Optimizing Data Aggregation for Uplink Machine-to-Machine Communication Networks

Malak, Derya; Dhillon, Harpreet S.; Andrews, Jeffrey G.

doi:10.1109/tcomm.2016.2517073

Cited by 76 publications

(65 citation statements)

References 32 publications

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“…Having obtained the predicted labelĉ = arg min c d(s, m c ), all the active component classifiers determining the current predicted label should satisfy the requirement of data alignment probability predefined in (19). Consequently, the single-threshold policy for importance ARQ defined in (21) can be then extended to a multi-threshold policy as defined below:…”

Section: Implementation Of Multi-class Classificationmentioning

confidence: 99%

Wireless Data Acquisition for Edge Learning: Importance-Aware Retransmission

Liu

Zhu

Huang

2019

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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By deploying machine-learning algorithms at the network edge, edge learning can leverage the enormous real-time data generated by billions of mobile devices to train AI models, which enable intelligent mobile applications. In this emerging research area, one key direction is to efficiently utilize radio resources for wireless data acquisition to minimize the latency of executing a learning task at an edge server. Along this direction, we consider the specific problem of retransmission decision in each communication round to ensure both reliability and quantity of those training data for accelerating model convergence. To solve the problem, a new retransmission protocol called data-importance aware automatic-repeat-request (importance ARQ) is proposed. Unlike the classic ARQ focusing merely on reliability, importance ARQ selectively retransmits a data sample based on its uncertainty which helps learning and can be measured using the model under training. Underpinning the proposed protocol is a derived elegant communication-learning relation between two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data uncertainty. This relation facilitates the design of a simple threshold based policy for importance ARQ. The policy is first derived based on the classic classifier model of support vector machine (SVM), where the uncertainty of a data sample is measured by its distance to the decision boundary. The policy is then extended to the more complex model of convolutional neural networks (CNN) where data uncertainty is measured by entropy. Extensive experiments have been conducted for both the SVM and CNN using real datasets with balanced and imbalanced distributions. Experimental results demonstrate that importance ARQ effectively copes with channel fading and noise in wireless data acquisition to achieve faster model convergence than the conventional channel-aware ARQ. The gain is more significant when the dataset is imbalanced. a classifier, the samples near decision boundaries are more critical than those far away [6].This fact motivates the proposed design principle of importance-aware resource allocation. In this work, we apply this principle to redesign the classic technique of automatic repeat-request (ARQ) for efficient wireless data acquisition in edge learning. A. Wireless Communications for Edge LearningConventional communication techniques are designed mostly for either reliable transmission or data-rate maximization without awareness of data utility for learning. Such a "communicationlearning separation" principle does not yield efficient solutions for acquiring large-scale distributed data in edge learning. Its increasingly critical communication bottleneck calls for redesigning communication techniques with a new objective of low-latency execution of learning tasks. Research opportunities in this largely uncharted area can be roughly grouped under three topics: radio resource allocation, multiple access, and signal encoding. The new idea in radio resource allocation for edge learning, the top...

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Section: Implementation Of Multi-class Classificationmentioning

confidence: 99%

Wireless Data Acquisition for Edge Learning: Importance-Aware Retransmission

Liu

Zhu

Huang

2019

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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“…8a and notice that h 1 , h 2 ≥ 0 are also restrictions. Therefore we can transform (27) to attain the result in (28) at the top of the next page.…”

Section: Appendixmentioning

confidence: 99%

Hybrid resource scheduling for aggregation in massive machine-type communication networks

2019

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Data aggregation is a promising approach to enable massive machine-type communication (mMTC). Here, we first characterize the aggregation phase where a massive number of machine-type devices transmits to their respective aggregator. By using non-orthogonal multiple access (NOMA), we present a hybrid access scheme where several machine-type devices (MTDs) share the same orthogonal channel. Then, we assess the relaying phase where the aggregatted data is forwarded to the base station. The system performance is investigated in terms of average number of MTDs that are simultaneously served under imperfect successive interference cancellation (SIC) at the aggregator for two scheduling schemes, namely random resource scheduling (RRS) and channel-dependent resource scheduling (CRS), which is then used to assess the performance of data forwarding phase.

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“…This deployment is shown in Fig. 2a, and it has been widely assumed in the literature, but under different distributions of devices [5], [6].…”

Section: A Deployment Strategiesmentioning

confidence: 99%

“…Performance Metrics 1) Average transmit power consumption: At the transmitting device side, we consider the average power consumption as a performance metric. Specifically, we model the power consumption as [6]…”

Section: A Deployment Strategiesmentioning

confidence: 99%

Performance Analysis of Uplink Cellular IoT Using Different Deployments of Data Aggregators

Hattab

Čabrić

2018

2018 IEEE Global Communications Conference (GLOBECOM)

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Data aggregation is an effective solution to enable cellular support of Internet-of-things (IoT) communications. Indeed, it helps alleviate channel congestion, reduce the communication range, and extend battery-lifetime. In this paper, we use stochastic geometry to analyze the performance of uplink cellular IoT using different deployment strategies of aggregators, including terrestrial and aerial ones, e.g., drones or unmanned aerial vehicles. We focus on IoT-specific performance metrics, that are typically used by 3GPP. Specifically, we derive closedform expressions of the average transmit power consumption, which is key to determine the lifetime of IoT devices, as well as the maximum coupling loss, which is essential to determine the maximum coverage the cellular system can support. Simulation results are presented to validate the derived theoretical expressions. It is shown that aerial aggregators can significantly extend the device lifetime and provide superior coverage compared to other deployment strategies. In addition, random deployment performs well when aggregators are densely deployed, whereas optimizing the location of a single terrestrial aggregator is beneficial when devices are more clustered.

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Optimizing Data Aggregation for Uplink Machine-to-Machine Communication Networks

Cited by 76 publications

References 32 publications

Wireless Data Acquisition for Edge Learning: Importance-Aware Retransmission

Wireless Data Acquisition for Edge Learning: Importance-Aware Retransmission

Hybrid resource scheduling for aggregation in massive machine-type communication networks

Performance Analysis of Uplink Cellular IoT Using Different Deployments of Data Aggregators

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