In this
article we would discuss potential future of wireless technology, where Radio
Environment can be made configurable / Smart, which is at present not playing
any role.
Future wireless networks are
expected to constitute of distributed intelligent wireless communications,
sensing, and computing platform and all are going to be interconnected, which
is going to be challenging as they will have challenging requirement of
interconnecting the physical and digital worlds in a seamless and sustainable
manner.
The major problem to get these
connected is to have seamless connectivity , which is mostly hampered because
of obstacles in signal path , those obstacles are generally buildings in urban
areas , also there is another problem of power , as more signal means radio
unit needs more power to transmit and receive those signal.
Above issues motivate the use of
Smart radio environment , which can be configured in such a way that signal which
get blocked by earlier obstacle /
environment , can now be reflected to the area where more subscribers are
present , so that those Subscribers can be benefitted with services and no need
of additional Base Station is needed. This can be achieved with a potential
wireless concept, where the environmental objects are coated with artificial
thin films of electromagnetic and reconfigurable material (that are referred to
as intelligent reconfigurable meta-surfaces), which are capable of sensing the
environment and of applying customized transformations to the radio waves.
Smart radio environments have the potential to provide future wireless networks
with uninterrupted wireless connectivity, and with the capability of
transmitting data without generating new signals but recycling existing radio
waves.
In a nutshell, this article is
focused on discussing how the availability of intelligent reconfigurable
meta-surfaces will allow wireless network operators to redesign common and
well-known network which can provide seamless connectivity with less antenna
devices, by recycling existing radio waves instead of generating them.
Let’s Understand future of wireless communication:
Wireless Futures - Beyond
Communications, but Without More Power and Radio Waves
As in current scenario user
traffic is increasing tremendously and to serve user service provider is using
different frequency and spectrum, with technology like carrier aggregation etc.
, which increases power consumption and emission of radio waves , which is environment.
To limit this this smart radio
environment concept can help service provider a lot and also will help service
provider to cut the cost as power consumption will go less.
What is Smart Radio Environment
???
In current wireless networks, the
radio environment, i.e., the physical objects that alter the propagation of the
electromagnetic waves, which is not controllable , and is perceived, in
addition, as an adversary to the communication process, i.e., it has usually a
negative effect that needs to be counteracted by the transmitters and receivers
. By contrast, we define a smart radio environment as a radio environment that
is turned into a smart reconfigurable space that plays an active role in transferring
and processing information, and that makes more reliable the exchange of data
between transmitters and receivers. This can be achieved by with the use of Reconfigurable
Meta-Surfaces.
Lets understand what is Meta-Surfaces
& Reconfigurable Meta-Surfaces
An example of meta-surface is
sketched in Fig. 1, where it is shown that it transforms an incident radio wave
into a reflected radio wave and a transmitted (or refracted) radio wave. The
specific arrangements of the scattering particles (e.g., full or slotted
patches, straight or curved strips, various types of crosses, etc.) determine
how the meta-surface transforms the incident wave into arbitrary specified
reflected and transmitted radio waves. The major difference between a surface
and a meta-surface lies in the capability of the latter of shaping the radio
waves.
A reconfigurable meta-surface is
a meta-surface in which the scattering particles are not fixed and engineered
at the manufacturing phase, but can be modified depending on the stimuli that
the meta-surface receives from the external world.
Lets see the example to
understand better , in fig.2 which is a conventional wireless network , where
smart radio is not present and to serve the user it will be served by BS2 since
BS1 signals is blocked by obstacle , which is very far hence the subscriber services
will get impacted , but when used Smart radio environment in fig. 2 then we can
see that the signal from BS1 is refracted in such a way that the user can be
served by BS1 only instead of BS2 , hence his experience will be better.
Now let us understand what we
meant by Smart radio environment, since till now there is nothing smart rather
than the fact that this meta surface can be configured based on requirement,
but the problem to configure / reconfigure such meta surface is very complex.
As we know that by leveraging the
AI concepts, this kind of complex problem can be solved hence with the help of
Artificial intelligence, we can configure this meta-surface in a way that it solves
the purpose of seamless coverage with less power and Radio waves.
