Intelligent Reflecting Surface Optimization in Cognitive Radio Networks

Intelligent reflecting surfaces (IRS) have emerged as a transformative technology for enhancing spectrum utilisation in cognitive radio (CR) networks. By reconfiguring the phase shifts of numerous passive elements, an IRS can steer incident electromagnetic waves towards intended receivers or away from primary users, thereby improving signal quality and reducing interference without additional power amplification. In cognitive radio settings, secondary users exploit underutilised spectrum bands under strict interference constraints imposed by licensed primary users. Joint optimisation of transmit power, passive beamforming and, where applicable, active beamforming or energy harvesting enables secondary networks to maximise achievable rate, energy efficiency and reliability. Recent advances have combined convex and non-convex optimisation techniques—such as alternating optimisation, penalty dual decomposition and gradient projection—with machine learning frameworks like deep reinforcement learning to address the high-dimensional and dynamic nature of practical environments. Applications span from mobile edge computing and unmanned aerial vehicle communications to secure transmission against eavesdroppers and jammers. The IRS-enhanced CR paradigm promises low-cost deployment, improved coverage in non-line-of-sight scenarios and scalable adaptation for sixth-generation (6G) networks and beyond.

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Intelligent Reflecting Surface Optimization in Cognitive Radio Networks publication trend

The graph below shows the total number of articles in intelligent reflecting surface optimization in cognitive radio networks across all publications each year (not limited to Nature Index journals).

Technical terms

Intelligent Reflecting Surface (IRS): A planar array of passive elements capable of electronically controlling the phase of reflected electromagnetic waves to shape the wireless propagation environment.

Cognitive Radio (CR): A spectrum-sharing paradigm where unlicensed (secondary) users opportunistically access under-utilised licensed bands without causing harmful interference to primary users.

Passive Beamforming: The process of adjusting the phase shifts of IRS elements to constructively steer reflected signals toward desired receivers.

Underlay Spectrum Sharing: A mode of cognitive radio operation where secondary users transmit concurrently with primary users under strict interference power constraints.

Deep Reinforcement Learning (DRL): A class of machine learning methods combining deep neural networks with reinforcement learning to make sequential decisions in complex, dynamic environments.

References

1. Achievable Rate Maximization for Underlay Spectrum Sharing MIMO System With Intelligent Reflecting Surface. IEEE Wireless Communications Letters (2022).
2. RIS-Assisted CR-MEC Systems Using Deep Reinforcement Learning Approach. IEEE Access (2024).
3. Enhancing Reconfigurable Intelligent Surface-Enabled Cognitive Radio Networks for Sixth Generation and Beyond: Performance Analysis and Parameter Optimization. Sensors (2024).