Massive Machine Type Communication Research Articles

User association and resource allocation in 5G (AURA-5G): A joint optimization framework

5G wireless networks will be extremely dense, given the projected increase in the number of users and access points (APs), as well as heterogeneous, given the different types of APs and the applications being accessed by the users. In such challenging environments, efficient mobility management (MM), and specifically user association, will be critical to assist the 5G networks in provisioning the Quality of Service (QoS) of diverse applications 5G targets to serve. Whilst determining the most suitable AP for the users, multiple constraints such as available backhaul capacity, link latency, etc., will need to be accommodated for. Hence, to provide an optimal user association solution, in this paper we present a joint optimization framework, namely AURA-5G. Under this framework we formulate our user association strategy as a Mixed Integer Linear Program (MILP) that aims to maximize the total sum rate of the network whilst optimizing the bandwidth assignment and access point selection. We analyze multiple active application profiles simultaneously, i.e. enhanced Mobile Broadband (eMBB) and massive Machine Type Communication (mMTC), in the network and study the performance of AURA-5G. Additionally, we provision a novel study on the multiple dual connectivity modes, wherein the user can be connected to either one macro cell and a possible small cell, or with any two favorable candidate access points. Utilizing the AURA-5G framework, we perform a novel comparative study of all the considered scenarios on the basis of total network throughput, performance against baseline scenario and system fairness. We show that the AURA-5G optimal solutions improve the performance of different network scenarios in terms of total network throughput (by 38×–690×) and system fairness as compared to the baseline scenario, which is a conventional user association solution. Further, we also present a fidelity analysis of the AURA-5G framework based on the backhaul utilization, latency compliance, convergence time distribution and solvability. And since, a given network cannot always guarantee to satisfy the future network loads and application constraints, we show that AURA-5G can be utilized by the operators/vendors to evaluate the myriad network re-dimensioning approaches for attaining a feasible and optimal solution. Henceforth, we then explore the possibility of network re-dimensioning and study its impact on system performance for scenarios where the performance of AURA-5G is severely impacted due to the extremely strict nature of the constraints imposed in the MILP.

Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry

Massive machine-type communications (mMTC) is a crucial scenario to support booming Internet of Things (IoTs) applications. In mMTC, although a large number of devices are registered to an access point (AP), very few of them are active with uplink short packet transmission at the same time, which requires novel design of protocols and receivers to enable efficient data transmission and accurate multi-user detection (MUD). Aiming at this problem, grant-free non-orthogonal multiple access (GF-NOMA) protocol is proposed. In GF-NOMA, active devices can directly transmit their preambles and data symbols altogether within one time frame, without grant from the AP. Compressive sensing (CS)-based receivers are adopted for non-orthogonal preambles (NOP)-based MUD, and successive interference cancellation is exploited to decode the superimposed data signals. In this paper, we model, analyze, and optimize the CS-based GF-MONA mMTC system via stochastic geometry (SG), from an aspect of network deployment. Based on the SG network model, we first analyze the success probability as well as the channel estimation error of the CS-based MUD in the preamble phase and then analyze the average aggregate data rate in the data phase. As IoT applications highly demands low energy consumption, low infrastructure cost, and flexible deployment, we optimize the energy efficiency and AP coverage efficiency of GF-NOMA via numerical methods. The validity of our analysis is verified via Monte Carlo simulations. Simulation results also show that CS-based GF-NOMA with NOP yields better MUD and data rate performances than contention-based GF-NOMA with orthogonal preambles and CS-based grant-free orthogonal multiple access.

On the Minimum Average Age of Information in IRSA for Grant-Free mMTC

We consider the optimal design of the frame-based irregular repetition slotted ALOHA (IRSA) protocol for minimizing the average age of information (AAoI) in grant-free massive machine-type communications (mMTC). To this end, we first characterize the AAoI as a function of the number of user elements (UEs), the frame duration, and the repetition distribution of IRSA. We present this characterization for IRSA schemes with packet recovery at the end of frame and packet generation either at the beginning of the frame or just in time before first transmission in a frame. We also propose and characterize the AAoI of a novel early packet recovery method which further reduces the average age of information. In all cases, the analysis reveals that, as a function of normalized channel traffic (defined as the ratio of number of UEs to frame duration), the AAoI first decreases linearly due to more frequent updates received from the UEs, and increases sharply beyond a critical point due to packet recovery failures caused by collisions. We then consider the problem of minimizing AAoI by optimizing over the normalized channel traffic and repetition distribution for all the proposed sampling and recovery schemes. The optimization problem is challenging since the objective function is semi-analytical and can only be completely characterized using simulations. In an asymptotic regime where the number of UEs as well as the frame size is large, we characterize the AAoI using upper and lower bounds. We also obtain a locally optimal normalized channel traffic and repetition distribution using differential evolution. Based on the insights obtained from the asymptotic analysis, we also propose a pragmatic approach to obtain a normalized channel traffic and repetition distribution for AAoI reduction in the non-asymptotic case. Finally, we empirically show that our AAoI minimizing schemes outperform conventional throughput optimal schemes.

