What is it about?
In this paper, we convert the original optimization problem into two problems. The primary problem is finding the desired IRS array factor without focusing on the binary weights. The secondary one is finding the binary weights of the IRS elements to reach the desired array factor with minimum error. We model and solve the secondary problem using an algorithm to activate/deactivate elements of the IRS. First, the proposed algorithm generates random matrices consisting of 0/1 weights, which create array factors with a tolerable error, and selects the matrix with minimum error. Second, it changes the weights of the matrix one by one up to the second predefined iteration number and saves it if the error is reduced.
Featured Image
Photo by Jakub Żerdzicki on Unsplash
Why is it important?
For 36 elements with 0.5 wavelength inter-element spacing, a tolerable amount of error of 0.1, and 1000 iterations, all-on, improved all-on, constrained random on-off, improved constrained random on-off, and Genetic solutions show 25%, 21%, 11%, 5%, and 7% error, respectively. In addition to the computational complexity, the complexity order of the proposed algorithm is derived and compared with both exhaustive search and the Genetic algorithm. Furthermore, the precision, processing time, and the difference between the obtained and desired weights are compared in 2 × 2-dimension to 10 × 10-dimension configurations.
Perspectives
ided wireless communications optimization problem into two primary and secondary problems. The first (primary) problem finds the desired IRS array factor and the second (secondary) problem derives the op timal solution for 0∕1 weights of the IRS elements to create an array factor close to the desired array factor obtained from the primary prob lem. In solving the proposed primary problem, the obtained desired array factor is far better than the solution obtained from the original problem, because the binary weights constraint is not considered. After solving the second problem and finding the binary weights, the obtained associated array factor is slightly different from the desired one, which is due to the binary nature of the weights in IRS, but it is very close to the solution of the original problem. Therefore, instead of solving one complicated optimization problem, two
Dr. Shahriar Shirvani Moghaddam
Shahid Rajaee Teacher Training University
Read the Original
This page is a summary of: A group-based coarse-fine algorithm for intelligent reflecting surface beamforming, Physical Communication, August 2025, Elsevier,
DOI: 10.1016/j.phycom.2025.102668.
You can read the full text:
Contributors
The following have contributed to this page
