arXiv subjects

Danijela Cabric

Publications and source records attributed to Danijela Cabric.

At least 37 records · Page 2Linked to original sources

arXiv2022

Multi-Mode Spatial Signal Processor with Rainbow-like Fast Beam Training and Wideband Communications using True-Time-Delay Arrays

Initial access in millimeter-wave (mmW) wireless is critical toward successful realization of the fifth-generation (5G) wireless networks and beyond. Limited bandwidth in existing standards and use of phase-shifters in analog/hybrid phased-antenna arrays (PAA) are not suited for these emerging standards demanding low-latency direction finding. This work proposes a reconfigurable true-time-delay (TTD) based spatial signal processor (SSP) with frequency-division beam training methodology and wideband beam-squint less data communications. Discrete-time delay compensated clocking technique is used to support 800~MHz bandwidth with a large unity-gain bandwidth ring-amplifier (RAMP)-based signal combiner. To extensively characterize the proposed SSP across different SSP modes and frequency-angle pairs, an automated testbed is developed using computer-vision techniques that significantly speeds up the testing progress and minimize possible human errors. Using seven levels of time-interleaving for each of the 4 antenna elements, the TTD SSP has a delay range of 3.8 ns over 800 MHz and achieves unique frequency-to-angle mapping in the beamtraining mode with nearly 12 dB frequency-independent gain in the beamforming mode. The SSP is prototyped in 65nm CMOS with an area of 1.98mm$^2$ consuming only 29 mW excluding buffers. Further, an error vector magnitude (EVM) of 9.8% is realized for 16-QAM modulation at a speed of 122.8 Mb/s.

eess.SP↗

arXiv2021

Wideband Beamforming with Rainbow Beam Training using Reconfigurable True-Time-Delay Arrays for Millimeter-Wave Wireless

The decadal research in integrated true-time-delay arrays have seen organic growth enabling realization of wideband beamformers for large arrays with wide aperture widths. This article introduces highly reconfigurable delay elements implementable at analog or digital baseband that enables multiple SSP functions including wideband beamforming, wideband interference cancellation, and fast beam training. Details of the beam-training algorithm, system design considerations, system architecture and circuits with large delay range-to-resolution ratios are presented leveraging integrated delay compensation techniques. The article lays out the framework for true-time-delay based arrays in next-generation network infrastructure supporting 3D beam training in planar arrays, low latency massive multiple access, and emerging wireless communications standards.

eess.SP↗

arXiv2021

Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval

As the Internet of Things (IoT) continues to grow, ensuring the security of systems that rely on wireless IoT devices has become critically important. Deep learning-based passive physical layer transmitter authorization systems have been introduced recently for this purpose, as they accommodate the limited computational and power budget of such devices. These systems have been shown to offer excellent outlier detection accuracies when trained and tested on a fixed authorized transmitter set. However in a real-life deployment, a need may arise for transmitters to be added and removed as the authorized set of transmitters changes. In such cases, the system could experience long down-times, as retraining the underlying deep learning model is often a time-consuming process. In this paper, we draw inspiration from information retrieval to address this problem: by utilizing feature vectors as RF fingerprints, we first demonstrate that training could be simplified to indexing those feature vectors into a database using locality sensitive hashing (LSH). Then we show that approximate nearest neighbor search could be performed on the database to perform transmitter authorization that matches the accuracy of deep learning models, while allowing for more than 100x faster retraining. Furthermore, dimensionality reduction techniques are used on the feature vectors to show that the authorization latency of our technique could be reduced to approach that of traditional deep learning-based systems.

cs.LG↗

arXiv2021

Distributed Transmit Beamforming: Design and Demonstration from the Lab to UAVs

Cooperating radios can extend their communication range by adjusting their signals to ensure coherent combining at a destination radio. This technique is called distributed transmit beamforming. Beamforming (BF) relies on the BF radios having frequency synchronized carriers and phases adjusted for coherent combining. Both requirements are typically met by exchanging preambles with the destination. However, since BF aims to increase the communication range, the individually transmitted preambles are typically at low SNR and their lengths are constrained by the channel coherence time. These noisy preambles lead to errors in frequency and phase estimation, which result in randomly changing BF gains. To build reliable distributed BF systems, the impact of estimation errors on the BF gains need to be considered in the design. In this work, assuming a destination-led BF protocol and Kalman filter for frequency tracking, we optimize the number of BF radios and the preamble lengths to achieve reliable BF gain. To do that, we characterize the relations between the BF gains distribution, the channel coherence time, and design parameters like the SNR, preamble lengths, and the number of radios. The proposed relations are verified using simulations and via experiments using software-defined radios in a lab and on UAVs.

