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Low-overhead Communications in IoT Networks [electronic resource] : Structured Signal Processing Approaches / by Yuanming Shi, Jialin Dong, Jun Zhang.

By: Shi, Yuanming [author.].
Contributor(s): Dong, Jialin [author.] | Zhang, Jun [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XIV, 152 p. 350 illus., 19 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811538704.Subject(s): Engineering | Computer engineering | Computer networks  | Machine learning | Technology and Engineering | Computer Engineering and Networks | Machine LearningAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 620 Online resources: Click here to access online
Contents:
Chapter 1. Introduction -- Chapter 2. Sparse Linear Model -- Chapter 3. Blind Demixing -- Chapter 4. Sparse Blind Demixing -- Chapter 5. Shuffled Linear Regression -- Chapter 6. Learning Augmented Methods -- Chapter 7. Conclusions and Discussions -- Chapter 8. Appendix. .
In: Springer Nature eBookSummary: The recent developments in wireless communications, networking, and embedded systems have driven various innovative Internet of Things (IoT) applications, e.g., smart cities, mobile healthcare, autonomous driving and drones. A common feature of these applications is the stringent requirements for low-latency communications. Considering the typical small payload size of IoT applications, it is of critical importance to reduce the size of the overhead message, e.g., identification information, pilot symbols for channel estimation, and control data. Such low-overhead communications also help to improve the energy efficiency of IoT devices. Recently, structured signal processing techniques have been introduced and developed to reduce the overheads for key design problems in IoT networks, such as channel estimation, device identification, and message decoding. By utilizing underlying system structures, including sparsity and low rank, these methods can achieve significant performance gains. This book provides an overview of four general structured signal processing models: a sparse linear model, a blind demixing model, a sparse blind demixing model, and a shuffled linear model, and discusses their applications in enabling low-overhead communications in IoT networks. Further, it presents practical algorithms based on both convex and nonconvex optimization approaches, as well as theoretical analyses that use various mathematical tools.
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Chapter 1. Introduction -- Chapter 2. Sparse Linear Model -- Chapter 3. Blind Demixing -- Chapter 4. Sparse Blind Demixing -- Chapter 5. Shuffled Linear Regression -- Chapter 6. Learning Augmented Methods -- Chapter 7. Conclusions and Discussions -- Chapter 8. Appendix. .

The recent developments in wireless communications, networking, and embedded systems have driven various innovative Internet of Things (IoT) applications, e.g., smart cities, mobile healthcare, autonomous driving and drones. A common feature of these applications is the stringent requirements for low-latency communications. Considering the typical small payload size of IoT applications, it is of critical importance to reduce the size of the overhead message, e.g., identification information, pilot symbols for channel estimation, and control data. Such low-overhead communications also help to improve the energy efficiency of IoT devices. Recently, structured signal processing techniques have been introduced and developed to reduce the overheads for key design problems in IoT networks, such as channel estimation, device identification, and message decoding. By utilizing underlying system structures, including sparsity and low rank, these methods can achieve significant performance gains. This book provides an overview of four general structured signal processing models: a sparse linear model, a blind demixing model, a sparse blind demixing model, and a shuffled linear model, and discusses their applications in enabling low-overhead communications in IoT networks. Further, it presents practical algorithms based on both convex and nonconvex optimization approaches, as well as theoretical analyses that use various mathematical tools.

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