Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness on the edge has the potential to serve as a key enabler for the emerging beyond 5G networks. State-of-the-art studies in this domain have (i) only focused on a single task - modulation or signal (protocol) classification - which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks based multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks while considering heterogeneous wireless signals such as radar and communication waveforms in the electromagnetic spectrum. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. We additionally include experimental evaluations of the model with over-the-air collected samples and demonstrate first-hand insight on model compression along with deep learning pipeline for deployment on resource-constrained edge devices. We demonstrate significant computational, memory, and accuracy improvement of the proposed model over two reference architectures. In addition to modeling a lightweight MTL model suitable for resource-constrained embedded radio platforms, we provide a comprehensive heterogeneous wireless signals dataset for public use.
Automatic RF modulation recognition is a primary signal intelligence (SIGINT) technique that serves as a physical layer authentication enabler and automated signal processing scheme for the beyond 5G and military networks. Most existing works rely on adopting deep neural network architectures to enable RF modulation recognition. The application of deep compression for the wireless domain, especially automatic RF modulation classification, is still in its infancy. Lightweight neural networks are key to sustain edge computation capability on resource-constrained platforms. In this letter, we pro…
We propose a joint feature compression and transmission scheme for efficient inference at the wireless network edge. Our goal is to enable efficient and reliable inference at the edge server assuming limited computational resources at the edge device. Previous work focused mainly on feature compression, ignoring the computational cost of channel coding. We incorporate the recently proposed deep joint source-channel coding (DeepJSCC) scheme, and combine it with novel filter pruning strategies aimed at reducing the redundant complexity from neural networks. We evaluate our approach on a classifi…
Orthogonal time frequency space (OTFS) modulation is a recently proposed multi-carrier transmission scheme, which innovatively multiplexes the information symbols in the delay-Doppler (DD) domain instead of the conventional time-frequency (TF) domain. The DD domain symbol multiplexing gives rise to a direct interaction between the DD domain information symbols and DD domain channel responses, which are usually quasi-static, compact, separable, and potentially sparse. Therefore, OTFS modulation enjoys appealing advantages over the conventional orthogonal frequency-division multiplexing (OFDM) m…
Automatic modulation classification is of crucial importance in wireless communication networks. Deep learning based automatic modulation classification schemes have attracted extensive attention due to the superior accuracy. However, the data-driven method relies on a large amount of training samples and the classification accuracy is poor in the low signal-to-noise radio (SNR). In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of differ…
We propose a low-cost reconfigurable metasurface transmitter to implement generalized polarization-space modulation (GPSM) that has been proposed for next-generation wireless communications. Unlike conventional multiple-input multiple-output GPSM, our electromagnetically-compliant, reduced form-factor design using Barium-Strontium-Titanate film yields more than two polarization states. Full-wave simulations show our optimized design achieves 0.08-0.95 polarization conversion ratio with 5% bandwidth at 28 GHz and bit error rates comparable or better than conventional systems.
Adaptive modulation and coding (AMC) is widely employed in modern wireless communication systems to improve the transmission efficiency by adjusting the transmission rate according to the channel conditions. Thus, AMC can provide very efficient use of channel resources especially over fading channels. Quadrature Amplitude Modulation (QAM) is an ef- ficient and widely employed digital modulation technique. It typically employs a rectangular signal constellation. Therefore the decision regions of the constellation are square partitions of the two-dimensional signal space. However, it is well kno…
In this paper we are interested to learn from a wireless digitally modulated signal the number of antennas that the transmitter (Tx) of this signal uses, as well as its specific modulation scheme (from phase-shift keying (PSK) or quadrature amplitude modulation (QAM)). Formally, these are modulation and antenna classification problems. We examine the problems with data-driven machine learning (ML)-based techniques. The two sub-problems of modulation and number of transmitter antenna classification are initially examined independently for a variety for system parameters, namely the SNR, number …
A Kerr microresonator frequency comb has enabled the generation of low-phase-noise millimeter- and terahertz-waves in conjunction with an ultrafast photodiode. It is intriguing to employ the new light source in wireless communication at above 100 GHz band, where a carrier signal with a high signal-to-noise ratio is desired to achieve higher data rates. In this study, we demonstrate two simple and efficient architectures of wireless links based on a microresonator comb. We show experimentally that simultaneous modulation and detection of multiple comb lines result in >10 times stronger modulati…
Index modulation (IM) is one of the candidate technologies for the upcoming sixth generation (6G) wireless communications networks. In this paper, we propose a space-time-modulated reconfigurable intelligent metasurface (RI-MTS) that is configured to implement various frequency-domain IM techniques in a multiple-input multiple-output (MIMO) array configuration. Unlike prior works which mostly analyze signal theory of general RI-MTS IM, we present novel electromagnetics-compliant designs of specific IMs such as sub-carrier index modulation (SIM) and MIMO orthogonal frequency-domain modulation I…
