In a world where wireless devices are proliferating at an unprecedented rate, the demand for spectrum resources has never been higher. Traditional methods of resource allocation are struggling to keep up, leading to inefficiencies and potential interference. Enter Cognitive Radio Networks (CRNs), a technology that enables secondary users to access licensed spectrum bands without compromising the quality of service for primary users. But even CRNs face their own set of challenges, notably limited battery life and the potential for interference. This is where the innovative work of Lilian Chiru Kawala, a researcher at the School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, comes into play.
Kawala’s recent research, published in the IEEE Open Journal of the Communications Society, explores the use of Intelligent Reflecting Surfaces (IRSs) and energy harvesting techniques to enhance the efficiency of CRNs. The study addresses a critical gap in current frameworks: the assumption of ideal continuous phase shifts, which is impractical due to hardware limitations that permit only discrete phase levels. “Most existing IRS-assisted CRN frameworks assume ideal continuous phase shifts, leading to quantization errors and increased design complexity,” Kawala explains. “Our work establishes a unified system model that considers practical constraints, including discrete IRS phase shifts, beamforming design, and energy causality.”
The research introduces an optimization problem aimed at maximizing secondary user throughput under various constraints. To tackle this non-convex problem, Kawala and her team developed a quantization-aware alternating optimization algorithm. This algorithm decomposes the problem into interrelated subproblems, addressing detection probability maximization, false alarm minimization, energy harvesting optimization, and secondary user throughput enhancement. Advanced techniques such as semidefinite relaxation (SDR), Successive Convex Approximation (SCA), and Nearest Point Search with Penalty (NPSP) were employed to address practical implementation constraints.
The simulation results are promising, demonstrating the superior performance of the proposed framework and the novel resource allocation algorithm based on alternating optimization. “Our results highlight the transformative potential of IRS with discrete phase shifts in enhancing EH-CRN efficiency,” Kawala notes. “This technology can significantly improve energy harvesting and secondary user throughput under practical constraints.”
The implications of this research are far-reaching, particularly for the energy sector. As the demand for wireless devices continues to grow, so too does the need for efficient and sustainable energy solutions. By integrating energy harvesting techniques with advanced wireless technologies, the energy sector can reduce its reliance on traditional power sources and move towards a more sustainable future. “This research opens up new avenues for enhancing the efficiency of wireless communication systems,” Kawala concludes. “It paves the way for future developments in the field, particularly in the context of energy harvesting and spectrum sensing.”
As the world grapples with the challenges of a rapidly evolving digital landscape, innovations like those pioneered by Kawala offer a glimpse into a future where technology and sustainability go hand in hand. The research not only addresses current inefficiencies but also sets the stage for future advancements in wireless communication and energy management.