Mathematical models for the analysis of dynamic channel selection for indoor mobile wireless communication systems

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

doi:10.1109/wncmf.1994.529421

Cited by 10 publications

(8 citation statements)

References 1 publication

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“…This paper extends the mathematical investigation from [7] and [8]. The models discussed in this paper give mathematical results for the performance of a DCS system at the moment of call setup.…”

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confidence: 64%

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

Punt

Sparreboom

Brouwer

et al. 1998

IEEE Trans. Veh. Technol.

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Abstract-Dynamic channel selection (DCS) is an algorithm for flexible resource sharing in mobile radio systems. The digital enhanced cordless telecommunications (DECT) standard implements a version of DCS based on time as well as frequency multiplexing. In this paper, mathematical models are developed to evaluate the probabilities of channel availability, desired carrier power, and the carrier-to-interference ratio (CIR) for a constant traffic load. These results can be used to compute the call setup blocking probability. The models reported in this study are based on a decentralized DCS according to the DECT standard specifications. The results show that blocking due to availability of a channel (resource blocking), not interference blocking, generally is the most important factor in overall call blocking. Furthermore, it is seen that the resource blocking is sensitive to the implementation of the portable sets. Also, with homogeneous static traffic, this type of DCS can be as good as fixed channel allocation (FCA) with respect to resource blocking.

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confidence: 64%

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

Punt

Sparreboom

Brouwer

et al. 1998

IEEE Trans. Veh. Technol.

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“…In order to deal with the traffic load variations, channel reassignment and negotiation (e.g., channel borrowing [19]) can be effectively done with the help of mobile switching centers. To further attain high channel efficiency, dynamic channel allocation schemes are proposed (e.g., dynamic channel selection [20]), where centralized control is usually needed to ensure the effectiveness of frequency reuse at the cost of computational complexity. Since the mesh routers of a large WMN are likely scattered around, the clustered WMN is not expected to be as structured as its counterpart.…”

Section: Related Workmentioning

confidence: 99%

Pareto optimal resource management for wireless mesh networks with QoS assurance: Joint node clustering and subcarrier allocation

Cheng

Zhuang

2009

IEEE Trans. Wireless Commun.

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Abstract-Node clustering and subcarrier allocation are imperative to ameliorate system throughput and facilitate qualityof-service (QoS) provisioning by means of effective interference control and maximum frequency reuse. In this paper, we propose a novel node clustering algorithm with effective tax-based subcarrier allocation tailored for wireless mesh networks with QoS support. With increased frequency reuse, our proposed approach is shown to achieve a higher system throughput than a conflictgraph approach and a baseline approach. Also, our approach is demonstrated promising in balancing packet delay and end-toend transmission rate. By carefully adjusting an upper bound of subcarriers allocated to each cluster, we can achieve improved system performance. The proposed resource allocation achieves the Pareto optimality, demonstrating efficient use of network resources. Further, our analysis reveals that how to allocate resources in a wireless network in a decentralized manner can affect the solution space of a performance tradeoff between QoS provisioning and throughput maximization.Index Terms-Frequency reuse, game theory, node clustering, quality-of-service (QoS) provisioning, subcarrier allocation, wireless mesh network (WMN).

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“…The channel occupancy information and interference measurement needs to be exchanged between adjacent BS's or/and between MHs and the corresponding BS's [13] [29]. In the mobile host based solutions [30] [31], MHs select a BS and a channel based on received signal power, probabilities of channel availability and co-channel interference. Signal strength and carrier-tointerference ratio (CIR) need to be measured and calculated on MHs.…”

Section: Introductionmentioning

confidence: 99%

PBCA - Prediction Based Channel Allocation

Wang

Mehrotra²,

Venkatasubramanian³

2007

IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference

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Cellular infrastructures as they exist today are statically configured, and do not provide scalable, flexible and adaptive radio resource reconfiguration. In GSM networks, many dynamic channel allocation (DCA) schemes have been proposed to improve the network performance and user experience. DCA schemes can be categorized into cell based centralized, cell based distributed, and mobile handset based solutions. However, most of these existing schemes are event-driven and are reactively triggered by the arrival of active calls, which has a delayed response to network traffic condition. In order to adjust network configuration to the changing traffic conditions, a new class of information driven adaptation schemes are needed. The emergence of next generation smart phones with considerable computation capability and the localization technologies adopted in cellular networks enable a new class of proactive and prediction-based information driven network reconfiguration schemes. This paper makes a case for Prediction Based Channel Allocation algorithm as a means to dynamically reconfigure cellular network channel resource based on location and traffic prediction. Simulation results show that the proposed scheme outperforms an existing DCA scheme in term of channel allocation efficiency under various network conditions, and meanwhile shows a good tolerance to prediction errors.

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Mathematical models for the analysis of dynamic channel selection for indoor mobile wireless communication systems

Cited by 10 publications

References 1 publication

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

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

Pareto optimal resource management for wireless mesh networks with QoS assurance: Joint node clustering and subcarrier allocation

PBCA - Prediction Based Channel Allocation

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