the postulate that the wireless environment separating two communicating devices is fixed by nature and cannot be modified, but only compensated through the design of sophisticated transmission/reception schemes and feedback mechanisms. However, after five generations of wireless networks, the improvements that can be expected by continuing to operate only on the end-points of the channel, are not adequate to meet the above requirements. Instead, 6G networks will require a new architectural platform to perform joint communication and sensing with throughput, latency, reliability, and localization that can be flexibly customized in real-time. Major performance gains can be expected by breaking free from the postulate that regards the wireless channels as uncontrollable factors. Serving such a vision, MetaWireless puts forth the disruptive idea to design wireless networks by treating the wireless environment as an optimization variable to be adapted to maximize the network performance. But how to implement this vision? How to modify in real-time the propagation properties of the environment between two communicating devices? The answer lies in realizing reconfigurable intelligent surfaces [3] (RISs), incorporating them into 6G wireless networks. To succeed, this vision needs to advance and merge the inter-disciplinary fields of wireless communications, physics, electromagnetic meta-surfaces, computational learning, software networks that have never been combined before.

What are RISs?
Present meta-surfaces are planar structures made of special materials, known as meta-materials, that contain elementary electromagnetic units. This non-uniformity in space enables meta-surfaces not to adhere to conventional reflection and diffraction laws, but they are able to modify the phase and wavefront of the radio waves impinging on them, in a fully customizable way [4], [5], [6]They can be used to coat objects or can be deployed on the facades of buildings, enabling the customization of the electromagnetic response of a wireless environment. While the use of meta-surfaces has been demonstrated in small indoor links [7] ,[8], [9], their use in real-world networks requires the capability of configuring their electromagnetic behavior in real-time, so as to compensate for channel fluctuations. As shown in the left figure, present (i.e. 2nd generation) meta-surfaces do not provide this possibility. RISs, are considered the 3rd (future) generation of meta-surfaces [10], granting the chance of configuring their electromagnetic response fast enough to follow the fluctuations of wireless channels. Merging together RISs and wireless networks provides the three major advantages (see figure below).

1) Increased degrees of freedom for system design. RISs can be deployed on the walls of buildings or can be used to coat objects between communicating devices, which effectively makes the wireless channel a new optimization variable, besides transmitters and receivers. Overcoming present Shannon’s paradigm that views wireless channels as fixed with given transition probabilities, MetaWireless regards channels as entities to be engineered for performance improvement. Customizing the wireless channel is a paradigm shift that has been the forbidden dream of wireless engineers.

2) Increased energy savings. RISs are nearly passive devices, which only require a small amount of energy that allows their uninterrupted reconfigurable functionality. In fact, such an amount of energy can be secured in a continuous fashion by harvesting the ambient electromagnetic energy. This constitutes a substantial advantage over competitive alternatives to RISs, such as relays, which require a dedicated energy source due to the use of active components (power amplifiers, mixers, local oscillators). In fact, relays are not a good option for massive deployment, especially for high frequencies, due to the high cost of RF electronics for the envisaged 5G and beyond frequency bands [11]. Apart from such small energy requirements, no additional energy is needed for operating RISs. Hence, the hardware components deployed are only those needed to enable the reconfiguration (low-power switches like PIN diodes or varactors [12]).

3) Increased wireless security. Controlling the angle of the departing signal from the RISs enables to focus the electromagnetic energy only towards selected directions. Besides increasing the energy received by the intended receiver, and reducing electromagnetic pollution, this enables a higher security, since it prevents malicious users from eavesdropping. Moreover, RISs can be configured to absorb the incoming signal, thus eliminating unwanted radiations. Again, this is achieved without using active components that focus the energy in a given direction through beamforming by large antenna arrays equipped with radio-frequency chains requiring dedicated energy sources.