Supervised learning in machine learning (ML) requires labelled data set. Further real-time data classification requires an easily available methodology for labelling. Wireless modulation and signal classification find their application in plenty of areas such as military, commercial and electronic reconaissance and cognitive radio. This paper mainly aims to solve the problem of real-time wireless modulation and signal class labelling with an active learning framework. Further modulation and signal classification is performed with machine learning algorithms such as KNN, SVM, Naive bayes. Activ…
In this work, we propose a joint collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We design near-optimal collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. We sho…
In this communication, we propose a framework for amalgamating optical-orthogonal frequency-division multiplexing (O-OFDM) and O-OFDM with index modulation (O-OFDM-IM) for optical wireless systems. Both schemes individually have some limitations, e.g., O-OFDM does not provide any granularity for spectral efficiency (SE)/energy efficiency (EE) trade-off, and O-OFDM-IM loses EE for higher order modulation alphabets. By combining O-OFDM and O-OFDM-IM together, the above limitations can be circumvented. Following on from providing the general framework, we use asymmetrically clipped (AC)O-OFDM and…
In this paper, we propose downlink signal design and optimal uplink scheduling for the wireless powered communication networks (WPCNs). Prior works give attention to resource allocation in a static channel because users are equipped with only energy receiver and users cannot update varying uplink schedulling. For uplink scheduling, we propose a downlink signal design scheme, called a power level modulation, which conveys uplink scheduling information to users. First, we design a downlink energy signal using power level modulation. Hybrid-access point (H-AP) allocates different power level in e…
The explosion of the number of low-power devices in the next decades calls for a re-thinking of wireless network design, namely, unifying wireless transmission of information and power so as to make the best use of the RF spectrum, radiation, and infrastructure for the dual purpose of communicating and energizing. This paper provides a novel learning-based approach towards such wireless network design. To that end, a parametric model of a practical energy harvester, accounting for various sources of nonlinearities, is proposed using a nonlinear regression algorithm applied over collected real …
Wireless all-analog biosensor design for concurrent microfluidic and physiological signal monitoring is presented in this work. The key component is an all-analog circuit capable of compressing two analog sources into one analog signal by Analog Joint Source-Channel Coding (AJSCC). Two circuit designs are discussed, including the stacked-Voltage Controlled Voltage Source (VCVS) design with the fixed number of levels, and an improved design, which supports a flexible number of AJSCC levels. Experimental results are presented on the wireless biosensor prototype, composed of Printed Circuit Board…
Deep learning-based automatic modulation classification (AMC) models are susceptible to adversarial attacks. Such attacks inject specifically crafted wireless interference into transmitted signals to induce erroneous classification predictions. Furthermore, adversarial interference is transferable in black box environments, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification model. In this work, we propose a novel wireless receiver architecture to mitigate the effects of adversarial interference in various black box a…
In this paper, a highly-efficient single-switch-regulated resonant wireless power receiver with hybrid modulation is proposed. To achieve both high efficiency and good output voltage regulation, phase shift and pulse width hybrid modulation are simultaneously applied. The soft switching operation in this topology is achieved by the cycle-by-cycle phase shift adjustment between the input current and the gate drive signal and also attributed to the reactive components such as the series-compensated secondary coil and the parasitic capacitor of the active switch . The soft switching operation als…
Resilient Machine-learning Institute (ReMI)（弹性机器学习研究所）
In wireless Internet of things (IoT), the sensors usually have limited bandwidth and power resources. Therefore, in a distributed setup, each sensor should compress and quantize the sensed observations before transmitting them to a fusion center (FC) where a global decision is inferred. Most of the existing compression techniques and entropy quantizers consider only the reconstruction fidelity as a metric, which means they decouple the compression from the sensing goal. In this work, we argue that data compression mechanisms and entropy quantizers should be co-designed with the sensing goal, s…
University of Maryland（马里兰大学）；Intelligent Automation, Inc.（智能自动化公司）
We consider a wireless communication system that consists of a transmitter, a receiver, and an adversary. The transmitter transmits signals with different modulation types, while the receiver classifies its received signals to modulation types using a deep learning-based classifier. In the meantime, the adversary makes over-the-air transmissions that are received as superimposed with the transmitter's signals to fool the classifier at the receiver into making errors. While this evasion attack has received growing interest recently, the channel effects from the adversary to the receiver have be…