eess.SP↗

arXiv2021

Signal Processing Based Deep Learning for Blind Symbol Decoding and Modulation Classification

Blindly decoding a signal requires estimating its unknown transmit parameters, compensating for the wireless channel impairments, and identifying the modulation type. While deep learning can solve complex problems, digital signal processing (DSP) is interpretable and can be more computationally efficient. To combine both, we propose the dual path network (DPN). It consists of a signal path of DSP operations that recover the signal, and a feature path of neural networks that estimate the unknown transmit parameters. By interconnecting the paths over several recovery stages, later stages benefit from the recovered signals and reuse all the previously extracted features. The proposed design is demonstrated to provide 5% improvement in modulation classification compared to alternative designs lacking either feature sharing or access to recovered signals. The estimation results of DPN along with its blind decoding performance are shown to outperform a blind signal processing algorithm for BPSK and QPSK on a simulated dataset. An over-the-air software-defined-radio capture was used to verify DPN results at high SNRs. DPN design can process variable length inputs and is shown to outperform relying on fixed length inputs with prediction averaging on longer signals by up to 15% in modulation classification.

eess.SP↗

arXiv2021

Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in Multipath Channels

Communication systems at millimeter-wave (mmW) and sub-terahertz frequencies are of increasing interest for future high-data rate networks. One critical challenge faced by phased array systems at these high frequencies is the efficiency of the initial beam alignment, typically using only phase-less power measurements due to high frequency oscillator phase noise. Traditional methods for beam alignment require exhaustive sweeps of all possible beam directions, thus scale communications overhead linearly with antenna array size. For better scaling with the large arrays required at high mmW bands, compressive sensing methods have been proposed as their overhead scales logarithmically with the array size. However, algorithms utilizing machine learning have shown more efficient and more accurate alignment when using real hardware due to array impairments. Additionally, few existing phase-less beam alignment algorithms have been tested over varied secondary path strength in multipath channels. In this work, we introduce a novel, machine learning based algorithm for beam alignment in multipath environments using only phase-less received power measurements. We consider the impacts of phased array sounding beam design and machine learning architectures on beam alignment performance and validate our findings experimentally using 60 GHz radios with 36-element phased arrays. Using experimental data in multipath channels, our proposed algorithm demonstrates an 88\% reduction in beam alignment overhead compared to an exhaustive search and at least a 62\% reduction in overhead compared to existing compressive methods.

eess.SP↗

arXiv2021

Open Set RF Fingerprinting using Generative Outlier Augmentation

RF devices can be identified by unique imperfections embedded in the signals they transmit called RF fingerprints. The closed set classification of such devices, where the identification must be made among an authorized set of transmitters, has been well explored. However, the much more difficult problem of open set classification, where the classifier needs to reject unauthorized transmitters while recognizing authorized transmitters, has only been recently visited. So far, efforts at open set classification have largely relied on the utilization of signal samples captured from a known set of unauthorized transmitters to aid the classifier learn unauthorized transmitter fingerprints. Since acquiring new transmitters to use as known transmitters is highly expensive, we propose to use generative deep learning methods to emulate unauthorized signal samples for the augmentation of training datasets. We develop two different data augmentation techniques, one that exploits a limited number of known unauthorized transmitters and the other that does not require any unauthorized transmitters. Experiments conducted on a dataset captured from a WiFi testbed indicate that data augmentation allows for significant increases in open set classification accuracy, especially when the authorized set is small.

eess.SP↗

arXiv2021

A 4-Element 800MHz-BW 29mW True-Time-Delay Spatial Signal Processor Enabling Fast Beam-Training with Data Communications

