An improved preamble detection method for LTE PRACH in high-speed railway scenario

This paper presents a configurable and optimized hardware architecture for computing Zadoff-Chu (ZC) complex sequences in the frequency domain. It is a hardware-efficient and accurate architecture for computing ZC sequences in realtime. The architecture is mainly based on the CORDIC algorithm for computing complex exponentials using only shift and add operations. Due to transformations applied to the Zadoff-Chu equation it is possible to eliminate the use of multipliers with non-constant terms. This hardware architecture is employed by the Physical Random Access Channel (PRACH) in LTE and LTE-A systems during the reception and detection of random access preambles. Its main advantage is that it eliminates the need for storing a large number of long complex ZC sequences. Simulation results show that the proposed architecture is accurate, efficient and renders the resulting PRACH receiver fully compliant with 3GPP's detection requirements.

A frequency-domain cepstrum correlation and temporal transient filtered radio frequency fingerprints (RFF) blind extraction method is proposed for long term evolution uplink device authentication in this work. With the merits of the constant-modulus nature of the Zadoff-Chu (ZC) sequence and the cyclic prefix (CP) structure of the physical random access channel (PRACH) frame, the device RFF integrated in both the frequency-domain ZC sequence and the transient ramp-on/ramp-off sequences are extracted. For the frequency-domain RFF extraction, the cepstrum of the ZC sequence is calculated to enlarge the gap between the constant RFF and the random fading effects, and the correlation between two cepstrum fragments is then calculated to mitigate the random fading components. For the temporal RFF extraction, the ramp-on and ramp-off features induced by the device-specific power amplifier are extracted by exploiting the CP structure of the PRACH frame, where the CP part and the duplicate part are adaptively filtered and the final adapted weight of the least mean square filter is used as RFF. No prior PRACH parameter knowledge is required in RFF extraction and both the frequency-domain and the temporal RFF are combined as the input of the convolutional neural network to enhance the classification accuracy. With four-months sampling of PRACH signals emitted from universal software radio peripheral devices, the experimental results show that the authentication accuracy of the proposed combined RFF can reach as high as 99.9% in the line-of-sight (LOS) scenario and 99.6% in the non-LOS scenario.

To support a high density of mobile devices and its connections by means of rapid increase of mobile devices as well as small cells, various studies related to random access such as the random access load, better uplink resource utilization and interference among random access attempts have been developed for enhanced long-term evolution (LTE) system and 5G communication technology. In this paper, we discuss about the technical issues of random access resource configuration in time division duplex (TDD) mode and propose an improved method of it to make better uplink resource utilization on frequency domains and to minimize interference among random access attempts for physical random access channel (PRACH) resource allocation point of view, but it can also easily extend the LTE system. The proposed method is compared with LTE TDD PRACH configuration method through theoretical analysis. It shows that the proposed method provides better resource utilization for PRACH and physical uplink shared channel resources (PUSCH), allows flexible resource utilization in uplink scheduling, and is intuitive and effective in cell deployment with random access channel optimization in terms of random access resource configuration.

The Physical random access channel (PRACH) of the fifth generation of mobile communication (5G) with a new radio-access (NR) is composed of cyclic prefix (CP) and preamble sequence. While the CP of PRACH guarantees sufficient length for full sequence detection to PRACH detector, it also hampers finding out the PRACH start point since the CP itself causes a considerable pseudo-peak at PRACH detection. Ruling out this pseudo-peak using threshold contains risk of mis-judgement depending on fading environment or PRACH searching time offset. In this paper, we propose a comprehensive PRACH detection procedure to find out PRACH sequence and the start point of uplink correctly.

This paper presents performance evaluation results of physical random access channel in 5G new radio standard defined in 3GPP, which was derived from simulation assumptions such as preamble formats, wireless channel, signal-to-noise ratio. We assumed sampling rate of 61.44 Msps, 30 kHz subcarrier spacing, 3.7 GHz center frequency, and 40 MHz system bandwidth in the simulation. As the official performance requirements for new radio physical random access channel is not available yet at the moment, which is being discussed in the 3GPP meeting and by email, this paper refers to the requirements described in 3GPP release-13. Simulation results indicates that the performance of NR PRACH defined in 3GPP release 15 meets the requirements in release 13 at least in additive white Gaussian noise and extended typical urban channel.

