[OPEN][RI-HOME_2019-NW-023] 5G radio transparent low energy windows

Modern energy saving windows’ glass, so called low-emissivity (Low-E) glass, uses a very thin metallic coating. This coating blocks the electromagnetic radiation in the infrared (IR) and ultraviolet (UV) regions and is transparent to the visible part of the spectrum giving rise to high thermal insulation glass. However, this metallic coating also attenuates wireless signals and has already impacted indoor coverage of cellular networks. Different types of Low-E windows exhibit typically 15 dB to 45 dB attenuation over 1-18 GHz range [1]. As the RF signal attenuation increases with the frequency, this problem will become harder in the coming years with the arrival of 5G which will make use of new higher frequency bands such as 3.5 GHz, 28 GHz and even 60 GHz [2-3]. The objective of the internship is to examine and evaluate potential solutions for improving wireless transmission through modern energy efficient windows without a significant degradation of its energy efficiency property. Two solutions are envisioned: • Solution 1: use Frequency Selective Surface (FSS) [4]concept to improve wireless transmission through Low-E glass In this approach, bandpass FSS structures are etched in the glass’s coating to improve the RF signal penetration in the desired frequency bands while keeping a good thermal insulation, by minimizing the surface of uncoated area. • Solution 2: use Flat Fresnel-type focusing lens on/into the window glass to improve the indoor signal strength In this approach rather than improving the RF signal penetration through the Low-E glass, we seek to improve the signal strength by focusing an incident plane wave at the indoor transceiver location, and thus, compensating the attenuation loss by the focusing gain. This could be done by using an optically transparent Fresnel-type zone plate lens or dielectric lens [5-6] flash mounted on, or integrated into, the window glass.

[1] P. Ängskog, M. Bäckström, and B. Vallhagen, ""Measurement of radio signal propagation through window panes and energy saving windows”, 2015 IEEE International Symposium on Electromagnetic Compatibility (EMC), , pp. 74-79, Dresden, 2015,.
[2] E. Semaan, F. Harrysson, A. Furusk¨ar, and H. Asplund, “Outdoor-to indoor coverage in high frequency bands”, in 2014 Globecom Workshops (GC Wkshps), pp. 393–398, Dec. 2014.
[3] Yanshen Du, Chang Cao, Xiongfei Zou, Jia He, Hua Yan, Guangjian Wang, and David Steer, ""Measurement and Modeling of Penetration Loss in the Range from 2 GHz to 74 GHz"", 2016 IEEE Globecom Workshops (GC Wkshps), pp. 1-6, 2016
[4] R. Mittra, C.H. Chan, and T. Cwik: “Techniques for analyzing frequency selective surfaces a review”, Proc. IEEE, , pp. 1593–1615, 1988.
[5] L. Buskirk, and C. E. Hendrix, “The Zone Plate as a Radio-Frequency Focusing Element”, IRE Transactions on Antennas and Propagation, vol. 9, no. 3, pp. 319-320, May 1961.
[6] James E. Garett, and James C. Wiltse “Performance characteristics of Phase-correcting Fresnel Zone Plates”, IEEE MTT-S, 1990

Skills  : • Senior B.E- or Master’s- student • Knowledge: Wireless systems, Antennas and Propagation, Microwave and RF design, background in Electromagnetic Simulation • A previous experience in using CST electromagnetic simulation SW or the like will be very appreciated • Self-starter, quick learner and good communicator • Research spirit and creative attitude

Keywords  : Antennas, Propagation, Frequency Selective Surface (FSS), Fresnel lens, Lens antennas, Low-E glass, 5G.


This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.

[OPEN][RI-HOME_2019-NW-004] Implementation of a workbench to experiment LTE-Unlicensed/WiFi coexistence in a residential environment.

The future deployment of LTE small cells using unlicensed bands will eventually give rise to coexistence problems between LTE-U for example and WiFi. The goal of this internship is to set up an experimental benchwork using an USRP-X Software Defined Radio (SDR) platform and an off-the-shelf WiFi access point to validate/experiment new LTE-U/WiFi coexistence algorithms based on machine learning techniques.

Skills  : Wireless communications, at least one programming language (e.g. C++, Python, Matlab). VHDL knowledge would be a plus.

Keywords  : LTE, 4G, 5G, LTE-U/WiFi coexistence.


This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.

[CLOSED][RI-HOME_2019-DM-NW-003] Network congestion control based on deep reinforcement learning

Congestion control is one fundamental building block of networking protocols. Over the last decades, network researchers have designed a wide variety of congestion control algorithms that each target a specific environment or application (e.g. datacenter, wifi, video…) or optimize a specific aspect in networking performance (bufferbloat, latency, bandwidth...). Most congestion control schemes were designed as classical deterministic algorithms based on expert knowledge using hardwired and predefined control responses. Only recently machine-learning based congestion control schemes have been proposed by the community. These efforts are quite recent and do not employ the latest deep reinforcement learning techniques. Deep Reinforcement Learning denotes the use of deep learning, a powerful class of learning algorithm, to develop reinforcement learning algorithms: algorithms that attempt to learn how to control a system optimally. These algorithms have been in a spotlight lately as they have achieved impressive results in a variety of tasks such as beating human experts at Go, competing against them in cooperative video games or reducing energy consumption by 40% in data centers. Considering the recent advances in Deep Reinforcement Learning and the availability of emulation and evaluation platforms for congestion control schemes, we believe that there is room to design a congestion control scheme based on deep reinforcement learning. The objective of this internship is therefore to design and evaluate deep reinforcement learning based congestion control schemes. Technicolor being a home gateway and settop-box manufacturer a specific focus in the evaluation of the proposed algorithms will be home environments.

Skills  : .

Keywords  : Deep learning, reinforcement learning algorithm


This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.