Power Control in Spectrum Sharing Systems with Almost-Zero Inter-System Signaling Overhead

TL;DR

This paper proposes a deep reinforcement learning-based power control method for spectrum sharing systems that minimizes inter-system signaling to almost zero, ensuring primary user QoS with reduced complexity.

Contribution

It introduces a novel, location-based power allocation approach that significantly reduces signaling overhead compared to traditional methods.

Findings

Achieves primary QoS guarantees with minimal signaling.

Operates effectively based solely on user location information.

More robust than centralized channel-based power allocation.

Abstract

Power allocation in spectrum sharing systems is challenging due to excessive interference that the secondary system could impose on the primary system. Therefore, an interference threshold constraint is considered to regulate the secondary system's activity. However, the primary receivers should measure the interference and inform the secondary users accordingly. These cause design complexities, e.g., due to transceiver's hardware impairments, and impose a substantial signaling overhead. We set our main goal to mitigate these requirements in order to make the spectrum sharing systems practically feasible. To cope with the lack of a model we develop a coexisting deep reinforcement learning approach for continuous power allocation in both systems. Importantly, via our solution, the two systems allocate power merely based on geographical location of their users. Moreover, the inter-system signaling requirement is reduced to exchanging only the number of primary users that their QoS requirements are violated. We observe that compared to a centralized agent that allocates power based on full (accurate) channel information, our solution is more robust and strictly guarantees QoS requirements of the primary users. This implies that both systems can operate simultaneously with almost-zero inter-system signaling overhead.