SPECTRUM SENSING WITH ENERGY DETECTION TECHNIQUES FOR COGNITIVE RADIO SYSTEM

A cognitive radio by virtue of its ability to sense and adapt to the dynamic spectrum scenario, can increase the spectral efficiency .In order to be non invasive, a cognitive radio must adhere to strict benchmark in the quality of spectrum sensing for primary users of a band.Thus, spectrum sensing has a major role to play in cognitive radio .Many algorithms have been proposed to enable spectrum sensing such as energy detection and cyclostationary detection. IEEE 802.22, the first standard for cognitive radio devices, imposes strict requirements for the detection and false alarm probability on all spectrum sensing devices at SNR up to -20 dB. This requires use of robust spectrum sensing techniques. Energy detection is the simplest and near optimum technique that is widely used for spectrum sensing. However its performance is drastically affected by uncertainty in noise variance due to SNR wall [1]. Cyclostationary detection can exploit detection spectral correlation present in most modulated signals to reliably detect signal even at low SNR .All general QAM signals exhibit distinct cyclic frequency depending on their carrier frequency, baud rate etc. which can help to distinguish between the SOI (signal of interest) and interference. The filter structures for optimum MSME estimation of cyclostationary signals are frequency shift (FRESH) filters. The theory of cyclic Wiener filtering theory, developed by Gardner [2], forms the basis of LCL (Linear Conjugate Linear) filtering used in fresh filters. By adding appropriately frequency shifted versions of a cyclostationary signals ,fresh filter can provide significant gains for cyclostationary detection .Hence is it intuitive to apply FRESH filter for spectrum sensing in the cognitive radio context . For upcoming wireless standard like WI-MAX and LTE, OFDM (Orthogonal Frequency Division Multiplexing) is used because of the advantage of multicarrier transmission . Spectrum sensing for OFDM signals is specially challenging due to the cancellation of cyclostationary features and efficient detection algorithm for OFDM need to be developed.