Physical Random Access Channel Research Articles

Multistage Decimator Design for the Low-PHY PRACH Receiver

This paper describes the design of a multistage decimator for the Physical Random Access Channel (PRACH) receiver based on the hybrid time/frequency domain architecture. Flexible structure of the multistage decimator allows to support all combinations of PRACH subcarrier spacings and carrier bandwidths defined in the 3rd Generation Partnership Project (3GPP) Release 15 for Frequency Range (FR) 1. Impulse responses of low pass filters for each decimation stage do not depend on a selected combination allowing to facilitate hardware implementation. The multistage decimator jointly uses the Cascaded Integrator-Comb (CIC) and Nyquist filters to minimize number of required multipliers. Acceptable degradation level due to decimation in the receiver performance is estimated based on requirements defined by the Radio Access Network (RAN) working group 4. Assuming no specific detection algorithm, it is shown that the proposed design introduces negligible degradation, less than 0.1 dB in the operating Signal-to-Noise Ratio (SNR) region.

Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning.

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.

LTE Device Radio Frequency Fingerprints Blind Extraction Based on Temporal-Frequency Domain PRACH Signals

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.

A Resource Optimization for Two-Step Random Access in 5G New Radio

This paper presents a resource optimization method for the 2-step random access (RA) procedure in the 5th generation new radio (5G NR). In 2-step RA, the MsgA includes a sequence as a preamble on the physical random access channel (PRACH) and uplink payload on the physical uplink shared channel (PUSCH). The mapping between PRACH slots and PUSCH slots is specified by system configuration parameters. The network reserves the corresponding PUSCH resource fixedly, regardless of the random access occasion (RO) occupancy rate and the uplink data traffic request. It deprives the uplink scheduling resource efficiency in cases of some RO unoccupied. To achieve higher utilization efficiency, this paper studies the reusing of the PUSCH slots and occasions fixed to RACH in MsgA. By detecting the preambles in RO before the corresponding PUSCH slots, the network acquires the occupancy of the RO. Then the PUSCH occasion (PO) in reserved PUSCH can be released, which conforms to the RO detection result. The release of the previously reserved PUSCH occasions can be rescheduled and reused by the user equipment (UE) for uplink data transmission. Hence more wireless resources can be scheduled in contrast to the existing allocation of uplink share channel in 2-step type random access. It suggests that using the method improves the efficiency of resource utilization.

Simplification of DFT and IDFT in PRACH

AbstractThe respective simplifications of prime‐point discrete Fourier transform (DFT) and ultra‐long‐point inverse discrete Fourier transform (IDFT) in physical random access channel (PRACH) are proposed. The former is an equivalent substitution of DFT function by using the property of Zadoff‐Chu (ZC) sequence, and the latter is an approximation based on cubic spline interpolation, which not only reduces the IDFT points, but also is easy to construct.

An Efficient Random Access Reception Algorithm for ToA Estimation in NB-IoT

Narrowband Internet of Things (NB-IoT) aims to provide wide coverage for a massive number of low-cost devices. Therefore, an NB-IoT physical random access channel (NPRACH) preamble based on a single tone signal with frequency hopping was designed, enabling the base station to estimate the time-of-arrival (ToA) values for realizing uplink synchronization among multiple users. However, due to residual carrier frequency offset (RCFO) in the NPRACH preamble, it is crucial to keep the accuracy of the ToA estimation. Recognizing this urgency, in this paper we first judiciously investigate the effect of the hooping distance on ToA estimation. With which, we propose an efficient receiving algorithm to improve the accuracy of ToA estimation. The main aim of the algorithm is to treat two consecutive symbol groups as a whole and then carry out difference calculations on the two newly constituted symbol groups to construct a more logical frequency hopping distance. Extensive numerical results validate the superiorly of our proposed scheme compared against conventional strategies, showing that the probability of the ToA estimation obtained by this method is 99% within the acceptable error range.

