摘要翻译：
我们考虑一个认知无线电系统，由一个主发射机(PUtx)、一个主接收机(PUrx)、一个副发射机(SUtx)和一个副接收机（SUrx）组成。二次用户（SUs）配备有可定向的定向天线。我们假设SUs和主要用户(PU)共存，并且SUtx知道网络的几何形状。我们得到了SUtx和SUrx之间信道的遍历容量，并研究了频谱感知误差对信道容量的影响。在我们的系统中，SUtx首先感知频谱，然后根据感知结果在两个功率级上传输数据。在平均发射功率和平均干扰功率约束下，通过最大遍历容量得到了最优的SUtx发射功率电平以及SUrx发射天线和SUrx接收天线的最优方向。为了研究衰落信道的影响，我们考虑了三种情况：1）当SUtx知道SUtx和PUrx、PUtx和SUrx、SUtx和SUrx之间的衰落信道时，2)当SUtx只知道SUtx和SUrx之间的信道和其他两个信道的统计量时，3)当SUtx只知道这三个衰落信道的统计量时，对于每种情况，我们探索了最佳的SUtx发射功率电平和最佳的SUtx和SUrx天线方向，使遍历容量最大化，同时满足功率约束。
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英文标题：
《On the Combined Effect of Directional Antennas and Imperfect Spectrum
  Sensing upon Ergodic Capacity of Cognitive Radio Systems》
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作者：
Hassan Yazdani, Azadeh Vosoughi
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  We consider a cognitive radio system, consisting of a primary transmitter (PUtx), a primary receiver (PUrx), a secondary transmitter (SUtx), and a secondary receiver (SUrx). The secondary users (SUs) are equipped with steerable directional antennas. We assume the SUs and primary users (PUs) coexist and the SUtx knows the geometry of network. We find the ergodic capacity of the channel between SUtx and SUrx , and study how spectrum sensing errors affect the capacity. In our system, the SUtx first senses the spectrum and then transmits data at two power levels, according to the result of sensing. The optimal SUtx transmit power levels and the optimal directions of SUtx transmit antenna and SUrx receive antenna are obtained by maximizing the ergodic capacity, subject to average transmit power and average interference power constraints. To study the effect of fading channel, we considered three scenarios: 1) when SUtx knows fading channels between SUtx and PUrx, PUtx and SUrx, SUtx and SUrx, 2) when SUtx knows only the channel between SUtx and SUrx, and statistics of the other two channels, and, 3) when SUtx only knows the statistics of these three fading channels. For each scenario, we explore the optimal SUtx transmit power levels and the optimal directions of SUtx and SUrx antennas, such that the ergodic capacity is maximized, while the power constraints are satisfied.
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PDF链接：
https://arxiv.org/pdf/1712.00115