Designing greener wireless systems

Over the last decades, communication networks have grown exponentially, starting with low-rate applications such as voice calls and now offering high-speed mobile internet based on 4G, 5G and soon 6G deployments. While expanding networks and embracing new frequency bands helps meeting user throughput demands, it also increases the network power consumption, raising environmental, economic and battery lifetime concerns.

In order to improve the energy efficiency of future wireless transceivers, we need to predict their power consumption before building them. Using such a tool, we can explore many possible architectures and configurations, assess their power consumption, select the most appropriate options and dimension them optimally. This also supports the development of run-time control mechanisms maximizing the transceiver energy efficiency in operation.

Power modelling and optimization of wireless transceivers has been successfully investigated at IMEC for more than 10 years, combining specific models for digital processing, analog front-end and power amplifiers. However, considering the diversity of communication devices to design and enhance in future networks, many challenges lie ahead and require fundamental investigations. More specifically, you will investigate the following points in your PhD:

• New frequency bands are opened towards 6G, e.g., FR3 (7 - 24 GHz). This comes with new transceiver architectures and technologies, but also increased flexibility, with a critical need to investigate the most energy-efficient solutions.
• Improving the accuracy of an exploratory power model deserves fundamental research and strong benchmarking to state-of-the-art. Digital power estimates based on operation count require a stronger connection to the digital hardware design. Within imec, advanced semiconductor research offers a natural link to deep-scaled CMOS models.
• Advanced algorithms and architectures need investigation of power-consumption trade-offs. For instance, digital predistortion (DPD) can enhance power amplifier efficiency but at the cost of significant extra digital complexity. A complete system power optimization is hence required.
• New communication networks will also support radar-like applications (joint communication and sensing) or come in distributed implementations. Both approaches require dedicated models and optimizations to improve energy efficiency.

To support this critical research, you will combine models from diverse sources and develop a flexible simulation environment allowing to model and optimize the power consumption of different wireless transceivers. You may also complement the model by using AI solutions extracting power consumption trends or including the energy penalty of machine learning solutions themselves. And the energy efficiency at the transceiver hardware will be put in a broader perspective of sustainability and life cycle analysis, where run-time power consumption is only a part of the total picture.

As a PhD student, you will be part of a large imec community working on the research, implementation and prototyping of future communications systems with experts in wireless communication, signal processing, digital, analog and mm-wave design, and machine learning. This is a unique opportunity to develop innovative, multi-disciplinary technology and shape future wireless networks. You will publish your research in top-level journals and conferences.