Low-latency Approach Research Articles

A Low-Latency Approach for RFF Identification in Open-Set Scenarios

Radio frequency fingerprint (RFF) identification represents a promising technique for lightweight device authentication. However, current research on RFF primarily focuses on the close-set recognition assumption. Moreover, the high computational complexity and excessive latency during the identification stage represent an intolerable burden for Internet of Things (IoT) devices. In this paper, we propose a deep-learning-based RFF identification framework in relation to open-set scenarios. Specifically, we leverage a simulated training scheme, in which we strategically designate certain devices as simulated unknowns. This allows us to fine-tune our extractor to better handle open-set recognition. Additionally, we construct an exemplar set that only contains representative RFF features to further reduce time consumption in the identification stage. The experiments are carried out on a hardware platform involving LoRa devices and using a USRP N210 software-defined radio receiver. The results show that the proposed framework can achieve 90.23% accuracy for rogue device detection and 93.85% accuracy for legitimate device classification. Furthermore, it is observed that using an exemplar set consisting of half the total data size can reduce the time overhead by 58% compared to using the entire dataset.

Vibrational Radar Backscatter Communications Theory and Bound

Vibrational radar backscatter communication (VRBC) utilizes millimeter-wave radar vibrometry to receive message signals from vibrating surfaces. So long as multiple transponding surfaces are separable by the system radar in angle and/or range, VRBC can simultaneously detect, isolate, and decode messages from these multiple sources. For anything-to-vehicle communications, VRBC is therefore a scalable, low-latency approach which leverages existing automotive radars to increase situational awareness without requiring any additional use of the RF spectrum. In this paper, we describe the modeling and processing of VRBC signals. We show that knowledge of surface resonances and coding constraints can be used to improve system performance by incorporating them into a Markov model. We provide insight into the rate capabilities of VRBC by providing a theoretical upper bound that incorporates the presence of intersymbol interference (ISI). Simulation results compare our upper bound to achievable communication rates using single symbol vs sequence detection maximum likelihood methods for a given symbol length, set of symbol excitations, and VRBC radar pulse repetition interval (PRI).

High spectrum utilization and low latency approach for multi-user-full-dimensional MIMO (MU-FD-MIMO) for 5G applications

Ultra-Reliable Low-Latency Communications (URLLC) network acts a significant role of extending new radio connectivity and improves 5 G applications instantly without increasing a latency of MIMO wireless framework. It is very much indeed for maximizing spectral utility and retains desired energy efficiency of the URLLC network. Due to technology add-ups, still, it needs improvement in the spectral property as well as energy efficiency under multiple delay constraints. Therefore, in this paper, a new MIMO wireless framework is proposed that influence two unique strategies namely multi-users (MU) and full dimensional (FD) are combined to develop an efficient URLLC network, which is referred as MU-FD-MIMO system. Firstly, an effective beamforming is achieved by activating partial CSI-RS transmission at every eNB antenna. Secondly, estimate 3D response pattern of receiving antenna by modifying array size which extracts adaptability of channel properties. Thereby, a proper angle adjustment is carried between individual antenna element and UEs. As a result, improves beamforming coverage ability and ensure an optimistic thresholding function generated after estimates subcarrier spacing and 3D channel gain model. The experimental validation of the proposed MU-FD-MIMO system is subjected under different delay constraints includes (i) allow user up to tolerable limit, (ii) allow user with delay constrained respectively. It reaches an impressive service of maximum spectral utility, energy efficiency and low latency even under the delay-constrained scenarios.

Low‐latency transformer model for streaming automatic speech recognition

Transformer models have made great progress in automatic speech recognition. However, it is challenging for streaming transformer models to make trade-off between output latency and recognition accuracy. In this letter, it is aimed to propose a low-latency transformer model with satisfactory recognition accuracy. First, a streaming transformer is designed and explain how it works streamingly. Second, the authors propose to use CTC during training to minimise the latency of transformer models. Finally, the authors also propose to utilise CTC as a backup during decoding to ensure that the low-latency characteristic is maintained. The authors fairly compare our streaming transformer model to existing streaming models, particularly the transducer model, which is a popular low-latency approach. The experiments show that, while having comparable output latency, the transformer model outperforms the transducer model by average relative character (or word) error rate reduction of 22.18%, 26.71% and 19.36% on HKUST, Switchboard and Call Home, respectively.

A Low Latency Approach for Blind Source Separation

We present a low latency approach for blind source separation (BSS). BSS algorithms generally require a long window to estimate the demixing parameters. In traditional approaches, the long analysis window leads to a long algorithmic delay. Hence, traditional BSS approaches cannot be used in real-time systems. In contrast, our approach reduces the algorithmic delay independently of the window length used for estimation, while retaining separation performance. The new method exploits that the information about the sources provided by additional microphones can be traded against algorithmic delay. The method can be integrated with existing BSS algorithms and can be implemented in the time domain or in the time-frequency domain. Our experimental results confirm the effectiveness of our approach.

