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Load Estimation in a Two-Priority mMTC Random Access Channel
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Abstract
The use of cellular networks for massive machine-type communications (mMTC) is an appealing solution due to the wide availability of cellular infrastructure. Estimating the number of devices (network load) is vital for efficient allocation of the available resources, especially for managing the random access channel (RACH) of the network. This paper considers a two-priority RACH and proposes two network load estimators: a maximum likelihood (ML) estimator and a reduced complexity (RCML) variant. The estimators are based on a novel model of the random access behavior of the devices coupled with a flexible analytical framework to calculate the involved probabilities. Monte Carlo simulations demonstrate the accuracy of the proposed estimators for different network configurations.
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The deployment of machine-to-machine (M2M) communications on cellular networks provides ubiquitous services to Internet-of-Things (IoT) systems. Cellular networks have been chosen as the best infrastructure for M2M communications due to the wide coverage and spectral efficiency. However, with the increased number of devices connecting to the network, massive number of devices are expected to simultaneously access the network resources. This massive access results in excessive congestion and collisions in the random access channel (RACH) which causes major degradation in systems performance. This article focuses on resolving the RACH collisions during the massive access scenarios for cellular M2M communications. We propose a collision resolution scheme using the backoff procedure which dynamically adjusts the backoff indicator (
BI
) based on the number of backlog devices and the available resources. The proposed scheme is integrated with three well-known random access schemes; standard random access (SRA), static access class barring (ACB) and dynamic access class barring (DAB). Furthermore, the paper presents an analysis for access success probability based on the dynamic backoff procedure. The optimal value of
BI
that achieves the highest access success probability is derived for the three different schemes. The analysis and simulation results indicate that the dynamic value of
BI
achieves approximately 99.9% access success rate with a slight increase in access delay of around 10%, which is considered a reasonable increment for delay-tolerant applications during the massive arrivals scenarios.
Massively dense deployment of Internet of Things (IoT) devices has put a stringent requirement on cellular networks to provide convenient service for not only human type traffic (HTC) but also for bursty traffic for IoT devices. Any bottleneck in the random access process means the bottleneck of the entire system because it is the first step before scheduled access. Access class barring (ACB) scheme is one of the key schemes in long term evolution advanced (LTE-A) to control the congestion in a random access process in which the access of some devices is barred based on a parameter, ACB factor, to relieve the congestion. In this paper, we analyze the ACB factor and criteria of its selection as the optimal selection of the ACB factor is essential for the maximum throughput of the system. The metrics used in the analysis of the ACB factor are total service time (TST), access delay and maximum collision, success, and idle probabilities with fixed and optimal ACB factor. Simulation results in MATLAB provide the complete picture of the behavior of the ACB factor and its control, used in the random access process.
The deployment of machine-type communications (MTC) together with cellular networks have great potential to create the ubiquitous Internet of Things environment. Nevertheless, the simultaneous activation of a large number of MTC devices (UEs) is a situation difficult to manage at the evolved Node B (eNB). The knowledge of the joint probability distribution function (PDF) of the number of successful and collided access requests within a random access opportunity (RAO) is a crucial piece of information for contriving congestion control schemes. A closed-form expression and an efficient recursion to obtain this joint PDF are derived in this paper. Furthermore, we exploit this PDF to design estimators of the number of contending UEs in a RAO. Our numerical results validate the effectiveness of our formulation and show that its computational cost is considerably lower than that of other related approaches. In addition, our estimators can be used by the eNBs to implement highly efficient congestion control methods.
Over the coming years, it is expected that the number of machine-to-machine (M2M) devices that communicate through LTE-A networks will rise significantly for providing ubiquitous information and services. However, LTE-A was devised to handle human-to-human traffic, and its current design is not capable of handling massive M2M communications. Access Class Barring (ACB) is a congestion control scheme included in the LTE-A standard that aims to spread the accesses of user equipments (UEs) through time so that the signaling capabilities of the eNB are not exceeded. Notwithstanding its relevance, the potential benefits of the implementation of ACB are rarely analyzed accurately. In this paper, we conduct a thorough performance analysis of the LTE-A random access channel (RACH) and ACB as defined in 3GPP specifications. Specifically, we seek to enhance the performance of LTE-A in massive M2M scenarios by modifying certain configuration parameters and by the implementation of ACB.We observed that ACB is appropriate for handling sporadic periods of congestion. Concretely, our results reflect that the access success probability of M2M UEs in the most extreme test scenario suggested by the 3GPP improves from approximately 30%, without any congestion control scheme, to 100% by implementing ACB and setting its configuration parameters properly.
