| 摘要: |
| 针对5G时代小基站的密集部署带来的复杂干扰问题，对下行的认知无线电超密集网络下的资源分配进行了研究。为减小网络干扰，提高次用户吞吐量，提出了一种改进的基于用户分簇的资源分配算法。基于基站的覆盖范围，选出用户的强干扰基站，以用户-基站干扰关系建立用户-用户干扰图，按用户受到的平均弱干扰划分优先级对用户分簇，再为簇集群预分配频段，为每个簇分配对应频段中效用最大的信道。该资源分配算法能准确反映用户间的干扰关系，保障资源分配公平性。仿真结果表明，当用户密度与基站密度均较大时，与相同场景的已有算法相比，该改进算法有较好的抗干扰能力，能有效提高次用户的吞吐量。 |
| 关键词: 超密集网络（UDN） 认知无线电(CR) 资源分配 干扰图 用户分簇 |
| DOI： |
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| 基金项目:国家自然科学基金面上项目(61671143) |
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| Resource allocation for cognitive radio ultra-dense network based on user clustering |
| ZHANG Junjie,QIU Runhe |
| (1.College of Information Science and Technology,Donghua University,Shanghai 201620,China;
2.Engineering Research Center of Digitized Textile & Apparel Technology,Ministry of Education,
Shanghai 201620,China;1.College of Information Science and Technology,Donghua University,Shanghai 201620,China;
2.Engineering Research Center of Digitized Textile & Apparel Technology,Ministry of Education,
Shanghai 201621,China) |
| Abstract: |
| The resource allocation in the downlink cognitive radio ultra-dense network(CR-UDN) is investigated in response to the complex interference problems caused by femtocell in the 5G era.In order to maximize the throughput of secondary users,an improved resource allocation algorithm based on user clustering is proposed.According to the coverage of base station,the strong interfering base station of user is selected,the user-user interference graph based on the user-femtocell interference relationship is established,the user is divided into clusters according to the average weak interference received by the user,and then frequency bands are pre-allocated for the cluster clusters.For each cluster,the channel with the most utility in the corresponding frequency band is allocated.The resource allocation algorithm can accurately express the interference relationship between users,guarantee the fairness of resource allocation,and improve the priority of secondary users in resource allocation.The simulation results show that when the user density and base station density are both large,the proposed algorithm has better anti-interference ability and can effectively improve the throughput of secondary users. |
| Key words: ultra-dense network(UDN) cognitive radio(CR) resource allocation interference graph user clustering |
