5,943 research outputs found
Dynamic Geospatial Spectrum Modelling: Taxonomy, Options and Consequences
Much of the research in Dynamic Spectrum Access (DSA) has focused on opportunistic access in the temporal domain. While this has been quite useful in establishing the technical feasibility of DSA systems, it has missed large sections of the overall DSA problem space. In this paper, we argue that the spatio-temporal operating context of specific environments matters to the selection of the appropriate technology for learning context information. We identify twelve potential operating environments and compare four context awareness approaches (on-board sensing, databases, sensor networks, and cooperative sharing) for these environments. Since our point of view is overall system cost and efficiency, this analysis has utility for those regulators whose objectives are reducing system costs and enhancing system efficiency. We conclude that regulators should pay attention to the operating environment of DSA systems when determining which approaches to context learning to encourage
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
On Myopic Sensing for Multi-Channel Opportunistic Access: Structure, Optimality, and Performance
We consider a multi-channel opportunistic communication system where the
states of these channels evolve as independent and statistically identical
Markov chains (the Gilbert-Elliot channel model). A user chooses one channel to
sense and access in each slot and collects a reward determined by the state of
the chosen channel. The problem is to design a sensing policy for channel
selection to maximize the average reward, which can be formulated as a
multi-arm restless bandit process. In this paper, we study the structure,
optimality, and performance of the myopic sensing policy. We show that the
myopic sensing policy has a simple robust structure that reduces channel
selection to a round-robin procedure and obviates the need for knowing the
channel transition probabilities. The optimality of this simple policy is
established for the two-channel case and conjectured for the general case based
on numerical results. The performance of the myopic sensing policy is analyzed,
which, based on the optimality of myopic sensing, characterizes the maximum
throughput of a multi-channel opportunistic communication system and its
scaling behavior with respect to the number of channels. These results apply to
cognitive radio networks, opportunistic transmission in fading environments,
and resource-constrained jamming and anti-jamming.Comment: To appear in IEEE Transactions on Wireless Communications. This is a
revised versio
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