Preamble Collision Research Articles

Support of resourses redistribution in NB-IоT LTE networks

The NB-IoT is expected to be used with the deployed LTE network as well as future 5G networks. The rapid development of the IoT concept has entailed the need to provide wireless communication to a huge number of devices included in the infrastructure. As part of the 5G NR standard for such devices, Massive Machine Communications (m MTC) technology is focused on optimizing the use of network resources to support a large number of stable connections per unit area. The Narrowband Internet of Things (NB-IoT) has been analyzed to enable the connectivity of a wide range of new IoT devices and services to the mobile network. The NB-IoT is also shown to be designed for fixed devices with low data transmission and low consumption, leading to an increase in the number of interconnected devices. In turn, standard NB-IoT modules attempting to simultaneously request radio channel resources for uplink data transmission may suffer from random access preamble collision. The proposed model describes the macro- and microcells of an NB-IoT LTE cluster. An increase in the efficiency of using the bandwidth of networks based on macro- and microcells with a high concentration of users of the NB-IoT LTE networks is shown. Numerical results of an analysis of identifying factors affecting system performance are presented. A linear increase in throughput has been revealed depending on the throughput of the macrocell when using the shared resource of the macrocell for a microcell of equal size without prior repacking of channels when servicing moving subscribers. In the absence of moving subscribers, the additional load is serviced, and the efficiency of using a macro cell increases almost 3 times. In addition, repacking channels significantly increases system throughput.

Grant-Free Random Access Enhanced by Massive MIMO and Non-Orthogonal Preambles

Massive multiple input multiple output (MIMO) enabled grant-free random access (mGFRA) stands out as a promising random access (RA) solution, thus effectively addressing the need for massive access in massive machine-type communications (mMTCs) while ensuring high spectral efficiency and minimizing signaling overhead. However, the bottleneck of mGFRA is mainly dominated by the orthogonal preamble collisions, since the orthogonal preamble pool is small and of a fixed-sized. In this paper, we explore the potential of non-orthogonal preambles to overcome limitations and enhance the success probability of mGFRA without extending the length of the preamble. Given the RA procedure of mGFRA, we analyze the factors influencing the success rate of mGFRA with non-orthogonal preamble and propose to use two types of sequences, namely Gold sequence and Gaussian distribution sequence, as the preambles for mGFRA. Simulation results demonstrate the effectiveness of these two types pf non-orthogonal preambles in improving the success probability of mGFRA. Moreover, the system parameters’ impact on the performance of mGFRA with non-orthogonal preambles is examined and deliberated.

Density-Based Clustering Resolution for Grant-Free Preamble Collision in Cell-Free mURLLC Systems

Density-Based Clustering Resolution for Grant-Free Preamble Collision in Cell-Free mURLLC Systems

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.

Medium Access Control Techniques for Massive Machine-Type Communications in Cellular IoT Networks

A key component of the Internet of things (IoT) ecosystem is wide-area network connectivity, for which cellular network technologies are a promising option through their support of massive machine-type communications (mMTC). However, numerous devices transmitting sporadically small data packets in a highly synchronized way can generate overload on the radio access network. This situation leads to a shortage of resources, especially those associated with the random access procedure for contention, control, and data transmission, causing preamble collision, control message blocking, and data collision. As a result, mMTC traffic can jeopardize the provisioning of quality of service (QoS) to end-devices, decrease the network efficiency, and increase access latency and device energy consumption. This paper proposes medium access control (MAC) techniques for addressing problems related to the support of mMTC in cellularnetworks. First, a solution for allocating control resources with QoS provisioning and access differentiation is provided, including a resource management model, a scheduling algorithm, and a message prioritization policy. Second, a mechanism for reducing data collisions is also proposed. This solution comprises a protocol in which every device with scheduled retransmission uses a probabilistic policy to decide whether to retransmit, a novel method at the device to estimate the number of nodes trying random access, and two different retransmission policies employing this estimation. Results show that the proposals support QoS, decrease access latency, decrease the device energy consumption, and increase resource utilization under massive random access.

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.

Joint Preamble and AOA Estimation-Based Random Access Scheme for Cellular-IoT Networks

In this letter, to overcome the preamble collision issue of massive random access (RA) in cellular-IoT networks, we design a clustered ESPRIT-based joint preamble and angle of arrival (AOA) estimator, in which the AOA information of the user equipment (UE) is regarded as a new RA label. With the assistance of the obtained AOA information provided by the designed joint estimator, the base station (BS) can recognize every detected preamble that collided and further reduce the preamble collision probability. Simulation results show that compared with the benchmark, the proposed joint estimator-based RA (JERA) scheme not only obtains the AOA estimation of UE that has successfully accessed but also achieves a better RA performance.

