Abstract: How to achieve efficient allocation of dynamically coupled frequency spectrum resources to meet user traffic demands in heterogeneous network overlapping coverage scenarios is an important challenge for next-generation wireless communication networks. This paper comprehensively considers the difference of spectral attributes in the network domain and the diversification of user domain requirements. With the goal of maximizing the total bandwidth obtained by users, spectrum resource allocation is modeled as a nonlinear multi-constraint conditional 0-1 integer programming problem, and two solving methods are designed and implemented. Firstly, a simplified method based on the improved Hungarian algorithm is designed. By simplifying the constraints, the complex model is transformed into a standard form 0-1 programming, and the Hungarian algorithm is improved to effectively solve the complex spectrum allocation problem. Secondly, an improved genetic algorithm is designed, which uses elitism to preserve excellent individuals, fuses primary network interference constraints and sub-user requirements into fitness assessment to correct non-conforming genes for iteratively evolving to excellent individuals. Finally, the performance of the proposed methods and the particle swarm optimization method are compared by experiments. The experimental results show that the simplification method has a greater efficiency advantage, while the improved genetic algorithm can obtain a greater bandwidth.

Key words: heterogeneous networks, full spectrum access, network access, dynamic spectrum allocation

摘要： 如何在异构网络重叠覆盖场景下实现动态耦合频谱资源高效分配以满足用户流量需求是下一代无线通信网络的重要挑战。综合考虑网络域频谱属性差异化及用户域需求多样化问题，以用户获得总带宽最大化为目标，将频谱资源分配建模为非线性多约束条件0-1整数规划问题，并设计了两种求解方法。首先，设计了一种基于改进匈牙利算法的化简方法，该方法通过对约束条件进行化简，将复杂模型转化为标准形式0-1规划，并通过对匈牙利算法进行改进，有效求解了该复杂的频谱分配问题；其次，设计了一种改进的遗传算法，把主网络干扰约束及次用户需求融合进适应度评估中，以修正不符合要求的基因，并利用精英主义思想保留优秀个体，以进化迭代到优秀个体。最后通过实验对提出的方法与粒子群优化方法的性能进行对比分析，实验结果显示化简方法具有较大的效率优势，而改进遗传算法可得到更大的带宽。

关键词: 异构网络, 全频谱接入, 网络接入, 动态频谱分配