Low Complexity Modified Viterbi Decoder with Convolution Codes for Power Efficient Wireless Communication

To attain high quality of service (QoS) with efficient power consumption with minimum delay through Wireless Local Area Network (WLAN) through mesh network is an important research area. But the existing real-time routing system involves multiple hops with time varying mobility channels for fastest data propagation is greatly degraded with power utilization factor through congestion traffic queue. Required allocation and resource management through desired access points plays vital roles in which multiple hops demands delay rates by interconnected data nodes. In order to achieve high throughput with minimum delay the QoS in real-time data communication have to be concentrated by using Viterbi decoder with convolution codes. By undertaking IEEE 802.11 WLAN physical layers afford multiple transmission rates by engaging various modulations and channel coding schemes, major point arises to pinpoint the desired transmission rate to enhance the performance. Because each node exhibits different dynamic characteristics based on the token rings passed from the server to the end links. In order to validate the real-time traffic with power consumption and average delay communication, an improved Viterbi decoder is designed with convolution codes to determine accurate channel estimation based on learning the utilization ration of the needed to execute the current wireless channel optimization. The proposed methodology can attain accurate channel estimation without additional implementation effort and modifications to the current 802.11 standard. And each node is capable to choose the optimized transmission rate, so that the system performance can be improved with very minimum power with high packet transmission ratio with minimum traffic rate to improve QoS. The proposed scheme also offers an appealing combination of the allocation of transmission rate and the current link condition. Based on the basic relationship between them, the proposed decoding scheme maximizes the throughput with periodic learning of channel variation and system status.