A multi-antenna wireless communication system beamforming method and system based on quantum approximate optimization

A quantum-classical hybrid optimization framework was constructed by using the quantum approximation optimization algorithm (QAOA) to solve the problems of computational complexity of beamforming and user service priority differences in multi-antenna wireless communication systems, and to achieve comprehensive system performance optimization under finite phase quantization accuracy.

CN121690307BActive Publication Date: 2026-05-01XIAMEN UNIV OF TECH
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Patent Information

Application Number
CN202610168195.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-02-05
Publication Date
2026-05-01
Estimated Expiration
2046-02-05

AI Technical Summary

Technical Problem

Existing multi-antenna wireless communication systems suffer from high computational complexity in beamforming, difficulty in optimization and control, and insufficient consideration of user service priority differences and wireless energy harvesting requirements, which limits their applicability in smart energy-carrying network application scenarios.

Method used

The Quantum Approximation Optimization Algorithm (QAOA) is adopted. By constructing a quantum-classical hybrid optimization framework, the parameterized quantum circuit is executed by a quantum processor and the circuit parameters are optimized on a classical processor. The optimal quantization phase configuration is searched to optimize the overall performance of the system.

Benefits of technology

Under limited phase quantization accuracy, the overall performance problem of beamforming is effectively solved, and the weighted sum rate and wireless energy harvesting performance of the system are improved.

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Abstract

The present application relates to a kind of quantum approximation optimization-based multi-antenna wireless communication system beam forming method, system, belong to multi-antenna wireless communication field, comprising the following steps: S1, according to the channel state information and the wireless resource weight, construct the weighted channel correlation matrix reflecting the characteristics of multi-user integrated channel;S2, the continuous phase of each transmitting antenna is discretized, and a gradually doubled binary variable is sequentially assigned to the discrete phase, the phase combination of all antennas is jointly constructed to determine the state space corresponding to quantum approximation optimization algorithm corresponding to quantum state representation of quantum approximation optimization algorithm;S3, by quantum approximation optimization algorithm, quantum circuit evolution and observation are executed, and the power allocation quantum optimization initial parameter is updated and optimized by the gradient descent of classical algorithm, to obtain power allocation quantum optimization optimal parameter;S4, power allocation quantum optimization optimal parameter is converted into the phase combination of all antennas, and the optimal power allocation beam is obtained.
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