Preamble Detection Research Articles

Non-orthogonal resource scheduling with enhanced preamble detection method for cellular random access networks

In this article, we propose a non-orthogonal resource scheduling (NORS) scheme with an enhanced preamble detection (PD) method for cellular random access (RA) systems. The enhanced PD method is designed based on a deep learning technique that can classify three PD statuses: idle, collision-free, and collision. Because of this collision resolution capability, the base station (BS) can exploit radio resources non-orthogonally in contention-based RA protocols. In addition, we mathematically analyze the performance of our proposed NORS scheme in terms of RA success probability and resource utilization efficiency and conduct a comparison with the baseline orthogonal resource scheduling (ORS) scheme. Through simulations, we verify the validity of our mathematical analysis. As a result, our results will be a useful guideline for resource scheduling and provide support for massive connectivity.

Preamble detection for broadband power line communication systems

Preamble detection for broadband power line communication systems

Corrections to “LoRa Preamble Detection With Optimized Thresholds”

Corrections to “LoRa Preamble Detection With Optimized Thresholds”

Automatic Dependent Surveillance-Broadcast Deceptive Jamming Detection Method Based on Track Data

The characteristics of automatic dependent surveillance-broadcast (ADS-B) technology, such as unencrypted channels and open data protocols, make it extremely vulnerable to various intentional or unintentional spoofing and jamming, which seriously threatens air traffic safety. Traditional approaches do not update their high-bit data for a long time, which causes the situations of the same encoding results to have different aircraft positions and it is difficult to determine the real position of the aircraft and effectively detect ADS-B jamming. Therefore, it is urgent to propose more effective detection methods for ADS-B spoofing and jamming. First, the methods and principles of frequency shift calculation and automatic monitoring algorithm are introduced. Then, signal consistency detection and error robustness are analyzed, and a track-data-based detection model for abnormal ADS-B data are proposed. The track-data-based detection methods can use multi-point sampling judgment algorithm to realize the reception and processing of data bits. This method makes full use of the information of multiple sampling values of each data bit and the reference power value obtained in the preamble detection, and determines the data value and confidence value through the relationship between the two. The detection methods for ADS-B spoofing and jamming distinguishes the true and false signals and the authenticity of target positions by comparing the arrival time difference of each signal at the ground station. Finally, the research results show that, with the increase of signal to noise ratio, no matter the estimation based on virtual array translation or the estimation based on circular stationary signal, their estimation accuracies are improved with gradually increasing trends.

SDR-LoRa, an open-source, full-fledged implementation of LoRa on Software-Defined-Radios: Design and potential exploitation

In this paper, we present SDR-LoRa, an open-source, full-fledged Software Defined Radio (SDR) implementation of a LoRa transceiver. First, we conduct a thorough analysis of the LoRa physical layer (PHY) functionalities, encompassing processes such as packet modulation, demodulation, and preamble detection. Then, we leverage on this analysis to create a pioneering SDR-based LoRa PHY implementation. Accordingly, we thoroughly describe all the implementation details. Moreover, we illustrate how SDR-LoRa can help boost research on the LoRa protocol by presenting three exemplary key applications that can be built on top of our implementation, namely fine-grained localization, interference cancellation, and enhanced link reliability. To validate SDR-LoRa and its applications, we test it on two different platforms: (i) a physical setup involving USRP radios and off-the-shelf commercial devices, and (ii) the Colosseum wireless channel emulator. Our experimental findings reveal that (i) SDR-LoRa performs comparably to conventional commercial LoRa systems, and (ii) all the aforementioned applications can be successfully implemented on top of SDR-LoRa with remarkable results. The complete details of the SDR-LoRa implementation code have been publicly shared online, together with a plug-and-play Colosseum container.