Massive Coded-NOMA for Low-Capacity Channels: A Low-Complexity Recursive Approach

In this paper, we present a low-complexity recursive approach for massive and scalable code-domain nonorthogonal multiple access (NOMA) with applications to emerging low-capacity scenarios. The problem definition in this paper is inspired by three major requirements of the next generations of wireless networks. Firstly, the proposed scheme is particularly beneficial in low-capacity regimes which is important in practical scenarios of utmost interest such as the Internet-of-Things (IoT) and massive machine-type communication (mMTC). Secondly, we employ code-domain NOMA to efficiently share the scarce common resources among the users. Finally, the proposed recursive approach enables code-domain NOMA with low-complexity detection algorithms that are scalable with the number of users to satisfy the requirements of massive connectivity. To this end, we propose a novel encoding and decoding scheme for code-domain NOMA based on factorizing the pattern matrix, for assigning the available resource elements to the users, as the Kronecker product of several smaller factor matrices. As a result, both the pattern matrix design at the transmitter side and the mixed symbols' detection at the receiver side can be performed over matrices with dimensions that are much smaller than the overall pattern matrix. Consequently, this leads to significant reduction in both the complexity and the latency of the detection. We present the detection algorithm for the general case of factor matrices. The proposed algorithm involves several recursions each involving certain sets of equations corresponding to a certain factor matrix. We then characterize the system performance in terms of average sum rate, latency, and detection complexity. Our latency and complexity analysis confirm the superiority of our proposed scheme in enabling large pattern matrices. Moreover, our numerical results for the average sum rate show that the proposed scheme provides better performance compared to straightforward code-domain NOMA with comparable complexity, especially at low-capacity regimes.

A Poisson Process-Based Random Access Channel for 5G and Beyond Networks

The 5th generation (5G) wireless networks propose to address a variety of usage scenarios, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Due to the exponential increase in the user equipment (UE) devices of wireless communication technologies, 5G and beyond networks (B5G) expect to support far higher user density and far lower latency than currently deployed cellular technologies, like long-term evolution-Advanced (LTE-A). However, one of the critical challenges for B5G is finding a clever way for various channel access mechanisms to maintain dense UE deployments. Random access channel (RACH) is a mandatory procedure for the UEs to connect with the evolved node B (eNB). The performance of the RACH directly affects the performance of the entire network. Currently, RACH uses a uniform distribution-based (UD) random access to prevent a possible network collision among multiple UEs attempting to access channel resources. However, in a UD-based channel access, every UE has an equal chance to choose a similar contention preamble close to the expected value, which causes an increase in the collision among the UEs. Therefore, in this paper, we propose a Poisson process-based RACH (2PRACH) alternative to a UD-based RACH. A Poisson process-based distribution, such as exponential distribution, disperses the random preambles between two bounds in a Poisson point method, where random variables occur continuously and independently with a constant parametric rate. In this way, our proposed 2PRACH approach distributes the UEs in a probability distribution of a parametric collection. Simulation results show that the shift of RACH from UD-based channel access to a Poisson process-based distribution enhances the reliability and lowers the network’s latency.

Massive Random Access With Sporadic Short Packets: Joint Active User Detection and Channel Estimation via Sequential Message Passing

This paper considers an uplink massive machine-type communication (mMTC) scenario, where a large number of user devices are connected to a base station (BS). A novel grant-free massive random access (MRA) strategy is proposed, considering both the sporadic user traffic and short packet features. Specifically, the notions of active detection time (ADT) and active detection period (ADP) are introduced so that active user detection can be performed multiple times within one coherence time. By taking sporadic user traffic and short packet features into consideration, we model the joint active user detection and channel estimation issue into a dynamic compressive sensing (CS) problem with the underlying sparse signals exhibiting substantial temporal correlation. This paper builds a probabilistic model to capture the temporal structure and establishes a corresponding factor graph. A novel sequential approximate message passing (S-AMP) algorithm is designed to sequentially perform inference and recover sparse signal from one ADT to the next. The Bayes active user detector and the corresponding channel estimator are then derived. Numerical results show that the proposed S-AMP algorithm enhances active user detection and channel estimation performances over competing algorithms under our scenario.

