This unified treatment of game theory focuses on finding state-of-the-art solutions to issues surrounding the next generation of wireless and communications networks. Future networks will rely on autonomous and distributed architectures to improve the efficiency and flexibility of mobile applications, and game theory provides the ideal framework for designing efficient and robust distributed algorithms. This book enables readers to develop a solid understanding of game theory, its applications and its use as an effective tool for addressing wireless communication and networking problems. The key results and tools of game theory are covered, as are various real-world technologies including 3G networks, wireless LANs, sensor networks, dynamic spectrum access and cognitive networks. The book also covers a wide range of techniques for modeling, designing and analysing communication networks using game theory, as well as state-of-the-art distributed design techniques. This is an ideal resource for communications engineers, researchers, and graduate and undergraduate students.

Ambient backscatter communication technology has been introduced recently, and is then quickly becoming a promising choice for self-sustainable communication systems as an external power supply or a dedicated carrier emitter is not required. By leveraging existing RF signal resources, ambient backscatter technology can support sustainable and independent communications and consequently open up a whole new set of applications that facilitate Internetof-Things (IoT). In this article, we study an integration of ambient backscatter with wireless powered communication networks (WPCNs). We first present an overview of backscatter communication systems with an emphasis on the emerging ambient backscatter technology. Then we propose a novel hybrid transmitter design by combining the advantages of both ambient backscatter and wireless powered communications. Furthermore, in the cognitive radio environment, we introduce a multiple access scheme to coordinate the hybrid data transmissions. The performance evaluation shows that the hybrid transmitter outperforms traditional designs. In addition, we discuss some open issues related to the ambient backscatter networking. Index TermsAmbient backscatter communications, modulated backscatter, RF energy harvesting, self-sustainable communications, wireless powered communications, Internet-of-Things. I. INTRODUCTIONInformation transmission based on modulated backscatter of incident signals from external RF sources has emerged as a promising solution for low-power wireless communications. The power consumption of a typical backscatter transmitter is less than 1 µW [1], which renders excessively long lifetime, e.g., 10 years, for an on-chip battery. This low power consumption well matches the harvestable wireless energy from RF sources, e.g., typically from 1 µW to tens of µW [2], [3]. This additionally renders RF energy harvesting to be an alternative to power backscatter transmitters. Furthermore, backscatter communications Dong In Kim is the corresponding author 2 can be embedded into small gadgets and objects, e.g., a radio frequency identification (RFID) and passive sensor. Therefore, backscatter communications is also envisioned as the last hop in the Internet-of-Things (IoT) [4], which requires low cost and ubiquitous deployment of small-sized devices [5].Due to recent dramatic increases in application demands, the requirement for backscatter communications has gone beyond the conventional RFID towards a more data-intensive way. This strongly raises the need for re-engineering backscatter transmitters for better reliability, higher data rates, and longer interrogation/transmission range. However, traditional backscatter communication techniques, e.g., RFID, are hindered by three major shortcomings: 1) The activation of backscatter transmitters relies on an external power supply such as an active interrogator (also called a reader or carrier emitter) which is costly and bulky.2) A backscatter transmitter passively responds only when inquired by a reader. The communication link ...

Due to the storage and retrieval efficiency, hashing has been widely deployed to approximate nearest neighbor search for large-scale multimedia retrieval. Supervised hashing, which improves the quality of hash coding by exploiting the semantic similarity on data pairs, has received increasing attention recently. For most existing supervised hashing methods for image retrieval, an image is first represented as a vector of hand-crafted or machine-learned features, followed by another separate quantization step that generates binary codes. However, suboptimal hash coding may be produced, because the quantization error is not statistically minimized and the feature representation is not optimally compatible with the binary coding. In this paper, we propose a novel Deep Hashing Network (DHN) architecture for supervised hashing, in which we jointly learn good image representation tailored to hash coding and formally control the quantization error. The DHN model constitutes four key components: (1) a sub-network with multiple convolution-pooling layers to capture image representations; (2) a fully-connected hashing layer to generate compact binary hash codes; (3) a pairwise cross-entropy loss layer for similarity-preserving learning; and (4) a pairwise quantization loss for controlling hashing quality. Extensive experiments on standard image retrieval datasets show the proposed DHN model yields substantial boosts over latest state-of-the-art hashing methods.

The purpose of Springer's new Wireless Networks book series is to establish the state of the art and set the course for future research and development in wireless communication networks. The scope of this series includes not only all aspects of wireless networks (including cellular networks, WiFi, sensor networks, and vehicular networks), but related areas such as cloud computing and big data. The series serves as a central source of references for wireless networks research and development. It aims to publish thorough and cohesive overviews on specific topics in wireless networks, as well as works that are larger in scope than survey articles and that contain more detailed background information. The series also provides coverage of advanced and timely topics worthy of monographs, contributed volumes, textbooks and handbooks.