Spatial signal processors (SSP) for emerging millimeter-wave wireless networks are critically dependent on link discovery. To avoid loss in communication, mobile devices need to locate narrow directional beams with millisecond latency. In this work, we demonstrate a true-time-delay (TTD) array with digitally reconfigurable delay elements enabling both fast beam-training at the receiver with wideband data communications. In beam-training mode, large delay-bandwidth products are implemented to accelerate beam training using frequency-dependent probing beams. In data communications mode, precise beam alignment is achieved to mitigate spatial effects during beam-forming for wideband signals. The 4-element switched-capacitor based time-interleaved array uses a compact closed-loop integrator for signal combining with the delay compensation implemented in the clock domain to achieve high precision and large delay range. Prototyped in TSMC 65nm CMOS, the TTD SSP successfully demonstrates unique frequency-to-angle mapping with 3.8ns maximum delay and 800MHz bandwidth in the beam-training mode. In the data communications mode, nearly 12dB uniform beamforming gain is achieved from 80MHz to 800MHz. The TTD SSP consumes 29mW at 1V supply achieving 122MB/s with 16-QAM at 9.8% EVM.

eess.SP↗

arXiv2021

A Schottky-Diode-Based Wake-Up Receiver for IoT Applications

This paper presents an always-on low-power wake-up receiver (WuRx) that activates the remainder of the system when a wake-up signal is detected. The proposed receiver has two phases of waking up. The first phase uses an integrated CMOS Schottky diodes to detect the signal power at a low bias current. The approach dissipates low quiescent power and allows the reuse of the design in multiple frequency bands with only modifying the matching network. In the second phase, a data-locked startable oscillator is proposed to correlate the received data with a target signature. This design eliminates the area and power dissipation of an external crystal oscillator and only turns on when the second phase is activated. By correlating to a target signature, the second phase also reduces the probability of a false alarm (PFA) that would otherwise wake up the high-power bulk of the system. The two-phase approach leads to significant reduction in average power consumption when compared to a single-phase design. This implementation targets sub-ms wake-up latency and operates in the unlicensed band at a 750-MHz carrier frequency with a data rate of 200 kbps. The design achieves $\sim$8.45pJ/bit and $<$-50 dBm of input sensitivity and average power of 1.69$μ$W. The system is implemented in 65-nm CMOS technology and occupies an area of 1mm$\times$0.75mm.

eess.SP↗

arXiv2021

UAV Swarm Position Optimization for High Capacity MIMO Backhaul

A swarm of cooperating UAVs communicating with a distant multiantenna ground station can leverage MIMO spatial multiplexing to scale the capacity. Due to the line-of-sight propagation between the swarm and the ground station, the MIMO channel is highly correlated, leading to limited multiplexing gains. In this paper, we optimize the UAV positions to attain the maximum MIMO capacity given by the single user bound. An infinite set of UAV placements that attains the capacity bound is first derived. Given an initial swarm placement, we formulate the problem of minimizing the distance traveled by the UAVs to reach a placement within the capacity maximizing set of positions. An offline centralized solution to the problem using block coordinate descent is developed assuming known initial positions of UAVs. We also propose an online distributed algorithm, where the UAVs iteratively adjust their positions to maximize the capacity. Our proposed approaches are shown to significantly increase the capacity at the expense of a bounded translation from the initial UAV placements. This capacity increase persists when using a massive MIMO ground station. Using numerical simulations, we show the robustness of our approaches in a Rician channel under UAV motion disturbances.

eess.SP↗

arXiv2020

Spatial Signal Strength Prediction using 3D Maps and Deep Learning

Machine learning (ML) and artificial neural networks (ANNs) have been successfully applied to simulating complex physics by learning physics models thanks to large data. Inspired by the successes of ANNs in physics modeling, we use deep neural networks (DNNs) to predict the radio signal strength field in an urban environment. Our algorithm relies on samples of signal strength collected across the prediction space and a 3D map of the environment, which enables it to predict the scattering of radio waves through the environment. While already extensive body of research exists in spatial signal strength prediction, our approach differs from most existing approaches in that it does not require the knowledge of the transmitter location, it does not require side channel information such as attenuation and shadowing parameters, and it is the first work, to the best of our knowledge, to use 3D maps to accomplish the task of signal strength prediction. This algorithm is developed for the purpose of placement optimization of a UAV or mobile robot to maximize the signal strength to or from a stationary transceiver but it also holds relevance to dynamic spectrum access networks, cellular coverage design, power control algorithms, etc.