Resource allocation for massive machine type communications in the finite blocklength regime

Massive machine type communications (mMTC) have been regarded as promising applications in the future. One main feature of mMTC is short packet communication. Different from traditional long packet communication, short packet communication suffers from transmission rate degradation and a significant error rate is introduced. In this case, traditional resource allocation scheme for mMTC is no longer applicable. In this paper, we explore resource allocation for cellular-based mMTC in the finite block- length regime. First, to mitigate the load of the base station (BS), we establish a framework for cellular- based mMTC, where MTCGs reuse the resources of cellular users (CUs), aggregate the packets generated by MTCDs, and forward them to the BS. Next, we adopt short packet theory to obtain the minimum required blocklength of a packet that transmits a certain amount of information. Then, by modeling the process of MTCGs-assisted communication as a queuing process, we derive the closed-form expression of the average delay of all MTCDs. Guided by this, we propose a joint power allocation and spectrum sharing scheme to minimize the average delay. Finally, the simulation results verify the correctness of the theoretical results and show that the proposed scheme can reduce the average delay efficiently.

Cross-Network-Slice Authentication Scheme for the 5 th Generation Mobile Communication System

The fifth-generation mobile network (5G) integrates various application services in a heterogeneous network environment. Compared to the traditional networks, 5G is not just an extension of the 4th generation, which contains three important properties, enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable and low latency communications (URLLC). 5G applies the functionalities of Network Function Virtualization and Software-Defined Networking to support multiple services and proposes a new concept called Network Slicing. Users can access different services quickly in the 5G network supported by network slicing. In a traditional network like 4G, if a user wants to access different services, it will be necessary to perform different authentication procedures that cause additional burden and operation cost in the user’s device. However, the 5G network inherits the previous network architecture. Hence, the user’s device still needs to be authenticated by the core network. Besides, providing a guarantee of connecting to a correct network slice is one of the prime concerns. The paper presents an authentication scheme tailored for the 5G network. In the proposed scheme, the authentication is decentralized to the edge clouds to achieve low latency. Moreover, the authentication flow is no longer attached to the operator all the time to reduce time latency. The proposed scheme is secure against the attackers who aim to impersonate users, network operators, or even network slices, and it also provides secure session key exchange. Empirical performance assessment in terms of its functionalities gains better acceptability of the proposed scheme than other existing ones.

Guest Editorial Massive Access for 5G and Beyond—Part I

Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. In the past few years, it has received considerable attention in academia and industry. This Special Issue (SI) of the IEEE Journal on Selected Areas in Communications (JSAC) on Massive Access for 5G and Beyond contains the latest results of researchers, industry practitioners, and individuals working on related research problems. Due to the extremely high response to the Call for Papers, this SI is split into two parts. The first part includes a guest editor-authored survey paper and 17 technical papers focusing on access models and access protocols, while the second part contains 18 papers focusing on access techniques and coverage enhancement approaches. We sincerely thank the authors, reviewers, JSAC staffs, and the Senior Editor, Prof. Wayne Stark, for their effort and time in preparing this SI.

Cognitive IoT relaying NOMA networks with user clustering and imperfect SIC

To implement machine type communications (MTCs) in next generation mobile networks, Narrowband-Internet of Things (NB-IoT) need be studied. Such NB-IoT has been released by the third generation partnership project (3GPP) as a promising method to exhibit extended coverage and low energy consumption, especially for cheap MTC devices. However, the existing NB-IoT using orthogonal multiple access (OMA) scheme which cannot provide connectivity for a massive MTC devices. In this paper, we consider NB-IoT using non-orthogonal multiple access (NOMA). We find that power allocation factors for devices in clustering users as main enabler to exhibit differences in term of performance of devices. The worse situation of imperfect successive interference cancellation (SIC) is further studied due to main reason regarding degraded system performance. We derive closed-form expressions of outage probability for devices in a cluster to highlight main parameters affecting quality of devices. We verify our mathematical analysis via Monte-Carlo simulations.

