Random Access Success Probability 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.

Pattern Division Random Access (PDRA) for M2M Communications With Massive MIMO Systems

In this work, we introduce the pattern-domain pilot design paradigm based on a superposition of orthogonal-building-blocks with significantly larger contention space to enhance the massive machine-type communications (mMTC) random access (RA) performance in massive multiple-input multiple-output (MIMO) systems. Specifically, the pattern-domain pilot is constructed based on the superposition of L cyclically-shifted Zadoff-Chu (ZC) sequences. The pattern-domain pilots exhibit zero correlation values between non-colliding patterns from the same root and low correlation values between patterns from different roots. The increased contention space, i.e., from N to C_N^L, where C_N^L denotes the number of all L-combinations of a set N, and low correlation values lead to a significantly lower pilot collision probability without compromising excessively on channel estimation performance for mMTC RA in massive MIMO systems. We present the framework and analysis of the RA success probability of the pattern-domain based scheme with massive MIMO systems. Numerical results demonstrate that the proposed pattern division random access (PDRA) scheme achieves an appreciable performance gain over the conventional one, while preserving the existing physical layer virtually unchanged. The extension of the superposition of orthogonal-building-blocks scheme to superposition of quasi-orthogonal-building- blocks is straightforward.

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.

TARA: An Efficient Random Access Mechanism for NB-IoT by Exploiting TA Value Difference in Collided Preambles

To meet the tremendous demand of Internet of Things (IoT) applications, the 3rd Generation Partnership Project (3GPP) has specified the Narrowband IoT (NB-IoT) standard. However, collisions in the random access (RA) channel of NB-IoT can be severe due to mismatch between frequent random access attempts by a huge number of devices and the limited radio resources. In this paper, a new random access mechanism called time-alignment-value based random access (TARA) is designed to improve the efficiency of random access of NB-IoT. The key mechanism of TARA is to conduct quick retries upon failure by exploiting the difference of time-alignment (TA) values in collided preambles. To analyze TARA rigorously, a theoretical model of TARA is derived, and its validity is verified via simulations. Further simulations are carried out to evaluate TARA with respect to different system parameters. Comparisons with existing schemes are also conducted. Both analytical and simulation results show that, under various system parameters, TARA achieves 30% higher success probability of random access, 75% higher throughput, and 40% lower access delay than the original slotted Aloha mechanism of NB-IoT. It also highly outperforms other random access schemes.

Performance Analysis of LTE Random Access Protocol With an Energy Harvesting M2M Scenario

In this article, we analyze the performance of the long-term evolution random access procedure with the Third Generation Partnership Project's access class barring (ACB) mechanism in an energy harvesting (EH) machine-to-machine (M2M) scenario. To circumvent the state-space explosion in the conventional Markov-chain-based analysis due to time-dependent traffic pattern and data and energy buffer status, we develop an analytical model that combines mean-value analysis with the Markov-based analysis. Based on the analytical model, the random access success probability, the access delay of the network, and the average time duration between two successive successful transmissions are derived. Our analysis suggests that in the EH scenario, despite the lower number of the contending nodes in comparison with the non-EH scenario, the ACB parameters must be chosen in a more conservative way to avoid excessive collisions. The ACB parameters include access barring rate and mean barring duration. We also study an energy threshold-based activation policy and investigate the joint effects of this policy and the ACB mechanism on the random access success probability. The extensive simulations were conducted to evaluate the accuracy of the analytical model.

Research on Election Interval of Distributed Wireless Ad Hoc Networks

The distributed scheduling mode possesses numerous advantages over the centralized scheduling mode in scenarios where the topology changes rapidly, such as low operation and maintenance costs, high robustness and improved delay performance, and thus has broad application prospects in robot swarms, device-to-device networks, industrial sensor networks, etc. The distributed election mechanism can effectively reduce collisions and improve the success probability of random access, which is one of the most commonly used channel access methods in distributed networks. The size of election interval directly affects the probability of successful election and the network performance. Specifically, with the expansion of the network scale, smaller election intervals will lead to more election nodes and reduce the probability of successful elections. Although expanding the election interval can reduce collisions to a certain extent, the number of legal election nodes will increase, which reduces the probability of successful elections. Therefore, the choice of the election interval needs to be explored in detail. This paper analyzes the relationship between the election interval and the number of effective election nodes, and establishes the analytical model of the election success probability, scheduling delay and effective throughput. In addition, the election interval size and the number of control slots are optimized to improve effective throughput and delay performance. Finally, the impact of the election interval on the network performance is verified by simulation, which provides guidance for parameter selection in realistic scenarios.

