Informatics and Applications
2019, Volume 13, Issue 1, pp 108-116
COMPARATIVE ANALYSIS OF PERFORMANCE MEASURES FOR A WIRELESS MACHINE-TO-MACHINE NETWORK MODEL OPERATING WITHIN TWO RADIO RESOURCE MANAGEMENT POLICIES
- E. V. Markova
- A. A. Golskaia
- I. L. Dzantiev
- I. A. Gudkova
- S. Ya. Shorgin
Abstract
Currently, information and communication technologies (ICT) deeply penetrate into many areas of modern life. For example, the concept of integrating ICT and the Internet of Things for managing Smart City infrastructure allows city authorities to monitor changes and the situation in the city using sensors. Thus, specialized systems collect data automatically without human intervention. An important parameter in determining the performance of wireless networks of machine-to-machine (M2M) interaction - data transfer rates, blocking probabilities, becomes the distance between the device (sensor) and the radio transmitting equipment (base station, BS). Therefore, describing such a network in the form of a queuing system with streaming (guaranteed data transfer rate) or elastic traffic (nonguaranteed speed), it is necessary to consider the incoming stream of requests for data transmission of M2M devices in such a way as to take into account the distance between devices and BS. In this paper, there is built a cell model of a wireless network with stationary M2M devices that are in a passive or active state, shown by points that appear randomly on a plane. The devices generate streaming traffic which depends on the distance from the BS, the device transmit power, and the noise level. The state of the system is described by the vector of variable length, the components of which are the distance of each active device to the BS. Two different disciplines of radio resource separation are considered - "round robin" and "full power," which differ in the distribution of the time interval for servicing an M2M device and the data transfer rate provided. There is built a random process with states enlarged by the number of users and a formula for calculating the probability of blocking a data transfer request is proposed.
[+] References (13)
- Laya, A., L. Alonso, and J. Alonso-Zarate. 2014. Is the random access channel of LTE and LTE-A suitable for M2M communications? A survey of alternatives. lEEECommun. Surv. Tut. 16(1):4-16. doi: 10.1109/ SURV.2013.111313.00244.
- Cisco Visual Networking Index: Forecast and trends, 2017-2022. November 26,2018. White Paper. Available at: https: //www.cisco.com/c/en/us/solutions/collateral/ service-provider/visual-networking-index-vni/white- paper-c11-741490.html (accessed January 15, 2019).
- Future technology trends of terrestrial IMT systems. November 2014. ITU-R Report M.2320. Available at: https://www.itu.int/pub/R-REP-M.2320-2014 (accessed January15, 2019).
- Aijaz, A., M. Tshangini, M. R. Nakhai, X. Chu, and A.-
H. Aghvami. 2014. Energy-efficient uplink resource allo-cation in LTE networks with M2M/H2H co-existence under statistical QoS guarantees. IEEE T. Commun. 62(7):2353-2365. doi: 10.1109/TCOMM.2014.2328338.
- Hamdoun, S., A. Rachedi, and Y. Ghamri-Doudane.
2016. A flexible M2M radio resource sharing scheme in LTE networks within an H2H/M2M coexistence scenario. Conference (International) on Communications. IEEE. 1-7. doi: 10.1109/ICC.2016.7511237.
- On the pulse of the networked society. June 2016. Ericsson mobility report. Available at: https://www. ericsson.com/assets/local/mobility-report/documents/ 2016/ericsson-mobility-report-june-2016.pdf (accessed January 15, 2019) .
- Requirements, evaluation criteria and submission templates for the development of IMT-2020. November 2017. ITU-R Report M.2411. Available at: https:// www.itu.int/pub/R-REP-M.2411-2017 (accessed January 15, 2019).
- Samouylov, K., I. Gudkova, E. Markova, and I. Dzantiev.
2016. On analyzing the blocking probability of M2M trans-missions for a CQI-based RRM scheme model in 3GPP LTE. Information technologies and mathematical modelling - queueing theory and applications. Eds. A. Dudin, A. Gortsev, A. Nazarov, and R. Yakupov. Communications in computer and information science ser. Springer 638:327-340. doi: 10.1007/978-3-319-44615-8_29.
- Markova, E., I. Dzantiev, I. Gudkova, and S. Shorgin.
2017. Analyzing impact of path loss models on probability characteristics of wireless network with licensed shared access framework. 9th Congress (International) on Ultra Modern Telecommunications and Control Systems Proceedings. Piscataway, NJ: IEEE. 20-25. doi: 10.1109/ICUMT.2017.8255189.
- Begishev, V., R. Kovalchukov, A. Samuylov, A. Ometov,
D. Moltchanov, Y. Gaidamaka, and S. Andreev. 2015. An analytical approach to SINR estimation in adjacent rectangular cells. Internet of things, smart spaces, and next generation networks and systems. Eds. S. I. Balandin, S. D. Andreev, and Y. Koucheryavy. Lecture notes in computer science ser. Springer. 9247:446-458. doi: 10.1007/978-3-319-23126-6_39.
- Samuylov, A., D. Moltchanov, Y. Gaidamaka, S. Andreev, and Y. Koucheryavy. 2016. Random triangle: A baseline model for interference analysis in heterogeneous networks. IEEE T. Veh. Technol. 65(8):6778-6782. doi: 10.1109/TVT.2015.2481795.
- Naumov, V., and K. Samouylov. 2017. Analysis of multi-resource loss system with state-dependent arrival and ser-vice rates. Probab. Eng. Inform. Sc. 31(4):413-419. doi: 10.1017/S0269964817000079.
- Markova, E., I. Gudkova, A. Ometov, I. Dzantiev, S. And-reev, Ye. Koucheryavy, and K. Samouylov. 2017. Flexible spectrum management in a smart city within licensed shared access framework. IEEE Access 5:22252-22261. doi: 10.1109/ACCESS.2017.2758840.
[+] About this article
Title
COMPARATIVE ANALYSIS OF PERFORMANCE MEASURES FOR A WIRELESS MACHINE-TO-MACHINE NETWORK MODEL OPERATING WITHIN TWO RADIO RESOURCE MANAGEMENT POLICIES
Journal
Informatics and Applications
2019, Volume 13, Issue 1, pp 108-116
Cover Date
2019-04-30
DOI
10.14357/19922264190115
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
wireless network; LTE; machine-to-machine communication; channel quality indicator; Shannon's formula; uniform distribution; round robin policy; full power policy; blocking probability
Authors
E. V. Markova , A. A. Golskaia ,
I. L. Dzantiev , I. A. Gudkova , ,
and S. Ya. Shorgin ,
Author Affiliations
Peoples' Friendship University of Russia, 6 Miklukho-Maklaya Str., Moscow 117198, Russian Federation
Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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