In wireless communications, energy requirements are an important issue. Wireless sensor service life significantly impacts the reliability and efficiency of wireless networks. Important progress in wireless networks self-maintaining their lifecycles has been made by extracting energy from ambient radio frequency ( RF) signals. Motivated by this and enhanced spectrum reuse by the combined use of cognitive radio network (CRN) overlay / underlay modes, this paper proposes a novel multi-channel (m-channel) efficiency maximisation algorithm for low-power mobile allocation. CRNs can harvest energy from RF signals via nearby active primary transmitters (PTs), called secondary transmitters (STs). PTs and STs are distributed as autonomous homogeneous Poisson point processes in the proposed scheme and contact their receivers at fixed distances. Each PT includes a guard zone to protect its intended receiver against ST interference and provides STs located in its harvesting zone with RF energy. With the rapid growth in use of both wireless devices and wireless networks, bandwidth demand has risen. Prioritization of STs during opportunistic channel allocation is important as properties such as energy level and capacity for harvesting enhance the efficiency of channel delivery. A new metric that prioritises STs based on initial energy levels, the capacity to harvest, and the number of channels from which they can be transmitted is proposed. Three algorithms were considered for comparison: the greedy mechanism for the non-harvesting m-channel allocation of hybrid CRNs, the proposed maximum independent sets (MIS) m-channel allocation schemes, and the proposed metric of hybrid CRNs with harvesting power. The simulations demonstrate that the suggested MIS-based m-channel allocation approach outperforms the greedy algorithm. Using the proposed metric on hybrid CRNs with energy harvesting efficiency, the suggested m-channel allocation provided the best performance of the three methods, proving the superiority of the proposed algorithm. The findings show that the proposed process and the proposed metric are superior.
Hakan Murat Karaca
Department of Computer Engineering, Celal Bayar University, Muradiye-Manisa, Turkey.
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