A Directed Information Learning Framework for Event-Driven M2M Traffic Prediction

Ali, Samad; Saad, Walid; Rajatheva, Nandana

doi:10.1109/lcomm.2018.2868072

Cited by 21 publications

(14 citation statements)

References 15 publications

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“…Therefore, we propose a novel solution, summarized in Algorithm 1. Here, we consider that the BS has a prediction algorithm (e.g., such as those proposed in [39], [26], and [40]) to determine the set of active MTDs at each given time. This algorithm provides the set of active MTDs with a certain probability.…”

Section: B Sleeping Bandits and Proposed Algorithmmentioning

confidence: 99%

“…As a first step to implement the fast uplink grant, one must investigate the problem of source traffic prediction. In this regard, in [26], an MTD traffic prediction method based on the so-called directed information is presented for source traffic prediction. By using the method proposed in [26] upon detection of an irregular transmission, a set of future active MTDs facing the same event can be detected.…”

Section: Introductionmentioning

confidence: 99%

“…In this regard, in [26], an MTD traffic prediction method based on the so-called directed information is presented for source traffic prediction. By using the method proposed in [26] upon detection of an irregular transmission, a set of future active MTDs facing the same event can be detected. The authors in [27] propose a predictive resource allocation scheme for event-driven MTC in which MTDs are physically located across a line where their traffic pattern can be predicted.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sleeping Multi-Armed Bandit Learning for Fast Uplink Grant Allocation in Machine Type Communications

Ali

Ferdowsi

Saad

et al. 2020

IEEE Trans. Commun.

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Scheduling fast uplink grant transmissions for machine type communications (MTCs) is one of the main challenges of future wireless systems. In this paper, a novel fast uplink grant scheduling method based on the theory of multi-armed bandits (MABs) is proposed. First, a single quality-of-service metric is defined as a combination of the value of data packets, maximum tolerable access delay, and data rate. Since full knowledge of these metrics for all machine type devices (MTDs) cannot be known in advance at the base station (BS) and the set of active MTDs changes over time, the problem is modeled as a sleeping MAB with stochastic availability and a stochastic reward function. In particular, given that, at each time step, the knowledge on the set of active MTDs is probabilistic, a novel probabilistic sleeping MAB algorithm is proposed to maximize the defined metric. Analysis of the regret is presented and the effect of the prediction error of the source traffic prediction algorithm on the performance of the proposed sleeping MAB algorithm is investigated. Moreover, to enable fast uplink allocation for multiple MTDs at each time, a novel method is proposed based on the concept of best arms ordering in the MAB setting. Simulation results show that the proposed framework yields a threefold reduction in latency compared to a maximum probability scheduling policy since it prioritizes the scheduling of MTDs that have stricter latency requirements. Moreover, by properly balancing the exploration versus exploitation tradeoff, the proposed algorithm selects the most important MTDs more often by exploitation. During exploration, the sub-optimal MTDs will be selected, which increases the fairness in the system, and, also provides a better estimate of the reward of the sub-optimal MTD.

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Section: B Sleeping Bandits and Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sleeping Multi-Armed Bandit Learning for Fast Uplink Grant Allocation in Machine Type Communications

Ali

Ferdowsi

Saad

et al. 2020

IEEE Trans. Commun.

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“…In [7], authors use directed information (DI) to identify the causal connectivity between transmission patterns of different MTDs. The idea of directed information was initially used to find the capacity of a discrete channel with feedback [8].…”

Section: Introductionmentioning

confidence: 99%

Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks

Senevirathna

Thennakoon

Sankalpa

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

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Source traffic prediction is one of the main challenges of enabling predictive resource allocation in machine-type communications (MTC). In this paper, a long short-term memory (LSTM) based deep learning approach is proposed for eventdriven source traffic prediction. The source traffic prediction problem can be formulated as a sequence generation task where the main focus is predicting the transmission states of machinetype devices (MTDs) based on their past transmission data. This is done by restructuring the transmission data in a way that the LSTM network can identify the causal relationship between the devices. Knowledge of such a causal relationship can enable event-driven traffic prediction. The performance of the proposed approach is studied using data regarding events from MTDs with different ranges of entropy. Our model outperforms existing baseline solutions in saving resources and accuracy with a margin of around 9%. Reduction in random access (RA) requests by our model is also analyzed to demonstrate the low amount of signaling required as a result of our proposed LSTM based source traffic prediction approach.

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“…First, the set of the MTDs that have data to transmit should be known to the BS which is known as source traffic prediction. To address this problem, in [13], the use of directed information for event-driven source traffic prediction is presented. Second, when the number of active MTDs is larger than the number of fast uplink grants that can be allocated, an optimal allocation policy must be developed.…”

Section: Introductionmentioning

confidence: 99%

Sleeping Multi-Armed Bandits for Fast Uplink Grant Allocation in Machine Type Communications

Ali

Ferdowsi

Saad

et al. 2018

2018 IEEE Globecom Workshops (GC Wkshps)

Self Cite

View full text Add to dashboard Cite

Scheduling fast uplink grant transmissions for machine type communications (MTCs) is one of the main challenges of future wireless systems. In this paper, a novel fast uplink grant scheduling method based on the theory of multi-armed bandits (MABs) is proposed. First, a single quality-of-service metric is defined as a combination of the value of data packets, maximum tolerable access delay, and data rate. Since full knowledge of these metrics for all machine type devices (MTDs) cannot be known in advance at the base station (BS) and the set of active MTDs changes over time, the problem is modeled as a sleeping MAB with stochastic availability and a stochastic reward function. In particular, given that at each time step, the knowledge on the set of active MTDs is probabilistic, a novel probabilistic sleeping MAB algorithm is proposed to maximize the defined metric. Numerical results show that the proposed algorithm has logarithmic regret, and hence is optimal. The results also show that the proposed framework yields a three-fold reduction in latency compared to a random scheduling policy since it prioritizes the scheduling of MTDs that have stricter latency requirements. Moreover, by properly balancing the exploration versus exploitation tradeoff, the proposed algorithm is able to provide system fairness by allowing the most important MTDs to be scheduled more often while also allowing the less important MTDs to be selected enough times to ensure the accuracy of estimation of their importance.

show abstract

A Directed Information Learning Framework for Event-Driven M2M Traffic Prediction

Cited by 21 publications

References 15 publications

Sleeping Multi-Armed Bandit Learning for Fast Uplink Grant Allocation in Machine Type Communications

Sleeping Multi-Armed Bandit Learning for Fast Uplink Grant Allocation in Machine Type Communications

Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks

Sleeping Multi-Armed Bandits for Fast Uplink Grant Allocation in Machine Type Communications

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