On Channel Sharing Policies in LEO Mobile Satellite Systems

Moscholios, Ioannis D.; Sagias, N.C.; Logothetis, Michael D.

doi:10.1109/taes.2018.2798318

Cited by 25 publications

(15 citation statements)

References 28 publications

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“…Needless to say, if the b kc b.u. are not available then the call will be blocked (see Figure 2, where the call admission mechanism of [38] does not include the grey parts).…”

Section: The Proposed Probabilistic Threshold Loss Modelmentioning

confidence: 99%

“…Multirate loss models (MLMs) are widely adopted in the literature for the determination of call blocking probabilities (CBP), a significant performance index in contemporary multiservice networks [1,2]. Among the MLMs, the basis is considered to be the Erlang MLM (EMLM) not only because CBP can be efficiently determined via the Kaufman-Roberts recursive formula [3,4] but also due to the fact that it has been widely adopted for the CBP computation in various types of networks (from wired [4][5][6][7][8][9][10][11][12][13][14][15][16], to optical [17][18][19][20], wireless [21][22][23][24][25][26][27][28][29][30][31][32][33][34] and even satellite networks [35][36][37][38]).…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Retry and Threshold Multirate Loss Models for Impatient Calls

Moscholios¹

2021

Telecom

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In this paper, a link of fixed capacity is considered that services calls from different service-classes. Calls arrive in the link according to a Poisson process, have an initial (peak) bandwidth requirement while their service time is exponentially distributed. We model this system as a multirate loss system and analyze two different multirate loss models. In the first model, named probabilistic retry loss model, if there is no available link bandwidth, a new call is blocked but retries with a lower bandwidth requirement and increased service time. To allow for the fact that a blocked call may be impatient, we assume that it retries with a probability. In the second model, named probabilistic threshold loss model, a call may reduce its bandwidth requirement (before blocking occurs) based on the occupied link bandwidth. To determine call blocking probabilities in both multirate loss models, we show that approximate but recursive formulas do exist that provide quite satisfactory results compared to simulation.

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“…Needless to say, if the b kc b.u. are not available then the call will be blocked (see Figure 2, where the call admission mechanism of [38] does not include the grey parts).…”

Section: The Proposed Probabilistic Threshold Loss Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Probabilistic Retry and Threshold Multirate Loss Models for Impatient Calls

Moscholios¹

2021

Telecom

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“…However, in modern networks it is significant to be able to study a multiservice environment where calls need more resources. The analysis at call-level of such contemporary networks is essential in network planning procedures [31][32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

et al. 2021

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In this paper, a cloud radio access network (C-RAN) is considered where the baseband units form a pool of computational resource units and are separated from the remote radio heads (RRHs). Based on their radio capacity, the RRHs may form one or many clusters: a single cluster when all RRHs have the same capacity and multi-clusters where RRHs of the same radio capacity are grouped in the same cluster. Each RRH services the so-called multiservice traffic, i.e., calls from many service classes with various radio and computational resource requirements. Calls arrive in the RRHs according to a quasi-random process. This means that new calls are generated by a finite number of mobile users. Arriving calls require simultaneously computational and radio resource units in order to be accepted in the system, i.e., in the serving RRH. If their requirements are met, then these calls are served in the (serving) RRH for a service time which is generally distributed. Otherwise, call blocking occurs. We start with the single-cluster C-RAN and model it as a multiservice loss system, prove that the model has a product form solution, and determine time congestion probabilities via a convolution algorithm whose accuracy is validated with the aid of simulation. Furthermore, the previous model is generalized to include the more complex case of more than one clusters.

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“…The BR policy favours calls of high subcarrier requirements since it permits the reservation of subcarriers for such calls. Contrary to the CS policy, which can be unfair to calls of high subcarrier requirements, the BR policy can guarantee QoS to specific service‐classes [18–22]. On the other hand, the application of the BR policy in the proposed c‐P–S model destroys the PFS of the steady‐state probabilities. (iv) We show that the determination of the main performance measures (TC and CC probabilities, resource utilisation) can be based on recursive formulas. The accuracy of the proposed formulas in both models (c‐P–S and c‐P–S/BR) is verified via simulation and found to be quite satisfactory.…”

Section: Introductionmentioning

confidence: 99%