Research on Resource Allocation for IRS-Assisted Energy Harvesting-Cognitive Radio Networks
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Date
2026
Authors
KAWALA CHIRU Lilian
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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.
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Keywords
Intelligent Reflecting Surface (IRS), Cognitive Radio Networks (CRNs), Energy Harvesting (EH), Resource Allocation