A novel design of multi-user sequence set for joint vehicular radar-communication based on Oppermann family

In this paper, a novel multi-user sequence set which are to be potentially used as communication preambles for joint vehicular radar and communication using Oppermann sequences is proposed. Herein, a set of Oppermann sequences with suitable auto-ambiguity functions (AF) and cross-ambiguity functions (AF) properties have been proposed. Moreover, a channel estimation algorithm using the designed Oppermann sequences is also proposed. The designed preamble, composed of optimized Oppermann sequences is an appropriate candidate for wireless standards, where both channel estimation and multi-user vehicular radar sensing are jointly pursued. The simulation results demonstrate that the designed sequences provide a significant performance improvement for vehicular radar sensing and don't degrade the channel estimation accuracy. The proposed optimized Oppermann sequences have better auto- and cross-AF compared existing Golay complementary sequences (used in IEEE 802.11ad as preamble), Zadoff-Chu sequences (used in 5G to generate preambles for the Physical Random Access Channel (PRACH)) and the non-optimized Oppermann sequences. Specifically, compared to the preamble composed of Golay complementary sequences in IEEE 802.11ad, the proposed sequences reduce the maximum sidelobe level of the auto-AF and maximum value level of cross-AF by at least 2 and 2.85 times, respectively. The target can still be accurately detected in mutual interference scenario caused by multiple radars in the vicinity. Both the sequences, when used for channel estimation achieve the CRLB limit. Both the proposed sequences and Golay complementary sequences, when used for channel estimation achieve the Cramer-Rao Lower Bound.

An Energy-Efficient DL-Aided Massive Multiple Access Scheme for IoT Scenarios in Beyond 5G Networks

In view of the challenges foreseen in futuristic massive IoT (mIoT) scenarios, characterized by a huge deployment of energy-constrained IoT devices, we propose an efficient Next Generation Multiple Access (NGMA) scheme where several innovative solutions combine and integrate. First, we aim to maximize the spectral efficiency of the Physical Uplink Shared Channel (PUSCH) by adopting the most appropriate Non-Orthogonal Multiple Access (NOMA) technique and by introducing an efficient contention-based approach for data transmission in the unused PUSCH resources. Second, on basis of the traffic load, the proposed scheme dynamically sets the UpLink (UL) radio resources dimensioning by searching the optimal trade-off between lowering the collision probability in the Physical Random Access Channel (PRACH) and providing enough radio resources in the PUSCH to permit the data transmission to all the succeeded access requests. Third, it is enriched by a series of strategic procedures to make feasible the traffic load estimation, even when the PRACH is in overload condition. Fourth, we exploit Artificial Intelligence technologies to further optimize the performances obtained. The proposed scheme is compared with other benchmark schemes available in the literature. It outperforms them in the most important metrics for the mIoT scenario (i.e., spectral efficiency and energy consumption), especially in high load conditions, which is the expected requirement for Beyond 5G and 6G networks.

Dynamic Preamble Resource Distribution for Random Access in 5G New Radio Systems

Millimeter wave (mmWave) spectrum promises unprecedented data rates in 5G New Radio (NR). However, mmWave links are susceptible to severe path and propagation losses. Implementation of directional antennas that can manifest multiple beams becomes necessary at 5G base stations (gNBs) and User Equipments (UEs). Random access procedure (RAP), which is the first stage to establish connections to gNB, in its current state would be inadequate for such systems. To support RAP, 5G NR introduced mapping of different subsets of preambles in Physical Random Access Channel (PRACH) to different beams. We explore changes in RAP due to preamble-beam mapping and present a novel online algorithm to dynamically distribute preambles amongst beams to achieve fair access opportunities. The proposed algorithm can also eliminate overload problem of the beams with large UE population especially for high arrival rates. We extend the proposed algorithm to be applicable for RAP with access class barring mechanism which enables UEs to transmit a preamble with an optimal probability and, therefore, reduce access congestion. Through extensive simulations, we validate the efficiency of our proposed algorithms in providing fair channel access opportunities, improving throughput (or stability region), and reducing access delay when the system has high rate of UE arrivals and has unbalanced UE arrivals over different beams.

