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Gardner定时算法 matlab仿真。QPSK、16qam，反馈环路(Gardner timing algorithm matlab simulation. QPSK, 16qam, the feedback loop)

分形插值曲面的MATLAB程序 这时pdf程序资料(MATLAB program of fractal interpolation of time pdf program information)

采用MATLAB编写的“剪刀石头布”小游戏，供学习MATLAB的程序员练习。解压后，在MATLAB命令窗口直接输入文件名即可运行。(It is a simple game coded by MATLAB. It is useful to the biginner of MATLAB programmer.)

实现劳斯稳定判定的全部过程；给出劳斯表格，并给出判定所给系统稳定性结论。(Rolls-determination to achieve stability in the entire process given Rolls-form, and give stability to the system to determine the conclusion.)

利用Capon, MUSIC进行窄带信号空域信号方向估计。比较分辨率性能。观察阵元数、信噪比、快拍数对估计器性能的影响。 (Using Capon, MUSIC for narrowband signal airspace signal direction estimates. Comparative resolution performance. Observe the number of array elements, signal to noise ratio, the number of snapshots on the estimator performance.)

说明：  信号处理,进行信号检测与处理，通过设定threshold来进行信号检测的可信度调整(Credibility adjustment signal processing, signal detection and processing, by setting threshold for signal detection)

MATLAB下通过impulse invariant transformation方法设计数字滤波器，包含低通和带通滤波器代码。(Using MATLAB m-file to design LP and BP digital filter by impulse invariant transformation method.)

一个matlabGUI，可以对mat文件中的多个信号进行线性分析（最多三个）(A matlabGUI, can document a number of mat-signal linear analysis (a maximum of three))

guidlines to how to do ofdm in matlab

The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given.