Mathematical analysis of dynamic channel selection in indoor mobile wireless communication systems

Punt, J.B.; Sparreboom, D.; Brouwer, Frank; Prasad, Ramjee

doi:10.1109/25.728520

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…To set-up and maintain a call with acceptable bit error rate (BER) (typically, less than 10 −3 ), a minimum SINR of 21 dB should be maintained in DECT [16], [17], [18]. During the SINR calculation, a transmission power of 24 dBm and a background noise floor of -100 dBm are considered.…”

Section: Propagation Modelmentioning

confidence: 99%

Evaluation of DECT for low latency real-time industrial control networks

Das

Havinga

2013

2013 IEEE International Workshop of Internet-of-Things Networking and Control (IoT-NC)

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Abstract-Wireless sensor networks (WSNs) have revolutionized the industrial networks by enabling wireless sensing and control to the machine parts where wiring is impossible. However, new challenges in terms of communication reliability and latency, appear with the advances in the industrial wireless control systems. Existing standards are found inadequate to support many of these demanding situations as most of those are based on IEEE 802.15.4 standard, which is unable to provide high communication reliability with low latency (milliseconds). Digital Enhanced Cordless Telecommunications (DECT), a communication standard developed by European Telecommunications Standards Institute (ETSI), seems to support the timing and reliability requirements of modern industrial wireless control networks. In this paper, we evaluate the performance of DECT in various industrial environments and found that it can maintain excellent communication reliability between sensors and control centre with low latency in such scenarios.

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Section: Propagation Modelmentioning

confidence: 99%

Evaluation of DECT for low latency real-time industrial control networks

Das

Havinga

2013

2013 IEEE International Workshop of Internet-of-Things Networking and Control (IoT-NC)

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“…In a dynamic channel assignment (DCA) method, in contrast, all channels are potentially available to all cells and are assigned to cells dynamically as calls arrive. If this is done rightly, it can take advantage of temporary changes in the distribution of calls in order to serve more users as discussed in [2][3][4][5][6][7]. Dynamic channel allocation offers the possibility of capturing unused channel capacity by allocating unused resources between competing network nodes.…”

Section: Introductionmentioning

confidence: 99%

Dynamic channel allocation in mobile cellular networks

Mou

Islam

Amin³

2008

Journal of Discrete Mathematical Sciences and Cryptography

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Channel assignment problem is one of the main issues in radio networks. Having limited number of possible channels and unlimited demands it has become a challenge for the network planners to allocate channels in optimized ways. In the early days of radio technology, conflict free fixed channel assignment was the main target. But in the present days, dynamic channel allocation is used. In this paper, an algorithm has been developed, which will be able to allocate channels dynamically. In a certain area, there can be different demands; in the morning the demand may be increased and in the evening the demand may be reduced. This algorithm, based on Greedy method, is able to assign the radio channels dynamically according to dynamic demands.

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“…It should be noted that spectrum agility addressed in this paper differs significantly from the problems of so-called dynamic frequency-selection, such as Dynamic Channel Selection [9] in cellular networks, Dynamic Frequency Selection [10] in the IEEE 802.11h standard or Auto Frequency Allocation [11] in the HiperLAN. These schemes address the problem of choosing a channel from a pre-defined spectral band to mitigate interference between devices.…”

mentioning

confidence: 99%

What and how much to gain by spectrum agility?

Chou

Shankar

Kim

et al. 2007

IEEE J. Select. Areas Commun.

170

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Abstract-Static spectrum allocation prohibits radio devices from using spectral bands designated for others. As a result, some bands are under-utilized while other bands are over-populated with radio devices. To remedy this problem, the concept of spectrum agility has been considered so as to enable devices to opportunistically utilize others' spectral bands. In order to help realize this concept, we establish an analytical model to derive performance metrics, including spectrum utilization and spectrum-access blocking time in spectral-agile communication systems. We then propose three basic building blocks for spectralagile systems, namely spectrum opportunity discovery, spectrum opportunity management, and spectrum usage coordination, and develop protocols for each blocks. These protocols are integrated with the IEEE 802.11 protocol, and simulated using ns-2 to evaluate the protocol overhead. The simulation results show that our proposed protocols can improve the throughput of an IEEE 802.11 wireless LAN by 90% for the simulated scenarios, and the improvements matched well our analytical model. These results demonstrate the great potential of using spectrum agility for improving spectral utilization in an efficient, distributed, and autonomous manner.

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Mathematical analysis of dynamic channel selection in indoor mobile wireless communication systems

Cited by 11 publications

References 7 publications

Evaluation of DECT for low latency real-time industrial control networks

Evaluation of DECT for low latency real-time industrial control networks

Dynamic channel allocation in mobile cellular networks

What and how much to gain by spectrum agility?

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