Random access channels (RACHs) in cellular networks are normally designed for Poisson-distributed arrivals with a constant rate. Unexpected bursty arrivals may result in severe collisions in RACHs and thus degrade users' service qualities. This paper presents an analytical model for investigating the transient behavior of the RACHs with bursty arrivals generated in a specific time interval for orthogonal frequency-division multiple access (OFDMA) wireless networks. The proposed model has considered the implementation details of the OFDMA random access procedure (such as periodic access characteristic, uniform random backoff policy, and power-ramping effect) and the effect of new arrivals. The performance metrics of collision probability, success probability, and average access delay and the cumulative distribution function of the number of preamble transmissions and access delay for the successfully accessed mobile station are then derived based on the analytical model. The accuracy of the proposed analytical model was verified through computer simulations, and the results show the effectiveness of the proposed model.
Machine-to-Machine (M2M) communication is now playing a market-changing role
in a wide range of business world. However, in event-driven M2M communications,
a large number of devices activate within a short period of time, which in turn
causes high radio congestions and severe access delay. To address this issue,
we propose a Fast Adaptive S-ALOHA (FASA) scheme for M2M communication systems
with bursty traffic. The statistics of consecutive idle and collision slots,
rather than the observation in a single slot, are used in FASA to accelerate
the tracking process of network status. Furthermore, the fast convergence
property of FASA is guaranteed by using drift analysis. Simulation results
demonstrate that the proposed FASA scheme achieves near-optimal performance in
reducing access delay, which outperforms that of traditional additive schemes
such as PB-ALOHA. Moreover, compared to multiplicative schemes, FASA shows its
robustness even under heavy traffic load in addition to better delay
performance.
Fifth generation (5G) networks provide connectivity for several services including enhanced mobile broadband (eMBB), ultra-reliable low latency communication (URLLC) and massive Internet of things (mIoT). With the accelerated increase in the number of devices connected to the networks, and the diversity in quality-of-service (QoS) requirements, an appropriate modification in protocol design is required. Initial access in 5G networks is carried out based on the random access (RA) procedure through the random access channel (RACH). However, the limited resources, i.e. preambles, of the RACH causes intensive collisions among the massive number of contending devices, which result in low success probability and high access delay, causing failure to meet QoS requirements for different services. This paper introduces a priority-based load-adaptive preamble separation (PLPS) RA scheme in which RACH resources are separated between devices according to the service class priority and load estimation. The number of preambles assigned for each class of device is updated before each random access opportunity (RAO), based on the arrival load estimation, aimed at increasing the RACH throughput and therefore reducing the access delay for eMBB and URLLC devices. The proposed scheme is evaluated through a mathematical analysis model and extensive simulations. The results show that the proposed PLPS RA scheme succeeds in achieving the different QoS requirements of the three considered services, while increasing the RACH throughput by approximately 140%, compared to the benchmark which indicates the efficiency of the proposed scheme.
Traffic control is regarded as a key issue to alleviate congestion in internet of vehicles (IoV) machine-type communications (MTC). Recently, many traffic control schemes have been studied, such as access class barring (ACB) scheme and back-off (BO) scheme. However, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid scheme, combining the priority dynamic ACB (PDACB) scheme and BO scheme. The IoV devices are classified depending on different delay characteristics, where the delay-sensitive devices are classified as high priority. The target is to maximum the successful transmission of packets with the success rate constraint by adjusting the various ACB factors. Proximal policy optimization (PPO) algorithm as a unique deep reinforcement learning (DRL) method is utilized in this paper, which can obtain continuous action space and solve for the optimal ACB factors without estimating backlog of nodes. A quick convergence is achieved by designing sensible state space, action space and reward. The access capability of the PDACB traffic control scheme is verified by simulations.
One of the pivotal services of the fifth generation (5G) of cellular technology is massive Machine-type Communications (mMTCs), which is intended to support connecting high density of machine-type devices (MTDs). Random access channel of long-term evolution (LTE)/LTE advanced needs to be modified in order to support the large simultaneous arrival of MTDs. 3GPP suggested access class barring (ACB) as a mechanism to inhibit network congestion in the mMTC or massive Internet of Things (IoT) scenario. Consequently, in devices that are repeatedly ignored by ACB, the queue of data packets keeps growing. In storage-constrained IoT nodes with limited buffer, this may lead to packet drop due to buffer overflow, causing a decline in the overall throughput of the system. To address this issue, a novel queue-aware prioritized access classification (QPAC)-based ACB technique is proposed in this article, where MTDs having data queue size close to its buffer limit are dynamically given higher priority in ACB. To study the queue build-up at each MTC device, a node-centric analysis of ACB in the buffer-constrained scenario is performed using a 2-D Markov chain. It is shown that the proposed QPAC scheme, with optimal model parameters obtained by maximizing overall system utility, offers up to 70% gain in throughput compared to the nearest competitive dynamic ACB scheme.
