Optimization of Detection Error Rate in Cooperative Sensing using ACO algorithm

Divyesh R. Keraliya, Balvant Makwana, Pankaj P. Prajapati

Abstract


Cognitive radio (CR) is the next generation communication technology that combined the use of radio technology and networking technology. One of the key elements of cognitive radio is Cooperative spectrum sensing which sensing results from a different node are combined either through hard decision fusion (HDF) scheme or through soft decision fusion(SDF) scheme at fusion center (FC). SDF has excellent performance, but a lot of overhead is required while HDF requires only one bit of overhead, but has the worst performance. There is a trade-off between overhead and accuracy in this conventional scheme. In this paper, ant colony optimization (ACO) based hybrid cooperative sensing framework is proposed which optimizes the weighting coefficient vector of sensing result from a different node. The novelty of this paper is to use the ACO algorithm as significant tools that evaluate the optimal values of sensing weight for cooperative sensing so that it minimizes the overall cooperative sensing error under min-max criteria. The performance of the proposed ACO based framework is thoroughly analyzed and compared with conventional HDF approaches i.e. AND, OR, majority as well as conventional SDF based approaches like equal gain combing (EGC), MRC, etc., through simulation. The experimental result shows the proposed framework outperforms with the conventional HDF scheme and it has a low overhead requirement compared to the conventional SDF scheme. Finally, analytical evaluation and validation for the performance of ACO algorithm in this framework is also examined and it gives the excellent convergence performance with lower computation time and less complexity which meet the real-time requirement of cooperative spectrum sensing.


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