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Decomposition based multiobjective spectrum allocation algorithm for cognitive vehicular networks

机译：认知车辆网络中基于分解的多目标频谱分配算法

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摘要

To solve the problem of low throughput and fairness in cognitive vehicular networks, we establish a multi-objective spectrum allocation model to maximize the average throughput and fairness of cognitive vehicular networks. By blending the cuckoo search algorithm (CS) with multi-objective optimization based on decomposition (MOEA/D), the multi-objective problem is decomposed into a series of scalar sub-problems with different weights. We verify the effectiveness of the algorithm by comparing it with the MOEA/D algorithm which generates new solutions by cross and mutation on standard test functions ZDT1, ZDT2 and ZDT3, the convergence curve demonstrates that the proposed algorithm has faster convergence rate and more stable convergence performance. We also simulate the spectrum allocation problem model of maximizing the average throughput and fairness of the cognitive vehicular network. The simulation results show that the solution obtained by the algorithm can dominate the optimal solution of some classical algorithms, that is to say this algorithm can improve the network average throughput and fairness at the same time.
机译：为了解决认知车辆网络的吞吐量和公平性低的问题，我们建立了一个多目标频谱分配模型，以最大化认知车辆网络的平均吞吐量和公平性。通过将杜鹃搜索算法（CS）与基于分解的多目标优化（MOEA / D）混合，将多目标问题分解为一系列权重不同的标量子问题。通过与标准测试函数ZDT1，ZDT2和ZDT3的交叉和变异生成新解的MOEA / D算法进行比较，验证了该算法的有效性，收敛曲线表明该算法收敛速度更快，收敛速度更稳定。表现。我们还模拟了最大化认知车辆网络的平均吞吐量和公平性的频谱分配问题模型。仿真结果表明，该算法所获得的解可以支配某些经典算法的最优解，即该算法可以同时提高网络平均吞吐量和公平性。

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