Why an ITN? In order to realize RIS-empowered wireless networks, simply developing RISs structures is only a pre-requisite. Incorporating RISs into a complex wireless network requires immense interdisciplinary efforts. RIS-specific methodologies need to be developed to design and operate all layers of the protocol stack. The high number of degrees of freedom to optimize is a capability of profound importance, but it also makes radio resource allocation more complex and necessitates improved optimization techniques. Simply the fact that the propagation channel becomes an optimization variable makes information-theory frameworks for conventional channels not applicable anymore, which requires the development of a new theory of communications. The fact that RISs are nearly passive enables notable energy savings, but also complicates channel estimation and feedback, since RISs have no active transmission and data processing ability. Furthermore, the possibility of realizing very sharp beams leads to increased security only if the end-users can be localized accurately. The deployment of RISs makes possible to customize the propagation channels, but how to exploit this possibility to boost the network performance is an enormous challenge that requires completely new theoretical and algorithmic frameworks. As a final challenge, the radio environment must become intelligent to predict and adapt to random service demands, traffic evolutions, and sudden changes of the propagation scenarios, as well as to simultaneously communicate and localize end-users and/or objects.

MetaWireless will lay the theoretical, algorithmic, and architectural foundation of RIS-enabled future wireless networks, and will develop the first open access system-level simulator for network optimization

The above overall objective is decomposed into the following four research objectives of MetaWireless:

  • Objective 1 – Developing RISs. Bring to light the third generation of meta-materials technology by developing RISs that can be reconfigured in real-time and are able to perform joint communication and sensing tasks.
  • Objective 2 – Theoretical frameworks. Develop new mathematical techniques to introduce a novel communication theory that overcomes conventional Shannon’s theory and unveils the ultimate performance of RIS-based networks.
  • Objective 3 – Algorithmic frameworks. Develop new communication schemes, optimization protocols, and algorithms for RIS-based networks, coping with the large degrees of freedom and the passive nature of RISs.
  • Objective 4 – System-level simulator. Develop RIS-tailored ray tracing modules and build the first open access simulation (software) platform to analyze, optimize, and test large-scale RIS-based intelligent radio environments.


[1] ITU, IMT traffic estimates for the years 2020 to 2030, Report ITU-R M.2370-0, 2015..
[2] K. B. Letaief et al., The Roadmap to 6G: AI Empowered Wireless Networks, IEEE Communications Magazine, Aug. 2019.
[3] M. Di Renzo, et al. Smart radio environments empowered by reconfigurable AI metasurfaces: an idea whose time has come, J. Wireless Com. Net. 2019.
[4] F. Liu et al. Intelligent metasurfaces with continuously tunable local surface impedance for multiple reconfigurable functions, Phy. Review Applied, 2019.
[5] N. Kaina et al. Shaping complex microwave fields in reverberating media with binary tunable metasurfaces, Science Rep., vol. 4, Oct. 2014
[6] S.A. Tretyakov et al. Metasurfaces for general control of reflection and transmission, World Scientific Book of Metamaterials and Plasmonic, 2018.
[7]  DOCOMO & Metawave successful demonstration of 28 GHz-band 5G using world’s first meta-structure technology, https://www.businesswire.com/, 2018.
[8] Z. Li et al. Towards programming the radio environment with large arrays of inexpensive antennas, USENIX NSDI, Feb. 2019.
[9] https://www.comsoc.org/what-will-6g-be?, 2018; M. Di Renzo Reflection Probability in Wireless Networks with Metasurfaces, https://arxiv.org/abs, 2019.
[10] C. Liaskos et al. A new wireless communication paradigm through software-controlled metasurfaces, IEEE Commun. Mag., vol 56, Sep. 2018.
[11] W. Khawaja et al.,, Coverage enhancement for mm wave communications using passive reectors,” Glombal Symp. On Millim. Wave, 2018.
[12] E. Basar, Wireless Communications Through RIS, IEEE Access, 2019; K. Achouri, Design, concepts, applications of EM metasurfaces, Nanophot. 2018