Generic Low-Latency Masking in Hardware

In this work, we introduce a generalized concept for low-latency masking that is applicable to any implementation and protection order, and (in its most extreme form) does not require on-the-fly randomness. The main idea of our approach is to avoid collisions of shared variables in nonlinear circuit parts and to skip the share compression. We show the feasibility of our approach on a full implementation of a one-round unrolled Ascon variant and on an AES S-box case study. Additionally, we discuss possible trade-offs to make our approach interesting for practical implementations. As a result, we obtain a first-order masked AES S-box that is calculated in a single clock cycle with rather high implementation costs (60.7 kGE), and a two-cycle variant with much less implementation costs (6.7 kGE). The side-channel resistance of our Ascon S-box designs up to order three are then verified using the formal analysis tool of [BGI+18]. Furthermore, we introduce a taint checking based verification approach that works specifically for our low-latency approach and allows us to verify large circuits like our low-latency AES S-box design in reasonable time.

Low-Latency Approach for Secure ECG Feature Based Cryptographic Key Generation

We propose a low-latency approach for generating secure electrocardiogram (ECG) feature-based cryptographic keys. This is done by taking advantage of the uniqueness and randomness properties of ECG’s main features. This approach achieves a low-latency since the key generation relies on four reference-free ECG’s main features that can be acquired in short time. We call the approach several ECG features (SEF)-based cryptographic key generation. SEF consists of: 1) detecting the arrival time of ECG’s fiducial points using Daubechies wavelet transform to compute ECG’s main features accordingly; 2) using a dynamic technique to specify the optimum number of bits that can be extracted from each main ECG feature, comprising of PR, RR, PP, QT, and ST intervals; 3) generating cryptographic keys by exploiting the above-mentioned ECG features; and 4) consolidating and strengthening the SEF approach with cryptographically secure pseudo-random number generators. Fibonacci linear feedback shift register and advanced encryption standard algorithms are implemented as the pseudo-random number generator to enhance the security level of the generated cryptographic keys. Our approach is applied to 239 subjects’ ECG signals comprising of normal sinus rhythm, arrhythmia, atrial fibrillation, and myocardial infraction. The security analyses of the proposed approach are carried out in terms of distinctiveness, test of randomness, temporal variance, and using National Institute of Standards and Technology benchmark. The analyses reveal that the normal ECG rhythms have slightly better randomness compared with the abnormal ones. The analyses also show that the strengthened SEF key generation approach provides a higher security level in comparison to existing approaches that rely only on singleton ECG features. For the normal ECG rhythms, the SEF approach has in average the entropy of about 0.98 while cryptographic keys which are generated utilizing the strengthened SEF approach offer the entropy of ~1. The execution time required to generate the cryptographic keys on different processors is also examined. The results reveal that our SEF approach is in average 1.8 times faster than existing key generation approaches which only utilize the inter pulse interval feature of ECG.

CELLA: FPGA Based Candidate Execution with Low Latency Approach for Soft MIMO Detector

This paper describes the design and Field Programmable Gate Array (FPGA) based 4 × 4 breadth heuristic Multiple-Input—Multiple-Output (MIMO) decoder using 16 and 64 Quadrature Amplitude Modulation (QAM) schemes. The intention of this work is to observe the performance of Candidate Execution with Low Latency Approach for soft MIMO detector in FPGA (CELLA). The Smart Ordering and Candidate Adding (SOCA), Parallel Candidate Adding (PCA) and Backward Candidate Adding (BCA) give better performance in terms of Bit Error Rate (BER) or chip level service. In order to attain both BER and FPGA level performance in a single system, CELLA is developed in this work. Simulation and experimental results demonstrate the effectiveness of the proposed work under the system 4 × 4 MIMO-OFDM employing 16 QAM and 64 QAM. The proposed experiment is implemented in Xilinx Virtex 5 C5VSX240T. The performance results, in terms of FPGA level 76% slice reduction, 58.76% throughput improvement, 75% power reduction and 87% latency reduction, are achieved. The BER performance is observed and compared with the conventional algorithms. Thus, the proposed work achieves better outcome than the conventional work.

A Quasi-Stationary Markov Chain Model of a Cooperative Multi-Hop Linear Network

We consider a quasi-stationary Markov chain as a model for a decode and forward wireless multi-hop cooperative transmission system that forms successive Opportunistic Large Arrays (OLAs). This paper treats a linear network topology, where the nodes form a one-dimensional horizontal grid with equal spacing. In this OLA approach, all nodes are intended to decode and relay. Therefore, the method has potential application as a high-reliability and low-latency approach for broadcasting in a line-shaped network, or unicasting along a pre-designated route. We derive the transition probability matrix of the Markov chain based on the hypoexponential distribution of the received power at a given time instant assuming that all the nodes have equal transmit power and the channel has Rayleigh fading and path loss with an arbitrary exponent. The state is represented as a ternary word, which indicates which nodes have decoded in the present hop, in a previous hop, or have not yet decoded. The Perron-Frobenius eigenvalue and the corresponding eigenvector of the sub-stochastic matrix indicates the signal-to-noise ratio (SNR) margin that enables a given hop distance.

Compact and low-latency scheme for optical steganography using chirped fibre Bragg gratings

A compact and low-latency approach for optical steganography has been experimentally demonstrated using a matched pair of chirped fibre Bragg gratings. A stealth WDM channel is temporally and spectrally hidden underneath a public optical code division multiple access signal. With the use of chirped fibre Bragg gratings, the length of the pulse compressor/decompressor is reduced by a factor of 2000. The power penalty on the public channel is <1 dB when the stealth signal has a power of 10 dB lower than the public channel.