Due to noise uncertainty, a dual-threshold energy detection based on adaptive sampling number is proposed. The two thresholds are given accordingly to noise uncertainty. And between the two thresholds, a novel adaptive sampling number algorithm is used. In order to avoid too much energy consumption, an optimal upper bound of the sampling number is given by trading-off the energy consumption and throughput. Both theoretical and emulational practicability and advantage are proved. Upon comparison between the proposed algorithm and traditional dual-threshold energy detection algorithm based on constant sampling size, even though the throughput difference is very small when the detection proba-bility is high, the corresponding false alarm probability and error probability of the proposed algorithm is less than the traditional algorithm's. Besides, comparison of the two algorithms in condition of same sampling number, it still shows that the proposed algorithm brings with better detection performance despite a little more energy consumption.%针对认知环境中能量感知的噪声不确定性问题,提出了一种基于自适应检测长度的双门限能量感知算法.算法首先根据噪声不确定性大小设置上下判决门限.当检测统计量位于双门限之外时直接判决,否则增加采样数并再次比较,直到得出判决结果或采样数达到上限;为了尽量减小由于采样数增加带来的系统能量开销的增加,给出了系统能量开销与吞吐量折中的最佳采样数上限.从理论上分析了算法的优越性,并进行了仿真验证,结果表明,该算法尽管增加了一定的能量开销,但是可以显著地提高系统检测性能.
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