2,224 research outputs found
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
Optimal Network Control in Partially-Controllable Networks
The effectiveness of many optimal network control algorithms (e.g.,
BackPressure) relies on the premise that all of the nodes are fully
controllable. However, these algorithms may yield poor performance in a
partially-controllable network where a subset of nodes are uncontrollable and
use some unknown policy. Such a partially-controllable model is of increasing
importance in real-world networked systems such as overlay-underlay networks.
In this paper, we design optimal network control algorithms that can stabilize
a partially-controllable network. We first study the scenario where
uncontrollable nodes use a queue-agnostic policy, and propose a low-complexity
throughput-optimal algorithm, called Tracking-MaxWeight (TMW), which enhances
the original MaxWeight algorithm with an explicit learning of the policy used
by uncontrollable nodes. Next, we investigate the scenario where uncontrollable
nodes use a queue-dependent policy and the problem is formulated as an MDP with
unknown queueing dynamics. We propose a new reinforcement learning algorithm,
called Truncated Upper Confidence Reinforcement Learning (TUCRL), and prove
that TUCRL achieves tunable three-way tradeoffs between throughput, delay and
convergence rate
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
Experimentation with MANETs of Smartphones
Mobile AdHoc NETworks (MANETs) have been identified as a key emerging
technology for scenarios in which IEEE 802.11 or cellular communications are
either infeasible, inefficient, or cost-ineffective. Smartphones are the most
adequate network nodes in many of these scenarios, but it is not
straightforward to build a network with them. We extensively survey existing
possibilities to build applications on top of ad-hoc smartphone networks for
experimentation purposes, and introduce a taxonomy to classify them. We present
AdHocDroid, an Android package that creates an IP-level MANET of (rooted)
Android smartphones, and make it publicly available to the community.
AdHocDroid supports standard TCP/IP applications, providing real smartphone
IEEE 802.11 MANET and the capability to easily change the routing protocol. We
tested our framework on several smartphones and a laptop. We validate the MANET
running off-the-shelf applications, and reporting on experimental performance
evaluation, including network metrics and battery discharge rate.Comment: 6 pages, 7 figures, 1 tabl
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