Using learning automata for adaptively adjusting the downlink-to-uplink ratio in IEEE 802.16e wireless networks

Sarigiannidis, Antonios; Nicopolitidis, Petros; Papadimitriou, Georgios I.; Sarigiannidis, Panagiotis; Louta, Malamati

doi:10.1109/iscc.2011.5983863

Cited by 13 publications

(18 citation statements)

References 9 publications

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“…A paradigm of developing traffic engineering using LA can be found in [10]. Lastly, in [11] the BaseStation (BS) of a WiMAX access network is strengthened by utilizing a LA in order to improve the bandwidth allocation process.…”

Section: Related Workmentioning

confidence: 99%

Predicting multimedia traffic in wireless networks: A performance evaluation of cognitive techniques

Sarigiannidis

Aproikidis

Louta

et al. 2014

IISA 2014, the 5th International Conference on Information, Intelligence, Systems and Applications

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Abstract-Traffic engineering in networking is defined as the process that incorporates sophisticated methods in order to ensure optimization and high network performance. One of the most constructive tools employed by the traffic engineering concept is the traffic prediction. Having in mind the heterogeneous traffic patterns originated by various modern services and network platforms, the need of a robust, cognitive, and error-free prediction technique becomes even more pressing. This work focuses on the prediction concept as an autonomous, functional, and efficient process, where multiple cutting-edge methods are presented, modeled, and thoroughly assessed. To this purpose, real traffic traces have been captured, including multiple multimedia traffic flows, so as to comparatively assess widely used methods in terms of accuracy.

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Section: Related Workmentioning

confidence: 99%

Predicting multimedia traffic in wireless networks: A performance evaluation of cognitive techniques

Sarigiannidis

Aproikidis

Louta

et al. 2014

IISA 2014, the 5th International Conference on Information, Intelligence, Systems and Applications

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“…Aimed to minimize the waste of utilized bandwidth, a group of researches in [16] proposed a method that dynamically adjusts the downlink-to-uplink ratio. Dynamic Ratio Determination (DRD) monitors the mapping operation of both downlink and uplink sub-frames, and while considering load balance it receives feedback from both processes and proceeds to the appropriate selection of the forthcoming downlink-to-uplink ratio.…”

Section: Related Research and Proposed Solutions For Bandwidth Utilizmentioning

confidence: 99%

Resource Request Mapping Techniques for OFDMA Networks

Basholli¹,

Λάγκας²

2014

Resource Management in Mobile Computing Environments

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Wireless broadband networks are designed to provide high quality services to multiple mobile users simultaneously. The IEEE 802.16e Mobile WiMAX standard uses Orthogonally Frequency Division Multiple Access (OFDMA) schema for frame structure. OFDMA defines rectangular resource allocation of time slots and frequency carriers, separating in this way the channel into multiple subcarriers. This structure is used for arranging the incoming user requests. Aimed to increase the bandwidth utilization, while arranging the incoming user requests into an effective way, we have considered and evaluated Bin-Packing algorithms. Subsequently in the following sections are presented analysis and design of various Bin packing algorithms developed in our simulator. Moreover, a combined algorithm is proposed, named Guillotine First Fit Algorithm and a new version of the Shelf First Fit Algorithm. Simulation's results concerning performance of implemented algorithms in different input values are gathered, analyzed and compared. The results are encouraging and provide indications regarding usage of the proposed algorithms in practice.

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“…computing and adaptable computing have put more stress on existing communication infrastructure [1][2][3][4][5][6][7]. Intelligent and real-time data acquisition is must for providing pervasive connectivity based applications.…”

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confidence: 99%

“…The effective deployment of pervasive connectivity based applications such as smart home, smart grid, smart surveillance, smart transport, smart farming and smart care, etc. depends heavily on data acquisition system and communication systems [1][2][3][4][5][6][7].…”

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confidence: 99%

“…The computing cost could be identified as processing time, overhead in data reading as well as in data writing, delay due to task switching and idle CPU cycles. The communication cost could be classified in few simpler terms such as overall bandwidth consumption, interference due to noise, the amount of data retransmission and overhead due to the control data such as acknowledgement, retransmission request and cyclic redundancy codes [2][3][4][5][6][7]. Finally, the capital cost could be categorized as the cost of hardware, software, implementation and maintenance.…”

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confidence: 99%

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Learning Automata-Based Cross Layer Framework with Context Awareness for Wireless Systems

Krishna¹,

Misra²,

Sivanesan³

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

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Due to the evolutionary nature of computing and communication technologies, smartness and intelligence have become inevitable requirements for all futuristic systems. How to network these futuristic systems and make them deliver their services effectively is a major challenge. This challenge could be achieved by using wireless communication technologies. But, these smart systems need to be interconnected in seamless manner with facilities such as dynamic connection and disconnection, re-configurability, self-configurability, bandwidth optimization, etc. The existing wireless communication provides few of these facilities for the interconnection of generic computing devices such as server computers, desktop computers and laptop computers. A smart system highly depends on the individual real-time data monitoring for their effective performance. Hence, existing wireless communication technologies need to be customized for heterogeneous, event-driven and proactive smart systems. Hence this paper proposes a learning automata based cross layer framework for wireless networks using context awareness which aids in the reduction of energy consumption and better management of resources. The proposed technique has been tested on a simulated wireless multimedia network.

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Using learning automata for adaptively adjusting the downlink-to-uplink ratio in IEEE 802.16e wireless networks

Cited by 13 publications

References 9 publications

Predicting multimedia traffic in wireless networks: A performance evaluation of cognitive techniques

Predicting multimedia traffic in wireless networks: A performance evaluation of cognitive techniques

Resource Request Mapping Techniques for OFDMA Networks

Learning Automata-Based Cross Layer Framework with Context Awareness for Wireless Systems

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