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

Thomsen, Henning; Manchón, Carles Navarro; Fleury, Bernard Henri

doi:10.1109/pimrc.2017.8292670

Cited by 15 publications

(11 citation statements)

References 8 publications

(11 reference statements)

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“…Consider a single coordinator and let the MTDs be deployed randomly and independently in its coverage area. We resort to homogeneous PPPs to model the position of devices and event epicenters [47]. Note that PPP has been the most popular spatial model for various types of wireless networks [48], [49] because of salient features such as the independence between points and the simple form of the probability generating functional [50].…”

Section: A Mtds Deployment and Event Sensingmentioning

confidence: 99%

“…The traffic exchanged between the coordinator and the MTDs is modeled using an ergodic Markov chain with two states [47], I and A, as illustrated in Fig. 1.…”

Section: Traffic and Wake-up Modelingmentioning

confidence: 99%

“…Furthermore, the parameter q can be used to study the impact of events on the temporal correlation of the total traffic at the coordinator. Focusing first on the case of small values of q, e.g., q ≤ 0.1, the traffic behaves similarly to a Bernoulli process (memoryless) [47], since it has a short memory. Increasing the value of q increases the memory, i.e., the total rate at a given time k is correlated with many past values.…”

Section: Traffic and Wake-up Modelingmentioning

confidence: 99%

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Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

Ruíz-Guirola

Rodríguez-López

Montejo‐Sánchez³

et al. 2022

IEEE Internet Things J.

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educing energy consumption is a pressing issueeducing energy consumption is a pressing issueR in low-power machine-type communication (MTC) networks. In this regard, the Wake-up Signal (WuS) technology, which aims to minimize the energy consumed by the radio interface of the machine-type devices (MTDs), stands as a promising solution. However, stateof-the-art WuS mechanisms use static operational parameters, so they cannot efficiently adapt to the system dynamics. To overcome this, we design a simple but efficient neural network to predict MTC traffic patterns and configure WuS accordingly. Our proposed forecasting WuS (FWuS) leverages an accurate long-short term memory (LSTM)-based traffic prediction that allows extending the sleep time of MTDs by avoiding frequent page monitoring occasions in idle state. Simulation results show the effectiveness of our approach. The traffic prediction errors are shown to be below 4%, being false alarm and miss-detection probabilities respectively below 8.8% and 1.3%. In terms of energy consumption reduction, FWuS can outperform the best benchmark mechanism in up to 32%. Finally, we certify the ability of FWuS to dynamically adapt to traffic density changes, promoting low-power MTC scalability.

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Section: A Mtds Deployment and Event Sensingmentioning

confidence: 99%

“…The traffic exchanged between the coordinator and the MTDs is modeled using an ergodic Markov chain with two states [47], I and A, as illustrated in Fig. 1.…”

Section: Traffic and Wake-up Modelingmentioning

confidence: 99%

Section: Traffic and Wake-up Modelingmentioning

confidence: 99%

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Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

Ruíz-Guirola

Rodríguez-López

Montejo‐Sánchez³

et al. 2022

IEEE Internet Things J.

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“…We consider the trafic model, introduced in [9], where the state of an MTD evolve between two states, alarm and regular modes, following a Markov Chain, given in Fig. 3, and the state transition matrix is:…”

Section: A Traffic Modelmentioning

confidence: 99%

Fast Uplink Grant for NOMA: A Federated Learning Based Approach

Habachi¹,

Adjif

Cances³

2020

Lecture Notes in Computer Science

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Recently, non-orthogonal multiple access (NOMA) technique have emerged and is being considered as a building block of 5G systems and beyond. In this paper, we focus on the resource allocation for NOMA-based systems and we investigate how Machine Type Devices (MTDs) can be arranged into clusters. Specifically , we propose three allocation technique to enable the integration of massive NOMA-based MTD in the 5G. Firstly, we propose a low-complexity schema where the BS assigns an MTD to a cluster based on its CSI and transmit power in order to ensure that the SIC can be performed in the uplink as well as the downlink. In the second framework, we propose a federatedlearning based traffic model estimation in order to extend the capacity of the system. In fact, the BS take into account the traffic model of the MTDs in order to use time multiplexing in addition to the power multiplexing to separate MTDs. After the BS allocation, we propose a learning algorithm to allow contending MTDs synchronize their transmissions. Finally, we focus on the Quality of Service (QoS) for NOMA based devices and we propose in the third framework a QoS-aware technique, where MTDs are served differently based on their mode, i.e. regular or alarm modes. Simulation results show that the proposed techniques outperform existing technique in the literature.

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“…However, the processes observed by different sensors are often correlated (e.g. due to alarm event [4]) . For example, in earthquake or flood detection [5], [6], nearby sensors will be more likely to transmit than sensors further away.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Resource Allocation for Outage Minimization in Random Access with Correlated Activation

Zheng

Egan

Clavier

et al. 2022

2022 IEEE Wireless Communications and Networking Conference (WCNC)

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A key challenge for random access communications arising in the monitoring of physical phenomena is optimizing the access policy. This is particularly the case when the activity of each sensor is correlated, contrasting with the independence assumption underpinning standard slotted ALOHA schemes. In this paper, we propose a stochastic resource allocation algorithm to reduce outages via maximization of the expected number of sensors that are able to reliably communicate with an access point. Allowing for devices to transmit data over multiple consecutive frames, we show that the proposed algorithm converges with probability one to a locally optimal solution. Moreover, our algorithm significantly outperforms existing methods in terms of the average number of successful transmissions when utilizing successive interference cancellation.

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

Cited by 15 publications

References 8 publications

Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

Fast Uplink Grant for NOMA: A Federated Learning Based Approach

Stochastic Resource Allocation for Outage Minimization in Random Access with Correlated Activation

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