Optimal resource allocation in Cognitive Smart Grid Networks
Jadliwala, Murtuza Shabbir
Kwon, Hyuck M.
Alamatsaz, Navid Reza
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A. Boustani, M. Jadliwala, H. M. Kwon and N. Alamatsaz, "Optimal resource allocation in Cognitive Smart Grid Networks," 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, 2015, pp. 499-506
Taking advantage of information and communication technologies, the power industry is moving towards the next generation power grid, the smart grid. This information-based power grid is expected to change the way electricity is generated, distributed, and transmitted to the consumers by enhancing the reliability, efficiency, sustainability, and economics of the grid. However, due to the high volume and high granularity of the data generated by smart electricity meters, careful planning and management of this communication network is necessary. Given the large scale future deployment of smart grid, utility companies face possible network capacity constraints. Due to this scarcity, an efficient spectrum allocation is often difficult, thus resulting in low overall bandwidth utilization in Smart Grid Networks (SGN). Hence, an efficient utilization of this communication network should be studied. Cognitive Radio Networks (CRN) enable Secondary Users (SU) to coexist with existing network infrastructures. Cognitive Smart Grid Networks (CSGN) use CRN to optimize resource allocation in SGNs. However, efficient utilization of available channel bandwidth by SUs, without interfering with the Primary Users (PU), remains an important open problem in CSGN. In this paper, we focus on CSGN as the Secondary Network (SN), coexisting with a Primary Network, and outlining the applicability of Code Division Multiple Access for overcoming the low Number of SUs (NSU) in SN. We propose a novel resource allocation technique to improve NSU in CSGN by using a specific kind of Orthogonal Chip Sequence (OCS) allocation in spread spectrum communications for SU transmissions. By means of extensive simulations and analysis, we show that our technique improves NSU on SN (or CSGNs) significantly.
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