Random Access Channel Research Articles

A Probabilistic Approach to Model SIC Based RACH Mechanism for Machine Type Communications in Cellular Networks

In a cellular Internet of Things, burst transmissions from millions of machine type communications (MTC) devices can result in channel congestion. The main bottleneck in such scenario is inefficient random access channel (RACH) mechanism that is used to attach MTC devices to a base station (BS). To address this issue of congestion in RACH mechanism, 3GPP has proposed an extended access barring (3GPP-EAB) mechanism. However, several works indicate that the performance of the 3GPP-EAB mechanism can be further improved. In this work, a successive interference cancellation (SIC) based RACH mechanism is considered to significantly increase the success rate and reduce congestion. In the proposed mechanism, the devices are allowed to transmit repeatedly for a finite number of times in a given cycle, and thereafter, the success rate is improved by applying back-and-forth SIC at the BS. A novel probabilistic approach of the proposed mechanism is presented with all transition and steady-state probabilities. Further, the probability of SIC for a given slot is derived. Through extensive numerical results, it is shown that the proposed mechanism significantly outperforms the existing ones in terms of the success rate. Moreover, to obtain the maximum success rate, the optimum number of devices to be entered in a cycle is also calculated.

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.

A Successive Interference Cancellation Based Random Access Channel Mechanism for Machine-to-Machine Communications in Cellular Internet-of-Things

In Cellular Internet-of-Things, random access channel (RACH) mechanism is used by machine-to-machine communication (M2M) devices to connect to a base station (BS) for any information exchange. However, increase in the number of M2M devices increases the network contention and reduces the number of RACH successes. In order to address this problem, we propose a successive interference cancellation based non-orthogonal random access (SIC-NORA) mechanism. In the proposed mechanism, each M2M device is allowed to repeat its transmission in a finite number of time slots within a radio frame. The messages of two devices that collide in the same slot can be decoded if the difference in the arrival time of their messages is greater than the predetermined threshold. Upon the successful RACH from an M2M device, the BS applies SIC in the current and all previous slots of the radio frame to decode messages of other devices for enhanced RACH success. A Markov chain model of the proposed mechanism is developed and the corresponding steady-state probabilities are derived. Through extensive numerical results, we show that the proposed mechanism performs better than state-of-the-art RACH mechanisms in terms of number of RACH successes and average access delay. Further, the proposed mechanism improves the success rate by 65.3% and 30.3% as compared to NORA and SIC-based RACH mechanism, respectively. Moreover, there is 29.6% and 15% reduction in the number of time slots required for 2×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> devices to get success with the proposed mechanism as compared to NORA and SIC-based RACH mechanism, respectively, at the corresponding optimal value of radio frame length.

Analysis of Random Access in NB-IoT Networks With Three Coverage Enhancement Groups: A Stochastic Geometry Approach

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks. To provide reliable connections with extended coverage, a repetition transmission scheme and up to three Coverage Enhancement (CE) groups are introduced into NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure, where each CE group is configured with different repetition values and transmission resources. To characterize the RACH performance of the NB-IoT network with three CE groups, this paper develops a novel traffic-aware spatio-temporal model to analyze the RACH success probability, where both the preamble transmission outage and the collision events of each CE group jointly determine the traffic evolution and the RACH success probability. Based on this analytical model, we derive the analytical expression for the RACH success probability of a randomly chosen IoT device in each CE group over multiple time slots with different RACH schemes, including baseline, back-off (BO), access class barring (ACB), and hybrid ACB and BO schemes (ACB&BO). Our results have shown that the RACH success probabilities of the devices in three CE groups outperform that of a single CE group network but not for all the groups, which is affected by the choice of the categorizing parameters.This mathematical model and analytical framework can be applied to evaluate the performance of multiple group users of other networks with spatial separations.

Математическая модель оценки вероятности коллизий преамбул при случайном доступе в сценарии массового межмашинного взаимодействия

The mass machine-to-machine communication (mMTC) scenario is one of the key ones in 5G cellular communication system. Tens of thousands of devices can operate simultaneously in one mMTC cell, sending data asynchronously to the data collection point. This is the reason for the occurrence of unavoidable collisions of preamble in the random access procedure. Mathematical models of traffic generation from a large number of devices within a cell are used to reliably estimate the probability of collisions. Most existing models are based on the approach of generating aggregated traffic from all devices and do not allow taking into account the current state of each individual device, which reduces the reliability of model studies. To solve this problem, we propose a mathematical model for generating traffic (preambles) in a random access channel, taking into account the transition state matrix of a discrete Markov chain. This approach allows to describe more reliably the synchronous and asynchronous transmission of preambles within a cell. The developed model is designed to estimate the collision probability of the random access procedure in the mMTC scenario, taking into account the synchronous and asynchronous transmission of preambles.

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.

