发帖
雷达卡
摘要翻译：
强大的频谱感知机制使认知无线电能够在专用于主用户的频谱资源中发现传输机会。本文在已知主用户存在和不存在概率的前提下，通过优化主用户的频谱感知时间来最大化主用户的平均吞吐量。对寻找传输机会所消耗的能量进行了评估，并讨论了主用户数量对次用户吞吐量和消耗能量的影响。为了避免解析方法带来的挑战，本文还提出了一种系统的基于神经网络的感知时间优化方法。所提出的自适应方案能够在不需要任何先验知识或对无线环境的假设的情况下找到信道感知时间的最优值。详细介绍了该方法中所用的人工神经网络的结构、性能和协作，并给出了一组示例性仿真结果，以验证分析结果和所提出的基于学习的优化方案的性能。
---
英文标题：
《Analytical and Learning-Based Spectrum Sensing Time Optimization in
Cognitive Radio Systems》
---
作者：
Hossein Shokri-Ghadikolaei, Younes Abdi, Masoumeh Nasiri-Kenari
---
最新提交年份：
2011
---
分类信息：
一级分类：Computer Science 计算机科学
二级分类：Networking and Internet Architecture 网络和因特网体系结构
分类描述：Covers all aspects of computer communication networks, including network architecture and design, network protocols, and internetwork standards (like TCP/IP). Also includes topics, such as web caching, that are directly relevant to Internet architecture and performance. Roughly includes all of ACM Subject Class C.2 except C.2.4, which is more likely to have Distributed, Parallel, and Cluster Computing as the primary subject area.
涵盖计算机通信网络的所有方面，包括网络体系结构和设计、网络协议和网络间标准（如TCP/IP）。还包括与Internet体系结构和性能直接相关的主题，如web缓存。大致包括除C.2.4以外的所有ACM主题类C.2，后者更有可能将分布式、并行和集群计算作为主要主题领域。
--
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
---
英文摘要：
Powerful spectrum sensing schemes enable cognitive radios (CRs) to find transmission opportunities in spectral resources allocated exclusively to the primary users. In this paper, maximizing the average throughput of a secondary user by optimizing its spectrum sensing time is formulated assuming that a prior knowledge of the presence and absence probabilities of the primary users is available. The energy consumed for finding a transmission opportunity is evaluated and a discussion on the impact of the number of the primary users on the secondary user throughput and consumed energy is presented. In order to avoid the challenges associated with the analytical method, as a second solution, a systematic neural network-based sensing time optimization approach is also proposed in this paper. The proposed adaptive scheme is able to find the optimum value of the channel sensing time without any prior knowledge or assumption about the wireless environment. The structure, performance, and cooperation of the artificial neural networks used in the proposed method are disclosed in detail and a set of illustrative simulation results is presented to validate the analytical results as well as the performance of the proposed learning-based optimization scheme.
---
PDF链接：
https://arxiv.org/pdf/1111.5296
京公网安备 11010802022788号
