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In this paper, we propose multiple antennas-based energy detector utilising adaptive double-threshold for spectrum sensing, which enhances detection performance and overcomes sensing failure problem as well. The detection threshold is made adaptive to the fluctuation of the received signal power in each local detector of cognitive radio (CR) user. Numerical results show that by using multiple antennas at the CRs, it is possible to significantly improve detection performance at very low signal-to-noise ratio (SNR). It is also found that when the number of antennas (Nr) increase, the probability of detection increases. Proposed work having Nr = 3 outperforms Nr = 1, 2 by 30.6 % and 10.5% at –12 dB SNR and probability of false alarm 0.1, respectively.

In this paper, an artificial neural network (ANN) design technique for a stub microstrip band-pass filter is presented. Essential dimensions of the microstrip filter layout are used to get the relationship of the input-outputs for ANN model. This paper presents the design and analysis of stub microstrip band-pass filter at mid-band frequency 1.8 GHz, which produced improved bandwidth and minimum insertion loss of –0.5899 dB and return loss of –36.67 dB. Artificial neural network architecture has been proposed to determine the magnitude variation of scattering parameters (S-parameters) of the microstrip band-pass filters for various dimensions. The ANN model produced has been exposed to be as exact and veracious as an EM simulator and it is computationally more effective in the design. The simulation is performed using the commercial software IE3D 14.1 and ANN training of S-parameters are performed in MATLAB 7.1.