Railway Cognitive Radio

Amanna, Ashwin; Gadhiok, Manik; Price, Matthew J.; Reed, Jeffrey H.; Siriwongpairat, W. Pam; Himsoon, T.

doi:10.1109/mvt.2010.938275

Cited by 31 publications

(17 citation statements)

References 10 publications

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“…The initial population is created either from random generation, or from the output of other modules of a cognitive engine, such as a case based reasoner [4].…”

Section: Ga Use In Crmentioning

confidence: 99%

“…CBR is founded on the belief that solutions to new situations can be identified based on solutions used in similar situations in the past. This is discussed in more detail in [22][23][24]. The decision process utilized in this article first attempts to utilize the CBR.…”

Section: Ce Architecturementioning

confidence: 99%

“…DOE requires testing of all the maximum and minimum values of each combination of parameters, while RSM requires the addition of nominal values. For example, a four-variable configuration with three levels each requires 3 4 = 81 individual test cases. Each test case will require at least two runs to determine variance, increasing the minimum required runs to 162.…”

Section: Introductionmentioning

confidence: 99%

“…Experimenters have utilized OAs since the 1940s; these are based on statistical designs that yield sufficient knowledge to determine a favorable parameter setting with a limited number of experimental runs [3]. The Taguchi method is implemented on the GA module within a CE designed around a railway application for transmission of packet data [4]. This CE utilizes a hybrid architecture of case-based reasoning (CBR) decision making with GA-based optimization.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Cognitive radio engine parametric optimization utilizing Taguchi analysis

Amanna

Ali

Gadhiok

et al. 2012

J Wireless Com Network

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Cognitive radio (CR) engines often contain multiple system parameters that require careful tuning to obtain favorable overall performance. This aspect is a crucial element in the design cycle yet is often addressed with ad hoc methods. Efficient methodologies are required in order to make the best use of limited manpower, resources, and time. Statistical methods for approaching parameter tuning exist that provide formalized processes to avoid inefficient ad hoc methods. These methods also apply toward overall system performance testing. This article explores the use of the Taguchi method and orthogonal testing arrays as a tool for identifying favorable genetic algorithm (GA) parameter settings utilized within a hybrid case base reasoning/genetic algorithm CR engine realized in simulation. This method utilizes a small number of test cases compared to traditional design of experiments that rely on full factorial combinations of system parameters. Background on the Taguchi method, its drawbacks and limitations, past efforts in GA parameter tuning, and the use of GA within CR are overviewed. Multiple CR metrics are aggregated into a single figure-of-merit for quantification of performance. Desirability functions are utilized as a tool for identifying ideal settings from multiple responses. Kiviat graphs visualize overall CR performance. The Taguchi method analysis yields a predicted best combination of GA parameters from nine test cases. A confirmation experiment utilizing the predicted best settings is compared against the predicted mean, and desirability. Results show that the predicted performance falls within 1.5% of the confirmation experiment based on 9 test cases as opposed to the 81 test cases required for a full factorial design of experiments analysis.

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“…The initial population is created either from random generation, or from the output of other modules of a cognitive engine, such as a case based reasoner [4].…”

Section: Ga Use In Crmentioning

confidence: 99%

Section: Ce Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cognitive radio engine parametric optimization utilizing Taguchi analysis

Amanna

Ali

Gadhiok

et al. 2012

J Wireless Com Network

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show abstract

“…Intelligent transport systems (ITS) are advanced applications which, without embodying intelligence as such, aim to provide innovative services relating to different modes of transport and traffic management and enable various users to be better informed and make safer, more coordinated, and 'smarter' use of transport networks [6], [7]. Interest in ITS comes from the problems caused by traffic congestion and a synergy of new information technology for simulation, real-time control, and communications networks.…”

Section: Introductionmentioning

confidence: 99%

Train Control System for Efficient Track Usage and Reduce Road Traffic

Maharaja¹

2013

IOSR-JCE

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Automatic Train Control (ATC) is the term for a general class of train protection systems for railways that involves some sort of speed control mechanism in response to external inputs. ATC systems continuously monitor all movements of the trains on lines and at stations and provide safe signaling. A large number of automatic functions support the high speeds possible and enabling the line and network resources to be utilized to maximum capacity. ATC use to schedule multiple trains between two stations with small meter gap between adjutant trains. So the railway department can run more train by using the same track without accident problem. Population explosion has resulted in a series of problems, such as traffic jam, environment pollution, and energy crisis. This project focus on channel estimation errors and the tradeoff between MIMO multiplexing gain and diversity gain in making handoff decisions. The handoff problem is formulated as a partially observable Markov decision process (POMDP), and the optimal handoff policy can be derived to minimize the handoff latency.

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