دانلود رایگان مقاله خوشه بندی هسته فازی c-means در تشخیص انرژی بر اساس سنجش طیف تعاونی

Abstract

Cooperation in spectral sensing (SS) offers a fast and reliable detection of primary user (PU) transmission over a frequency spectrum at the expense of increased energy consumption. Since the fusion center (FC) has to handle a large set of data, a cluster based approach, specifically fuzzy c-means clustering (FCM), has been extensively used in energy detection based cooperative spectrum sensing (CSS). However, the performance of FCM degrades at low signal-to-noise ratios (SNR) and in the presence of multiple PUs as energy data patterns at the FC are often found to be non-spherical i.e. overlapping. To address the problem, this work explores the scope of kernel fuzzy c-means (KFCM) on energy detection based CSS through the projection of non-linear input data to a high dimensional feature space. Extensive simulation results are shown to highlight the improved detection of multiple PUs at low SNR with low energy consumption. An improvement in the detection probability by ∼6.78% and ∼6.96% at −15 dBW and −20 dBW, respectively, is achieved over the existing FCM method.

5. Conclusions and scope of future works

The proposed work shows the efficacy of KFCM over FCM on an energy detection based CSS scheme at low SNR and multiple PU detection. For a single PU, it is observed that the proposed KFCM method offers detection probability value above 0.9 at false alarm probability 0.3 when PU power is at −10 dBW. It is also observed that the KFCM based CSS offers a higher detection probability when the number of relays (L) and the number of samples (N) are relatively less compared to the existing FCM based and analytic methods. For multiple PU detection, it is observed that the KFCM based method offers, on average, ∼0.86 individual detection probability at a very low PU power ∼−16 dB and it is also noted that average energy consumption is reduced by ∼75.02% through SUs clustering. The proposed KFCM based CSS is not only energy efficient but also provides faster sensing compared to optimal FCM which in turn increases the data transmission duration. The KFCM algorithm improves the energy consumption (Es) minimization by ∼1.18% over the optimal FCM, while meeting the same detection probability ∼0.90 and false alarm probability ∼0.05. Some of the future works may be as follows: • The proposed work may be extended in the joint SS and data transmission framework to evaluate the gain in energy minimization and throughput improvement over the FCM based method. • Similar to [40], the proposed work may be extended as an energy minimization problem under the constraints of detection reliability for an individual PU.