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Register a new userProgrammable Metasurface-based RF Chain-free 8PSK Wireless Transmitter
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Added by
Wankai Tang
Publication date
2019
fields
Electronic Engineering
and research's language is
English
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In this letter, a wireless transmitter using the new architecture of programmable metasurface is presented. The proposed transmitter does not require any filter, nor wideband mixer or wideband power amplifier, thereby making it a promising hardware architecture for cost-effective wireless communications systems in the future. Using experimental results, we demonstrate that a programmable metasurface-based 8-phase shift-keying (8PSK) transmitter with 8*32 phase adjustable unit cells can achieve 6.144 Mbps data rate over the air at 4.25 GHz with a comparable bit error rate (BER) performance as the conventional approach without channel coding, but with less hardware complexity.
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Metasurfaces have drawn significant attentions due to their superior capability in tailoring electromagnetic waves with a wide frequency range, from microwave to visible light. Recently, programmable metasurfaces have demonstrated the ability of manipulating the amplitude or phase of electromagnetic waves in a programmable manner in real time, which renders them especially appealing in the applications of wireless communications. To practically demonstrate the feasibility of programmable metasurfaces in future communication systems, in this paper, we design and realize a novel metasurface-based wireless communication system. By exploiting the dynamically controllable property of programmable metasurface, we firstly introduce the fundamental principle of the metasurface-based wireless communication system design. We then present the design, implementation and experimental evaluation of the proposed metasurface-based wireless communication system with a prototype, which realizes single carrier quadrature phase shift keying (QPSK) transmission over the air. In the developed prototype, the phase of the reflected electromagnetic wave of programmable metasurface is directly manipulated in real time according to the baseband control signal, which achieves 2.048 Mbps data transfer rate with video streaming transmission over the air. Experimental result is provided to compare the performance of the proposed metasurface-based architecture against the conventional one. With the slight increase of the transmit power by 5 dB, the same bit error rate (BER) performance can be achieved as the conventional system in the absence of channel coding. Such a result is encouraging considering that the metasurface-based system has the advantages of low hardware cost and simple structure, thus leading to a promising new architecture for wireless communications.
Many emerging technologies, such as ultra-massive multiple-input multiple-output (UM-MIMO), terahertz (THz) communications are under active discussion as promising technologies to support the extremely high access rate and superior network capacity in the future sixth-generation (6G) mobile communication systems. However, such technologies are still facing many challenges for practical implementation. In particular, UM-MIMO and THz communication require extremely large number of radio frequency (RF) chains, and hence suffering from prohibitive hardware cost and complexity. In this article, we introduce a new paradigm to address the above issues, namely wireless communication enabled by programmable metasurfaces, by exploiting the powerful capability of metasurfaces in manipulating electromagnetic waves. We will first introduce the basic concept of programmable metasurfaces, followed by the promising paradigm shift in future wireless communication systems enabled by programmable metasurfaces. In particular, we propose two prospective paradigms of applying programmable metasurfaces in wireless transceivers: namely RF chain-free transmitter and space-down-conversion receiver, which both have great potential to simplify the architecture and reduce the hardware cost of future wireless transceivers. Furthermore, we present the design architectures, preliminary experimental results and main advantages of these new paradigms and discuss their potential opportunities and challenges toward ultra-massive 6G communications with low hardware complexity, low cost, and high energy efficiency.
With the development and widespread use of wireless devices in recent years (mobile phones, Internet of Things, Wi-Fi), the electromagnetic spectrum has become extremely crowded. In order to counter security threats posed by rogue or unknown transmitters, it is important to identify RF transmitters not by the data content of the transmissions but based on the intrinsic physical characteristics of the transmitters. RF waveforms represent a particular challenge because of the extremely high data rates involved and the potentially large number of transmitters present in a given location. These factors outline the need for rapid fingerprinting and identification methods that go beyond the traditional hand-engineered approaches. In this study, we investigate the use of machine learning (ML) strategies to the classification and identification problems, and the use of wavelets to reduce the amount of data required. Four different ML strategies are evaluated: deep neural nets (DNN), convolutional neural nets (CNN), support vector machines (SVM), and multi-stage training (MST) using accelerated Levenberg-Marquardt (A-LM) updates. The A-LM MST method preconditioned by wavelets was by far the most accurate, achieving 100% classification accuracy of transmitters, as tested using data originating from 12 different transmitters. We discuss strategies for extension of MST to a much larger number of transmitters.
Wireless signals contain transmitter specific features, which can be used to verify the identity of transmitters and assist in implementing an authentication and authorization system. Most recently, there has been wide interest in using deep learning for transmitter identification. However, the existing deep learning work has posed the problem as closed set classification, where a neural network classifies among a finite set of known transmitters. No matter how large this set is, it will not include all transmitters that exist. Malicious transmitters outside this closed set, once within communications range, can jeopardize the system security. In this paper, we propose a deep learning approach for transmitter authorization based on open set recognition. Our proposed approach identifies a set of authorized transmitters, while rejecting any other unseen transmitters by recognizing their signals as outliers. We propose three approaches for this problem and show their ability to reject signals from unauthorized transmitters on a dataset of WiFi captures. We consider the structure of training data needed, and we show that the accuracy improves by having signals from known unauthorized transmitters in the training set.
Due to imperfections in transmitters hardware, wireless signals can be used to verify their identity in an authorization system. While deep learning was proposed for transmitter identification, the majority of the work has focused on classification among a closed set of transmitters. Malicious transmitters outside this closed set will be misclassified, jeopardizing the authorization system. In this paper, we consider the problem of recognizing authorized transmitters and rejecting new transmitters. To address this problem, we adapt the most prominent approaches from the open set recognition and anomaly detection literature to the problem. We study how these approaches scale with the required number of authorized transmitters. We propose using a known set of unauthorized transmitters to assist the training and study its impact. The evaluation procedure takes into consideration that some transmitters might be more similar than others and nuances these effects. The robustness of the RF authorization with respect to temporal changes in fingerprints is also considered in the evaluation. When using 10 authorized and 50 known unauthorized WiFi transmitters from a publicly accessible testbed, we were able to achieve an outlier detection accuracy of 98% on the same day test set and 80% on the different day test set.
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