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The next-generation communication,  i.e.  fifth generation (5G), will be manifesting the advertisers in near future. The Device to Device communication would be a proportion of 5G to provide communication requirements for trillions of devices connected in a significant manner to hold immense data rate. Optimization and Machine learning are said to be the most promising mechanisms for furnishing the best solutions to master the predominant aspects and explicit parameters of 5G communication. In this work, a Bio-inspired Conic Optimized and Distributed Latency Q Learning method is proposed for D2D communication in 5G with higher energy efficiency and minimum latency. Initially, a Bio-inspired Conic Particle Swarm Optimization model is to provide resource efficiency and energy efficiency by calculating the fitness function in terms of transmission power and data loss rate through updating the position and velocity to achieve optimized D2D communication. After that, the Distributed Latency Managed Q Learning model is employed for better connectivity with minimal latency which is achieved by two factors. They are SINR function to measure probability factor when choosing corresponding actions and design of reward function assuming neighbor device and communication range. With these two functions, a latency enhanced with minimum data loss is achieved to attain better connectivity for D2D communication. At last, the neighbor device and communication range for corresponding data stream are employed to reduce data loss and latency for D2D communication. The simulation result illustrates that the BCO-DLQL method increases the energy efficiency by 7% and reduces the latency by 24% as compared to state-of-the-art works.

Latency and Energy Efficient Bio-Inspired Conic Optimized and Distributed Q Learning for D2D Communication in 5G