Analog Communication Systems By P Chakrabarti Pdf
Roseanne Gennett
Storing all network weights locally would eliminate overhead of the off-chip communication and lead to unprecedented system-level energy efficiency and speed for large-scale networks. For example, the crude estimates showed that energy-delay product for the inference operation of a large-scale deep learning neural networks implemented with mixed-signal circuits based on the 200-nm memristor technology similar to the one discussed in this paper could be six orders of magnitude smaller as compared to that of the advanced digital circuits, while more than eight orders of magnitude smaller when utilizing three-dimensional 10-nm memristor circuits51.
Supplementary Fig. 2 shows additional details of the MLP implementation and the measurement setup. We have used AD8034 discrete operational amplifiers for the CMOS-based neurons and ADG1438 discrete analog multiplexers to implement on-board switch matrix.
Dr. Bishnu Prasad De is an Assistant Professorat the Department of Electronics and Communication Engineering, Haldia Institute of Technology, West Bengal, India. Dr. De has 7 years of teaching and 2 years of research experience. His areas of interest include VLSI circuits & systems, analog electronics, electronic design automation, and soft computing. He has published several papers in journals of national and international repute.
Mr. Banibrata Bag is an Assistant Professor at the Department of Electronics and Communication Engineering, Haldia Institute of Technology, West Bengal, India. He has 6 years of teaching and 4 years of industry experience. His main area of research is optical wireless communications and networks. He has published 1 book, 5 journal papers, and 9 conference papers.
The text book on Optical Fiber Communication describes the optical fiber with its low-loss and highbandwidth characteristics which has the potential to provide enormous capacity of transmitted data as compared to electronic means. This book will describe the fundamental operation and recent advances in the exciting area of optical fiber communication systems.
Dr. Gitanjali Chandwani Manocha is working with ECE Department, Thapar Institute of Engineering and Technology, Patiala since 2019. She did her PhD degree in Optical Networks from IIT Kharagpur in April 2019. Her research interests include Software Defined Networks, Optical Networks, Molecular Communication & Wireless communication.
Several potential advantages of the proposed smart WPT system with intelligent metasurface are remarked here. Firstly, the smart WPT system can be guaranteed to human exposure under the level of EM safety. For instance, the smart WPT system can be designed to be able to instantly shut off the power delivering when detecting a person moving close or falling into the charging region, and resume as the human leaves. Secondly, the smart WPT system can be optimized as simultaneous wireless information and power transfer system, when it is integrated with wireless sensor networks, communication modules and advanced algorithms. Thirdly, the WPT can be further extended to meet various needs such as automatic charging, monitoring, and microwave hyperthermia. In a word, the proposed WPT strategy could open a new avenue for the WPT with the high efficiency, safety, and intelligence.
NMMBCs work similar to the conventional wireless communication systems, but with the use of intelligent metasurfaces for recycling the energy dissipated in space that was conventionally thought to be useless and further improving SNR. Similar to conventional wireless communication systems, in NMMBC, an intended RF carrier carrying the information to be transferred is required for information transfer, where the signal modulation or demodulation is made by using nonlinear RF mixers. However, as opposed to the conventional wireless system, in NMMBC, the intelligent metasurface is deployed to shape the ambient environment such that the effective number of information channels can be increased (see Fig. 3a, b). In NMMBC, the intelligent metasurface is utilized to extend the aperture of the antenna in the conventional wireless communication systems in a distributed manner. In other words, the intelligent metasurface can be regarded as an extension part of antenna arrays of the conventional wireless communication systems, which is connected with the intended RF source using air rather than transmission lines [177].
Besides, the intelligent metasurface has several ubiquitous properties. First, the intelligent metasurface can be optimized to match any RF source and associated modules, since it improves the communication performance by tailoring the surrounding environment for all nearby devices instead of modifying the transmitting and receiving devices. Second, unlike the transmission lines in the conventional communication systems, the intelligent metasurface does not involve high-speed signals [177], and thus it can be easily incorporated into the ambient environment and remarkably improve SNR and thus the information capacity of the conventional systems. For instance, Tang et al. demonstrated theoretically that the intelligent metasurfaces were helpful in improving the energy efficiency of power allocation of the base station [177]. Hougne et al. demonstrated that the one-bit reconfigurable metasurface can be optimized to improve remarkably the equivalent number of channels of MIMO wireless communication systems [167]. More recently, in the community of wireless communication, the RIS has been numerically demonstrated to be helpful in enhancing the secure transfer [173, 174] (see Fig. 3c), reducing the mobile edge computing [175, 176] (see Fig. 3d), and so on. Overall, there are rapidly growing interests in this topic, and we would like to refer the readers of interest to Refs. [170,171,172] for more comprehensive reviews about recent progress.
We consider the utilization of linear embedding techniques in intelligent metasurface sensors [122]. As explored in Sect. 2.4, the intelligent metasurface is capable of generating nearly arbitrarily radiation patterns or the measurement modes desired by the machine learning techniques. Inspired by this, we proposed the concept of a machine-learning reprogrammable imager (see Fig. 4b), in which the intelligent metasurface is trained with a vast number of training data using the PCA such that the machine-learning-desired radiation patterns can be achieved on the physical level. Then, the intelligent metasurface serves as a physical computing device: which outputs the low-dimensional PCA features from the input of the high-dimensional raw data in an analog computing way. As such, the resultant sensing strategy is almost free of digital computation.
The linear-machine-learning-driven metasurface imager relies on the assumption of linear mapping from the data to results, which to some extent limits itself to handle relatively simple sensing tasks. It is believed that the deep networks have much more powerful representation capability than shallow networks do, let alone linear networks [96]. Recently, we have witnessed rapid progress in all-wave (specifically, all-optical) physical deep networks that are optimized to match the modern deep acritical networks in optics [194]. However, one of the remaining challenges is the difficulty of the physical implementation of the nonlinear activation functions, although nonlinear materials (e.g. crystals, polymers, semiconductor materials) are available. Thus, we considered the powerful capability of deep learning in the digital world, and proposed the intelligent sensing scheme by exploring the hybrid computing scheme [129, 220]: the analog high-dimensional data preprocessing (e.g., data compression) with the intelligent metasurface on the physical level, and the digital postprocessing with the modern deep acritical neural networks on the digital level. Note that the compressive-sensing-inspired computational metasurface sensors [207,208,209,210,211, 221] can be treated as hybrid-computing-based intelligent sensing, in the sense that the data compression is accomplished on the metasurface level, and the sparsity-aware data processing is implemented on the digital level.
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