PROJECT TITLE :
Deadline-Aware Scheduling With Adaptive Network Coding for Real-Time Traffic
We study deadline-aware scheduling with adaptive network coding (NC) for real-time traffic over a single-hop wireless network. To meet exhausting deadlines of real-time traffic, the block size for NC is customized based on the remaining time to the deadline so as to strike a balance between maximizing the throughput and minimizing the danger that the whole block of coded packets could not be decodable by the deadline. This sequential block size adaptation downside is then solid as a finite-horizon Markov decision method. One attention-grabbing finding is that the optimal block size and its corresponding action house monotonically decrease as the deadline approaches, and that the optimal block size is bounded by the “greedy” block size. These unique structures build it attainable to significantly slender down the search space of dynamic programming, building on which we have a tendency to develop a monotonicity-based mostly backward induction algorithm (MBIA) that may notice the optimal block size in polynomial time. Furthermore, a joint real-time scheduling and channel learning scheme with adaptive NC is developed to adapt to channel dynamics in an exceedingly mobile network surroundings. Then, we tend to generalize the analysis to multiple flows with laborious deadlines and long-term delivery ratio constraints. We tend to devise a coffee-complexity online scheduling algorithm integrated with the MBIA, and then establish its asymptotical utility optimality. The analysis and simulation results are corroborated by high-fidelity wireless emulation tests, where actual radio transmissions over emulated channels are performed to demonstrate the feasibility of the MBIA in finding the optimal block size in real time.
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