Deep learning method for delay minimization in MANET

Deep learning method for delay minimization in MANET

Blog Article

A transmission delay is a critical metric when dealing with ad hoc networks in 5G, particularly for real-time applications and multimedia.In this paper, we describe the challenge of managing mobile ad-hoc networks (MANET) based on multi-hop time-slotted time-division multiple access (TDMA) google pixel 7 freedom under routing delay minimization with heterogeneous traffic flows.In particular, we consider the challenge of request scheduling and power control in TDMA, for minimizing the overall weighted end-to-end packet delay when the weights are determined according to the priorities of the requests.

A hp s14 delay minimization network that uses deep learning is also introduced (DMNet).Simulations show that DMNet outperforms other state-of-art methods.Our approach is one of the first to utilize a DNN to solve end-to-end delay minimization through scheduling and power control.