Coupled Markovian Arrival Process for Automotive Machine Type Communication traffic modeling

Grigoreva, Elena; Laurer, Maximilian; Vilgelm, Mikhail; Gehrsitz, Thomas; Kellerer, Wolfgang

doi:10.1109/icc.2017.7996498

Cited by 31 publications

(27 citation statements)

References 14 publications

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“…The MAP is defined by an infinitesimal generator matrix D 0 in case of no arrivals, and a rate matrix D 1 in the case of an arrival leading to possible state change of the Markov chain. To account for the spatial and temporal correlation in the arrival stream of an IED i, the convex combination of the matrices in the regular and event phase is considered with the aid of β i [t], as in [12]. Therefore, MAP is characterized by the rate matrices D 0 , D 1 where…”

Section: Substation Automation Scenario and Traffic Modelmentioning

confidence: 99%

“…However, mode selection should also consider the interference level among the D2D-overlay links due to the resource sharing of their N d RBs. Therefore, to account for both, the mode selection probabilities for each link i ∈ D, are given by (12) and Pr(m = c) = 1 − Pr(m = d), where p c,th denotes the threshold value for the preamble collision probability and I n th is the interference tolerance level for RB n. Note that both p c,th and I n th can be optimized to maximize the overall system achievable rate. In this work, we consider p c,th and I n th as predefined parameters that depend on the reliability of the supported substation-automation service and we leave their joint optimization within the MSRA problem for future work.…”

Section: The Mode Selection and Resource Allocation Problem A Smentioning

confidence: 99%

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On the transmission mode selection for substation automation traffic in cellular networks

Kalalas

Alonso-Zárate

Bag

2017

2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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Abstract-In this paper, we propose device-to-device (D2D) communication over dedicated cellular resources, i.e., D2D-overlay mode, as a key enabling technology for reliable information exchange in power distribution grids. Motivated by the stringent requirements of substation automation traffic, we jointly address two fundamental issues: i) the seamless transition from cellular (i.e., communication via the base station) to D2D-overlay mode for smart grid entities upon detection of a surge of channel access attempts, and ii) the efficient orthogonal resource partition for cellular and D2D links. An analytical framework capturing the event-driven nature of substation automation traffic and both phases of uplink communication is introduced and the joint problem of mode selection and resource allocation (MSRA) is formulated as a sum-rate maximization problem. A dynamic heuristic MSRA mechanism is then proposed that adaptively allocates uplink resources for D2D links to prevent spectrum under-utilization and guarantees a minimum rate requirement for cellular users. The performance of our proposed scheme is evaluated through extensive simulations under different performance criteria and numerical results demonstrate the rate gains of a dynamic switch between D2D-overlay and conventional cellular mode for substation automation traffic.

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Section: Substation Automation Scenario and Traffic Modelmentioning

confidence: 99%

Section: The Mode Selection and Resource Allocation Problem A Smentioning

confidence: 99%

On the transmission mode selection for substation automation traffic in cellular networks

Kalalas

Alonso-Zárate

Bag

2017

2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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“…Basically, the approach can also serve as a basis for aggregate models. An example of IAT modeling with a Gaussian Mixture Model (GMM) is provided in . However, relations between PS and IAT parameters are not considered, as well as temporal properties of the packet sequence.…”

Section: Introductionmentioning

confidence: 99%

Comparison of HMM and RNN models for network traffic modeling

Bikmukhamedov

Nadeev

Maione

et al. 2020

Internet Technology Letters

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Major applications for statistical modeling of network traffic flows can be found in network testing and imitating of unavailable devices. Since packet‐level modeling is considered, packet size (PS) and inter‐arrival time (IAT) features are sufficient for accurate statistics. Two models are compared based on the hidden Markov model (HMM) framework and a recurrent neural network (RNN). In the RNN model, the feature space is encoded with latent components of a Gaussian mixture model (GMM). The comparison is carried out with a voice Skype call and traffic of an IoT device, and evaluated with the rolling entropy and Kulback‐Leibler divergence (KLD) metrics that are derived from the generated PS and IAT parameters. The results show that the RNN is applicable for the packet‐level modeling task, but it underperforms the HMM.

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“…A two-state traffic model for automotive M2M applications based on a coupled MAP is proposed in [10]. Here, any car can trigger an event; then other cars go into event mode if the distance to the car triggering the event is less than a specified threshold.…”

Section: Introductionmentioning

confidence: 99%

“…We also define a function which models the influence of events on device traffic. Even though spatial modeling has been considered in [6], [10], our use of PPPs allows us to obtain tractable analytical expressions for the total rate, and thus avoids computationally expensive simulations. Also, we include the temporal aspect in the model via defining a Markov chain for each device, derive an approximation of the expected total rate, and show via numerical simulations that this approximation is quite close.…”

Section: Introductionmentioning

confidence: 99%

A traffic model for machine-type communications using spatial point processes

Thomsen

Manchón

Fleury

2017

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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A source traffic model for machine-tomachine communications is presented in this paper. We consider a model in which devices operate in a regular mode until they are triggered into an alarm mode by an alarm event. The positions of devices and events are modeled by means of Poisson point processes, where the generated traffic by a given device depends on its position and event positions. We first consider the case where devices and events are static and devices generate traffic according to a Bernoulli process, where we derive the total rate from the devices at the base station. We then extend the model by defining a two-state Markov chain for each device, which allows for devices to stay in alarm mode for a geometrically distributed holding time. The temporal characteristics of this model are analyzed via the autocovariance function, where the effect of event density and mean holding time are shown.

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Coupled Markovian Arrival Process for Automotive Machine Type Communication traffic modeling

Cited by 31 publications

References 14 publications

On the transmission mode selection for substation automation traffic in cellular networks

On the transmission mode selection for substation automation traffic in cellular networks

Comparison of HMM and RNN models for network traffic modeling

A traffic model for machine-type communications using spatial point processes

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