Analyzing Random Access Collisions in Massive IoT Networks

The cellular-based infrastructure is regarded as one of the potential solutions for massive Internet of Things (mIoT), where the random access (RA) procedure is used for requesting channel resources in the uplink data transmission. Due to the nature of the mIoT network with the sporadic uplink transmissions of a large amount of IoT devices, massive concurrent channel resource requests lead to a high probability of RA failure. To relieve the congestion during the RA in mIoT networks, we model RA procedure and analyze as well as evaluate the performance improvement due to different RA schemes, including power ramping (PR), back-off (BO), access class barring (ACB), hybrid ACB and back-off schemes, and hybrid power ramping and back-off (PR&BO). To do so, we develop a traffic-aware spatio-temporal model for the contention-based RA analysis in the mIoT network, where the signal-to-noise-plus-interference ratio (SINR) outage and collision events jointly determine the traffic evolution and the RA success probability. Compared to existing literature that only models collision from the single-cell perspective, we model both SINR outage and the collision from the network perspective. Based on this analytical model, we derive the analytical expression for the RA success probabilities to show the effectiveness of different RA schemes. We also derive the average queue lengths and the average waiting delays of each RA scheme to evaluate the packets accumulation status and packets serving efficiency. Our results show that our proposed PR&BO scheme outperforms other schemes in heavy traffic scenarios in terms of the RA success probability, the average queue length, and the average waiting delay.

SARA: Sparse Code Multiple Access-Applied Random Access for IoT Devices

In this paper, we study a random access (RA) procedure to support the massive connectivity of the Internet of Things (IoT) devices, also known as the IoT connectivity. Compared with the previous RA procedures that have limitations to support the IoT connectivity due to the exponentially increased access delay, we develop an RA procedure by applying the sparse code multiple access to reduce the access delay and increase the ratio of the IoT devices that successfully complete their RA procedures. We provide the theoretical performance analysis of the proposed RA procedure with the performance metrics, such as the RA success probability, the average access delay, the RA throughput, and the average number of preamble transmissions. Then, we provide the numerical results to evaluate the performance of the proposed RA procedure based on our analysis and the ns-3 simulator. Numerical results show that our proposed RA procedure is able to support the massive connectivity requirement with improved RA performance metrics compared with the conventional RA procedures.

A Preamble Collision Resolution Scheme via Tagged Preambles for Cellular IoT/M2M Communications

In this paper, we propose a preamble (PA) collision resolution (PACR) scheme based on multiple timing advance (TA) values captured via tagged PAs. In the proposed PACR scheme, tags are embedded in random access (RA) PAs and multiple TA values are captured for a single detected PA during a tag detection procedure. The proposed PACR scheme significantly improves RA success probability for stationary machine nodes since the nodes using collided PAs can successfully complete the corresponding RAs using exclusive data resource blocks.

Dynamic Preamble Subset Allocation for RAN Slicing in 5G Networks

The random access (RA) mechanism of Long Term Evolution (LTE) networks is prone to congestion when a large number of devices attempt RA simultaneously, due to the limited set of preambles. If each RA attempt is made by means of transmission of multiple consecutive preambles (codewords) picked from a subset of preambles, as proposed, collision probability can be significantly reduced. Selection of an optimal preamble set size can maximize RA success probability in the presence of a trade-off between codeword ambiguity and code collision probability, depending on load conditions. In light of this finding, this paper provides an adaptive algorithm, called Multi-preamble RA , to dynamically determine the preamble set size in different load conditions, using only the minimum necessary uplink resources. This provides high RA success probability, and makes it possible to isolate different network service classes by separating the whole preamble set into subsets each associated to a different service class; a technique that cannot be applied effectively in LTE due to increased collision probability. This motivates the idea that preamble allocation could be implemented as a virtual network function, called vPreamble, as part of a random access network slice. The parameters of a vPreamble instance can be configured and modified according to the load conditions of the service class it is associated to.

Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications.

Cellular-based machine-to-machine (M2M) communication is expected to facilitate services for the Internet of Things (IoT). However, because cellular networks are designed for human users, they have some limitations. Random access channel (RACH) congestion caused by massive access from M2M devices is one of the biggest factors hindering cellular-based M2M services because the RACH congestion causes random access (RA) throughput degradation and connection failures to the devices. In this paper, we show the possibility exploiting the capture effects, which have been known to have a positive impact on the wireless network system, on RA procedure for improving the RA performance of M2M devices. For this purpose, we analyze an RA procedure using a capture model. Through this analysis, we examine the effects of capture on RA performance and propose an Msg3 power-ramping (Msg3 PR) scheme to increase the capture probability (thereby increasing the RA success probability) even when severe RACH congestion problem occurs. The proposed analysis models are validated using simulations. The results show that the proposed scheme, with proper parameters, further improves the RA throughput and reduces the connection failure probability, by slightly increasing the energy consumption. Finally, we demonstrate the effects of coexistence with other RA-related schemes through simulation results.

Energy-efficient game-theoretical random access for M2M communications in overlapped cellular networks

The unprecedented growth of machine-to-machine (M2M) devices has brought a heavy burden to traditional cellular networks. In this paper, we focus on the overload problem caused by massive connections of M2M devices in overlapped cellular networks. We formulate the joint base station (BS) selection and power allocation optimization problem for each M2M device as a noncooperative access game. The utility function of each M2M device is described as the success probability of random access weighted by the energy efficiency (EE). We propose an iterative energy-efficient game-theoretical random access algorithm, in which each M2M device searches its optimal strategies in turn until no M2M device is able to improve its individual utility with a unilateral deviation. Numerical results demonstrate that significant performance enhancements on both the delay and energy consumption can be achieved simultaneously.

An Early Preamble Collision Detection Scheme Based on Tagged Preambles for Cellular M2M Random Access

In this paper, we propose an early preamble collision detection (e-PACD) scheme at the first step of random access (RA) procedure based on tagged preambles (PAs), which consist of both PA and tag Zadoff–Chu sequences using different root numbers, respectively. The proposed e-PACD scheme enables faster PA collision detection and notification, compared with the conventional RA scheme. Accordingly, it can reduce the RA delay and remove resource wastes which occur at the third step of the conventional RA procedure. The reduced RA delay achieved by the proposed e-PACD scheme can contribute to an ultralow latency requirement in fifth-generation (5G) cellular networks. In addition, the proposed e-PACD scheme enables the eNodeB to monitor the number of RA-attempting nodes (RA load) on each physical RA channel slot. The PACD probability and the RA load monitoring accuracy are mathematically analyzed, and the RA performance enhancement of the proposed scheme is evaluated in terms of RA success probability, average RA delay, and RA resource efficiency, compared with the conventional RA scheme.

Overload control of massive random access for machine-type communications

We show that random access retransmissions can lead to performance degradation.A Markov model is proposed to evaluate the performance of LTE random access.There is an optimal number of retransmissions for massive random access.Random access resources are separated into two subsets for MTC overload control.Conventional and proposed schemes can be used adaptively depending on traffic load. Traditional cellular systems may not be appropriate to support machine-type communications (MTC) due to a large number of devices and relatively small, infrequent data transmissions. The 3GPP has identified the MTC as an important area of the LTE system and has discussed several mechanisms that control random access (RA) overload caused by massive MTC devices. In this paper, we show that a retransmission mechanism of RA may lead to performance degradation in an overload situation, and propose two RA solutions that relieve the RA overload. Since the RA success probability is closely related with the number of simultaneous RA attempts, the first solution adjusts the maximum number of RA retransmissions to control the amount of RA attempts. The second solution separates the RA resources into two subsets that MTC devices can access according to the number of consecutive RA failures and distributes the RA traffic over the two subsets. The two proposed solutions are analyzed by a mathematical model assuming a simplified operation, and a more realistic environment is considered by protocol-level simulations. Since the performance of the proposed solutions depends on the system configurations and parameters, the base station may adaptively adjust them for an optimal operation.