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arXiv2020

Penetrating RF Fingerprinting-based Authentication with a Generative Adversarial Attack

Physical layer authentication relies on detecting unique imperfections in signals transmitted by radio devices to isolate their fingerprint. Recently, deep learning-based authenticators have increasingly been proposed to classify devices using these fingerprints, as they achieve higher accuracies compared to traditional approaches. However, it has been shown in other domains that adding carefully crafted perturbations to legitimate inputs can fool such classifiers. This can undermine the security provided by the authenticator. Unlike adversarial attacks applied in other domains, an adversary has no control over the propagation environment. Therefore, to investigate the severity of this type of attack in wireless communications, we consider an unauthorized transmitter attempting to have its signals classified as authorized by a deep learning-based authenticator. We demonstrate a reinforcement learning-based attack where the impersonator--using only the authenticator's binary authentication decision--distorts its signals in order to penetrate the system. Extensive simulations and experiments on a software-defined radio testbed indicate that at appropriate channel conditions and bounded by a maximum distortion level, it is possible to fool the authenticator reliably at more than 90% success rate.

cs.CR↗

arXiv2020

Band Assignment in Ultra-Narrowband (UNB) Systems for Massive IoT Access

In this work, we consider a novel type of Internet of Things (IoT) ultra-narrowband (UNB) network architecture that involves multiple multiplexing bands or channels for uplink transmission. An IoT device can randomly choose any of the multiplexing bands and transmit its packet. Due to hardware constraints, a base station (BS) is able to listen to only one multiplexing band. The hardware constraint is mainly due to the complexity of performing fast Fourier transform (FFT) at a very small sampling interval over the multiplexing bands in order to counter the uncertainty of IoT device frequency and synchronize onto transmissions. The objective is to find an assignment of BSs to multiplexing bands in order to maximize the packet decoding probability (PDP). We develop a learning-based algorithm based on a sub-optimal solution to PDP maximization. The simulation results show that our approach to band assignment achieves near-optimal performance in terms of PDP, while at the same time, significantly exceeding the performance of random assignment. We also develop a heuristic algorithm with no learning overhead based on the locations of the BSs that also outperforms random assignment and serves as a performance reference to our learning-based algorithm.

eess.SP↗

arXiv2020

Hybrid Vehicular and Cloud Distributed Computing: A Case for Cooperative Perception

In this work, we propose the use of hybrid offloading of computing tasks simultaneously to edge servers (vertical offloading) via LTE communication and to nearby cars (horizontal offloading) via V2V communication, in order to increase the rate at which tasks are processed compared to local processing. Our main contribution is an optimized resource assignment and scheduling framework for hybrid offloading of computing tasks. The framework optimally utilizes the computational resources in the edge and in the micro cloud, while taking into account communication constraints and task requirements. While cooperative perception is the primary use case of our framework, the framework is applicable to other cooperative vehicular applications with high computing demand and significant transmission overhead. The framework is tested in a simulated environment built on top of car traces and communication rates exported from the Veins vehicular networking simulator. We observe a significant increase in the processing rate of cooperative perception sensor frames when hybrid offloading with optimized resource assignment is adopted. Furthermore, the processing rate increases with V2V connectivity as more computing tasks can be offloaded horizontally.

cs.DC↗

arXiv2020

Combining Deep Learning and Linear Processing for Modulation Classification and Symbol Decoding

