Access Success Probability Research Articles

Two-Phase Random Access Procedure for LTE-A Networks

Simultaneous random access attempts from massive machine-type communications (mMTC) devices may severely congest a shared physical random access channel (PRACH) in mobile networks. This paper presents a novel two-phase random access (TPRA) procedure to deal with the congestion caused by mMTC devices accessing the PRACH. During the first phase, the TPRA splits the mMTC devices into smaller groups according to a preamble selected randomly by the devices. Then, in the second phase, each group of devices is assigned with a dedicated channel to complete the random access procedure. The proposed concept allows a base station to adjust the number of dedicated channels in real-time according to the actual network load. We then present an analytical model to estimate the access success probability and the average access delay of the TPRA. Finally, we propose a simple formula to determine the optimal number of random access resources for the second phase of the proposed TPRA. Simulations are carried out to validate the analytical models and to demonstrate the benefits of the TPRA compared to competitive techniques.

Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks

With the rapid growth of Internet-of-things (IoT) devices, IoT communication has become an increasingly crucial part of 5G wireless communication systems. The large-scale IoT devices access results in system overload and low utilization of energy efficiency under the existing network framework. In this paper, the cluster head uses the LTE-M protocol, and the intra-cluster uses the low-power wide-area network (LPWAN) self-networking protocol in the wireless sensor network. By a detailed analysis of the messages exchanged between the device and the base station, we describe the causes of overload and the steps of data aggregate combined with the physical channel. Then, we explore the cluster head quantity and the optimal scale in the intra-cluster under the traditional K-mean algorithm. When K is 30 under specific resources, the simulation results show that the system’s access success probability and resource utilization are optimal. Also, we propose a distributed dynamic cluster-head selection and clustering scheme based on an improved K-means algorithm. Simulation results show that the proposed scheme can reach 88.07% on the access success probability. The throughput and resource utilization are 3.5 times high than that of the optimal K-means.

Performance Study and Enhancement of Access Barring for Massive Machine-Type Communications

Machine-type communications (MTC) is an emerging technology that boosts the development of the Internet of Things by providing ubiquitous connectivity and services. Cellular networks are an excellent choice for providing such hyper-connectivity thanks to their widely deployed infrastructure, among other features. However, dealing with a large number of connection requests is a primary challenge in the cellular-based MTC. Severe congestion episodes can occur when a large number of devices try to access the network almost simultaneously. Extended access barring (EAB) is a congestion control mechanism for the MTC that has been proposed by the 3GPP. In this paper, we carry out a thorough performance analysis of the EAB and show the limitations of its current specification. To overcome these limitations, we propose the two enhanced EAB schemes: the combined use of the EAB and access class barring, and the introduction of a congestion avoidance backoff after the barring status of a UE is switched to unbarred. It is shown through extensive simulations that our proposed solutions improve the key performance indicators. A high successful access probability can be achieved even in heavily congested scenarios, the access delay is shortened, and, most importantly, the number of required preamble retransmissions is reduced, which results in significant energy savings. Furthermore, we present an accurate congestion estimation method that solely relies on the information available at the base station. We show that this method permits a realistic and effective implementation of the EAB.

Dynamic Speed Control of Unmanned Aerial Vehicles for Data Collection under Internet of Things.

With the new advancements in flight control and integrated circuit (IC) technology, unmanned aerial vehicles (UAVs) have been widely used in various applications. One of the typical application scenarios is data collection for large-scale and remote sensor devices in the Internet of things (IoT). However, due to the characteristics of massive connections, access collisions in the MAC layer lead to high power consumption for both sensor devices and UAVs, and low efficiency for the data collection. In this paper, a dynamic speed control algorithm for UAVs (DSC-UAV) is proposed to maximize the data collection efficiency, while alleviating the access congestion for the UAV-based base stations. With a cellular network considered for support of the communication between sensor devices and drones, the connection establishment process was analyzed and modeled in detail. In addition, the data collection efficiency is also defined and derived. Based on the analytical models, optimal speed under different sensor device densities is obtained and verified. UAVs can dynamically adjust the speed according to the sensor device density under their coverages to keep high data collection efficiency. Finally, simulation results are also conducted to verify the accuracy of the proposed analytical models and show that the DSC-UAV outperforms others with the highest data collection efficiency, while maintaining a high successful access probability, low average access delay, low block probability, and low collision probability.

