3,047 research outputs found
Adaptive Network Coding for Scheduling Real-time Traffic with Hard Deadlines
We study adaptive network coding (NC) for scheduling real-time traffic over a
single-hop wireless network. To meet the hard deadlines of real-time traffic,
it is critical to strike a balance between maximizing the throughput and
minimizing the risk that the entire block of coded packets may not be decodable
by the deadline. Thus motivated, we explore adaptive NC, where the block size
is adapted based on the remaining time to the deadline, by casting this
sequential block size adaptation problem as a finite-horizon Markov decision
process. One interesting finding is that the optimal block size and its
corresponding action space monotonically decrease as the deadline approaches,
and the optimal block size is bounded by the "greedy" block size. These unique
structures make it possible to narrow down the search space of dynamic
programming, building on which we develop a monotonicity-based backward
induction algorithm (MBIA) that can solve for the optimal block size in
polynomial time. Since channel erasure probabilities would be time-varying in a
mobile network, we further develop a joint real-time scheduling and channel
learning scheme with adaptive NC that can adapt to channel dynamics. We also
generalize the analysis to multiple flows with hard deadlines and long-term
delivery ratio constraints, devise a low-complexity online scheduling algorithm
integrated with the MBIA, and then establish its asymptotical
throughput-optimality. In addition to analysis and simulation results, we
perform high fidelity wireless emulation tests with real radio transmissions to
demonstrate the feasibility of the MBIA in finding the optimal block size in
real time.Comment: 11 pages, 13 figure
An Examination of the Benefits of Scalable TTI for Heterogeneous Traffic Management in 5G Networks
The rapid growth in the number and variety of connected devices requires 5G
wireless systems to cope with a very heterogeneous traffic mix. As a
consequence, the use of a fixed TTI during transmission is not necessarily the
most efficacious method when heterogeneous traffic types need to be
simultaneously serviced.This work analyzes the benefits of scheduling based on
exploiting scalable TTI, where the channel assignment and the TTI duration are
adapted to the deadlines and requirements of different services. We formulate
an optimization problem by taking individual service requirements into
consideration. We then prove that the optimization problem is NP-hard and
provide a heuristic algorithm, which provides an effective solution to the
problem. Numerical results show that our proposed algorithm is capable of
finding near-optimal solutions to meet the latency requirements of mission
critical communication services, while providing a good throughput performance
for mobile broadband services.Comment: RAWNET Workshop, WiOpt 201
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