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PROJECT TITLE :

Large-Scale Multi-Cluster MIMO Approach for Cognitive Radio Sensor Networks

ABSTRACT:

This paper proposes a massive-scale cooperative multiple-input multiple-output (CMIMO) beamforming scheme for uplink (UL) access in broadband cognitive radio wireless sensor networks (CR-WSNs) sharing the same spectrum with a primary network and employing orthogonal frequency-division multiplexing. The CR-WSN is divided into clusters every consisting of cooperative nodes that form a virtual antenna array. Using particle swarm optimization (PSO), every cluster seeks the optimal transmit weight vectors that maximize the UL channel capacity of each cluster, whereas controlling the interference levels to the first network. Below the belief of terribly large range of sensor nodes at each cluster, semi-analytic expressions for the image error rate and the ergodic channel capacity of the CMIMO-based mostly CR-WSN are derived and validated with Monte-Carlo simulation. The PSO-based mostly capacity-aware (PSO-CA) scheme is compared with the one based mostly on the traditional gradient search theme (GS-CA) and also the results show that PSO-CA needs considerably less computational complexity while achieving basically the same level of performance because the GS-CA.


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