Research Engineer in 5G Broadcast Experimental Platforms and Validation.

The Inria Center at the University of Rennes is one of Inria’s eight research centers and is home to more than thirty research teams. The center is a major and internationally recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem, bringing together highly innovative SMEs, large industrial groups, competitiveness clusters, research and higher education institutions, laboratories of excellence, and a technological research institute.

Funding Context: ROBIN Project – Bpifrance i-Démo

The Bpifrance i-Démo program, funded under the France 2030 investment plan, supports ambitious collaborative research, development, and innovation (R&D&I) projects with strong technological and economic impact. It aims to accelerate the development of breakthrough technologies and their transfer to the market by fostering collaboration between industrial companies and research organizations.

The ROBIN project is funded under this program and brings together a consortium of two academic partners (INSA Rennes and the University of Rennes) and three industrial partners (TDF, Ateme, and ENENSYS). Together, they are developing innovative solutions for 5G broadcast technologies, with a particular focus on the delivery of broadcast services to 5G terminals.

Scientific Context

The evolution of digital broadcasting and mobile networks has led to the emergence of 5G Broadcast technology, enabling the delivery of high-quality multimedia services, including TV and video content, directly to 5G-enabled terminals. This technology relies on a combination of advanced terminal architectures, communication protocols, and standardization frameworks defined by organizations such as 3GPP.

However, several scientific challenges remain to be addressed, including the optimization of physical and protocol layers, the design of efficient and robust 5G Broadcast base station architectures, and the development of adaptive transmission strategies capable of addressing heterogeneous and dynamic propagation conditions across indoor and outdoor environments.

Artificial Intelligence (AI) and Machine Learning (ML) techniques offer promising approaches for improving system performance through data-driven optimization, intelligent resource management, and adaptive configuration. Nevertheless, challenges related to robustness, generalization, reliability, and computational complexity must be considered to ensure the deployment of AI-based solutions in real-world 5G Broadcast systems.

The Research Engineer will be responsible for the design, deployment, and validation of a 5G Broadcast experimental testbed based on USRP platforms, OpenAirInterface (OAI), and associated radio communication equipment. The mission will involve setting up an end-to-end experimental infrastructure, including hardware integration, 5G protocol stack configuration, signal generation and analysis, and performance evaluation.

The engineer will contribute to the deployment of indoor and outdoor test environments for evaluating 5G Broadcast services under realistic propagation conditions. Activities will include the configuration and operation of radio equipment, antenna systems, measurement tools, and network components, as well as the management of frequency resources, spectrum access, and required experimental licenses.

The position will involve:

• Deployment and maintenance of a 5G Broadcast testbed using USRP-based platforms and OpenAirInterface software.
• Integration and configuration of radio access network components, antennas, signal processing modules, and measurement equipment.
• Implementation and validation of broadcast transmission scenarios in indoor and outdoor environments.
• Characterization of radio propagation conditions, signal coverage, quality indicators, and system performance.
• Management of experimental frequency licenses and coordination of spectrum usage for field trials.
• Collection and analysis of experimental data to evaluate reliability, robustness, and optimization strategies.
• Collaboration with academic and industrial partners within the ROBIN project consortium.

• Strong knowledge of wireless communication systems, particularly 4G/5G technologies and radio access networks.
• Experience with Software-Defined Radio (SDR) platforms, preferably USRP, and OpenAirInterface (OAI) or similar open-source mobile network platforms.
• Good understanding of 5G physical and MAC layers, radio protocols, and network architectures.
• Experience in deploying and operating experimental testbeds, including hardware integration and system configuration.
• Knowledge of RF measurements, signal analysis, spectrum analyzers, vector signal generators, and other radio test and measurement equipment.
• Familiarity with indoor and outdoor radio propagation measurements, coverage assessment, and performance evaluation.
• Experience with Linux environments, scripting, and programming (Python, Bash, and/or C/C++).
• Understanding of 5G Broadcast, multimedia delivery, or broadcast/multicast technologies is an asset.
• Knowledge of spectrum regulations, frequency planning, or experimental licensing procedures is desirable.
• Ability to troubleshoot complex hardware and software systems and work independently.
• Excellent teamwork and communication skills, with the ability to collaborate effectively with academic and industrial partners.
• Good written and spoken English.

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

Net monthly salary ranging from approximately €2,500 to €3,100, depending on experience.