Benchmarking of OFDM Spectrum Exchange for Mobile Cognitive Radio Networks

Arief Marwanto, Sharifah Kamilah, M. Haikal Satria

Abstract


The local spectrum sensing objective in spectrum sensing is to detect the PU's signal. The sensing node's (SN) capacity to detect the PU's signal is of paramount importance. However, it is presumed to be stationary in the majority of SN in cognitive radio networks. The detection performance on local observation is significantly influenced by the mobility of the PUs and SNs. The SNs' movement generates spatial diversity in the PU's signal observation. The signal's condition would fluctuate during the sensing process as a result of Doppler effect, spatial distance, velocity, movement, and geolocation information. Therefore, a benchmark is required to compare the primary user signal detection level of stationary and moving SNs from each sensing node. The performance results have demonstrated that static nodes with SCM are superior to conventional subcarrier mapping (SCM) methods in the case of a subcarrier mapping width of α = 2. Additionally, the quantization width is uniform. It has been determined that the performance disparity is substantial, ranging from 2 dB to 4 dB. The results indicate that the static nodes SCM have achieved acceptable performance detection at a low subcarrier detection threshold (SDT) value of 0 dB up to 5 dB. Conversely, the probability of conventional SCM detection is less than 1 of probability detection (PD) value at the same low SDT value. The detection probability (PD) of static nodes with SCM is satisfactory at an SDT value of 15 dB. Moreover, the probability begins to decline until 20 dB at an SDT value of 11.5 dB, a substantial decrease that is rendered negligible. In contrast to the new subcarrier mapping (N-SCM) method, which has a false alarm probability (PFA) of approximately 0 dB to 9.5 dB, conventional subcarrier mapping (SCM) has a high false alarm probability in mobility networks. Furthermore, it is evident that the PFA curves for the conventional SCM method are lower than those of other methods at low speeds, as they approach the null value at SDT 7.5 dB. The PFA curve for both methods is higher than other velocities by attaining a null value at 10 dB, in contrast to high velocity. In general, the mobility parameter has the potential to meet the detection performance and perform well in the false alarm probability of mobile spectrum exchange. Consequently, it could be employed to provide information on spectrum exchange in the future.

Keywords


Spectrum Sensing; Cooperative Sensing; Mobility Node; Spatial Diversity; Spectrum Exchange

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