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 air jordan retro 11 low ie basketball shoes (TDMA) 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 mac miller kids poster weighted end-to-end packet delay when the weights are determined according to the priorities of the requests.A 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.