Preamble Detection Performance of LoRa Under Low-Complexity Receiver

Preamble Detection Performance of LoRa Under Low-Complexity Receiver

Preamble detection method for RSS data synchronization in WLAN monitoring

Preamble detection method for RSS data synchronization in WLAN monitoring

Composite Preambles Based on Differential Phase Rotations for Grant-Free Random Access Systems

With the advantages of low signaling overhead and latency, grant-free random access (GFRA) becomes a promising technology for supporting massive machine-type communications (mMTC), but poses new challenges for active user detection (AUD) and channel estimation (CE), whose performance mainly depends on the preamble detection. In this paper, we design the composite preamble based on differential phase rotations by aggregating orthogonal Zadoff-Chu (ZC) sequences and multiple root ZC sequences with differential phase rotations to reduce the probability of preamble collisions, thereby improving the performance of AUD and CE. In particular, differential phase rotations extend the preamble set size so that users colliding in orthogonal sequences can be distinguished by phase rotations. In addition, it also reduces non-orthogonal interference and thus reduces CE errors. The preamble detection algorithm and CE scheme are proposed, along with the theoretical analysis of AUD and CE performance to verify the effectiveness of the designed preamble. In addition, the proposed preamble is extended to combine phase rotations with cyclic shifts to further enlarge the preamble set size with low non-orthogonality. Simulation results show that the proposed composite preamble outperforms existing preambles in terms of the probability of detection and CE accuracy.

The Design and Implementation of a Steganographic Communication System over In-Band Acoustical Channels

This article presents SoundSticker, a system for steganographic, in-band data communication over an acoustic channel. In contrast with recent works that hide bits in inaudible frequency bands, SoundSticker embeds hidden bits in the audible sounds, making them more reliably survive audio codecs and bandpass filtering, while achieving a higher data rate and remaining imperceptible to a listener. The key observation behind SoundSticker is that the human ear is less sensitive to the audio phase changes than the frequency and amplitude changes, which leaves us an opportunity to alter the phase of an audio clip to convey hidden information. We take advantage of this opportunity and build an OFDM-based physical layer. To make this PHY-layer design work for a variety of end devices with heterogeneous computation resources, SoundSticker addresses multiple technical challenges including perceivable waveform artifacts caused by the phase-based modulation, bit rate adaptation without channel sounding and real-time preamble detection. Our prototype on both smartphones and ESP32 platforms demonstrates SoundSticker’s superior performance against the state of the arts, while preserving excellent sound quality and remaining unaffected by common audio codecs like MP3 and AAC. Audio clips produced by SoundSticker can be found at https://soundsticker.github.io/ .

MagneComm+: Near-Field Electromagnetic Induction Communication With Magnetometer

Near-field communication (NFC) technology emerges as a vital role with appealing benefits for users to improve mobile device's functionality. Although today's most smartphones and smartwatches come with NFC support, other mobile devices (e.g., PC and laptops) and IoT devices that don't equip with dedicated radio modules cannot take advantage of wide-scale NFC capability. We design and develop MagneComm+, an NFC implementation scheme without dedicated hardware and propose a novel near-field communication protocol that is applicable to almost all mobile devices and IoT devices. The key idea is to utilize the electromagnetic induction (EMI) signal emitted from the computing devices (e.g., CPUs) and captured by magnetometers on mobile devices for communication. We tackle challenges in data encoding/decoding, preamble detection, retransmission and error correction, multi-transmitter, and full-duplex schemes, to efficiently generate and reliably receive EMI signal with the hardware available on devices. We prototype MagneComm+ on both between laptops and smartphones, as well as between two laptops with an external magnetometer. Extensive evaluation results show that our MagneComm+ supports around 10cm communication distance with average 110 bit per second (bps) data rate on the normal-speed mode, and maximum 17.28kbps on the full-speed mode.

Deep Learning-Based User Activity Detection and Channel Estimation in Grant-Free NOMA

In the uplink machine-type communication (MTC) system, a combination of grant-free transmission and non-orthogonal multiple access (NOMA) emerges to reduce the control overhead and transmission latency. In the grant-free scenario, the base station needs to identify the active devices and estimate the channel state information before the data detection. However, due to the lack of a scheduling process, the user activity detection (UAD) and channel estimation (CE) are both challenging, especially when short non-orthogonal preambles are adopted. In this paper, by exploiting the framework of the compressive sensing-based algorithm, we propose a novel deep learning architecture, namely UAD and CE Neural Network (UAD-CE-NN), to effectively solve the joint UAD and CE problem for grant-free NOMA. In the proposed scheme, the user activity and channel state information hidden in the received data signals are also exploited to aid the preamble for higher detection accuracy. Specifically, UAD-CE-NN is composed of two stages: we first build a preamble detection neural network for a tentative UAD-CE; a data detection neural network is then deployed to exploit the data signals. Compared with the conventional schemes, the proposed scheme obtains much higher accuracy for both the UAD and CE, especially when short preamble sequences are employed.

