Performance of a hierarchical cellular network with mobility-dependent hand-over strategies

Lagrange, Xavier; Godlewski, Philippe

doi:10.1109/vetec.1996.504082

Cited by 51 publications

(47 citation statements)

References 6 publications

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“…Such a homogenous model can be used to model the central cells in a large array of cells in which the nonhomogeneity is only in the boundary cells. Note that the models analyzed in [9] and [13] are also homogenous.…”

Section: A the Approximate Analysis Approachmentioning

confidence: 99%

“…Furthermore, a slow call in the macrolayer retains its channel if it becomes fast. With these observations we define the rates (12) (13) It is now clear that, with the assumptions made and the notation defined, has the transition diagram shown in Fig. 2.…”

Section: ) Stationary Analysis Of the Microlayermentioning

confidence: 99%

“…A call is identified as fast or slow by the cellular system. Approaches for carrying out such classification are proposed in [10], [13], and [22]; we assume, as in [9], that such classification has already been done on call arrival. A fast call is allocated to a macrolayer channel in the macrocell that it is located, and a call that is identified as slow is allocated to a microlayer channel in the microcell that it is located.…”

mentioning

confidence: 99%

“…Related work on this problem has appeared in [4], [5], [9], [10], [13], [21], and [22]. In [21], a cellular system model with call overflow and repacking between two layers of overlapping cells is considered.…”

mentioning

confidence: 99%

“…This information is used to determine the base station (at the macrocell or at the microcell) which will handle the call during origination or handoff of the call. A similar approach for identifying fast calls is proposed in [13], and in addition analysis of grade-of-service is done for a two-layer system. The latter paper, however, does not consider slow call repacking and mobility changes.…”

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confidence: 99%

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Performance analysis of microcellization for supporting two mobility classes in cellular wireless networks

Maheshwari¹,

Kumar

2000

IEEE Trans. Veh. Technol.

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Abstract-We study the call blocking performance obtained by microcellizing a macrocell network. Each macrocell is partitioned into microcells, and some of the channels originally allocated to the macrocell are assigned to the microlayer cells according to a reuse pattern. The arriving calls are classified as fast or slow; fast calls are always assigned only to macrocell channels, whereas for slow calls a microcell channel is first sought. Slow calls may be allowed to overflow to the macrolayer, but may be repacked to vacated microcell channels. Calls can change their mobility class during a conversation. We develop an approximate analysis for computing the slow and fast call blocking probabilities in such a system. We adopt the technique of analyzing an isolated macrocell with the Poisson arrival assumption and then iterating on the stationary analysis of the isolated macrocell to obtain stationary results for the multicell system. Simple, but accurate approximations are developed for analyzing the isolated macrocell and its associated microcells. The analyses based on the approximate isolated cell model are validated against simulations of a multicell model.

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Section: A the Approximate Analysis Approachmentioning

confidence: 99%

Section: ) Stationary Analysis Of the Microlayermentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Performance analysis of microcellization for supporting two mobility classes in cellular wireless networks

Maheshwari¹,

Kumar

2000

IEEE Trans. Veh. Technol.

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Performance Evaluation

2004

2‐D and 3‐D Image Registration

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We present a performance evaluation study of hierarchical cellular networks using Performance Evaluation Process Algebra (PEPA). These networks constitute a new application area for this process algebra formalism. We show that this formalism can easily be used to model such systems. We also show that the strong equivalence aggregation technique behind PEPA allows a significant reduction of the state space of the underlying Markov process. Using the resulting model, we derive performance criteria such as new call blocking and dropping handover probabilities.

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