Abstract

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 source-relay, relay-destination and loop interference channels, thus resulting in increased overheads. Aiming to offer a low-complexity alternative for power control in such networks, we adopt reward-based learning in the sense of multi-armed bandits. More specifically, we present bandit-based power control, relying on acknowledgements/negative-acknowledgements observations by the relay. Our distributed algorithms avoid channel state information acquisition and exchange, and can alleviate the impact of outdated channel state information. Two cases are examined regarding the channel statistics of the wireless network, namely, strict-sense stationary and non-stationary channels. For the latter, a sliding window approach is adopted to further improve the performance. Performance evaluation highlights a performance-complexity trade-off, compared to optimal power control with full channel knowledge and significant gains over cases considering channel estimation and feedback overheads, outdated channel knowledge, no power control and random power level selection. Finally, it is shown that the sliding-window bandit-based algorithm provides improved performance in non-stationary settings by efficiently adapting to abrupt changes of the wireless channels.

Highlights

  • Various antenna solutions and digital loop interference (LI) cancellation algorithms have shown the feasibility of FD relay communication with lowcost deployments in mobile networks [6]–[9]

  • In the majority of the considered scenarios, bandit-based power control (BB-PC) outperforms optimal power control when channel estimation and feedback overheads are taken into consideration, while the impact of non-stationary wireless environments is efficiently mitigated from the sliding window approach

  • Complexity concerns and possible errors in the channel state information (CSI) acquisition and exchange process, as well as issues related to outdated CSI for power control are completely eliminated, as BB-PC relies on only 1-bit ACK/NACK packets

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Summary

Background

S IXTH generation (6G) mobile networks are envisioned to support dense topologies of small cells where coexisting user devices and machines will compete for wireless resources [2], [3]. Performance evaluation reveals improved performance over conventional DPA schemes in scenarios with fast time-varying channels, high modulation order and large packets In such complex networks, a significant amount of signaling and feedback messages is necessary for efficient operation, threatening the network’s performance when centralized solutions are adopted [15]. In settings where FD relays were equipped with buffers, statistical and instantaneous CSI availability was exploited to conduct power adaptation at both the source and the selected relay, in order to maximize the chances of LI cancellation or avoidance and improve the average throughput of the network [28], [29]. In the majority of the considered scenarios, BB-PC outperforms optimal power control when channel estimation and feedback overheads are taken into consideration, while the impact of non-stationary wireless environments is efficiently mitigated from the sliding window approach. Complexity concerns and possible errors in the CSI acquisition and exchange process, as well as issues related to outdated CSI for power control are completely eliminated, as BB-PC relies on only 1-bit ACK/NACK packets

Contributions
System Model
Estimation and feedback errors
Outdated CSI
CSI-based power allocation
The MAB Problem
Upper Confidence Bound Policies
Online Learning Model
Online Learning Algorithms
PERFORMANCE EVALUATION
Strict-sense stationary case
Non-stationary case
Conclusions
Findings
Future Directions

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