A layered grouping random access scheme based on dynamic preamble selection for massive machine type communications

Massive machine type communication (mMTC) has been identified as an important use case in Beyond 5G networks and future massive Internet of Things (IoT). However, for the massive multiple access in mMTC, there is a serious access preamble collision problem if the conventional 4-step random access (RA) scheme is employed. Consequently, a range of grantfree (GF) RA schemes were proposed. Nevertheless, if the number of cellular users (devices) significantly increases, both the energy and spectrum efficiency of the existing GF schemes still rapidly degrade owing to the much longer preambles required. In order to overcome this dilemma, a layered grouping strategy is proposed, where the cellular users are firstly divided into clusters based on their geographical locations, and then the users of the same cluster autonomously join in different groups by using optimum energy consumption (Opt-EC) based K-means algorithm. With this new layered cellular architecture, the RA process is divided into cluster load estimation phase and active group detection phase. Based on the state evolution theory of approximated message passing algorithm, a tight lower bound on the minimum preamble length for achieving a certain detection accuracy is derived. Benefiting from the cluster load estimation, a dynamic preamble selection (DPS) strategy is invoked in the second phase, resulting the required preambles with minimum length. As evidenced in our simulation results, this two-phase DPS aided RA strategy results in a significant performance improvement

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.

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.

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.

Random Power Back-Off for Random Access in 5G Networks

In the massive Machine Type Communications (mMTC) scenario of the 5G network, a huge number of devices communicate with each other. The massive access requests will cause frequent access collisions, which will reduce the efficiency of Random Access (RA) procedure. In this paper, we propose the random power back-off scheme that utilizes power domain Non-orthogonal Multiple Access (NOMA) scheme to solve the RA collision problem in 5G networks. In the proposed method, we attempt to mitigate the RA conflict situation when devices send the same preamble even if the Base Station (BS) does not detect the preamble collision. The proposed method is shown to improve the RA efficiency and average delay.

Two-Step Random Access for 5G System: Latest Trends and Challenges

The 3rd Generation Partnership Project (3GPP) finalized Release 15 specifications for the 5th Generation New Radio (5G NR) in June 2018. In Release 16, the 3GPP worked on not only technical improvements over the previous release but also the introduction of new features. One of the new features is the use of Two-step Random Access Channel (2-step RACH) that enhances 4-step random access with respect to radio resource control connection setup and resume procedures. In this article, we first look into details of 2-step random access defined in the 3GPP Release 16, and briefly introduce recent literature related to 2-step random access. Second, we present challenges of the above random access schemes. Among the challenges, we focus on how a User Equipment (UE) performs self-uplink synchronization with the next-generation Node B (gNB) to resolve preamble collisions, which occur when multiple UEs transmit the same preamble. Specifically, we propose a framework that helps the UE estimate the Timing Advance (TA) command using a deep neural network model and to determine the TA value. Finally, we evaluate the proposed framework in terms of the accuracy of TA command estimation, the inference time, and the battery consumption.

Machine Learning Enabled Preamble Collision Resolution in Distributed Massive MIMO

Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA). In this paper, by leveraging distributed massive multiple input multiple output (mMIMO) together with machine learning, a novel machine learning based framework solution is proposed to address the preamble collision problem in GFRA. The key idea is to identify and employ the neighboring access points (APs) of a collided RA UE for its data decoding rather than all the APs, so that the mutual interference among collided RA UEs can be effectively mitigated. To this end, we first design a tailored deep neural network (DNN) to enable the preamble multiplicity estimation in GFRA, where an energy detection (ED) method is also proposed for performance comparison. With the estimated preamble multiplicity, we then propose a K-means AP clustering algorithm to cluster the neighboring APs of collided RA UEs and organize each AP cluster to decode the received data individually. Simulation results show that a decent performance of preamble multiplicity estimation in terms of accuracy and reliability can be achieved by the proposed DNN, and confirm that the proposed schemes are effective in preamble collision resolution in GFRA, which are able to achieve a near-optimal performance in terms of uplink achievable rate per collided RA UE, and offer significant performance improvement over traditional schemes.

On Throughput Improvement Using Immediate Re-Transmission in Grant-Free Random Access With Massive MIMO

To support machine-type communication (MTC), massive multiple-input multiple-output (MIMO) has been considered for grant-free random access. In general, the performance of grant-free random access with massive MIMO is limited by the number of preambles and the number of active devices. In particular, when there are a number of active devices transmitting data packets simultaneously, the signal-to-interference-plus-noise ratio (SINR) cannot be high enough for successful decoding. In this paper, in order to improve performance, we consider immediate re-transmissions for an active device that has a low SINR although it does not experience preamble collision to exploit re-transmission diversity (RTD) gain. To see the performance of the proposed approach, we perform throughput analysis with certain approximations and assumption. Since the proposed approach can be unstable due to immediate re-transmissions, conditions for stable systems are also studied. Simulations are carried out and it is shown that analysis results reasonably match simulation results.