The physical random access channel (PRACH) of a mobile hotspot network (MHN) is based on a single carrier frequency division multiple access (SC-FDMA). In this paper, we introduce a practical filter design of interpolation-based SC-FDMA transmitter for random access preamble generation. A multistage interpolation filter is used to increase the preamble sampling rate of 12.96 MHz to the system sampling rate of 184.32 MHz, while lowering the sidelobes below −60 dB. In the implementation point of view, this interpolation-based PRACH transmitter provides efficiency and reduction in complexity.

In this paper, we apply access class barring (ACB) scheme and sparse code multiple access (SCMA) technique to massive machine type communication devices (MTCDs) communications, and propose a resource allocation scheme to increase the number of successful accesses. The proposed scheme employs ACB to control the number of massive random access (RA) attempts during preamble transmission in physical random access channel (PRACH), then allocates SCMA codebook resource in physical uplink shared channel (PUSCH) to those MTCDs with successful preamble transmission for data transmission. We formulate a joint PRACH and PUSCH resource allocation optimization problem. Finally, we propose a detailed algorithm to solve the optimization problem and optimize the length of SCMA codeword to maximize the number of successful accesses. Simulation results show that the proposed scheme can increase the number of successful accesses even in the overload scenario.

The performance and implementation of PRACH (physical random access channel) acquisition in WCDMA system is investigated. The analysis shows that the conventional methods are not satisfying. Thus we proposed the quasi-matched filter acquisition scheme of PRACH preamble which based on fast Hadamard transform. We implement this method by hardware in the practical WCDMA field trial system. The simulation and test results show that the proposed scheme achieves the following performance: the detection probability with E/sub b//N/sub 0/ =7 dB is not less than 95%, and the mean acquisition time is less than 1.33 ms.

In 5G networks, the Physical Random Access Channel (PRACH) significantly affects mobile network performance.The base station is responsible for detecting the preamble by calculating the correlation among predefined PRACH preamble sequences, which are essential for successful communication.However, this traditional method presents challenges, as it is computationally complex and resource-intensive.Additionally, inaccuracies in threshold settings can lead to increased energy consumption, primarily due to issues such as preamble collisions and the need for retransmissions.This paper explores the potential of replacing the current detection method with a deep-learning model to improve the efficiency of PRACH preamble detection.Experiments were conducted by varying the layers of Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN) across four different SNR environments to evaluate their performance in detecting PRACH preambles.As a result, the DNN model, in particular, demonstrated high accuracy, averaging between 97.3% and 99.8%.Moreover, in low SNR environments, the DNN model exhibited a notable convergence speed reduction of up to 3.22 times with increased layers.Meanwhile, the CNN model demonstrated that employing fewer convolution layers led to a convergence speed that was 2.14 times faster in terms of loss values.These findings indicate that the design approaches for DNN and CNN models in PRACH preamble detection differ, potentially improving the initial access procedure through tailored strategies.

The employment of additional spectrum is considered the most simple and effective way to boost network capacity for next-generation wireless communications. Towards this end, ongoing research work has been undertaken by the 3rd Generation Partnership Project (3GPP) on enabling 5G new radio (NR) in the unlicensed band, which is called NR-U. In particular, some key physical layer technologies based on the NR air interface must be enhanced, by reason of the different regulatory requirements in the unlicensed band. In this paper, a comprehensive physical random access channel (PRACH) sequence design scheme is presented for the NR-U system, which is characterized by low cubic metric (CM), enhanced coverage, resistance to carrier frequency offset (CFO), and low-complexity detection. For the proposed sequence, a joint timing offset and CFO estimation scheme is proposed, furthermore, the theoretical derivation of CM and maximum coupling loss (MCL) is conducted. Results show that the proposed sequence can maintain a comparatively low CM value with an increment of the number of frequency-domain sequence repetitions while having remarkably improved PRACH coverage and detection performance.

The fifth-generation (5G) mobile cellular network is vulnerable to various security threats. Radio frequency fingerprint (RFF) identification is an emerging physical layer authentication technique which can be used to detect spoofing and distributed denial of service attacks. In this paper, the performance of RFF identification is studied for 5G mobile phones. The differential constellation trace figure (DCTF) is extracted from the physical random access channel (PRACH) preamble. When the database of all 64 PRACH preambles is available at the gNodeB (gNB), an index-based DCTF identification scheme is proposed, and the classification accuracy reaches 92.78% with a signal-to-noise ratio of 25 dB. Moreover, due to the randomness in the selection of preamble sequences in the random access procedure, when only a portion of the preamble sequences can be trained, a group-based DCTF identification scheme is proposed. The preamble sequences generated from the same root value are grouped together, and the untrained sequences can be identified based on the trained sequences within the same group. The classification accuracy of the group-based scheme is 89.59%. An experimental system has been set up using six 5G mobile phones of three models. The 5G gNB is implemented on the OpenAirInterface platform.

