By 2020, there will be more than 200 billion sensor enabled objects world-wide in the Internet of Things (IoT). The biggest challenge of future IoT is to provide ultra scalable wireless access for a massive number of devices. The goal of this thesis is to build up a model for systems with massive access, study the fundamental limits, and design practical signaling schemes and signal processing algorithms. The thesis mainly consists of two parts, which are presented in Chapter 2 and Chapter 3, respectively. Chapter 2 is devoted to the modeling of the IoT and the study of the fundamental limits from the perspective of information theory. Chapter 3 is devoted to the design of low-complexity practical signal processing algorithms for neighbor discovery. Classical multiuser information theory studies the fundamental limits of models with a fixed (often small) number of users as the coding blocklength goes to innity. In Chapter 2, we introduce a new many-user paradigm, where the number of users and the block length simultaneously tend to innity. This paradigm is motivated by emerging systems whose massive number of users is comparable or far exceeds the blocklength, such as in machineto-machine communication systems and the IoT. The focus of the thesis is the Gaussian many-access channel, which is used to model the uplink transmission of the IoT. The many-access channel consists of a single receiver and many transmitters with fixed power, where all or a subset of users may transmit in a given block and need to be identied. The conventional notion of capacity in bits per channel use is ill-suited for the task, as Cover and Thomas recognized that the rate per sender vanishes. A new notion of capacity is introduced and characterized for the Gaussian many-access channel. The capacity can be achieved by rst detecting the set of active users and then decoding their messages. To achieve the capacity of the many-access channels, an essential step is device identification, also known as neighbor discovery. In wireless neighbor discovery, an access point needs to identify all the active devices in its surrounding areas. In Chapter 3, a novel low-complexity wireless neighbor discovery scheme, referred to as sparse orthogonal frequency division multiplexing (sparse-OFDM) is proposed. In the IoT, the number of devices is very large while every device accesses the network with a small probability, so the number of active devices in a frame is much smaller than the total local device population. Sparse OFDM is a one-shot transmission scheme with low complexity, which exploits both the parallel channel access oered by OFDM and the bursty nature of transmissions. The scheme is inspired by the sublinear algorithms for computing sparse Fourier transform and compressive sensing. When the transmission delay of each device is an integer number of symbol intervals, analysis and simulation show that sparse OFDM enables successful asynchronous neighbor discovery using a much smaller transmission length than the random access schemes

Last modified

• 02/19/2018

Creator

• Chen Xu

DOI

• https://doi.org/10.21985/N2M09H

Subject

• Electrical Engineering

Keyword

• sparse OFDM
• neighbor discovery
• compressed sensing
• Internet of Things
• many user

Date created

• 2016-01-01

Resource type

• Dissertation

Rights statement

• In Copyright