Interference mitigation in wideband radios using spectrum correlation and neural network

Toma, Andrea; Nawaz, Tassadaq; Gao, Yue; Regazzoni, Carlo S.

doi:10.1049/iet-com.2018.5720

Cited by 9 publications

(3 citation statements)

References 41 publications

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“…It is widely recognized in the literature for its popularity owing to several advantages. These include ease of implementation and robustness (Koçkaya and Develi, 2020;Toma et al, 2019;Carrick, 2018;Garg and Saluja, 2018;Wang et al, 2018;Gul et al, 2020;Zhao and Zhou, 2022;Latif et al, 2021).…”

Section: Spectrum Allocation Optimization Using Particle Swarm Algorithmmentioning

confidence: 99%

A Spectrum Sensing and Allocation Model for Primary User Detection and Interference Mitigation in Television Whitespaces

Notcker,

Adetiba,

Adetiba

et al. 2024

Journal of Computer Science

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Television White Space (TVWS) emerges as an encouraging solution to address the challenge of a restricted wireless communication spectrum. It denotes the frequency range spanning from 54-790 MHz and researchers have increasingly explored its propagation characteristics in recent years. Nonetheless, a notable hindrance to its effective utilization lies in the interference between primary and secondary users, as well as interference among secondary users themselves. Approaches involving spectrum sensing and resource allocation have been extensively employed independently to tackle these issues, yet they have not been integrated or utilized in combination. Hence, in this study, we formulated an architectural model that combines spectrum sensing and allocation components. This integrated model aims to detect the presence of primary users while simultaneously minimizing interference among secondary users. The spectrum sensing component utilized an energy detection model to identify primary users, mitigating interference with secondary users. Meanwhile, the spectrum allocation component employed the Particle Swarm Optimization (PSO) algorithm to determine the optimal distribution of channels among secondary users. We implemented the architectural model in a simulated TVWS network using MATLAB R2020a. Its performance was then evaluated and compared with that of matched filter and Artificial Bee Colony (ABC) algorithms, which were utilized for spectrum sensing and allocation, respectively. Based on the simulation findings, when the Signal-to-Noise Ratio (SNR) was configured at -10 dB, the detection probability for the energy detection model reached 98.23%, surpassing the matched filter's detection probability of 92.55%. With a false alarm probability of 0.51, the energy detection model exhibited a misdetection probability of 0.13%, outperforming the matched filter which had a higher misdetection probability of 2.61%. In scenarios with 10 channels and 100 secondary users, the particle swarm optimization algorithm attained a maximum throughput of 279.9 Mbps, slightly outperforming the artificial bee colony algorithm, which achieved 278.7 Mbps. In scenarios with 30 channels and 200 secondary users, the particle swarm optimization algorithm achieved throughputs of 1.575 Gbps, whereas the artificial bee colony algorithm achieved a comparable throughput of 1.571 Gbps. In the scenario where the number of channels was set to 50 and users to 300, the particle swarm optimization algorithm achieved a throughput of 3.879 Gbps, slightly surpassing the artificial bee colony algorithm, which achieved 3.864 Gbps. While the designed components consistently outperformed the matched filter and artificial bee colony algorithms across all cases, it's important to note that the model faced limitations. Specifically, it was unable to detect more than one primary user or allocate spectrum for a new incoming secondary user.

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Section: Spectrum Allocation Optimization Using Particle Swarm Algorithmmentioning

confidence: 99%

A Spectrum Sensing and Allocation Model for Primary User Detection and Interference Mitigation in Television Whitespaces

Notcker,

Adetiba,

Adetiba

et al. 2024

Journal of Computer Science

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show abstract

“…On the other hand, CFD not only detects the signals in low-SNR conditions but also has the potential to differential the legitimate signals from interference [14,15]. CFD exploits the built-in periodicity in man-made signals, such as carrier frequencies, and symbol rates, to differentiate between communications signals [16]. It computes the spectral correlation function (SCF) of signals that is, in fact, the Fourier Transform of cyclic auto-correlation function (CAF), and hence it produces different patterns for different communications signals under the consideration [17,18].…”

