Abstract: Aiming at the problem of maximizing system throughput of inter-cell interference in OFDMA multi-cell system, a cooperative rand quantum genetic algorithm particle swarm optimization algorithm is proposed for subcarriers and power co-allocation under the constraint of system power based on cooperative quantum genetic algorithm particle swarm optimization algorithm. Particle swarm optimization algorithm is used to optimize power allocation of subcarrier independently and improved quantum genetic algorithm is used to optimize user subcarrier allocation. At the same time, by using the stochastic cooperative strategy, the local optimal solution is avoided, and the global optimal solution is achieved. Simulation results show that the proposed algorithm can get more system throughput and higher resource utilization compared with the traditional fractional step algorithm.

Key words: multi-cell Orthogonal Frequency Division Multiple Access（OFDMA） system, cooperative rand quantum genetic algorithm particle swarm optimization algorithm, resource allocation

摘要： 针对OFDMA多小区系统中相邻小区同频干扰下的吞吐量最大化问题，在系统功率的约束条件下，基于协同量子粒子群算法提出一种子载波和功率联合分配的协同随机量子粒子群算法（CRQP）。分别利用粒子群算法独立优化子载波的功率分配，并利用改进的量子遗传算法独立优化用户的子载波分配。在独立优化的同时，通过随机协同策略避免陷入局部最优解，达到全局最优。仿真结果表明，与传统的分步求解算法相比，CRQP算法能获得更多的系统吞吐量和更高的资源利用率。

关键词: 多小区正交频分多址（OFDMA）系统, 协同随机量子粒子群算法, 资源分配