Enhanced Random Access for Massive-Machine-Type Communications

In this article, we study a random access (RA) scheme to alleviate the RA channel (RACH) overload problem in the massive-machine-type communication (mMTC) environment. We first propose a timing advance-based preamble resource expansion (TAPRE) scheme which effectively increases preamble resources and reduces the preamble collision probability by adjusting preamble transmission timing with timing advance (TA) information. We also propose a resource allocation wait (RAW) scheme which efficiently reduces the number of RA failures due to the lack of physical uplink shared channel (PUSCH) resources. We then propose an overall procedure for enhanced RA with TAPRE and RAW (ERATAR). In addition, we provide the analysis of RA performance by applying more practical assumptions than the existing analysis. We validate our analysis with the system level simulation based on NS-3, and compare the various performances of our ERATAR to those of existing works. Numerical results show that our analysis provides more accurate results than the existing work and our ERATAR provides significantly improved performances compared with those of existing works.

An Approach to Preamble Collision Reduction in Grant-Free Random Access With Massive MIMO

In this paper, we study grant-free random access with massive multiple input multiple output (MIMO) systems. We first show that the performance of massive MIMO based grant-free random access is mainly decided by the probability of preamble collision. The implication of this is that although the number of antennas can be arbitrarily large, the (average) number of successful packet transmissions can be limited by the number of preambles. Then, we propose an approach to preamble collision reduction without increasing the number of preambles using the notion of superpositioned preambles (S-preambles). Since the channel estimation for conjugate beamforming can be carried out when unused S-preambles are known, a simple approach is derived to detect unused S-preambles and its performance is analyzed for a special case (superpositions with 2 preambles). Based on analysis and simulation results, it is confirmed that the proposed approach with S-preambles can increase the success probability with the same spectral efficiency as the conventional approach.

A Collision Resolution Protocol for Random Access in Massive MIMO

In 5G and beyond wireless scenarios, it is expected to support tremendous amount of communicating machines. With an exponential increase of machine-to-machine (M2M) system deployments, efficient approaches to massive access by a large number of devices are to be studied. In this paper, we propose a massive multiple-input multiple-output (MIMO) based grant-free random access (RA) with resolution of preamble collision for massive access. Based on the channel hardening and favorable propagation characteristics of massive MIMO, collided signals processed at the base station (BS) can be viewed as a variation of superposition modulation, and are to be recovered by successive interference cancellation (SIC) techniques. Besides, taking into consideration the effect of pass loss and fractional power control (FPC), analytic expressions of success probability of the proposed collision resolution with conjugate beamforming (CB) and zero-forcing beamforming (ZFB) are derived. With simulation results, we verify the analyses and show that the proposed protocol can resolve most preamble collisions.

Triangular Non-Orthogonal Random Access in mMIMO Systems

Massive multiple-input multiple-output (mMIMO) based grant-free random access (RA) (mGFRA) has been studied for massive machine-type communication (mMTC). In the existing works of mGFRA, it is implicitly assumed that each RA user equipment (UE) in mMTC has the same data-packet size. However, this may not be the case in practice as RA UEs in different applications can have different numbers of bits to send, which may result in data packets of different sizes. In addition to that, preamble collision is a key issue that degrades the performance of mGFRA when orthogonal preamble is used. In this article, we aim to address the preamble collision issue under a practical mGFRA scenario that consists of two different types of RA UEs (with short and large data packets). Specifically, by exploiting different sizes of data packets, we propose a triangular non-orthogonal RA (T-NORA) scheme, where the preamble transmission of RA UEs with small-sized data packet is embedded into the data transmission of RA UEs with large-sized data packet so that the preamble collision between two different types of RA UEs can be avoided. As a result, an improved performance over conventional mGFRA can be achieved by T-NORA with the assistance of mMIMO. Through analytical and simulation results, we can confirm that the proposed T-NORA scheme outperforms the conventional mGFRA scheme.

A Novel Random Access Framework for Uplink Cellular IoT: Non-Orthogonal Preambles and Multi-Antennas

Traditionally, even though the base station (BS) is equipped with multiple antennas, it has not been able to apply antenna technique such as an uplink multi-user multiple-input multiple-output (MU-MIMO) during the random access (RA) procedure due to the contention feature of the RA. To overcome such limitation, this letter proposes a novel RA framework leveraging both non-orthogonal preambles and multi-antenna technique. In our proposed framework, the non-orthogonal preambles effectively increase the total amount of contention resources, i.e., RA preambles, and thus the occurrence of preamble collisions can be significantly reduced. Accordingly, the BS can take an opportunity to exploit an uplink MU-MIMO even in the contention-based RA procedure under the belief that the contention may be resolved. We mathematically analyze the proposed framework in terms of packet collision probability and resource efficiency. Analysis and simulation results reveal that our proposed framework is able to accommodate more internet-of-things (IoT) devices in a resource-efficient way, compared to the conventional one.