Resource Allocation for IRS-assisted Full-Duplex Cognitive Radio Systems (2003.07467v1)

Abstract: In this paper, we investigate the resource allocation design for intelligent reflecting surface (IRS)-assisted full-duplex (FD) cognitive radio systems. In particular, a secondary network employs an FD base station (BS) for serving multiple half-duplex downlink (DL) and uplink (UL) users simultaneously. An IRS is deployed to enhance the performance of the secondary network while helping to mitigate the interference caused to the primary users (PUs). The DL transmit beamforming vectors and the UL receive beamforming vectors at the FD BS, the transmit power of the UL users, and the phase shift matrix at the IRS are jointly optimized for maximization of the total sum rate of the secondary system. The design task is formulated as a non-convex optimization problem taking into account the imperfect knowledge of the PUs' channel state information (CSI) and their maximum interference tolerance. Since the maximum interference tolerance constraint is intractable, we apply a safe approximation to transform it into a convex constraint. To efficiently handle the resulting approximated optimization problem, which is still non-convex, we develop an iterative block coordinate descent (BCD)-based algorithm. This algorithm exploits semidefinite relaxation, a penalty method, and successive convex approximation and is guaranteed to converge to a stationary point of the approximated optimization problem. Our simulation results do not only reveal that the proposed scheme yields a substantially higher system sum rate for the secondary system than several baseline schemes, but also confirm its robustness against CSI uncertainty. Besides, our results illustrate the tremendous potential of IRS for managing the various types of interference arising in FD cognitive radio networks.

• The paper proposes a novel block coordinate descent algorithm that transforms a complex non-convex problem into tractable convex subproblems.
• The paper demonstrates significant sum rate improvements and robust interference mitigation even with imperfect primary user channel information.
• The paper highlights the role of Intelligent Reflecting Surfaces in enhancing spectrum and energy efficiency in full-duplex cognitive radio networks.

Resource Allocation for IRS-assisted Full-Duplex Cognitive Radio Systems

This paper presents a novel approach to resource allocation in Intelligent Reflecting Surface (IRS)-assisted full-duplex (FD) cognitive radio systems. The work addresses a secondary network employing a full-duplex base station (FD-BS) to serve both half-duplex downlink (DL) and uplink (UL) users simultaneously, utilizing an IRS to enhance the secondary network's performance and mitigate interference with primary users (PUs).

Core Research Problem

The paper formulates the resource allocation design as a non-convex optimization problem, targeting the maximization of the total sum rate of the secondary network. The problem entails optimizing downlink transmit beamforming vectors, uplink receive beamforming vectors, uplink user transmit power, and phase shifts at the IRS. The complexity arises from the need to account for imperfect knowledge of the PUs' channel state information (CSI) and their maximum interference tolerance levels. The work tackles these challenges by applying a safe approximation to transform the intractable interference tolerance constraints into convex constraints.

Methodology

Employing a block coordinate descent (BCD)-based iterative algorithm, this paper addresses the non-convexity of the optimization problem. The algorithm leverages techniques such as semidefinite relaxation (SDR) and successive convex approximation (SCA), guaranteeing convergence to a stationary point. The methodology divides the optimization variables into subsets, solving each subset iteratively while fixing the others. This segmentation allows for tackling complex interdependencies in power and phase shift allocation effectively.

Key Findings

Simulation results demonstrate that the proposed IRS-assisted resource allocation framework significantly outperforms several baseline schemes, both in terms of sum rate enhancements for the secondary system and robustness against CSI uncertainty. Importantly, the research highlights the potential role of IRSs in efficiently managing interference types specific to FD cognitive radio networks, thereby underscoring IRSs' utility in enhancing spectrum and energy efficiency.

Implications and Future Directions

The investigation opens several avenues for future exploration in IRS-assisted communication systems:

• Interference Management: The IRS's ability to intelligently shape the wireless channel presents intriguing possibilities for more refined interference management strategies in densely populated frequency bands.
• Robust Network Design: The demonstrated robustness to CSI uncertainty suggests further paper into deterministic models that can cater to a wider range of practical deployment scenarios and environmental conditions.
• Advanced Optimization Techniques: Future work may benefit from exploring adaptive algorithms that tune optimization parameters based on real-time network performance metrics, potentially enhancing the adaptability and efficiency of cognitive radio systems.

The paper makes a meaningful contribution to the theoretical and practical exploration of IRS usage in enhancing cognitive radio networks, paving the way for future technological advancements in wireless communications infrastructure.