Irs transmit power optimization method based on quasi-affine transformation evolutionary algorithm

By optimizing IRS phase shift and AP beamforming using a quasi-affine transformation evolution algorithm, the problems of high computational complexity in traditional methods and easy getting trapped in local optima by heuristic algorithms are solved, achieving efficient transmit power optimization, which is suitable for intelligent reflector-assisted multi-user communication systems.

CN121261752BActive Publication Date: 2026-02-17YANGO UNIV +1
2 Cites 0 Cited by

Patent Information

Application Number
CN202511818500.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-12-04
Publication Date
2026-02-17
Estimated Expiration
2045-12-04

AI Technical Summary

Technical Problem

In downlink multi-user systems of multi-antenna base stations assisted by intelligent reflectors, traditional optimization methods have high computational complexity and existing heuristic algorithms are prone to getting trapped in local optima, making it difficult to effectively optimize transmit power in high dimensions to meet user signal-to-noise ratio constraints.

Method used

The algorithm is based on the Quasi-Affine Transform Evolution (QUATRE) framework. By constructing a system model, channel modeling and optimizing the objective function, combined with label matrix control dimension perturbation and adaptive scaling mechanism, the IRS phase shift vector and AP beamforming vector are optimized.

Benefits of technology

It effectively reduces base station transmit power in high-dimensional environments, meets user signal-to-noise ratio constraints, improves optimization efficiency and robustness, and is suitable for high-energy-efficiency, low-power multi-user MISO communication scenarios.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The application provides an IRS transmission power optimization method based on a quasi-affine transformation evolution algorithm, and comprises the following steps: step 1, system model construction and parameter initialization; step 2, channel modeling; step 3, constructing a target for minimizing transmission power while meeting the constraint condition of the signal-to-noise ratio of all users; step 4, searching by using a quasi-affine evolution-based meta-heuristic algorithm; and step 5, outputting an IRS phase shift vector and an AP beamforming vector matrix. The application has the beneficial effect of improving the quasi-affine transformation evolution algorithm framework, aiming to overcome the problems of the traditional optimization method, such as the dramatic increase in the calculation complexity under the condition of a large number of IRS unit numbers N and user numbers K, and the defects of the existing heuristic algorithm (such as the particle swarm optimization) such as being prone to local optimization and low search efficiency.
Need to check novelty before this filing date? Find Prior Art