[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.

[OPEN][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.

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