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1. A Study of Cooperative and Non-Cooperative Wideband Spectrum Sensing Radio Networks in the Cognitive Radio 5G Era

   https://doi.org/10.5121/csit.2024.141903  

   C_Dike, Blessing; M_Akujuobi, Cajetan (October 2024, Academy & Industry Research Collaboration Center)

   The advent of 5G technologies has ushered in unprecedented demands for efficient spectrum utilization to accommodate a surge in data traffic and diverse communication services. In this context, accurate and reliable spectrum sensing is crucial. We investigated wideband spectrum sensing strategies by comparing non-cooperative cognitive radio (CR) approaches with cooperative methods across multiple sub-bands. Our research led to the development of a sophisticated cooperative wideband spectrum sensing framework that incorporates a K-out-of-N fusion rule at the fusion center to make optimal decisions, selecting an appropriate K for a given number of cooperating CRs. This method aims to combat the noise uncertainty typically affecting traditional non-cooperative energy detection methods in 5G environments under Additive White Gaussian Noise (AWGN) conditions, assumed to be identically and independently distributed (i.i.d). However, our findings indicate that while cooperative sensing significantly improves detection in scenarios with poor signal-to-noise ratios (SNRs) and higher false alarm rates (between 0.5 and 1), it does not consistently outperform non-cooperative methods at very low false alarm rates (0.01 and 0.1). This finding suggests the limited effectiveness of the cooperative sensing method under certain conditions, underscoring the need for further research to optimize these strategies for diverse operational environments. 

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2. THE EFFECTS OF DIVERSITY SCHEMES ON ENHANCINGENERGY DETECTOR-BASED COOPERATIVE WIDEBAND SPECTRUM SENSING IN 5G NETWORKS

   Dike, Blessing C; Akujuobi, Cajetan M; Foreman, Justin; Cui, Suxie; Chouikha, Mohamed (July 2025, CS&IT)

   Wyld, David C; Nagamalai, Dhinaharan (Ed.)

   The proliferation of 5G technologies and the vast deployment of Internet of Things (IoT) devices have heightened the demand for optimal spectrum utilization, necessitating robust spectrum management strategies. In this context, an efficient energy detector employing wideband spectrum sensing within a 5G environment is essential for identifying underutilized frequency bands suitable for cognitive radio applications across multiple subbands. While cooperative spectrum sensing (CSS) can enhance the detection capabilities of energy detectors amidst noise uncertainty, its performance often deteriorates under low signal-to-noise ratio (SNR) conditions. This study proposes an improved CSS framework that combines Maximal Ratio Combining (MRC) with the K-out-of-N fusion rule to address noise uncertainty in a complex Gaussian environment across multiple sub-bands in cooperative wideband spectrum sensing. Comparative performance analysis confirms that this integrated approach enhances detection probability and maintains a low false alarm rate across various low SNR scenarios, significantly outperforming traditional cooperative and non-cooperative wideband spectrum sensing methods. These results highlight the potential for advancing cognitive radio technologies by optimizing detection algorithms to improve performance under challenging conditions. 

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3. An Energy-Detection-Based Cooperative Spectrum Sensing Scheme for Minimizing the Effects of NPEE and RSPF

   https://doi.org/10.1145/2988287.2989169  

   Olaleye, Oladiran G.; Iqbal, Muhammad; Aly, Ahmed; Perkins, Dmitri; Bayoumi, Magdy (January 2016, Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems - MSWiM '16)

   For improved spectrum utilization, the key technique for acquiring spectrum situational awareness (SSA) — spectrum sensing — is greatly improved by cooperation among the active spectrum users, as network size increases. However, the many cooperative spectrum sensing (CSS) schemes that have been proposed are based on the assumptions of accurate noise power estimates, characterizable variation in noise level and absence of false or malicious users. As part of a series of SSA research projects, in this research work, we propose a novel scheme for minimizing the effects of noise power estimation error (NPEE) and received signal power falsification (RSPF) by energy-based reliability evaluation. The scheme adopts the Voting rule for fusing multiple spectrum sensing data. Based on simulation results, the proposed scheme yields significant improvement, 68.2—88.8%, over the conventional CSS schemes, when compared on the basis of the schemes’ stability to uncertainties in noise and signal power. 

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4. Robust AI-Assisted Successive Interference Cancellation for Coexistence of RF Sensors and 5G Signals in Shared Spectrum

   https://doi.org/10.1109/DySPAN64764.2025.11115917  

   Mohammadi, Hossein; Faisal, Fahad; Marojevic, Vuk (May 2025, IEEE International Symposium on Dynamic Spectrum Access Networks)

   The coexistence of active 5G communication signals and passive sensors in shared spectrum environments presents significant challenges due to radio frequency interference (RFI). This paper introduces a novel two-stage successive interference cancellation (SIC) architecture leveraging artificial intelligence (AI) to mitigate interference and preserve the integrity of passive sensing. The first stage employs a deep multilayer perceptron (D-MLP) trained with the Levenberg-Marquardt (LM) algorithm to reconstruct dominant active signals. The second stage, powered by a Bayesian Regularization (BR)-trained D-MLP, addresses residual non-linear and weak interference. Together, these stages achieve superior interference cancellation, ensuring robust separation of active and passive signals. The proposed architecture is evaluated in scenarios with varying interference complexities, including single and multiple active sources. Results demonstrate that the AI-assisted SIC framework significantly outperforms conventional methods, effectively reconstructing and removing 5G signals even under challenging conditions, such as low signal-to-noise ratios. The system also showcases adaptability, maintaining high performance when trained on one gain level and tested on another. This research advances the field by providing a scalable and robust solution for enabling reliable spectrum coexistence, particularly for Earth observation and environmental monitoring. 

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5. A Machine Learning Approach for Detecting and Classifying Jamming Attacks Against OFDM-based UAVs

   https://doi.org/10.1145/3468218.3469049  

   Pawlak, Jered; Li, Yuchen; Price, Joshua; Wright, Matthew; Al Shamaileh, Khair; Niyaz, Quamar; Devabhaktuni, Vijay (June 2021, Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning)

   null (Ed.)

   In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks on unmanned aerial vehicles (UAVs). Four attack types are implemented using software-defined radio (SDR); namely, barrage, single-tone, successive-pulse, and protocol-aware jamming. Each type is launched against a drone that uses orthogonal frequency division multiplexing (OFDM) communication to qualitatively analyze its impacts considering jamming range, complexity, and severity. Then, an SDR is utilized in proximity to the drone and in systematic testing scenarios to record the radiometric parameters before and after each attack is launched. Signal-to-noise ratio (SNR), energy threshold, and several OFDM parameters are exploited as features and fed to six ML algorithms to explore and enable autonomous jamming detection/classification. The algorithms are quantitatively evaluated with metrics including detection and false alarm rates to evaluate the received signals and facilitate efficient decision-making for improved reception integrity and reliability. The resulting ML approach detects and classifies jamming with an accuracy of 92.2% and a false-alarm rate of 1.35%. 

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