Author(s)

YuXin Cheng

Affiliation(s)

School of Intelligent Equipment, Shandong University of Science and Technology, Taian 271000, Shandong, China.

Corresponding Author

YuXin Cheng

ABSTRACT

In information science and electrical engineering, energy detection is a foundational tool for dynamic spectrum access and environmental awareness, playing a critical role in modern wireless communication systems. While Local Spectrum Energy Detection (LSED) is commonly regarded as superior to Full Spectrum Energy Detection (FSED), its performance advantage is not guaranteed across all real-world conditions. In this study, we derive closed-form expressions for the false alarm and detection probabilities of both FSED and LSED under the constant false alarm rate (CFAR) criterion. To address varying signal distributions, we develop an analytical framework based on the SNR ratio and categorize the frequency band selection problem into two typical cases: complete and partial capture of signal energy. Monte Carlo simulations validate our theoretical findings and reveal the specific conditions under which LSED provides measurable benefits over FSED, such as in narrowband signal environments or when prior knowledge of the signal's spectral characteristics is available. Furthermore, we propose a novel frequency-band energy ratio metric, which quantifies the relative energy distribution across different bands, enabling adaptive and resource-efficient detection method selection in complex electromagnetic environments. This metric facilitates intelligent switching between FSED and LSED based on real-time spectrum conditions. This work offers theoretical insights and practical guidance for signal detection, spectrum sensing, and data-driven decision-making in electronic communication and IoT-enabled systems, contributing to more efficient spectrum utilization and improved reliability in next-generation wireless networks.

KEYWORDS

Constant false alarm rate; Energy detection; Full spectrum; Local spectrum

CITE THIS PAPER

YuXin Cheng. Selection strategy for full-spectrum versus local-spectrum energy detection under constant false alarm rate conditions. 2025, 7(4): 47-59. DOI: https://doi.org/10.61784/jcsee3068.

REFERENCES

[1] Yucek T, Arslan H. A survey of pectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials 2009, 11, 116-130. DOI: https://doi.org/10.1109/SURV.2009.090109.

[2] Urkowitz H. Energy detection of unknown deterministic signals. Proceedings of the IEEE, 1967, 55, 523-531. DOI: https://doi.org/10.1109/PROC.1967.5573.

[3] Dikmese S, Ilyas Z, Sofotasios P C, et al. Sparse frequency domain spectrum sensing and sharing based on cyclic preffx autocorrelation. IEEE Journal on Selected Areas in Communications 2017, 35, 159-172. DOI: https://doi.org/10.1109/JSAC.2016.2633058.

[4] Hou C, Liu G, Tian Q, et al. Multisignal modulation classiffcation using sliding window detection and complex convolutional network in frequency domain. IEEE Internet of Things Journal, 2022, 9, 19438-19449. DOI: https://doi.org/10.1109/JIOT.2022.3167107.

[5] Li H, Hu Y, Wang S. A novel blind signal detector based on the entropy of the power spectrum subband energy ratio. Entropy, 2021, 23, 448. DOI: https://doi.org/10.3390/e23040448.

[6] Li H, Hu Y, Wang S. Signal detection based on power-spectrum sub-band energy ratio. Electronics, 2021, 10, 64. DOI: https://doi.org/10.3390/electronics10010064.

[7] Ejaz W, ul Hasan N, Azam M A, et al. Improved local spectrum sensing for cognitive radio networks. EURASIP Journal on Advances in Signal Processing 2012, 2012, 242. DOI: https: //doi.org/10.1186/1687-6180-2012-242.

[8] Adardour H E, Meliani M, Hachemi M H. Improved local spectrum sensing in cluttered environment using a simple recursive estimator. Computers & Electrical Engineering 2017, 61, 208-222. DOI: https://doi.org/10.1016/j.compeleceng.2016.11.037.

[9] Quan Z, Zhang W, Shellhammer S J, et al. Optimal spectral feature detection for spectrum sensing at very low SNR. IEEE Transactions on Communications 2011, 59, 201-212. DOI: https://doi.org/10.1109/TCOMM.2010.112310.090306.

[10] Zhang B, Wu J, Su M, et al. An efffcient cooperative spectrum sensing for cognitive wireless sensor networks. IEEE Access, 2023, 11, 132544-132556. DOI: https://doi.org/10.1109/ACCESS.2023.3336654.

[11] Besson O. Impact of covariance mismatched training samples on constant false alarm rate detectors. IEEE Transactions on Signal Processing, 2021, 69, 755-765. DOI: https://doi.org/10.1109/ TSP.2021.3050567.

[12] Diskin T, Beer Y, Okun U, et al. CFARnet: deep learning for target detection with constant false alarm rate. SIGNAL PROCESSING, 2024, 223, 109543. DOI: https://doi.org/10.1016/j. sigpro.2024.109543.

[13] Li K, Zhang P, Yang Z. Semiparametric constant false alarm rate method for radar and sonar images. Electronics Letters, 2024, 60, e13146. DOI:  https://doi.org/10.1049/ell2.13146.

[14] Zhong C, Wu C, Li X, et al. A novel frequency hopping prediction model based on TCN-GRU. IEICE Transactions on Fundamentals, 2024, E107-A, 1577-1581. DOI: https: //doi.org/10.1587/transfun.2023EAL2095.

[15] Zhou Z, Huang L, Christensen M G, et al. Robust spectral analysis of multi-channel sinusoidal signals in impulsive noise environments. IEEE Transactions on Signal Processing, 2022, 70, 919-935. DOI: https://doi.org/10.1109/TSP.2021.3101989.

[16] Ravve I, Koren Z. Analytical hilbert-transform attributes of ricker and gabor wavelets. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61, 1-16. DOI: https://doi.org/10.1109/TGRS.20 40723.3309248.