Based on the Berlekamp-Massy （BM） algorithm for Reed-Solomon（RS） decoding, an improved version is proposed, which focuses on how to find the error locator polynomial using least iterative operations. The condition...Based on the Berlekamp-Massy （BM） algorithm for Reed-Solomon（RS） decoding, an improved version is proposed, which focuses on how to find the error locator polynomial using least iterative operations. The conditions to end the iterative operations is derived. As a special case, criterion of only one error symbol in one received codeword is derived as well. Steps are listed concerning the implementation of the improved iterative decoding algorithm, which is carried out as software on the platform of TI＇s C6416 DSP. Decoding performance and decoding-delay of both improved and original algorithms under different （n,k） conditions are simulated. The results of simulations demonstrate that the improved algorithm has less computational complexity when the number of errors in a received codeword is relatively small. Therefore, in channels with low noise power spectrum density, the improved algorithm results in less decoding-delay than BM algorithm.展开更多
Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the node...Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).展开更多
基金Sponsored by the National High Technology Research and Development Program of China ("863"Program) (2007AA01Z293)
文摘Based on the Berlekamp-Massy （BM） algorithm for Reed-Solomon（RS） decoding, an improved version is proposed, which focuses on how to find the error locator polynomial using least iterative operations. The conditions to end the iterative operations is derived. As a special case, criterion of only one error symbol in one received codeword is derived as well. Steps are listed concerning the implementation of the improved iterative decoding algorithm, which is carried out as software on the platform of TI＇s C6416 DSP. Decoding performance and decoding-delay of both improved and original algorithms under different （n,k） conditions are simulated. The results of simulations demonstrate that the improved algorithm has less computational complexity when the number of errors in a received codeword is relatively small. Therefore, in channels with low noise power spectrum density, the improved algorithm results in less decoding-delay than BM algorithm.
文摘Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).