Evolutionary Random Access Game With Objective and Subjective Players

We consider an evolutionary game on a graph under the condition that players perception of uncertainty and decision making can follow either an objective expected utility theory (EUT) model or a subjective prospect theory (PT) model. Each player chooses one of two strategies with probabilities $x$ and $1-x$ , where the subjective players bias their choices of the probabilities to be $w(x)$ and $w(1-x)$ to reflect the probability weighting effect of PT. We derive the conditions to achieve a locally asymptotically stable Nash equilibrium (NE) and the necessary conditions to achieve an evolutionary stable strategy (ESS). We illustrate these results through a simple random access channel game. We find that players’ behavior is affected by the total number of players as well as the number of each type of players (objective or subjective). We show that increasing the number of interacting players induces an aggressive transmission strategy and the behavior becomes similar for both types of players. Moreover, we let the objective and subjective players adjust their transmission under throughput rewards, delay penalties, and energy costs. The EUT players’ throughput is higher than the PT players’ throughput for a small number of interacting players and equal to if the number of interacting players increases.

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

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

Random Access Channel Coding in the Finite Blocklength Regime

Consider a random access communication scenario over a channel whose operation is defined for any number of possible transmitters. As in the model recently introduced by Polyanskiy for the Multiple Access Channel (MAC) with a fixed, known number of transmitters, the channel is assumed to be invariant to permutations on its inputs, and all active transmitters employ identical encoders. Unlike the Polyanskiy model, in the proposed scenario, neither the transmitters nor the receiver knows which transmitters are active. We refer to this agnostic communication setup as the Random Access Channel (RAC). Scheduled feedback of a finite number of bits is used to synchronize the transmitters. The decoder is tasked with determining from the channel output the number of active transmitters, $k$, and their messages but not which transmitter sent which message. The decoding procedure occurs at a time $n_t$ depending on the decoder's estimate, $t$, of the number of active transmitters, $k$, thereby achieving a rate that varies with the number of active transmitters. Single-bit feedback at each time $n_i, i \leq t$, enables all transmitters to determine the end of one coding epoch and the start of the next. The central result of this work demonstrates the achievability on a RAC of performance that is first-order optimal for the MAC in operation during each coding epoch. While prior multiple access schemes for a fixed number of transmitters require $2^k - 1$ simultaneous threshold rules, the proposed scheme uses a single threshold rule and achieves the same dispersion.

Optimization of 5G Accessibility in Non Standalone and Standalone Mode

In this paper, we have analyzed and developed different types of algorithms related to 5G accessibility procedures for Non Standalone (NSA) and Standalone (SA) mode. The 5G accessibility depends on 5G radio parameters of each procedure, such as contention based Random access (CBRA), contention free random access (CFRA), Radio admission control (RAC), Radio resource control (RRC) and Radio bearer reconfiguration. The random access procedure for NSA is similar to SA mode. The goal is to improve the accessibility by optimizing timers and tune the main 5G radio parameters related to Random access channel (RACH), RRC reconfiguration and RAC procedures.

RACH in Self-Powered NB-IoT Networks: Energy Availability and Performance Evaluation

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for a massive number of low-throughput low-cost devices in delay-tolerant applications with low power consumption. To provide reliable connections with extended coverage, a repetition transmission scheme is introduced to NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure. To avoid the difficulty in replacing the battery for IoT devices, the energy harvesting is considered as a promising solution to support energy sustainability in the NB-IoT network. In this work, we analyze RACH success probability in a self-powered NB-IoT network taking into account the repeated preamble transmissions and collisions, where each IoT device with data is active when its battery energy is sufficient to support the transmission. We model the temporal dynamics of the energy level as a birth-death process, derive the energy availability of each IoT device, and examine its dependence on the energy storage capacity and the repetition value. We show that in certain scenarios, the energy availability remains unchanged despite randomness in the energy harvesting. We also derive the exact expression for the RACH success probability of a randomly chosen IoT device under the derived energy availability, which is validated under different repetition values via simulations. We show that the repetition scheme can efficiently improve the RACH success probability in a light traffic scenario, but only slightly improves that performance with very inefficient channel resource utilization in a heavy traffic scenario.

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

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

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.

Modeling MTC and HTC Radio Access in a Sliced 5G Base Station

In this article, we develop a modeling framework to describe the uplink behavior of radio access in a sliced cell, including most features of the standard 3GPP multiple access procedures. Our model allows evaluating throughput and latency of each slice, as a function of cell parameters, when resources are in part dedicated to individual slices and in part shared. The availability of an accurate model is extremely important for the automated run time management of the cell and for the correct setting of its parameters. Indeed, our model considers most details of the behavior of sliced 5G cells, including Access Class Barring (ACB) and Random Access CHannel (RACH) procedures, preamble decoding, Random Access Response (RAR), and Radio Resource Control (RRC) procedures. To cope with a number of slices devoted to serve various co-deployed tenants, we derive a multi-class queueing model of the network processor. We then present ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) an accurate and computationally efficient technique to derive the performance measures of interest using continuous-time Markov chains, which scales up to a few slices only, and ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) tight performance bounds, which are useful to tackle the case of more than a fistful of slices. We prove the accuracy of the model by comparison against a detailed simulator. Eventually, with our performance evaluation study, we show that our model is very effective in providing insight and guidelines for allocation and management of resources in cells hosting slices for services with different characteristics and performance requirements, such as machine type communications and human type communications.

