[论文解读] Calculate the Optimum Threshold for Double Energy Detection Technique in Cognitive Radio Networks (CRNs)

Morteza Alijani, Anas Osman|arXiv (Cornell University)|Apr 15, 2022

Cognitive Radio Networks and Spectrum Sensing被引用 4

一句话总结

本文提出了一种在认知无线电网络中优化的双能量检测技术，利用二分法算法确定低能量阈值与高能量阈值之间模糊区域的最优阈值。通过根据噪声统计特性动态调整决策阈值，该方法提升了频谱感知性能，相较于传统的双阈值方案，提高了主用户检测概率并降低了次用户碰撞概率。

ABSTRACT

One of the most important technical challenges when designing a Cognitive Radio Networks (CRNs) is spectrum sensing, which has the responsibility of recognizing the presence or absence of the primary users in the frequency bands. A common technique used for spectrum sensing is double energy detection since it can operate without any prior information regarding the characteristics of the primary user signals. A double threshold energy detection algorithm is based on the use of two thresholds, to check the energy of the received signals and decided whether the spectrum is occupied or not. Furthermore, thresholds play a key role in the energy detection algorithm, by considering the stochastic features of noise in this model, as a result calculating the optimal threshold is a crucial task. In this paper, the Bi-Section algorithm was used to detect the optimum energy level in the fuzzy region which is an area between the low and high energy threshold. For this purpose, the decision threshold was determined by the use of the Bisection function for cognitive users. Numerical simulations show that the proposed method achieves better detection performance than the conventional double-threshold energy-sensing schemes. Moreover, the presented technique has advantages such as increasing the probability of detection of primary users and decreasing the probability of Collison between primary and secondary users.

研究动机与目标

• 为解决认知无线电网络（CRNs）中主用户信号未知情况下的频谱感知挑战。
• 通过优化低能量阈值与高能量阈值之间模糊区域的决策阈值，提升检测性能。
• 通过精确的阈值选择，降低主用户与次用户之间的碰撞概率。
• 在不预先掌握其信号特征的情况下，提高主用户检测的概率。
• 开发一种基于二分法的鲁棒、自适应阈值选择机制，用于能量检测。

提出的方法

• 将二分法应用于迭代优化低能量阈值与高能量阈值之间模糊区域内的决策阈值。
• 利用噪声的随机特性，确定能最小化虚警概率与漏检概率的最优阈值。
• 将接收信号的能量测量值与动态调整的阈值进行比较，以判断频谱占用情况。
• 通过基于能量水平比较反复缩小区间，使算法收敛至最优阈值。
• 所提出的技术无需预先掌握主用户信号参数，确保在动态环境中具有良好的适应性。
• 数值仿真验证了在不同信噪比（SNR）条件下阈值优化过程的有效性。

实验结果

研究问题

• RQ1 在双能量检测中，低能量阈值与高能量阈值之间的模糊区域，最优阈值是多少？
• RQ2 二分法算法如何提升认知无线电网络中的频谱感知性能？
• RQ3 所提出的方法在多大程度上降低了主用户与次用户之间碰撞的概率？
• RQ4 优化后的阈值如何影响主用户的检测概率？
• RQ5 基于二分法的阈值选择是否能优于传统的双阈值能量检测方案？

主要发现

• 所提出的基于二分法的阈值优化方法，相较于传统的双阈值方法，实现了更高的主用户检测概率。
• 该方法通过最小化模糊区域内的错误决策，降低了主用户与次用户之间的碰撞概率。
• 仿真结果表明，该方法在各种信噪比（SNR）水平下均表现出改进的检测性能。
• 该算法能高效收敛至最优阈值，确保了可靠且自适应的频谱感知。
• 该技术在无需预先掌握主用户信号特征的情况下有效运行，增强了实际部署的可行性。
• 优化后的阈值选择显著降低了虚警率与漏检率。