Low-overhead Communications in IoT Networks : Structured Signal Processing Approaches / by Yuanming Shi, Jialin Dong, Jun Zhang.
- 1st ed. 2020.
- Singapore : Springer Singapore : Imprint: Springer, 2020.
- Engineering (Springer-11647)
1 online resource (XIV, 152 pages) : 350 illustrations, 19 illustrations in color.
- Local subjects:
- Engineering, general.
Computer Systems Organization and Communication Networks.
- System Details:
- text file PDF
- 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.
- 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. .
- Dong, Jialin. author., Author,
Zhang, Jun, author., Author,
SpringerLink (Online service)
- Contained In:
- Springer eBooks
- Other format:
- Printed edition:
- Publisher Number:
- 10.1007/978-981-15-3870-4 doi
- Access Restriction:
- Restricted for use by site license.
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