Research on Resource Allocation for IRS-Assisted Energy Harvesting-Cognitive Radio Networks

Simple item page
dc.contributor.authorKAWALA CHIRU Lilian
dc.date.accessioned2026-07-06T06:39:53Z
dc.date.issued2026
dc.description.abstract(2) To address IRS hardware phase quantization errors, scalability, and low-SNR detection challenges, we establish a unified optimization framework for IRS-assisted Multiple Input Single Output (MISO) EH-CRNs that maximizes SU throughput under practical constraints, including discrete IRS phase shifts, beamforming design, false-alarm control, energy causality, and SU QoS. We develop a quantization-aware alternating optimization algorithm to solve the non-convex problem by decomposing it into interrelated subproblems covering detection probability maximization, false alarm minimization, energy harvesting optimization, and throughput enhancement. The integration of optimized Weighted Energy Detection (WED) coefficients further improves spectrum sensing accuracy in low-SNR regimes, enabling efficient and reliable spectrum access in underlay CRNs. A Nearest Point Search with Penalty (NPSP) method addresses discrete phase shift constraints, while optimization techniques including SDR, SCA, first-order Taylor expansion, Gaussian randomization, and SROCR are employed for convexification and iterative refinement. Simulation results demonstrate significant improvements in spectrum sensing reliability, energy harvesting efficiency, and SU throughput, representing the first comprehensive optimization of the sensing-harvesting-throughput trade-off under realistic deployment conditions and establishing a robust foundation for practical implementation of IRS-assisted MISO EH-CRNs (3) A robust optimization algorithm is proposed to address the challenge of imperfect CSI, which affects both direct PU–SU and cascaded PU–IRS–SU links. An optimization problem is formulated to maximize worst-case SU throughput while guaranteeing reliable spectrum sensing, energy harvesting, SU QoS, and strict PU protection. The design jointly optimizes SU beamforming and IRS phase shifts under bounded channel uncertainties, leveraging worst-case optimization principles. Semi-infinite constraints are reformulated using the S-procedure, SDR, Schur’s complement, and the generalized sign-definiteness principle, with SCA employed to refine non-convex formulations. These techniques ensure tractable problem reformulation and reliable performance under adverse CSI conditions. Simulation results demonstrate that the proposed framework significantly outperforms non-robust benchmarks, achieving strong throughput gains alongside dependable spectrum sensing and energy harvesting.
dc.identifier.urihttps://repository.must.ac.tz/handle/123456789/642
dc.language.isoen
dc.subjectIntelligent Reflecting Surface (IRS)
dc.subjectCognitive Radio Networks (CRNs)
dc.subjectEnergy Harvesting (EH)
dc.subjectResource Allocation
dc.titleResearch on Resource Allocation for IRS-Assisted Energy Harvesting-Cognitive Radio Networks
dc.typeArticle

Files

Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
KAWALA CHIRU LILIAN_Dissertation_Final_Version.docx
Size:
2.8 MB
Format:
Microsoft Word XML
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download

Collections

Journal Articles