Differentiated Service-Aware Group Paging for Massive Machine-Type Communication

Massive machine-type communication (mMTC) has been identified as one of the three generic 5G services, with the aim of providing connectivity to a large number of devices. The concurrent massive access in mMTC may lead to congestion due to limited access resources. Group paging (GP) is emerging as one of the promising solutions to alleviate network congestion by controlling access load. However, the performance of GP deteriorates drastically with the number of devices per paging group. This paper explores GP with a pre-backoff strategy for a general mMTC scenario in which devices are allowed to have diverse access success probability (ASP) requirements, and proposes a differentiated service-aware GP scheme with specific pre-backoff times (GPSP), with the aim of maximizing the total access rate while guaranteeing the ASP requirements for individual devices. An optimal solution to the GPSP problem is derived to provide a performance upper bound. As low-complexity algorithms are of key importance for mMTC applications, an efficient heuristic algorithm is further designed. Numerical results demonstrate that the proposed GPSP scheme can effectively improve the system performance in terms of average ASP, average access delay, and the average number of preamble transmissions.

On Multiple Access of a Large Number of Machine-Type Devices in Cellular Networks

Introduction: Machine-to-machine communication assumes data transmission from various wireless devices and attracts attention of cellular operators. In this regard, it is crucial to recognize and control overload situations when a large number of such devices access the network over a short time interval.Purpose:Analysis of the radio network overload at the initial network entry stage in a machine-to-machine communication system.Results: A system is considered that features multiple smart meters, which may report alarms and autonomously collect energy consumption information. An analytical approach is proposed to study the operation of a large number of devices in such a system as well as model the settings of the random-access protocol in a cellular network and overload control mechanisms with respect to the access success probability, network access latency, and device power consumption. A comparison between the obtained analytical results and simulation data is also offered.

Performance Analysis and Optimal Access Class Barring Parameter Configuration in LTE-A Networks With Massive M2M Traffic

Over the coming years, it is expected that the number of machine-to-machine (M2M) devices that communicate through long term evolution advanced (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 evolved Node B 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 and ACB as defined in the 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.

A Group-Based Massive Multiple Access Scheme in Cellular M2M Networks

With the rapid development of machine-to-machine (M2M) applications, there is an explosion in the number of M2M devices emerging in the network. It poses a significant challenge for the network to support random access requests of a huge number of M2M devices. In this paper, we propose a novel group-based random access scheme for cellular M2M communications, where the beamforming technology enhanced by massive multiple-input multiple-output (MIMO) is applied. The key idea of the proposed scheme is that the evolved Node-B (eNB) makes use of multiple beams to divide M2M devices into different groups. Benefited from the spatial selectivity of beams, the interference among different groups is limited, which can efficiently reduce collisions during the random access procedure. Numerical results demonstrate that our proposed group-based random access scheme has the higher access success probability of devices compared with the legacy random access scheme.

Optimization of Random Access Channel in NB-IoT

Narrowband Internet of Things is a new access technology introduced by 3GPP. This paper presents an analytical model to estimate the access success probability and average access delay of the random access channels by considering maximum number of preamble transmissions, size of backoff windows, and number of subcarriers in each coverage enhancement levels. A joint optimization technique is proposed to configure the parameters to maximize the access success probability under a target delay constraint. The accuracy of the analysis and the effectiveness of the proposed optimization technique are verified by computer simulations and benchmarked with exhaustive search. The result shows that the proposed optimization is able to find the optimal configuration under various conditions.

Cluster-Based Group Paging for Massive Machine Type Communications Under 5G Networks

Machine-type communications (MTCs) have been envisaged to play a key role within the future 5G cellular network. To handle the massive MTCs (mMTCs) and alleviate the congestion of the radio access network, the group paging (GP) scheme was proposed by the third-generation partnership project. However, its performance quickly decreases in the face of massive simultaneous channel accesses. In this paper, we propose a two-phase cluster-based GP (CBGP) scheme. First, owing to the advantages of low-cost, high-access capacity and handy deployment, IEEE 802.11ah is introduced to increase the capability of coping with massive access attempts. The separation of inner cluster data collection and header-based data transmission phases greatly alleviates the access congestion of cellular networks, reducing the access delay and increasing the successful access probability for mMTC devices. Besides, we also derive mathematical models of the CBGP scheme in terms of the successful access probability and average access delay. Moreover, effects from different numbers of clusters on the performance of the CBGP scheme are investigated and the optimal number of clusters is also derived, adaptive to different access scales. At last, numerical results are presented to validate the accuracy of our analytical models, demonstrate the effectiveness of the proposed CBGP scheme, and verify the optimal number of clusters, providing insights for the coming 5G cellular system design.

