Neural-based downlink scheduling algorithm for broadband wireless networks

Fiengo, Pasquale; Giambene, Giovanni; Trentin, Edmondo

doi:10.1016/j.comcom.2006.08.009

Cited by 20 publications

(11 citation statements)

References 27 publications

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“…Traditionally, several dispatching rules have been developed for scheduling and selection of the most appropriate one for a given circumstance is not obvious, hence Priore et al (2006) evaluate feed forward neural networks, case based reasoning and inductive learning methods that aim to learn to select the most appropriate dispatching method based on past experience. Fiengo et al (2007) take a more direct approach and use reinforcement learning to train a neural network for scheduling multimedia traffic for mobile phone users based on previous examples. Li et al (2009) also take a similar approach in training a feed forward network with the aim of allocating bandwidth optimally.…”

Section: Neural Network In Schedulingmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Neural Network In Schedulingmentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…Fiengo et al proposed an artificial neural network traffic scheduling scheme with multi-objective requirements in the presence of errors over both wireless ATM and WiFi networks. The results obtained in scheduling concomitant voice, video and web traffic show a significant capacity improvement by the scheme as compared with other techniques [6]. Chang and Chen used a scheme that makes an adaptive decision for bandwidth reservation and call admission by employing fuzzy inference mechanism, timing-based reservation strategy and round-borrowing strategy in each base station in multimedia (audio and video) wireless networks.…”

Section: Introductionmentioning

confidence: 99%

An intelligent MPEG rate control in 2.4 GHz frequency

Kazemian

Chantaraskul

2009

Adaptive Control & Signal

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Transmitting Moving Picture Expert Group (MPEG) Variable Bit Rate (VBR) video stream over 2.4 GHz wireless frequency demands a large bandwidth and may result in data loss and delay, because of excessive fluctuation in bit rate. Transmission over 2.4 GHz frequency further causes uncertainty due to interferences by other wireless devices in the same frequency band and/or common dynamic channel noises. As a result, an intelligent system has been developed for MPEG video transmission over 2.4 GHz frequency band. A novel fuzzy controller automatically determines the quantization scale for each Group Of Picture (GOP) of the encoder during the transmission period. Moreover, a fuzzy controller and a neural-fuzzy controller stream the MPEG video data to conform to the current condition of dynamic wireless channel to maintain image quality during the transmission. In the intelligent system, rather than choosing the parameters associated with a given membership function arbitrarily, these parameters are chosen as such to tailor the membership functions to the desired input/output data. The intelligent system computes and trains the system in such a way that its output will be equal to the desired value. Computer simulation results demonstrate that the proposed intelligent technique increases transmission rate and reduces data loss, excessive delay and image quality degradation as compared with a conventional video transmission over 2.4 GHz frequency band.

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“…The QoS control field in the MAC frame format is a 16 bits field which facilitates the description of QoS requirements of application flows. Its TID (4 bits) identifies the TC (0-7) or the TS (8)(9)(10)(11)(12)(13)(14)(15) to which the corresponding MSDU in the FB field belongs. The last eight bits are used usually by QAP to receive the queue size of QSTAs.…”

Section: Qos Comparison Between Wifi and Wimax Mesh Modementioning

confidence: 99%

“…[12] uses fuzzy hopfield neural network technique to solve the TDMA broadcast scheduling problem in wireless sensor networks. Artificial neural network with reinforcement learning has been introduced in [13] to schedule downlink traffic of wireless networks. A genetic algorithm approach is used in [2] to find the schedule related to each link in a WMN.…”

Section: Related Workmentioning

confidence: 99%