Effects of Differentiated 5G Services on Computational and Radio Resource Allocation Performance

5G is poised to support new emerging service types that help in the realization of futuristic applications. These services include enhanced Mobile BroadBand (eMBB), ultra-Reliable Low Latency Communication (uRLLC), and massive Machine-Type Communication (mMTC). Even though the new services offer a variety of new use-cases to be implemented, it is still a challenge to guarantee the Quality of Service (QoS) they demand. Moreover, as considerable amount of computational resources are introduced in the evolved Radio Access Network (RAN) following the Mobile Edge Computing (MEC) concept, computational resource allocation optimization along with radio allocation becomes essential. In this paper, we examine the characteristics of the new 5G services and propose a joint computational and radio resource allocation framework that analyzes the QoS performance of each 5G service individually. The framework is developed based on per-service load characterization. Therefore, a computational load distribution algorithm is developed that balances the workloads subject to user association constraint. Further, radio resource allocation performs load-based eMBB-mMTC slicing and uRLLC puncturing. The simulation results show that the proposed solution reduces the packet loss ratio by up to 15% and increases the user data rate by up to 7% for 4G-like services. Furthermore, the effect of resource granularity in radio allocation has been identified as crucial factor for effective allocation of services with small data loads. Finally, the problem of small granularity has been solved by adapting the allocation interval.

DeepMuD: Multi-User Detection for Uplink Grant-Free NOMA IoT Networks via Deep Learning

In this letter, we propose a deep learning-aided multi-user detection (DeepMuD) in uplink non-orthogonal multiple access (NOMA) to empower the massive machine-type communication where an offline-trained Long Short-Term Memory (LSTM)-based network is used for multi-user detection. In the proposed DeepMuD, a perfect channel state information (CSI) is also not required since it is able to perform a joint channel estimation and multi-user detection with the pilot responses, where the pilot-to-frame ratio is very low. The proposed DeepMuD improves the error performance of the uplink NOMA significantly and outperforms the conventional detectors (even with perfect CSI). Moreover, this gain becomes superb with the increase in the number of Internet of Things (IoT) devices. Furthermore, the proposed DeepMuD has a flexible detection and regardless of the number of IoT devices, the multi-user detection can be performed. Thus, an arbitrary number of IoT devices can be served without a signaling overhead, which enables the grant-free communication.

Distance-Aware Relay Selection in an Energy-Efficient Discovery Protocol for 5G D2D Communication

Massive machine-type communications (mMTC) is one of the main services delivered by the fifth Generation (5G) mobile network. The traditional cellular architecture where all devices connect to the base station is not energy efficient. For this reason, the use of device-to-device (D2D) communications is considered to reduce the energy consumption of mMTC devices. The main idea is to use nearby user equipment (UE) as a relay and establish with it D2D communication. However, the relay selection process also consumes energy, and this consumption can be significant compared to the energy consumed during the data transmission phase. In this paper, we propose a distributed energy-efficient D2D relaying mechanism for mMTC applications. This mechanism favors the selection of the UEs with low path loss with the mMTC device. Through mathematical analysis and simulations, we show that our mechanism allows a reduction of the total energy consumption of mMTC devices (up to 75% compared to direct transmission) when they have an unfavorable link budget. Moreover, our mechanism achieves almost constant energy consumption for a large range of UE densities and distances between the mMTC device and the base station.

Modeling and Analysis of Tagged Preamble Transmissions in Random Access Procedure for mMTC Scenarios

With the emerging of the massive Machine Type Communication usage scenario, the legacy Random Access (RA) procedures need to be fully renewed to meet the huge number of accesses and the energy-constrained device requirements. In this context, the strategy of transmitting tagged preambles as access requests is gaining importance. In fact, it can reduce the number of signaling transmissions per access attempt, the overall number of phases in the access procedure, and, accordingly, energy consumption per device. However, the effectiveness of detection strategies of tagged preambles by the gNB receiver plays a fundamental role on the RA procedure performance. Up to now, the performance of these advanced detection procedures has been obtained only by running a large number of simulations that are typically highly time-consuming. To the best of our knowledge, this paper presents the first analytical model to analyze the correct detection of both the preamble and the tag transmitted by each device in the presence of interference, due to other preambles and tags, and of noise. The high accuracy of the proposed model is verified through simulations. In addition, we show how our analytical study can be a good tool to investigate and derive innovative detection strategies.

