Expectation-Maximization Aided Modified Weighted Sequential Energy Detector for Distributed Cooperative Spectrum Sensing
Energy detector (ED) is a popular choice for distributed cooperative spectrum sensing because it does not need to be cognizant of the primary user (PU) signal characteristics. However, the conventional ED-based sensing usually requires large number of observed samples per energy statistic, particula...
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2025-01-01
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| author | Mohammed Rashid Jeffrey A. Nanzer |
| author_facet | Mohammed Rashid Jeffrey A. Nanzer |
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| description | Energy detector (ED) is a popular choice for distributed cooperative spectrum sensing because it does not need to be cognizant of the primary user (PU) signal characteristics. However, the conventional ED-based sensing usually requires large number of observed samples per energy statistic, particularly at low signal-to-noise ratios (SNRs), for improved detection capability. This is due to the fact that it uses the energy only from the present sensing interval for the PU detection. Previous studies have shown that even with fewer observed samples per energy statistics, improved detection capabilities can be achieved by aggregating both present and past ED samples in a test statistic. Thus, a weighted sequential energy detector (WSED) has been proposed, but it is based on aggregating all the collected ED samples over an observation window. For a highly dynamic PU over the consecutive sensing intervals, that involves also combining the outdated samples in the test statistic that do not correspond to the present state of the PU. In this paper, we propose a modified WSED (mWSED) that uses the primary user states information over the window to aggregate only the highly correlated ED samples in its test statistic. In practice, since the PU states are a priori unknown, we also develop a joint expectation-maximization and Viterbi (EM-Viterbi) algorithm based scheme to iteratively estimate the states by using the ED samples collected over the window. The estimated states are then used in mWSED to compute its test statistics, and the algorithm is referred to here as the EM-mWSED algorithm. Simulation results show that EM-mWSED outperforms other schemes and its performance improves by increasing the average number of neighbors per SU in the network, and by increasing the SNR or the number of samples per energy statistic. |
| format | Article |
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| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-c37c01db94ef456498a1a7e3ef4193f32025-02-12T00:01:49ZengIEEEIEEE Access2169-35362025-01-0113248802489310.1109/ACCESS.2025.353761110869443Expectation-Maximization Aided Modified Weighted Sequential Energy Detector for Distributed Cooperative Spectrum SensingMohammed Rashid0https://orcid.org/0000-0003-4413-3596Jeffrey A. Nanzer1https://orcid.org/0000-0002-8096-6600Department of Electrical and Computer Engineering and Technology, Minnesota State University, Mankato, MN, USADepartment of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USAEnergy detector (ED) is a popular choice for distributed cooperative spectrum sensing because it does not need to be cognizant of the primary user (PU) signal characteristics. However, the conventional ED-based sensing usually requires large number of observed samples per energy statistic, particularly at low signal-to-noise ratios (SNRs), for improved detection capability. This is due to the fact that it uses the energy only from the present sensing interval for the PU detection. Previous studies have shown that even with fewer observed samples per energy statistics, improved detection capabilities can be achieved by aggregating both present and past ED samples in a test statistic. Thus, a weighted sequential energy detector (WSED) has been proposed, but it is based on aggregating all the collected ED samples over an observation window. For a highly dynamic PU over the consecutive sensing intervals, that involves also combining the outdated samples in the test statistic that do not correspond to the present state of the PU. In this paper, we propose a modified WSED (mWSED) that uses the primary user states information over the window to aggregate only the highly correlated ED samples in its test statistic. In practice, since the PU states are a priori unknown, we also develop a joint expectation-maximization and Viterbi (EM-Viterbi) algorithm based scheme to iteratively estimate the states by using the ED samples collected over the window. The estimated states are then used in mWSED to compute its test statistics, and the algorithm is referred to here as the EM-mWSED algorithm. Simulation results show that EM-mWSED outperforms other schemes and its performance improves by increasing the average number of neighbors per SU in the network, and by increasing the SNR or the number of samples per energy statistic.https://ieeexplore.ieee.org/document/10869443/Cognitive radiosdistributed cooperative spectrum sensingdynamic primary userenergy detectorexpectation-maximizationmodified weighted sequential energy detector |
| spellingShingle | Mohammed Rashid Jeffrey A. Nanzer Expectation-Maximization Aided Modified Weighted Sequential Energy Detector for Distributed Cooperative Spectrum Sensing IEEE Access Cognitive radios distributed cooperative spectrum sensing dynamic primary user energy detector expectation-maximization modified weighted sequential energy detector |
| title | Expectation-Maximization Aided Modified Weighted Sequential Energy Detector for Distributed Cooperative Spectrum Sensing |
| title_full | Expectation-Maximization Aided Modified Weighted Sequential Energy Detector for Distributed Cooperative Spectrum Sensing |
| title_fullStr | Expectation-Maximization Aided Modified Weighted Sequential Energy Detector for Distributed Cooperative Spectrum Sensing |
| title_full_unstemmed | Expectation-Maximization Aided Modified Weighted Sequential Energy Detector for Distributed Cooperative Spectrum Sensing |
| title_short | Expectation-Maximization Aided Modified Weighted Sequential Energy Detector for Distributed Cooperative Spectrum Sensing |
| title_sort | expectation maximization aided modified weighted sequential energy detector for distributed cooperative spectrum sensing |
| topic | Cognitive radios distributed cooperative spectrum sensing dynamic primary user energy detector expectation-maximization modified weighted sequential energy detector |
| url | https://ieeexplore.ieee.org/document/10869443/ |
| work_keys_str_mv | AT mohammedrashid expectationmaximizationaidedmodifiedweightedsequentialenergydetectorfordistributedcooperativespectrumsensing AT jeffreyananzer expectationmaximizationaidedmodifiedweightedsequentialenergydetectorfordistributedcooperativespectrumsensing |