LoRa Preamble Detection With Optimized Thresholds

LoRa Preamble Detection With Optimized Thresholds

Random Access Preamble Detection with Noise Suppression for 5G-Integrated Satellite Communication Systems

Thanks to its capability of providing seamless massive access and extended coverage, satellite communication has been envisioned as a promising complementary part of the future 6G network. Due to the large satellite-to-ground propagation loss, noise mitigation is one of the most important considerations for implementing key interface technologies on boarding a satellite, e.g., random access (RA). This paper aims at developing an effective preamble detection method with noise suppression for 5G-ntegrated satellite RA systems. Specifically, according to the satellite ephemeris and user equipment location, we first perform the pre-compensation of timing and frequency offset before preamble transmission to determine all the possible correlation peak positions in advance. By leveraging the advantage of the wavelet transform in signal-to-noise separation, we further design a novel detection framework based on wavelet denoising, which can efficiently reconstruct preamble signature from the noisy power delay profile. Simulation results validate the feasibility of the proposed method, and show that our method can achieve a notably improved detection performance under extremely low signal-to-noise ratio conditions, in comparison with the conventional one.

Two-Stage Preamble Detector for LEO Satellite-Based NTN IoT Random Access

In this paper, we propose an enhanced random access preamble (PA) detector for the low earth orbit (LEO) satellite-based non-terrestrial network (NTN) Internet-of-Things (IoT) that efficiently detects non-orthogonal PAs while suppressing interference. The proposed detector operates in two stages to efficiently suppress the interference caused by non-orthogonal PAs. In the first stage, reconstruction and cancellation of non-orthogonal PAs are performed. In the second stage, final PA detection is performed iteratively using the reconstructed PAs and received signal. In addition, we theoretically derive the probability of PA misdetection, PA collision, and successful PA detection under typical NTN line-of-sight (LoS) channel conditions. Simulation results verify that the proposed two-stage PA detector (TSPD) achieves performance close to the theoretical upper bound in terms of successful PA detection probability. The TSPD also shows a significantly greater successful PA detection probability than the conventional PA detector using orthogonal PAs while satisfying the random access requirement for the 3rd generation partnership project (3GPP) standard. Moreover, compared to the conventional PA detector for non-orthogonal PA, which adopts an interference cancellation scheme, the TSPD achieves up to 25.6 times lower detection complexity.

PRACH in Self-Powered Satellite Internet of Things: Low-Cubic Metric Preamble Design and Detection

This paper focuses on the energy-efficient physical random access channel (PRACH) design for self-powered sensors integrated satellite internet of things (S-IoT), where two novel preamble design schemes are proposed by jointly considering cubic metric (CM) reduction, coverage enhancement, resistance to carrier frequency offset (CFO), and low-complexity detection. Specifically, both of schemes possess a uniform preamble format by repeatedly cascading a series of conjugate Zadoff-Chu sequence pairs in the frequency domain, which can effectively mitigate the adverse effect of large CFO under the premise of ensuring PRACH coverage. Furthermore, phase rotation optimization and inverse discrete Fourier transform scaling are respectively leveraged in the two schemes to cope with the problem of CM performance degradation caused by sequence repetitions. We also develop corresponding preamble detection mechanisms that are capable of performing joint timing advance and CFO estimation, and conduct mathematical analysis in terms of CM and maximum coupling loss. Simulation results verify the feasibility of our schemes in self-powered S-IoT, and demonstrate that with the increment of the number of sequence repetitions, the proposed schemes can maintain relatively lower CM values in comparison to the state-of-the-art ones, while having significantly enhanced PRACH coverage and detection performance.

High-Throughput Cross-Technology Communication via Chip-Level Side Channel

Cross-technology Communication (CTC) is a novel technique enabling data transmission among devices equipped with different physical layers in a heterogeneous wireless network. Particularly, by enabling cross-technology communication between ZigBee and WiFi, the energy-saving problem and media access control under cross-technology interference would be resolved easily. However, the current CTC designs are either too slow or destructive to ongoing ZigBee transmissions. In this paper, we propose a new Cross-technology Communication framework based on ZigBee chip-level side channel. The proposed design enables the high-throughput ZigBee-to-WiFi cross-technology communication while maintaining transparency to the existing ZigBee network. Specifically, we modify some ZigBee chips within one ZigBee symbol to create a side channel at the CTC sender (ZigBee) for carrying CTC bits while the CTC receiver (WiFi) can detect those modifications and demodulate the CTC bits at WiFi preamble detection. Both theoretical analysis and experiments show that our design would be transparent to ZigBee and throughput can reach 31.25 Kbps—1000× the state-of-the-art CTC design.