RACH Session Initiation Procedure with Retranslations

Due to the rapidly growing number of user’s equipments, operating in the LTE (Long-Term Evolution) mobile radio network, it became necessary to find methods to reduce the user load and signaling message’s traffic. New rules and procedures of devices communication are created to downsize network resources. This article explores the procedure for establishing a connection between mobile user’s equipment and the LTE base station. Such possible solution of transferring small data generated by machine-to-machine connections (M2M, Machine-to-Machine) was presented in the technical report TR 37.868 of the 3GPP consortium (3rd Generation Partnership Project). This approach implies the use of the random access channel (RACH) to interconnect between devices. Effective usage of the procedure for establishing a connection through a random access radio channel is based on the analysis of time-probability characteristics, such as the access success probability and failed access probability, average access delay. In this article we proposed analytical model in term of Markov chain to find these characteristics and present numerical analysis, considering collisions of preamble message, retransmissions of signalling messages and the boundary conditions on the number of possible retransmissions. The accuracy of the proposed analytical model was verified using programming environment R.

A High Throughput Pilot Allocation for M2M Communication in Crowded Massive MIMO Systems

A new scheme to resolve the intra-cell pilot collision for machine-to-machine (M2M) communication in crowded massive multiple-input multiple-output (MIMO) systems is proposed. The proposed scheme permits those failed user equipments (UEs), judged by a strongest-user collision resolution (SUCR) protocol, to contend for the idle pilots, i.e., the pilots that are not selected by any UE in the initial step. This scheme is called as SUCR combined idle pilots access (SUCR-IPA). To analyze the performance of the SUCR-IPA scheme, we develop a simple method to compute the access success probability of the UEs in each random access slot. The simulation results coincide well with the analysis. It is also shown that, compared with the SUCR protocol, the proposed SUCR-IPA scheme increases the throughput of the system significantly, and thus decreases the number of access attempts dramatically.

Non-Orthogonal Random Access for 5G Networks

The massive amounts of machine-type user equipments (UEs) will be supported in the future fifth generation (5G) networks. However, the potential large random access (RA) delay calls for a new RA scheme and for a detailed assessment of its performance. Motivated by the key idea of non-orthogonal multiple access, the non-orthogonal RA (NORA) scheme based on successive interference cancellation (SIC) is proposed in this paper to alleviate the access congestion problem. Specifically, NORA utilizes the difference of time of arrival to identify multiple UEs with the identical preamble, and enables power domain multiplexing of collided UEs in the following access process, while the base station performs SIC based on the channel conditions obtained through preamble detection. Our analysis shows that the performance of the NORA is superior to the conventional orthogonal RA (ORA) scheme in terms of the preamble collision probability, access success probability, and the throughput of RA. Simulation results verify our analysis and further show that our NORA scheme can improve the number of the supported UEs by more than 30%. Moreover, the number of preamble transmissions and the access delay for successfully accessed UEs are also reduced significantly by using the proposed RA scheme.

Метод оценки времени установления соединения по радиоканалу случайного доступа

We observe nowadays increasing the number of devices using Internet for data transmission in LTE network. Researchers offer their algorithms and methods for base station load reduction. Our approach allows minimizing network resources using Random Access Channel for session setup procedure. We obtain all performance measures for this procedure between user equipment and LTE base station. Procedure includes four messages: preamble transmission on the physical random access channel (Msg1), random access response of existing RACH procedure (Msg2), connection request, a.k.a. Hybrid automatic repeat request on the physical uplink shared channel (Msg3) and connection resolution (Msg4). This approach is used for small data transmission. If two or more devices select the same preamble, then collision occurs, and it is necessary to retransmit another preamble. Similarly, Msg3 may be blocked due to lack of resources. Our analytical model includes the dependence on collision probability. This solution has been proposed by 3GPP technical report 37.868. We obtain such performance measures as collision probability, dependent upon the number of attempts and the overall number of attempts in the period; access success probability; statistics of number of preamble transmissions; statistics of access delay. Numerical experiment demonstrate the accuracy of the proposed analytical model in the form of Markov chains.