The massive deployment of machine type communication devices (MTCDs) on the 5G wireless network without proper access control will result in congestion and network overload in the front haul. The envisaged deployment is also expected to generate large power consumption due to high signaling which poses a major problem for machine type communication (MTC). The challenge of meeting the latency requirement for one of the usage scenarios in fifth generation (5G) which is ultra-reliable and low-latency communication(URLLC) is compounded during massive access for MTC. To address the massive access needs of MTCDs while considering their different service requirement, we propose a priority-based spatial aggregation algorithm to adequately maintain a balanced trade-off between the power consumed by MTCDs and their quality of service (QoS) requirements. The proposed scheme gives priority to URLLC devices (UMTCDs) through power ramping in the physical random access channel (PRACH). The performance measures show a decrease in the average power consumed by UMTCDs during uplink massive access and a significant reduction in their latency without barring access to non-delay sensitive MTCDs.

The computational complexity of FFT (Fast Fourier Transform) processing in an OFDM (Orthogonal Frequency Division Multiplex) based receiver is large with a large amount of receiver antennas. In LTE (Long Term Evolution) release 8, FFTs of different size are used for user data and random-access preambles [1] requiring additional FFTs to be implemented for random-access reception. Within the current paper, a 5G random-access preamble format is proposed based on a short sequence of the same length as the length of the OFDM symbols that are used for other uplink physical channels, such as user data, control signaling, and reference signals. The preamble sequence is constructed by repeating the short sequence multiple times. A corresponding preamble detector in which FFTs of the same size as for other uplink channels and signals are used is also described. In this way, the amount of special random-access related processing and hardware support is significantly reduced for multi-antenna systems with frequency-domain beamforming. This preamble detector is also robust against inter-carrier interference from other uplink channels and signals. Furthermore, the proposed preamble detector scheme can be used in 5G scenarios with a high amount of phase noise and frequency errors. For time-domain beamforming, the beamforming weights can be changed during preamble reception such that the number of spatial directions is increased for which preamble detection is done. Simulation results are used to compare preamble formats for different lengths of the sequences.

Satellite-based non-terrestrial networks (NTNs) can provide connectivity to geographical regions with inadequate cellular coverage. The 3rd Generation Partnership Project is developing specifications for NTNs based on 5G New Radio (NR). The NTN scenario entails large Doppler shifts and long propagation delays as compared to a terrestrial network. This necessitates revisiting the existing NR physical layer including the Physical Random Access Channel (PRACH), which was not designed to operate amid large carrier frequency offsets. In this paper, the design rationale is discussed for an NTN-specific PRACH preamble consisting of two concatenated preambles with different Zadoff-Chu root sequences. Simulation results validate that the proposed PRACH preamble meets the NTN performance requirements in terms of preamble detection, and timing and frequency offset estimation.

The 3rd Generation Partnership Project (3GPP) has started to investigate feasibility of machine type communication terminal class in Long Term Evolution (LTE) networks in the Release 12 to address the requirements of the Internet of Tings (IoT) targeting low-end device market with improved coverage. Along with that, the 3GPP has defined reference signals to support device discovery, communication and synchronization. These signals are transmitted by either an eNB within the coverage area, or a peer User Equipment (UE) in partial and out of coverage scenarios. The regular LTE synchronization signals, built from Zadoff-Chu (ZC) sequences and M-sequences, are reused in Rel-12. In this paper, we deal with low- complexity synchronization approach based on ZC sequences and tailored to LTE signal. The presented approach carries out Simply-Differential (SD) correlation-based metric that allows the detection of a ZC sequence pattern within the received signal. The SD metric correlates the received signal and compensates the output with an adjustment frequency whose value depends on the training ZC sequence. To reduce the huge computational load resulting from differential correlation operations, a recursive implementation of the SD metric is proposed. The performance is here evaluated in terms of theoretical probability of correct detection and compared to the experimental rate of correct detection to validate the analysis. The obtained results show good match between analytical and experimental probabilities.