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.

Random access optimization for initial access and seamless handover for 5G-satellite network

Satellite communication is becoming an indispensable component of the 5G global networks. Considering the unique characteristics of the satellite communications such as long round trip delay, large cell radius and high satellite speed, initial access and handover are necessary to be improved to guarantee the stable communication for 5G-satellite network, which usually use the random access procedure in traditional terrestrial communication and is not suitable for satellite scenario. Therefore, a 2-step random access for 5G-satellite network (TRAS) and a seamless MC-controlled handover for 5G-satellite network (SMHOS) are proposed in this paper. In particular, the access delay can be reduced and the success rate of access can be improved since the access procedure is simplified and the contention occurs only when the UEs select the same Physical Random Access Channel (PRACH) occasion and Physical Uplink Shared Channel (PUSCH) occasion in TRAS. Moreover, in SMHOS, the management center (MC) can predict the handover of the UE and inform the next satellite to send a broadcast message with embedded information about the reserved PUSCH time–frequency resource. The UE can access to the target satellite immediately when the handover occurs, therefore, seamless handover is realized. Simulation results demonstrate the effectiveness of our TRAS and SMHOS.

An Improved Random Access Scheme Using Directional Beams for 5G Massive Machine-Type Communications

In the 5th generation (5G) massive machine-type communication (mMTC), random access is the limiting factor of performance because there may be massive user equipments (UEs) up to 1 million unit competing for the access opportunities. Existing random access schemes in the 4th generation (4G) or 5G systems are not able to handle such large amount of concurrent random access requests. This paper proposes a random access scheme based on directional beams, which offers a new spatial degree of freedom to improve the random access performance in 5G mMTC. In this scheme, a cell is divided into beam zones in space domain. UEs in different beam zones choose a certain physical random access channel (PRACH) resource with different probability. When preamble collision occurs, uplink radio resource is allocated to the beam zone which has low collision probability. We provide theoretical performance analysis of the proposed scheme using different performance metrics. Comparisons are carried out between the proposed and several existing random access schemes. The proposed scheme can be easily deployed without any modification to 5G framework. Existing random access optimizing algorithms can also be applied to the proposed scheme.

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.

Deep Learning-Based Cellular Random Access Framework

Random access (RA) or preamble collision is one of the crucial problems in massive internet-of-things (IoT) at the network entry stage. Since a massive number of IoT nodes simultaneously attempt RAs on the same physical random access channel (PRACH), preambles may be selected by multiple nodes, incurring preamble collisions at the first step of the RA procedure. However, conventional RA models are limited to binary preamble detections which poses severe RA performance loss in the massive IoT environment. In this paper, we propose a deep learning (DL)-based end-to-end RA framework which has detection and resolution abilities for the collided preambles. In particular, advanced preamble classification and timing advance (TA) classifications are performed using deep neural networks (DNNs) for improving the probability of RA success while reducing the delay of the entire RA procedure. The effectiveness of the proposed DNN-based preamble and TA classifiers are demonstrated through extensive simulations. We further evaluate the system-level performance of the proposed DL-based RA model. It shows a significantly higher probability of instant RA success, which makes every node succeed in RA with very limited reattempts, and also maintains a significantly lower RA delay in massive IoT environment.

A PRACH Preamble Generation and Detection Model for 5GNR Systems

The basic idea of 5th generation New Radio (5GNR) is to have very high data rate and to make it work efficiently for all Internet of Things (IOT) applications like healthcare, Automotive, Industrial etc. applications. This paper provides the Orthogonal Frequency Division Multiple Access (OFDM) baseband signal generation and detection method for Physical Random-Access Channel (PRACH). The proposed model provides four scenarios of preamble detection i.e., Preamble detection probability, Miss-detection probability, False alarm probability and null. We achieved the target of 99% of Probability of Detection and less than 0.1% of False-alarm probability at certain SNR as specified according to 3gpp standard requirements when tested in Additive White Gaussian Noise (AWGN) channel and Extended Typical Urban (ETU) channel.