A Non-Orthogonal Resource Allocation Scheme in Spatial Group Based Random Access for Cellular M2M Communications

Cellular machine-to-machine (M2M) communication can be one of the major candidate technologies to develop an Internet of Things (IoT) platform. A massive number of machine nodes access the cellular networks and typically send/receive small-sized data. In this situation, severe random access (RA) overload and radio resource shortage problems may occur if there is no evolution in the conventional cellular system. Focusing on RA, we need a larger number of preambles, as well as a more efficient resource allocation scheme in order to accommodate a significantly large number of RA requests from machine nodes. In this paper, we propose a non-orthogonal resource allocation (NORA) scheme, combined with our spatial group based RA (SGRA) mechanism, in order to provide a sufficiently large number of preambles at the first step of the RA procedure and non-orthogonally allocate physical uplink shared channel resources at the second step of the RA procedure. As a result, the proposed SGRA-NORA scheme can significantly increase the RA success probability, compared with that of the conventional RA scheme.

RoBiN: Random Access using Border Routers in Cellular Networks

Introducing Machine-to-Machine (M2M) communications over traditional 4G cellular networks make the cellular random access channel more congested and collision-prone. In order to resolve this random access congestion, we propose RoBiN - Random access using Border router in M2M cellular Networks. RoBiN proposes an architectural modification of introducing small cells, called Border Routers (BR), in cellular networks, with complete frequency reuse capability. We formulate the aforementioned challenge in terms of collision probability and system capacity. Subsequently, we propose an efficient solution for M2M communications in cellular networks. Exhaustive mathematical analysis shows that RoBiN significantly improves the random access success probability, by 50 % over existing 4G cellular systems. Simulation results on typical 4G networks corroborate our mathematical analysis and demonstrate almost 15 dB increase in Signal-to-Interference-plus-Noise Ratio (SINR) and 3 times throughput improvements over legacy 4G cellular systems. Furthermore, RoBiN also achieves 50 %ź80 % improvement in collision probability over existing time alignment matching work.

Estimation and Adaptation for Bursty LTE Random Access

With the potential to generate numerous connection requests, an explosive growth in the volume of data traffic and the number of mobile and machine-to-machine devices has drawn new attention to the radio access network. Surging random-access attempts cause not only severe preamble collisions but downlink resource shortage as well, thus degrading the performance of the random-access procedure. However, the effect of downlink resource shortage on system performance has yet to be comprehensively studied. In addition, most existing random-access contention resolution mechanisms sacrifice random-access channel (RACH) throughput for a high success probability, and thus, the price is that low-throughput mechanisms need a lot of time to deal with access attempts. In this paper, we evaluate the medium-access-control (MAC)-level performance for the four-step random-access procedure in Long-Term Evolution (LTE) systems, both with and without constrained downlink resources. Furthermore, we propose a novel RACH contention resolution scheme, i.e., the dynamic backoff (DB) scheme. DB can achieve high RACH throughput, yielding a high random-access success probability under various RACH overloaded scenarios.

Random Access Control for M2M in LTE System

With the implementation of 4G mobile networks, the number of M2M users will be much large in a cell. When massive M2M users try to randomly access simultaneously, this will impose a great influence on H2H communication. Without effective control, these M2M users will lead to a system breakdown. In this paper, we put forward a model, which allows low delay tolerance of users to have a higher priority for random access, when contending with high delay tolerance of users for limited random access resources. In this model, by controlling access rates of different delay tolerance of users, the random access success probability will be improved efficiently. In the meantime, it will achieve the highest success probability and optimize the system performance by setting optimal access rates for different delay tolerance of users. This will be a practical guide to M2M communication in LTE system.