9,081 research outputs found
Delay-Optimal Buffer-Aware Probabilistic Scheduling with Adaptive Transmission
Cross-layer scheduling is a promising way to improve Quality of Service (QoS)
given a power constraint. In this paper, we investigate the system with random
data arrival and adaptive transmission. Probabilistic scheduling strategies
aware of the buffer state are applied to generalize conventional deterministic
scheduling. Based on this, the average delay and power consumption are analysed
by Markov reward process. The optimal delay-power tradeoff curve is the Pareto
frontier of the feasible delay-power region. It is proved that the optimal
delay-power tradeoff is piecewise-linear, whose vertices are obtained by
deterministic strategies. Moreover, the corresponding strategies of the optimal
tradeoff curve are threshold-based, hence can be obtained by a proposed
effective algorithm. On the other hand, we formulate a linear programming to
minimize the average delay given a fixed power constraint. By varying the power
constraint, the optimal delay-power tradeoff curve can also be obtained. It is
demonstrated that the algorithm result and the optimization result match each
other, and are further validated by Monte-Carlo simulation.Comment: 6 pages, 4 figures, accepted by IEEE ICCC 201
A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning
In this tutorial paper, a comprehensive survey is given on several major
systematic approaches in dealing with delay-aware control problems, namely the
equivalent rate constraint approach, the Lyapunov stability drift approach and
the approximate Markov Decision Process (MDP) approach using stochastic
learning. These approaches essentially embrace most of the existing literature
regarding delay-aware resource control in wireless systems. They have their
relative pros and cons in terms of performance, complexity and implementation
issues. For each of the approaches, the problem setup, the general solution and
the design methodology are discussed. Applications of these approaches to
delay-aware resource allocation are illustrated with examples in single-hop
wireless networks. Furthermore, recent results regarding delay-aware multi-hop
routing designs in general multi-hop networks are elaborated. Finally, the
delay performance of the various approaches are compared through simulations
using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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