Proactive approach for preamble detection in 5G-NR PRACH using supervised machine learning and ensemble model

The physical random access channel (PRACH) is used in the uplink of cellular systems for initial access requests from the users. It is very hard to achieve low latency by implementing conventional methods in 5G. The performance of the system degrades when multiple users try to access the PRACH receiver with the same preamble signature, resulting in a collision of request signals and dual peak occurrence. In this paper, we used two machine learning classification technique models with signals samples as big data to obtain the best proactive approach. First, we implemented three supervised learning algorithms, Decision Tree Classification (DTC), naïve bayes (NB), and K-nearest neighbor (KNN) to classify the outcome based on two classes, labeled as ‘peak’ and ‘false peak’. For the second approach, we constructed a Bagged Tree Ensembler, using multiple learners which contributes to the reduction of the variance of DTC and comparing their asymptotes. The comparison shows that Ensembler method proves to be a better proactive approach for the stated problem.

IIFNet: A Fusion-Based Intelligent Service for Noisy Preamble Detection in 6G

In this article, we present our vision of preamble detection in a physical random access channel for next-generation (Next-G) networks using machine learning techniques. Preamble detection is performed to maintain communication and synchronization between devices of the Internet of Everything and next-generation nodes. Considering the scalability and traffic density, Next-G networks have to deal with preambles corrupted by noise due to channel characteristics or environmental constraints. We show that when injecting 15 percent random noise, the detection performance degrades to 48 percent. We propose an informative instance-based fusion Network (IIFNet) to cope with random noise and to improve detection performance simultaneously. A novel sampling strategy for selecting informa-tive instances from feature spaces has also been explored to improve detection performance. The proposed IIFNet is tested on a real dataset for preamble detection that was collected with the help of a reputable commercial company.

On the LoRa Modulation for IoT: Optimal Preamble Detection and Its Performance Analysis

This article investigates the problem of preamble detection for the long-range (LoRa) modulation and analyzes its performance. For analysis, the inevitable multiuser interference is reasonably modeled as correlated noise. First, the optimal preamble detector is derived and the best preamble is designed to maximize the detection probability while achieving a target value of the false alarm rate. To reduce the required computational complexity and to gain more insights, the preamble detection problem is further investigated for two important special scenarios with: 1) a large spreading factor (SF) and 2) uncorrelated noise, respectively. The analysis is then extended to the case in the presence of phase offset. Our work reveals that the existing preamble detection methods are strictly suboptimal. In addition, various useful and interesting engineering insights into preamble detection with LoRa modulation are provided from the derived results, and the theoretical performance limit of the preamble detection with LoRa modulation is quantified. Extensive numerical results demonstrate the effectiveness and superiority of the proposed scheme. Particularly, the proposed scheme outperforms the existing schemes by more than 10 dB in terms of signal-to-noise ratio (SNR) at the detection probability of 80% and the target false alarm rate of 10%.

Analyzing Novel Grant-Based and Grant-Free Access Schemes for Small Data Transmission

Fifth Generation (5G) New Radio (NR) does not support data transmission during random access (RA) procedures, which results in unnecessary control signalling overhead and power consumption, especially for small data transmission (SDT). Motivated by this, 3GPP has proposed 4/2-step SDT RA schemes based on the existing grant-based (4-step) and grant-free (2-step) RA schemes, with the aim to enable data transmission during RA procedures in Radio Resource Control (RRC) Inactive state. To compare the 4/2-step SDT RA schemes with the benchmark 4/2-step RA schemes, we provide a spatio-temporal analytical framework to evaluate the RA schemes, which jointly models the preamble detection, Physical Uplink Shared Channel (PUSCH) decoding, and data transmission procedures. Based on this analytical model, we derive the analytical expressions for the overall packet transmission success probability and average throughput in each RACH attempt. We also derive the average energy consumption in each RACH attempt. Our results show that 2-step SDT RA scheme provides the highest overall packet transmission success probability, and the lowest average energy consumption, but the performance gain decreases with the increase of device intensity.