Section: Introductionmentioning

confidence: 99%

AI-Enabled Compressive Spectrum Classification for Wideband Radios

Nawaz,

Naidu

2023

Technologies

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Cognitive radio is a promising technology that emerged as a potential solution to the spectrum shortage problem by enabling opportunistic spectrum access. In many cases, cognitive radios are required to sense a wide range of frequencies to locate the spectrum white spaces; hence, wideband spectrum comes into play, which is also an essential step in future wireless systems to boost the throughput. Cognitive radios are intelligent devices and therefore can be opted for the development of modern jamming and anti-jamming solutions. To this end, our article introduces a novel AI-enabled energy-efficient and robust technique for wideband radio spectrum characterization. Our work considers a wideband radio spectrum made up of numerous narrowband signals, which could be normal communications or signals disrupted by a stealthy jammer. First, the receiver recovers the wideband from significantly low sub-Nyquist rate samples by exploiting compressive sensing technique to decrease the overhead caused by the high complexity analog-to-digital conversion process. Once the wideband is recovered, each available narrowband signal is given to a cyclostationary feature detector that computes the corresponding spectral correlation function and extracts the feature vectors in the form of cycle and frequency profiles. Then profiles are concatenated and given as input features set to an artificial neural network which in turn classifies each NB signal as legitimate communication with a specific modulation or disrupted by a stealthy jammer. The results show a classification accuracy of about 0.99 is achieved. Moreover, the algorithm highlights significantly high performances in comparison to recently reported spectrum classification techniques. The proposed technique can be used to design anti-jamming systems for military communication systems.

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“…The simple yet commonly adopted method is Neyman-Pearson (NP) energy detection (ED) on each channel's spectrum energy, which determines the channel occupancy in a soft decision manner by setting a proper threshold [24]. Some work in CSS proposes cyclostationary feature extraction on the recovered spectrum and then perform ED [17], [25]. Some prior information on the noise statistics is essential to optimal threshold setting.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Subspace-Aided Compressive Spectrum Sensing Over Wideband Whitespace

Zhang

Gao

2019

IEEE Trans. Veh. Technol.

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Compressive sensing (CS) techniques have been proposed for wideband spectrum sensing applications to achieve sub-Nyquist-rate sampling. The complexity of CS recovery algorithm and the detection performance against noise are two of the main challenges of the implementation of compressive spectrum sensing (CSS). Greedy algorithms have been of particular interest in CSS due to low complexity. We firstly propose a novel spectrum sparsity estimation scheme directly from sub-Nyquist measurements, with which the computational effort of greedy pursuit algorithms can be saved and recovery performance improved. Besides, the spectrum sparsity estimates also enable hard detection of channel occupancy where threshold adaption for energy detection is avoided. Moreover, with the detected dimension of signal subspace, we propose to implement joint-block-sparse multiple-measurementvector (MMV) model of CSS whose dimension can be reduced to minimum and meanwhile a large portion of noise is removed. The proposed MMV model with noise and dimension reduction further improves the detection performance and also keeps the complexity low. Finally, we generalize the hard thresholding pursuit (HTP) algorithm to recover joint-block-sparse signals. In simulations, the detection performance and complexity of the proposed CSS scheme show striking superiority against multiple benchmarking schemes.

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Interference mitigation in wideband radios using spectrum correlation and neural network

Cited by 9 publications

References 41 publications

A Spectrum Sensing and Allocation Model for Primary User Detection and Interference Mitigation in Television Whitespaces

A Spectrum Sensing and Allocation Model for Primary User Detection and Interference Mitigation in Television Whitespaces

AI-Enabled Compressive Spectrum Classification for Wideband Radios

Low-Complexity Subspace-Aided Compressive Spectrum Sensing Over Wideband Whitespace

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