认知无线电自适应频谱检测及新型联合检测算法研究
发布时间：2018-08-19 12:24
【摘要】：目前世界各国主流的频谱分配策略是静态分配方式,大多采用授权许可制度。然而许多授权用户,并非一直占用授权频段,许多频段处于空闲状态,这直接导致了的频谱利用率低下。在这样的情况下,Joseph Mitola于1999年在软件无线电的基础上提出了认知无线电(CR,Cognitive Radio)的概念,阐述了机会式频谱接入的方法。然而,要实现动态频谱接入,首先要解决的问题就是如何有效地检测频谱空穴,避免对主用户的干扰。本文对认知无线电网络中的频谱检测技术进行研究,并且分别在单用户及多用户的模型下提出了控制干扰的最大化利用频谱资源的算法,通过对其数学模型分析和推导,给出了下面的科研成果:1.提出了一种自适应调整能量检测器门限的算法。固定的能量判决门限是传统能量检测器的特征之一,虽然能保证主用户所受干扰限制在一定范围之内,但未深入考虑频谱的效率问题。自适应门限的能量检测技术通过动态地调整能量判决门限,在保证主用户所受干扰满足限制条件的同时,最大化频谱利用率。2.提出了基于幂集置信度(PSB,Power Set Belief)的联合检测技术。传统的联合检测技术中,K/N联合检测缺乏对每个用户表现的置信度的认识。基于幂集置信度的联合检测技术从Dempster-Shafer证据理论出发,根据每个用户的置信度进行有效地数据融合。3.提出了基于Adaboost的联合检测技术。Adaboost算法常用于图像识别,人工智能等领域。结合了Adaboost的联合检测技术,可以通过训练测试,达到十分优异的检测性能。
[Abstract]:At present, the main spectrum allocation strategy in the world is static allocation, mostly using authorized license system. However, many authorized users do not always occupy the authorized band, many bands are idle, which directly leads to low spectrum utilization. In this case, Joseph Mitola in 1999 in the software radio base. In this paper, the concept of cognitive radio (CR) is proposed, and the method of opportunistic spectrum access is described. However, to realize dynamic spectrum access, the first problem to be solved is how to effectively detect spectrum holes and avoid interference to primary users. Based on the analysis and derivation of its mathematical model, the following scientific research results are given: 1. An adaptive algorithm for adjusting the threshold of energy detector is proposed. Fixed threshold of energy decision is the characteristic of traditional energy detector. Firstly, although the interference of the primary user is limited to a certain range, the efficiency of the spectrum is not considered deeply. The adaptive threshold energy detection technique can maximize the spectrum utilization while ensuring that the interference of the primary user meets the limitation conditions by dynamically adjusting the energy decision threshold. In the traditional joint detection technology, K/N joint detection lacks the knowledge of the confidence of each user's performance. Based on the power set confidence, the joint detection technology proceeds from Dempster-Shafer evidence theory and carries on the effective data fusion according to the confidence of each user. Adaboost joint detection technology. Adaboost algorithm is often used in image recognition, artificial intelligence and other fields. Combined with Adaboost joint detection technology, can be trained and tested to achieve very good detection performance.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN925
本文编号：2191643
[Abstract]:At present, the main spectrum allocation strategy in the world is static allocation, mostly using authorized license system. However, many authorized users do not always occupy the authorized band, many bands are idle, which directly leads to low spectrum utilization. In this case, Joseph Mitola in 1999 in the software radio base. In this paper, the concept of cognitive radio (CR) is proposed, and the method of opportunistic spectrum access is described. However, to realize dynamic spectrum access, the first problem to be solved is how to effectively detect spectrum holes and avoid interference to primary users. Based on the analysis and derivation of its mathematical model, the following scientific research results are given: 1. An adaptive algorithm for adjusting the threshold of energy detector is proposed. Fixed threshold of energy decision is the characteristic of traditional energy detector. Firstly, although the interference of the primary user is limited to a certain range, the efficiency of the spectrum is not considered deeply. The adaptive threshold energy detection technique can maximize the spectrum utilization while ensuring that the interference of the primary user meets the limitation conditions by dynamically adjusting the energy decision threshold. In the traditional joint detection technology, K/N joint detection lacks the knowledge of the confidence of each user's performance. Based on the power set confidence, the joint detection technology proceeds from Dempster-Shafer evidence theory and carries on the effective data fusion according to the confidence of each user. Adaboost joint detection technology. Adaboost algorithm is often used in image recognition, artificial intelligence and other fields. Combined with Adaboost joint detection technology, can be trained and tested to achieve very good detection performance.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN925
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，本文编号：2191643
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