# Intelligent Reflecting Surface-Assisted Cognitive Radio System

arXiv: https://arxiv.org/abs/1912.10678 · HTML: https://mlsanity.com/paper/1912.10678/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Automated Metaheuristic Algorithm Design with Autoregressive Learning](https://mlsanity.com/paper/2405.03419/) (2024)
- [AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms with Diverse Structures](https://mlsanity.com/paper/2204.00998/) (2022)
- [AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design](https://mlsanity.com/paper/2303.06536/) (2023)
- [Geometry-Informed Optimization of Binary RIS Configurations for Communication and Sensing](https://mlsanity.com/paper/2608.04133/) (2026)

## Abstract (arXiv)

Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users. Meanwhile, intelligent reflecting surface (IRS), also known as reconfigurable intelligent surface (RIS), has been recently proposed as a promising approach to enhance energy efficiency (EE) of wireless communication systems through intelligently reconfiguring the channel environment. To improve both SE and EE, in this paper, we introduce multiple IRSs to a downlink multiple-input single-output (MISO) CR system, in which a single SU coexists with a primary network with multiple primary user receivers (PU-RXs). Our design objective is to maximize the achievable rate of SU subject to a total transmit power constraint on the SU transmitter (SU-TX) and interference temperature constraints on the PU-RXs, by jointly optimizing the beamforming at SU-TX and the reflecting coefficients at each IRS. Both perfect and imperfect channel state information (CSI) cases are considered in the optimization. Numerical results demonstrate that the introduction of IRS can significantly improve the achievable rate of SU under both perfect and imperfect CSI cases.