As a green and secure wireless transmission method, secure spatial modulation (SM) is becoming a hot research area. Its basic idea is to exploit both the index of activated transmit antenna and amplitude phase modulation signal to carry messages, improve security, and save energy. In this paper, we review its crucial challenges: transmit antenna selection (TAS), artificial noise (AN) projection, power allocation (PA) and joint detection at the desired receiver. As the size of signal constellation tends to medium-scale or large-scale, the complexity of traditional maximum likelihood detector be…
Efficient Wireless Federated Learning via Low-Rank Gradient Factorization
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；School of Computing Science, University of Glasgow（格拉斯哥大学计算科学学院）
This paper proposes an ordered sequence detection (OSD) for digital pulse interval modulation (DPIM) in optical wireless communications. Leveraging the sparsity of DPIM sequences, OSD shows comparable performance to the optimal maximum likelihood sequence detection (MLSD) with much lower complexity. Compared with the widely adopted sample-by-sample optimal threshold detection (OTD), it considerably improves the bit error rate (BER) performance by mitigating error propagation. Moreover, this paper proposes a barrier signal-aided digital pulse interval modulation (BDPIM), where the last of every…
Multi-colour light-emitting diode (LED) based visible light communication (VLC) benefits from wavelength diversity while providing indoor illumination. Colour shift keying (CSK) is a well-researched IEEE standardised multi-colour VLC modulation technique. This paper presents an investigation into the performance of tri-chromatic LED (TLED) CSK standardised in IEEE 802.15.7 and a quad-chromatic LED (QLED) CSK, over a range of diffuse optical wireless channels, and proposes the use of frequency domain equalisation (FDE) at the receiver to combat multipath dispersion. The investigation results sh…
The limited bandwidth of optical wireless communication (OWC) front-end devices motivates the use of multiple-input-multiple-output (MIMO) techniques to enhance data rates. It is known that very high multiplexing gains could be achieved by spatial multiplexing (SMX) in exchange for exhaustive detection complexity. Alternatively, in spatial modulation (SM), a single light emitting diode (LED) is activated per time instance where information is carried by both the signal and the LED index. Since only an LED is active, both transmitter (TX) and receiver (RX) complexity reduces significantly while…
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Utah State University（犹他州立大学）
Automatic modulation classification enables intelligent communications and it is of crucial importance in today's and future wireless communication networks. Although many automatic modulation classification schemes have been proposed, they cannot tackle the intra-class diversity problem caused by the dynamic changes of the wireless communication environment. In order to overcome this problem, inspired by face recognition, a novel automatic modulation classification scheme is proposed by using the multi-scale network in this paper. Moreover, a novel loss function that combines the center loss …
Terahertz (THz) wireless communication is envisioned as a promising technology, which is capable of providing ultra-high-rate transmission up to Terabit per second. However, some hardware imperfections, which are generally neglected in the existing literature concerning lower data rates and traditional operating frequencies, cannot be overlooked in the THz systems. Hardware imperfections usually consist of phase noise, in-phase/quadrature imbalance, and nonlinearity of power amplifier. Due to the time-variant characteristic of phase noise, frequent pilot insertion is required, leading to decre…
In this paper, we present a deep learning based wireless transceiver. We describe in detail the corresponding artificial neural network architecture, the training process, and report on excessive over-the-air measurement results. We employ the end-to-end training approach with an autoencoder model that includes a channel model in the middle layers as previously proposed in the literature. In contrast to other state-of-the-art results, our architecture supports learning time synchronization without any manually designed signal processing operations. Moreover, the neural transceiver has been tes…
As an excellent aided communication tool, intelligent reflecting surface (IRS) can make a significant rate enhancement and coverage extension. In this paper, we present an investigation on beamforming in an IRS-aided directional modulation (DM) network. To fully explore the advantages of IRS, two beamforming methods with enhanced secrecy rate (SR) performance are proposed. The first method of maximizing secrecy rate (Max-SR) alternately optimizes confidential message (CM) beamforming vector, artificial noise (AN) beamforming vector and phase shift matrix. The first optimization vector is direc…
Federated learning (FL) is an emerging machine learning technique that aggregates model attributes from a large number of distributed devices. Several unique features such as energy saving and privacy preserving make FL a highly promising learning approach for power-limited and privacy sensitive devices. Although distributed computing can lower down the information amount that needs to be uploaded, model updates in FL can still experience performance bottleneck, especially for updates via wireless connections. In this work, we investigate the performance of FL update with mobile edge devices t…
In this paper, we discuss the feasibility of creating an UltraSound (US) communication circuit to wireless transmit outside the body diagnostic information from multiplexed biosensors chip built on the top layer of a drinkable CMOS Body Dust cube. The system requires to be small enough (lateral size of less than 100 um) to mimic the typical size of a larger blood cell (diameter around 30 um for white cells) and so support free circulation of the cube in human tissues. The second constraint came from the low-power consumption requirement, with the energy provided by an external US base station.…
