547 research outputs found
Free Energy Approximations for CSMA networks
In this paper we study how to estimate the back-off rates in an idealized
CSMA network consisting of links to achieve a given throughput vector using
free energy approximations. More specifically, we introduce the class of
region-based free energy approximations with clique belief and present a closed
form expression for the back-off rates based on the zero gradient points of the
free energy approximation (in terms of the conflict graph, target throughput
vector and counting numbers). Next we introduce the size clique free
energy approximation as a special case and derive an explicit expression for
the counting numbers, as well as a recursion to compute the back-off rates. We
subsequently show that the size clique approximation coincides with a
Kikuchi free energy approximation and prove that it is exact on chordal
conflict graphs when . As a by-product these results provide us
with an explicit expression of a fixed point of the inverse generalized belief
propagation algorithm for CSMA networks. Using numerical experiments we compare
the accuracy of the novel approximation method with existing methods
Throughput Analysis of CSMA Wireless Networks with Finite Offered-load
This paper proposes an approximate method, equivalent access intensity (EAI),
for the throughput analysis of CSMA wireless networks in which links have
finite offered-load and their MAC-layer transmit buffers may be empty from time
to time. Different from prior works that mainly considered the saturated
network, we take into account in our analysis the impacts of empty transmit
buffers on the interactions and dependencies among links in the network that is
more common in practice. It is known that the empty transmit buffer incurs
extra waiting time for a link to compete for the channel airtime usage, since
when it has no packet waiting for transmission, the link will not perform
channel competition. The basic idea behind EAI is that this extra waiting time
can be mapped to an equivalent "longer" backoff countdown time for the
unsaturated link, yielding a lower link access intensity that is defined as the
mean packet transmission time divided by the mean backoff countdown time. That
is, we can compute the "equivalent access intensity" of an unsaturated link to
incorporate the effects of the empty transmit buffer on its behavior of channel
competition. Then, prior saturated ideal CSMA network (ICN) model can be
adopted for link throughput computation. Specifically, we propose an iterative
algorithm, "Compute-and-Compare", to identify which links are unsaturated under
current offered-load and protocol settings, compute their "equivalent access
intensities" and calculate link throughputs. Simulation shows that our
algorithm has high accuracy under various offered-load and protocol settings.
We believe the ability to identify unsaturated links and compute links
throughputs as established in this paper will serve an important first step
toward the design and optimization of general CSMA wireless networks with
offered-load control.Comment: 6 pages. arXiv admin note: text overlap with arXiv:1007.5255 by other
author
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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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