7,591 research outputs found
Optimal Compression and Transmission Rate Control for Node-Lifetime Maximization
We consider a system that is composed of an energy constrained sensor node
and a sink node, and devise optimal data compression and transmission policies
with an objective to prolong the lifetime of the sensor node. While applying
compression before transmission reduces the energy consumption of transmitting
the sensed data, blindly applying too much compression may even exceed the cost
of transmitting raw data, thereby losing its purpose. Hence, it is important to
investigate the trade-off between data compression and transmission energy
costs. In this paper, we study the joint optimal compression-transmission
design in three scenarios which differ in terms of the available channel
information at the sensor node, and cover a wide range of practical situations.
We formulate and solve joint optimization problems aiming to maximize the
lifetime of the sensor node whilst satisfying specific delay and bit error rate
(BER) constraints. Our results show that a jointly optimized
compression-transmission policy achieves significantly longer lifetime (90% to
2000%) as compared to optimizing transmission only without compression.
Importantly, this performance advantage is most profound when the delay
constraint is stringent, which demonstrates its suitability for low latency
communication in future wireless networks.Comment: accepted for publication in IEEE Transactions on Wireless
Communicaiton
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
A Non-Cooperative Game Theoretical Approach For Power Control In Virtual MIMO Wireless Sensor Network
Power management is one of the vital issue in wireless sensor networks, where
the lifetime of the network relies on battery powered nodes. Transmitting at
high power reduces the lifetime of both the nodes and the network. One
efficient way of power management is to control the power at which the nodes
transmit. In this paper, a virtual multiple input multiple output wireless
sensor network (VMIMO-WSN)communication architecture is considered and the
power control of sensor nodes based on the approach of game theory is
formulated. The use of game theory has proliferated, with a broad range of
applications in wireless sensor networking. Approaches from game theory can be
used to optimize node level as well as network wide performance. The game here
is categorized as an incomplete information game, in which the nodes do not
have complete information about the strategies taken by other nodes. For
virtual multiple input multiple output wireless sensor network architecture
considered, the Nash equilibrium is used to decide the optimal power level at
which a node needs to transmit, to maximize its utility. Outcome shows that the
game theoretic approach considered for VMIMO-WSN architecture achieves the best
utility, by consuming less power.Comment: 12 pages, 8 figure
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