Efficient Data Forwarding for Machine Type Communications in Internet of Things Networks

The MTC (Machine Type Communications) system is one of the most promising technologies to provide IoT (Internet of Things) applications. The MTC system suffers from congestion due to limited data transmission capacity and burst traffic. The congestion disturbs data delivery, results in an increase of energy consumption due to data retransmission, and finally poses a threat to the providers of IoT applications. In this paper, we focus on the congestion problem and present an efficient data forwarding mechanism to regulate the burst channel access from a large number of MTC devices. To regulate the communication channel access from the devices, we predict the number of devices by separating traffic load into the prediction of the number of devices that newly attempt to have access and the prediction of the number of devices that retry to access the channel. Through simulations, we show that our mechanism improves access success probability and reduces collision probability and access delay.

Gateway-assisted two-stage radio access for machine type communication in LTE-Advanced network

In Long Term Evolution-Advanced (LTE-A) network, Machine Type Communication (MTC) provides random access-based communication for automation applications. One of the most critical issues in MTC is the emergence of large number of MTC devices which may send requests to the base station simultaneously, and incur severe collision at the base station. When collision occurs, the data delivery fails, and the message delay accumulates.In this paper, we propose a gateway-assisted two-stage (GATS) radio access scheme to alleviate collision for delay-tolerant MTC applications. In GATS, MTC devices are divided into several groups, where random access in each group (as the first stage) is controlled by a device called MTC gateway. Collision of random access to the base station (as the second stage) is then effectively alleviated. Then we propose analytical model and simulation model to investigate the performance of GATS in terms of utilization of random access slots, access success probability and average message delay. Simulation results show that, by sacrificing a little message delay, GATS scheme significantly improves access success probability. Besides, when the group number increases, the delay becomes smaller.

Performance of Dynamic Access Class Barring Method in Cellular M2M Networks

Cellular based machine-to-machine (M2M) systems generate massive number of access requests which create congestion in the cellular network. The contention-based random access procedures are designed for cellular networks which cannot accommodate a large number of M2M traffic. In this paper, a contention-based slotted Aloha random access procedure for M2M network is first analyzed using different performance metrics. The impact of massive M2M traffic over cellular traffic is studied based on different arrival rate, random access opportunity and throughput. Then, an analytical model of selecting a base station (eNB) along with load balancing is developed. Finally, a dynamic access class barring method and RAN level congestion control mechanism by selecting the appropriate eNB is presented and evaluated with M2M traffic. Numerical results show that frame transmission rate influences the selection probability of the base stations. Moreover, the dynamic access class barring parameter along with frame transmission rate improve the overall throughput and access success probability among base stations as well as avoid overload situation in a particular base station.

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.

Two-Stage Resource Allocation for Random Access M2M Communications in LTE Network

Deployment of massive machine-to-machine (M2M) user equipments (UEs) in the current cellular network may cause overload in the radio access network (RAN). Access class barring (ACB) is an effective solution for reducing the RAN overload. In this letter, we propose an extended random access (RA) scheme to increase access success probability of M2M UEs by efficient use of available uplink radio resources. The proposed scheme allocates the available radio resources to the access-attempting UEs in two stages. In the first stage, the evolved node B (eNB) grants the available uplink resources to the UEs that have passed the ACB check. Then in the second stage, UEs that did not pass the ACB check utilize the remained unscheduled resources from the first stage. Simulation results show that the proposed scheme increases the number of successful requests and decreases the total service time of a traffic burst.

Dynamic RACH Partition for Massive Access of Differentiated M2M Services.

In machine-to-machine (M2M) networks, a key challenge is to overcome the overload problem caused by random access requests from massive machine-type communication (MTC) devices. When differentiated services coexist, such as delay-sensitive and delay-tolerant services, the problem becomes more complicated and challenging. This is because delay-sensitive services often use more aggressive policies, and thus, delay-tolerant services get much fewer chances to access the network. To conquer the problem, we propose an efficient mechanism for massive access control over differentiated M2M services, including delay-sensitive and delay-tolerant services. Specifically, based on the traffic loads of the two types of services, the proposed scheme dynamically partitions and allocates the random access channel (RACH) resource to each type of services. The RACH partition strategy is thoroughly optimized to increase the access performances of M2M networks. Analyses and simulation demonstrate the effectiveness of our design. The proposed scheme can outperform the baseline access class barring (ACB) scheme, which ignores service types in access control, in terms of access success probability and the average access delay.