ADAPTIVE FUSION SCHEME FOR COOPERATIVE SPECTRUM SENSING

This project is design based on the paper "A Novel Adaptive Fusion Scheme for Cooperative Spectrum Sensing".In cognitive radio systems, the accuracy of spectrum sensing depends on the received primary signal strength at the secondary user (SU). In fact, a single node sensing would be compromised if the received signal strength is not high enough to be detected by this node. In this project, we propose a cooperative decision fusion rule based on adaptive linear combiner. The weights which correspond to confidence levels affected to SUs, are determined adaptively using the various algorithms as given below,
a)    NLMS algorithm
b)    KLMS algorithm
c)    LMS algorithm
d)    RLS algorithm
e)    KRLS Algorithm
f)    Kalman Filter
g)    EKF Algorithm
The proposed algorithms combine the SUs decisions with the adaptive confidence levels to track the surrounding environment. Simulation is performed using Matlab software and result are shown. Performance of the proposed approach are evaluated on below cases,
a)    Qd and versus time (K = 10) for (NLMS, LMS, KLMS, RLS, KRLS, Kalman, EKF, OR Rule)
b)     Qf versus time (K = 10). for (NLMS, LMS, KLMS, RLS, KRLS, Kalman, EKF, OR Rule
c)    Qd versus time (K = 10).  for (NLMS, LMS, KLMS, RLS, KRLS, Kalman, EKF, AND Rule.
d)    Qf versus time (K = 10).  for (NLMS, LMS, KLMS, RLS, KRLS, Kalman, EKF, AND Rule.
e)    Qd and versus time (K = 10) for (NLMS, RLS, LMS, KRLS, Kalman, EKF, Majority Rule)
f)    Qf versus time (K = 10). For (NLMS, KLMS, LMS, RLS, KRLS, Kalman, EKF, Majority Rule)
g)    ROC curves (K = 10 and Pf = 0.1).
h)    Received SNR at the 1st SU changes (K = 3 and Pf = 0.1) for all adaptive filter (NLMS, LMS, KLMS, RLS, KRLS, Kalman Filter, EKF Filters)
i)    PU status changes (K = 10 and Pf = 0.1) for all adaptive filter (NLMS, LMS, KLMS, RLS, KRLS, Kalman Filter, EKF Filter)
Reference Paper: A Novel Adaptive Fusion Scheme for Cooperative Spectrum Sensing
Author’s Name: Imen NASR and Sofiane CHERIF
Source: IEEE
Year: 2012

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SIMULATION VIDEO DEMO