Asynchronous iterative water filling for cognitive smart grid communications

Asynchronous iterative water filling (AIWF), which is based on Shannon Theory, distributes resources fairly among users. Unlike Game Theory that requires users to exchange packets, AIWF does not require users to exchange packets for achieving Nash equilibrium. Conventionally, AIWF computes optimal transmit power for multiple users in Gaussian Wireless Channel. In this paper, AIWF was implemented upon cognitive radio for smart grid communications. Smart grid data in Advanced Metering Infrastructure (AMI) can be as large as 1000 kbps or 500 kbps for backhaul. Locating a block of spectrum which is available at all smart gird areas for AMI communication is infeasible. Although cognitive radio allows users to detect and utilize idle channels in licensed and unlicensed spectrum bands, the ability of cognitive radio to support high traffic load in AMI is a concern. Hence, AIWF was implemented to maximize the throughput performance in to order to cope with high traffic load environments. AIWF perform repetitive calculations to determine the transmit power of each user. Too low a transmit power causes unsuccessful transmission; while too high a transmit power increases delivery ratio, but it also causes interference to neighboring users. Using AIWF, interference from neighboring nodes are treated as noise, thus, no control packet exchange is required. AIWF coding was implemented in NS-2 simulator. An AMI communication scenario was simulated. The results show that AIWF improves the throughput performance by 20.35%.