Projects per year
Full-duplex relaying is an enabling technique of sixth generation (6G) mobile networks, promising tremendous rate and spectral efficiency gains. In order to improve the performance of full-duplex communications, power control is a viable way of avoiding excessive loop interference at the relay. Unfortunately, power control requires channel state information of both source-relay and relay-destination channels, as well as of the loop interference channel, thus resulting in increased overheads. Aiming to offer a low-complexity alternative for power control in full-duplex relay networks, we adopt reward-based learning in the sense of multi-armed bandits. More specifically, we provide bandit-based power control algorithms, relying on acknowledgements/negative-acknowledgements observations by the relay. The proposed algorithms avoid the need for channel state information acquisition and exchange, and can be employed in a distributed manner. Performance evaluation results in terms of outage probability, average throughput and accumulated regret over time highlight an interesting performance-complexity trade-off compared to optimal power control with full channel knowledge and significant performance gains over the cases without power control and random power level selection.
|Title of host publication||ICC 2021 - IEEE International Conference on Communications, Proceedings|
|Number of pages||6|
|Publication status||Published - Jun 2021|
|MoE publication type||A4 Article in a conference publication|
|Event||IEEE International Conference on Communications - Online, Montreal, Canada|
Duration: 14 Jun 2021 → 23 Jun 2021
|Name||IEEE International Conference on Communications|
|Conference||IEEE International Conference on Communications|
|Abbreviated title||ICC Workshops|
|Period||14/06/2021 → 23/06/2021|
- Full-duplex relaying
- machine learning
- multi-armed bandits
- power control
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- 1 Active
Co-design of control and communication systems for wireless networked control systems
Charalambous, T., Royyan, M. & Farjam, T.
01/09/2018 → 31/08/2023
Project: Academy of Finland: Other research funding