Название: Low-overhead Communications in IoT Networks: Structured Signal Processing Approaches
Автор: Yuanming Shi, Jialin Dong, Jun Zhang
Издательство: Springer
Год: 2020
Страниц: 164
Язык: английский
Формат: pdf (true), epub
Размер: 14.8 MB

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.

The IoT architecture is established by the proliferation of low-cost and small-size mobile devices. With the explosion of IoT devices, a heavy burden is placed on the wireless access. A key characteristic of IoT data traffic is the sporadic pattern, i.e., only a portion of all the devices are active at a given time instant. In particular, in many IoT applications, devices are designed to be inactive most of the time to save energy and only be activated by external events. Thus, with massive IoT devices, it is of vital importance to manage their random access procedures, detect the active ones, and decode their data at the access point. Massive IoT connectivity has been regarded as one of the key performance requirements of 5G and beyond networks.

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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