Deep learning has been recently applied to many problems in wireless communications including modulation classification and symbol decoding. Many of the existing end-to-end learning approaches demonstrated robustness to signal distortions like frequency and timing errors, and outperformed classical signal processing techniques with sufficient training. However, deep learning approaches typically require hundreds of thousands of floating points operations for inference, which is orders of magnitude higher than classical signal processing approaches and thus do not scale well for long sequences. Additionally, they typically operate as a black box and without insight on how their final output was obtained, they can't be integrated with existing approaches. In this paper, we propose a novel neural network architecture that combines deep learning with linear signal processing typically done at the receiver to realize joint modulation classification and symbol recovery. The proposed method estimates signal parameters by learning and corrects signal distortions like carrier frequency offset and multipath fading by linear processing. Using this hybrid approach, we leverage the power of deep learning while retaining the efficiency of conventional receiver processing techniques for long sequences. The proposed hybrid approach provides good accuracy in signal distortion estimation leading to promising results in terms of symbol error rate. For modulation classification accuracy, it outperforms many state of the art deep learning networks.

eess.SP↗

arXiv2020

mmRAPID: Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam Alignment

Millimeter-wave communication has the potential to deliver orders of magnitude increases in mobile data rates. A key design challenge is to enable rapid beam alignment with phased arrays. Traditional millimeter-wave systems require a high beam alignment overhead, typically an exhaustive beam sweep, to find the beam direction with the highest beamforming gain. Compressive sensing is a promising framework to accelerate beam alignment. However, model mismatch from practical array hardware impairments poses a challenge to its implementation. In this work, we introduce a neural network assisted compressive beam alignment method that uses noncoherent received signal strength measured by a small number of pseudorandom sounding beams to infer the optimal beam steering direction. We experimentally showcase our proposed approach with a 60GHz 36-element phased array in a suburban line-of-sight environment. The results show that our approach achieves post alignment beamforming gain within 1dB margin compared to an exhaustive search with 90.2 percent overhead reduction. Compared to purely model-based noncoherent compressive beam alignment, our method has 75 percent overhead reduction.

eess.SP↗

arXiv2020

Spectrum Sharing for Massive Access in Ultra-Narrowband IoT Systems

Ultra-narrowband (UNB) communications has become a signature feature for many emerging low-power wide-area (LPWA) networks. Specifically, using extremely narrowband signals helps the network connect more Internet-of-things (IoT) devices within a given band. It also improves robustness to interference, extending the coverage of the network. In this paper, we study the coexistence capability of UNB networks and their scalability to enable massive access. To this end, we develop a stochastic geometry framework to analyze and model UNB networks on a large scale. The framework captures the unique characteristics of UNB communications, including the asynchronous time-frequency access, signal repetition, and the absence of base station (BS) association. Closed-form expressions of the transmission success probability and network connection density are presented for several UNB protocols. We further discuss multiband access for UNB networks, proposing a low-complexity protocol. Our analysis reveals several insights on the geographical diversity achieved when devices do not connect to a single BS, the optimal number of signal repetitions, and how to utilize multiple bands without increasing the complexity of BSs. Simulation results are provided to validate the analysis, and they show that UNB communications enables a single BS to connect thousands of devices even when the spectrum is shared with other networks.

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arXiv2020

True-Time-Delay Arrays for Fast Beam Training in Wideband Millimeter-Wave Systems

The best beam steering directions are estimated through beam training, which is one of the most important and challenging tasks in millimeter-wave and sub-terahertz communications. Novel array architectures and signal processing techniques are required to avoid prohibitive beam training overhead associated with large antenna arrays and narrow beams. In this work, we leverage recent developments in true-time-delay (TTD) arrays with large delay-bandwidth products to accelerate beam training using frequency-dependent probing beams. We propose and study two TTD architecture candidates, including analog and hybrid analog-digital arrays, that can facilitate beam training with only one wideband pilot. We also propose a suitable algorithm that requires a single pilot to achieve high-accuracy estimation of angle of arrival. The proposed array architectures are compared in terms of beam training requirements and performance, robustness to practical hardware impairments, and power consumption. The findings suggest that the analog and hybrid TTD arrays achieve a sub-degree beam alignment precision with 66% and 25% lower power consumption than a fully digital array, respectively. Our results yield important design trade-offs among the basic system parameters, power consumption, and accuracy of angle of arrival estimation in fast TTD beam training.

eess.SP↗