An Achievability Bound of Energy Per Bit for Stabilized Massive Random Access Gaussian Channel

Developing communication standards highlight a scenario called massive Machine-Type Communication within the framework of the Internet of Things. Under this scenario, a large number of devices with autonomous power supplies will operate in a random multiple access mode. It is crucial to keep the system stable, while consuming minimal energy. We describe a model of a system with a Gaussian Random Access Channel and consider an algorithm allowing message retransmissions. We provide a stability condition for the system and introduce an optimization problem to determine an achievability bound for energy per bit.

A Decoupled Learning Strategy for Massive Access Optimization in Cellular IoT Networks

Cellular-based networks are expected to offer connectivity for massive Internet of Things (mIoT) systems. However, their Random Access CHannel (RACH) procedure suffers from unreliability, due to the collision from the simultaneous massive access. Despite that this collision problem has been treated in existing RACH schemes, these schemes usually organize IoT devices' transmission and re-transmission along with fixed parameters, thus can hardly adapt to time-varying traffic patterns. Without adaptation, the RACH procedure easily suffers from high access delay, high energy consumption, or even access unavailability. With the goal of improving the RACH procedure, this paper targets to optimize the RACH procedure in real-time by maximizing a long-term hybrid multi-objective function, which consists of the number of access success devices, the average energy consumption, and the average access delay. To do so, we first optimize the long-term objective in the number of access success devices by using Deep Reinforcement Learning (DRL) algorithms for different RACH schemes, including Access Class Barring (ACB), Back-Off (BO), and Distributed Queuing (DQ). The converging capability and efficiency of different DRL algorithms including Policy Gradient (PG), Actor-Critic (AC), Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG) are compared. Inspired by the results from this comparison, a decoupled learning strategy is developed to jointly and dynamically adapt the access control factors of those three access schemes. This decoupled strategy integrates predicted traffic into the learning process to improve training efficiency, where a Recurrent Neural Network (RNN) model is first employed to predict the real-time traffic values of the network environment, and then multiple DRL agents are employed to cooperatively configure parameters of each RACH scheme. Our results demonstrate that the decoupled strategy remarkably accelerate the training speedy.

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

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

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 Enhancement of IEEE 802.15.6 Using Collision Avoidance Technique

Research related to Wireless Body Area Networks (WBAN) has recently gained more attention due to its application in enabling smart healthcare systems. A WBAN consists of several sensing nodes and a dedicated coordinator. The distributed nodes communicate with the coordinator by accessing the physical communication channel in a randomly distributed fashion. Random channel access may cause frame re-transmission of corrupted frames due to frame collisions. As a result of that, there will be degradation in the WBAN throughput, an increase in delay, and a waste of node energy. Nodes within a WBAN can be classified using specific user priorities allowing for prioritized communication to reduce possible frame collisions. To improve the performance and energy efficiency, this work aims to reduce collisions among nodes that belong to the same users’ priority (homogeneous collisions) and collisions among nodes of different users’ priorities (heterogeneous collisions). Homogeneous collisions can be reduced by scaling the minimum Contention Window (CW) among nodes within the same user priority, whereas heterogeneous collisions can be reduced by allowing higher user priority nodes to transmit while lower user priority nodes enter into a backoff state. This paper presents an analytical model and extensive simulations to show the enhanced performance of the proposed collision avoidance mechanism. The results show that the throughput and node energy efficiency is improved by a factor of three and two times, respectively.

Joint Control of Random Access and Dynamic Uplink Resource Dimensioning for Massive MTC in 5G NR Based on SCMA

The massive machine-type communication (mMTC) usage scenario, also known as the massive Internet of Things (mIoT), involves a huge number of MTC devices having high requirements on increased battery lifetime, which autonomously transfer small amounts of data with relaxed delay requirements, without human intervention. The current cellular network is unsuitable for this scenario, due to the limited uplink resources allocated to the physical random access channel (PRACH) and to the physical uplink shared channel (PUSCH). With this in mind, in this article, we propose a new framework, customized for massive MTC services, that includes a joint control of the dynamic resource allocation between the PRACH and the PUSCH, and a new random access (RA) procedure based on an adaptive access class barring (ACB) scheme that appropriately spreads RA reattempts in time. In addition, to further increase the transmission efficiency, we adopt the sparse code multiple access (SCMA) technique for PUSCH resources, because SCMA results as the most promising nonorthogonal multiple access (NOMA) technique to support massive MTC connectivity with small-size data. The simulation results show that the proposed control framework significantly improves the number of succeeded communications in comparison with the other proposals available in the literature. Furthermore, an energy consumption model is introduced to show the considerable energy saving achieved by the MTC devices which adopt our joint control scheme.