Preamble Design and Collision Resolution in a Massive Access IoT System.

How to support massive access efficiently is one of the challenges in the future Internet of Things (IoT) systems. To address such challenge, this paper proposes an effective preamble collision resolution scheme to sustain massive random access (RA) for an IoT system. Specifically, a new sub-preamble structure is first proposed to reduce the preamble collision probability. To identify different devices that send the same preamble to the gNB on the same physical random access channel (PRACH), a multiple timing advance (TA) capturing scheme is then proposed. Thereafter, an RA scheme is designed to sustain massive access and the performance of the scheme is studied analytically. Finally, the effectiveness of the proposed RA scheme is validated by extensive simulation experiments in terms of preamble detection probability, preamble collision probability, RA success probability, resource efficiency and TA capturing.

Small-Cell Assisted Group Paging for Massive MTC in LTE Networks: Design and Analysis

Long-Term Evolution cellular networks are the main enabler for the massive Machine-Type Communications service and therefore must handle a large number of Machine-Type Devices (MTDs). To control the number of devices allowed to contend on the Physical Random Access Channel (PRACH), the group paging scheme that divides the MTDs into smaller groups and lets the network sequentially trigger the groups has been studied. However, as the number of PRACH preambles is limited, a group’s size must be kept relatively small compared to the MTD population. This paper exploits the possibility that a significant portion of the MTDs is also covered by densely deployed small-cells such that a Small-cell Base Station (SBS) may act as a representative for its MTDs during the preamble transmission step to reduce the load on PRACH. Once the SBS succeeds, its MTDs then contend locally to send their own signaling messages on the corresponding reserved uplink resources. Computer simulations show that the manageable group size can be significantly increased at a reasonable cost on the Physical Uplink Shared Channel. A theoretical model to quickly predict the effect of the ratio of MTDs that are under the coverage of the SBSs is also derived and verified.

A New Contention-Based PUSCH Resource Allocation in 5G NR for mMTC Scenarios

This letter addresses the uplink radio resource allocation problem in a 5G massive Machine Type Communication (mMTC) scenario, where a large number of MTC devices generate small-sized packets. The current cellular networks are unsuitable for this scenario, due to the limited resources allocated to the Physical Random Access Channel (PRACH) and to the Physical Uplink Shared Channel (PUSCH). To overcome this issue, instead of improving the succeeded access attempts in the PRACH, as widely addressed in literature, we focus on the PUSCH. We present the innovative idea to exploit the unused PUSCH resources to serve also the MTC devices that have failed their access attempt, by assigning them a pool of resources, in a contention-based mode. The simulation results show that the proposed PUSCH Resource Reallocation Algorithm improves significantly any static and dynamic system performance in terms of number of succeeded communications, at the cost a negligible increment of energy consumption.

Overcoming 5G PRACH Capacity Shortfall: Supersets of Zadoff–Chu Sequences With Low-Correlation Zone

5G physical random access channel (PRACH) will use new preamble formats based on short Zadoff-Chu (ZC) sequences to enable new use cases. These formats result however in a PRACH capacity shortfall where the number of sequences available for network planning may just be enough to support few cells without sequence reuse. To overcome this issue, two enlarged constant-amplitude sequence constructions that include ZC sequences, and thus being backward-compatible with the current 5G PRACH design, are presented. Both constructions feature a desirable subset structure from which follows a natural cellular network sequence allocation where each cell-specific sequence subset has a controllable low-correlation zone similar to ZC sequences, and thus enables a similar detection performance inside a cell. The two extensions are meanwhile structurally different and offer different network allocations, as for one, new sequences are distributed among all cells, while for the other, they are in separated cells. With both proposed supersets and the new 5G PRACH formats, hundred-times more cells without sequence reuse can be supported compared to ZC sequences for a moderately larger inter-cell correlation than intra-cell correlation. Simulation results confirm that using the proposed supersets instead of reusing sequences in the network can significantly improve detection performance.