AI for Smart Radio
Environments:
As discussed, smart radio
environments are a very complex system to design. This complexity originates
from the large number of parameters to be optimized based on the contextual
information that is gathered by the intelligent reconfigurable meta-surfaces
and that is made available to the network controller. In current era where Machine
learning is considered a answer for almost all complex problem, so its better
to consider ML in implementing Smart Radio Networks.
We can use Reinforcement Learning
Framework, to implement reconfigurable Meta-surface. As we know Reinforcement
learning, implements the learning and decision-making procedures by interacting
with the environment: Taking actions and receiving feedback on the result of
the actions that are taken. By using this approach, we can implement
reconfigurable meta-surface.
By following steps.
1) 1) Interact
with the environment through their embedded sensors.
2) 2) make
decisions and take actions, in a distributed way, in order to optimize the wave
transformations that they apply to the radio waves
3) 3) modify
the radio waves based on the subsequent response from the environment.
Above RL approach seems to be
perfect to solve this complex problem and implement smart Radio network, but
only problem occurs with RL is that since wireless network behaviour is highly
dynamic in nature , to get the model converge will take long time because of
exploration and exploitation phenomena of RL model and only using supervised
model will not help because they need lot of data to be trained on.
Hence based on some preliminary
research done by researcher on this domain, suggest that to use transfer learning
approach to solve this complex problem, The approach which researcher have
proposed in consists of combining together model-based and data-driven
optimization methods.
Let’s understand a bit more how
researcher has suggest this approach.
The green box is model based
approach where we create a approximate mathematical model of network , which we
generate with the help of Deep Neural Network and then feed the output of that
model as a input to data driven
model which is also a neural
network , to get the optimal function.
Conclusion and Potential
Impact:
Smart radio environments largely
expand the concept of network softwarization from the logical domain into the
physical domain: The radio environment itself is viewed as a software entity,
which can be remotely programmed, configured, and optimized. The concept of
smart radio environments is not restricted to enhancing wireless
communications, but is aimed at introducing a truly distributed intelligent
wireless communications, sensing, and computing platform that interconnects the
physical and digital worlds.
By recycling, e.g., the
reflections of radio waves and embedding the data of sensors into them at a zero-energy
cost, the potential impact of smart radio environments is beyond
communications. Imagine a smart radio space where the walls of rooms are coated
with sensing meta-surfaces that monitor the health status of people. This will
allow us to develop a truly pervasive and preventive e-health system.
It has potential to low the
radiation caused by conventional network as service provider need to increase
base station and use different frequency to fulfil the raising demand.
the vision of smart radio
environments are reality:
• How to integrate the
reconfigurable meta-surfaces into wireless networks?
• What are the ultimate
performance limits of wireless networks in the presence of reconfigurable
meta-surfaces?
• How to attain such performance limits in
practice?.
As experiment on this has already
been started by NTT DoCoMo and Metawave who have recently run some experimental tests
related to this technology, by using innovative 5G equipment provided by
Ericsson and Intel.
Future view of wireless
Network with Smart Radio Network in place , where user don’t have to struggle
with coverage.
Reference:
1) Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead Marco Di Renzo, Fellow, IEEE, Alessio Zappone, Senior Member, IEEE, Merouane Debbah, Fellow, IEEE, Mohamed-Slim Alouini, Fellow, IEEE, Chau Yuen, Senior Member, IEEE, Julien de Rosny, and Sergei Tretyakov, Fellow, IEEE
2) A. Zappone, M. Di Renzo, F. Shams, X. Qian, and M. Debbah, “Overhead-aware design of reconfigurable intelligent surfaces in smart radio environments,” arXiv, Mar. 2020. [Online]. Available: https://arxiv.org/abs/2003.02538
3) D. Dardari, “Communicating with large intelligent surfaces: Fundamental limits and models,” arXiv, Dec. 2019. [Online]. Available: https://arxiv.org/abs/1912.01719.
4) NTT DOCOMO, “DOCOMO conducts world’s first successful trial of transparent dynamic metasurface,” Jan. 2020. [Online]. Available: https://www.nttdocomo.co.jp/english/info/media center/pr/ 2020/0117 00.html