When incorporating machine-to-machine (M2M) communications into the Third-Generation Partnership Project (3GPP) Long-Term Evolution (LTE) networks, one of the challenges is the traffic overload since many machine-type communication (MTC) devices activated in a short period of time may require access to an evolved node B (eNodeB) simultaneously. One approach to tackle this problem is by using an access class barring (ACB) mechanism with an ACB factor to defer some activated MTC devices transmitting their access requests. In this paper, we first present an analytical model to determine the expected total service time, i.e., the time used by all MTC devices to successfully access the eNodeB. In the ideal case that the eNodeB is aware of the number of backlogged MTC devices, we determine the optimal value of the ACB factor to reduce traffic overload. To better utilize the random access resources shared among human users and MTC devices in LTE networks, we propose to dynamically allocate a number of random access preambles for MTC devices. We further propose two dynamic ACB (D-ACB) algorithms for fixed and dynamic preamble allocation schemes to determine the ACB factors without a priori knowledge of the system backlog. Simulation results show that the proposed D-ACB algorithms achieve almost the same performance as the optimal performance obtained in the ideal case. The proposed D-ACB for dynamic preamble allocation algorithm can reduce both the total time to serve all MTC devices and the average number of random access opportunities required by each MTC device.
The current wireless cellular networks can be used to provide machine-to-machine (M2M) communication services. However, the Long Term Evolution (LTE) networks, which are designed for human users, may not be able to handle a large number of bursty random access requests from machine-type communication (MTC) devices. In this paper, we propose a scheme that uses both access class barring (ACB) and timing advance information to prevent random access overload in M2M systems. We formulate an optimization problem to determine the optimal ACB parameter, which maximizes the expected number of MTC devices successfully served in each random access slot. Hence, the number of random access slots required to serve all MTC devices can be minimized. To reduce the computational complexity and improve the practicability of the proposed scheme, we propose a closed-form approximate solution to the optimization problem and present an algorithm to estimate the number of active MTC devices requiring access in each random access slot. The correctness of the analytical model and the accuracy of the estimation algorithm are validated via simulations. Results show that both numerical and approximate solutions provide the same performance. Our proposed scheme can reduce nearly half of the random access slots required to serve all MTC devices compared to the existing schemes, which use timing advance information only, ACB only, or cooperative ACB.
To address random access channel (RACH) congestion and high signaling overhead problems of machine-to-machine (M2M) communication in cellular networks, we propose a new design of a random access procedure that is exclusively engineered for the M2M communication. Our design has two prominent features. One is a fast signaling process that allows M2M user equipment to transmit data right after preamble transmission on a physical RACH to reduce the signaling overhead. The other is a self-optimization feature that allows the cellular system to produce optimal M2M throughput by adaptively changing resource block (RB) composition and an access barring parameter according to the amount of available RBs and the M2M traffic load. We derive a closed-form analytic formula for the M2M traffic throughput and propose a joint adaptive resource allocation and access barring scheme based on the analytic results. By simulation, we show that the proposed scheme exhibits a near-optimal performance in terms of the capacity.
When incorporating machine-to-machine (M2M) communications into Long Term Evolution (LTE) networks, one of the challenges is the traffic overload due to a large number of machine type communications (MTC) devices with bursty traffic. One approach to tackle this problem is to use the access class barring (ACB) mechanism to regulate the opportunity of MTC devices to transmit request packets. In this paper, we first present an analytical model to determine the expected total access delay of all the MTC devices. For the ideal case that the LTE base station (eNodeB) knows the number of backlogged users, we determine the optimal value of the ACB factor, which can best reduce the congestion and access delay. For the practical scenario, we propose a heuristic algorithm to adaptively change the ACB factor without the knowledge of the number of backlogged users. Results show that the proposed heuristic algorithm achieves near optimal performance in reducing access delay.
Due to the huge amount of machine-type communications (MTC) devices, radio access network (RAN) overload is a critical issue in cellular-based MTC. The prioritized random access with dynamic access barring (PRADA) framework was proposed to efficiently tackle the RAN overload problem and provide quality of service (QoS) for different classes of MTC devices in Third-Generation Partnership Project (3GPP) Long-Term Evolution Advanced (LTE-A) networks. The RAN overload issue is solved by preallocating random access channel (RACH) resources for different MTC classes with class-dependent back-off procedures and preventing a large number of simultaneous random access requests using dynamic access barring. In this paper, the performances of LTE-A without access barring, extended access barring (EAB), and PRADA are mathematically derived and compared, showing the superior performance of the PRADA scheme.
In this paper, we consider a wireless cellular system with an unbounded population of machine-type users. The system provides a number of non-interfering slotted-time channels which users contend for when sending their uplink data packets. We propose a provably stable control procedure for the channel access probability in the sense that it maintains a finite number of unserviced users in the system. We also compare the proposed algorithm against the optimal multi-channel slotted Aloha to conclude that our solution demonstrates near-optimum performance.
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