4,957 research outputs found
Maximum Multipath Routing Throughput in Multirate Wireless Mesh Networks
In this paper, we consider the problem of finding the maximum routing
throughput between any pair of nodes in an arbitrary multirate wireless mesh
network (WMN) using multiple paths. Multipath routing is an efficient technique
to maximize routing throughput in WMN, however maximizing multipath routing
throughput is a NP-complete problem due to the shared medium for
electromagnetic wave transmission in wireless channel, inducing collision-free
scheduling as part of the optimization problem. In this work, we first provide
problem formulation that incorporates collision-free schedule, and then based
on this formulation we design an algorithm with search pruning that jointly
optimizes paths and transmission schedule. Though suboptimal, compared to the
known optimal single path flow, we demonstrate that an efficient multipath
routing scheme can increase the routing throughput by up to 100% for simple
WMNs.Comment: This paper has been accepted for publication in IEEE 80th Vehicular
Technology Conference, VTC-Fall 201
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
Joint Coding and Scheduling Optimization in Wireless Systems with Varying Delay Sensitivities
Throughput and per-packet delay can present strong trade-offs that are
important in the cases of delay sensitive applications.We investigate such
trade-offs using a random linear network coding scheme for one or more
receivers in single hop wireless packet erasure broadcast channels. We capture
the delay sensitivities across different types of network applications using a
class of delay metrics based on the norms of packet arrival times. With these
delay metrics, we establish a unified framework to characterize the rate and
delay requirements of applications and optimize system parameters. In the
single receiver case, we demonstrate the trade-off between average packet
delay, which we view as the inverse of throughput, and maximum ordered
inter-arrival delay for various system parameters. For a single broadcast
channel with multiple receivers having different delay constraints and feedback
delays, we jointly optimize the coding parameters and time-division scheduling
parameters at the transmitters. We formulate the optimization problem as a
Generalized Geometric Program (GGP). This approach allows the transmitters to
adjust adaptively the coding and scheduling parameters for efficient allocation
of network resources under varying delay constraints. In the case where the
receivers are served by multiple non-interfering wireless broadcast channels,
the same optimization problem is formulated as a Signomial Program, which is
NP-hard in general. We provide approximation methods using successive
formulation of geometric programs and show the convergence of approximations.Comment: 9 pages, 10 figure
Towards Optimal Distributed Node Scheduling in a Multihop Wireless Network through Local Voting
In a multihop wireless network, it is crucial but challenging to schedule
transmissions in an efficient and fair manner. In this paper, a novel
distributed node scheduling algorithm, called Local Voting, is proposed. This
algorithm tries to semi-equalize the load (defined as the ratio of the queue
length over the number of allocated slots) through slot reallocation based on
local information exchange. The algorithm stems from the finding that the
shortest delivery time or delay is obtained when the load is semi-equalized
throughout the network. In addition, we prove that, with Local Voting, the
network system converges asymptotically towards the optimal scheduling.
Moreover, through extensive simulations, the performance of Local Voting is
further investigated in comparison with several representative scheduling
algorithms from the literature. Simulation results show that the proposed
algorithm achieves better performance than the other distributed algorithms in
terms of average delay, maximum delay, and fairness. Despite being distributed,
the performance of Local Voting is also found to be very close to a centralized
algorithm that is deemed to have the optimal performance
Throughput-Optimal Broadcast on Directed Acyclic Graphs
We study the problem of broadcasting packets in wireless networks. At each
time slot, a network controller activates non-interfering links and forwards
packets to all nodes at a common rate; the maximum rate is referred to as the
broadcast capacity of the wireless network. Existing policies achieve the
broadcast capacity by balancing traffic over a set of spanning trees, which are
difficult to maintain in a large and time-varying wireless network. We propose
a new dynamic algorithm that achieves the broadcast capacity when the
underlying network topology is a directed acyclic graph (DAG). This algorithm
utilizes local queue-length information, does not use any global topological
structures such as spanning trees, and uses the idea of in-order packet
delivery to all network nodes. Although the in-order packet delivery constraint
leads to degraded throughput in cyclic graphs, we show that it is throughput
optimal in DAGs and can be exploited to simplify the design and analysis of
optimal algorithms. Our simulation results show that the proposed algorithm has
superior delay performance as compared to tree-based approaches.Comment: To appear in the proceedings of INFOCOM, 201
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