Traffic Reduction Technologies and Data Aggregation Control to Minimize Latency in IoT Systems

The spread of the Internet of Things (IoT) has led to the generation of large amounts of data, requiring massive communication, computing, and storage resources. Cloud computing plays an important role in realizing most IoT applications classified as massive machine type communication and cyber-physical control applications in vertical domains. To handle the increasing amount of IoT data, it is important to reduce the traffic concentrated in the cloud by distributing the computing and storage resources to the network edge side and to suppress the latency of the IoT applications. In this paper, we first present a recent literature review on fog/edge computing and data aggregation as representative traffic reduction technologies for efficiently utilizing communication, computing, and storage resources in IoT systems, and then focus on data aggregation control minimizing the latency in an IoT gateway. We then present a unified modeling for statistical and nonstatistical data aggregation and analyze its latency. We analytically derive the Laplace-Stieltjes transform and average of the stationary distribution of the latency and approximate the average latency; we subsequently apply it to an adaptive aggregation number control for the time-variant data arrival. The transient traffic characteristics, that is, the absorption of traffic fluctuations realizing a stable optimal latency, were clarified through a simulation with a time-variant Poisson input and non-Poisson inputs, such as a Beta input, which is a typical IoT traffic model.

AI-Driven Provisioning in the 5G Core

Network slicing enables communication service providers to partition physical infrastructure into logically independent networks. Network slices must be provisioned to meet the service-level objectives (SLOs) of disparate offerings, such as enhanced mobile broadband, ultrareliable low-latency communications, and massive machine-type communications. Network orchestrators must customize service placement and scaling to achieve the SLO of each network slice. In this article, we discuss the challenges encountered by network orchestrators in allocating resources to disparate 5G network slices, and propose the use of artificial intelligence to make core placement and scaling decisions that meet the requirements of network slices deployed on shared infrastructure. We explore how artificial intelligence-driven scaling algorithms, coupled with functionality-aware placement, can enable providers to design closed-loop solutions to meet the disparate SLOs of future network slices.

Modern Random Access: An Age of Information Perspective on Irregular Repetition Slotted ALOHA

Age of information (AoI) is gaining attention as a valuable performance metric for many IoT systems, in which a large number of devices report time-stamped updates to a central gateway. This is the case, for instance, of remote sensing, monitoring, or tracking, with broad applications in the industrial, vehicular, and environmental domain. In these settings, AoI provides insights that are complementary to those offered by throughput or latency, capturing the ability of the system to maintain an up-to-date view of the status of each transmitting device. From this standpoint, while a good understanding of the metric has been reached for point-to-point links, relatively little attention has been devoted to the impact that link layer solutions employed in IoT systems may have on AoI. In particular, no result is available for modern random access protocols, which have recently emerged as promising solutions to support massive machine-type communications. To start addressing this gap we provide in this paper the first study of the AoI of a scheme in this family, namely irregular repetition slotted ALOHA (IRSA). By means of a Markovian analysis, we track the AoI evolution at the gateway, prove that the process is ergodic, and derive a compact closed form expression for its stationary distribution. Leaning on this, we compute exact formulations for the average AoI and the age violation probability. The study reveals non-trivial design trade-offs for IRSA and highlights the key role played by the protocol operating frame size. Moreover, a comparison with the performance of a simpler slotted ALOHA strategy highlights a remarkable potential for modern random access schemes in terms of information freshness.

SCMA System Design With Index Modulation via Codebook Assignment

Sparse code multiple access (SCMA) is a promising non-orthogonal multiple access (NOMA) scheme for massive machine-type communications for 5G-and-beyond communication systems. Innovative use of orthogonal frequency division multiplexing with index modulation (OFDM-IM) takes advantage of using tone indices in data transmission. In this study, we propose a novel multiplexing and multiple access scheme, namely SCMA based on OFDM-IM (SCMA-IM). SCMA-IM method can be implemented in uplink NOMA systems in an efficient way with similar computational complexity as SCMA. In this method constellation differences and diversity are obtained via SCMA codebooks (CBs) and the employment of CBs is transferred to index modulation (IM). Performance improvement is achieved via CBs so that the number of data bits carried by indices increases more than one bit with different constellations, and also with the number of CBs assigned. In order to determine the indices used in IM, the receiver structure is simplified by the message passing algorithm and log-likelihood ratio detection process used in SCMA receivers without indexing the receiver. Thus, a new NOMA scheme is obtained by coding with IM. Bit error rate performance of the system is studied for 150% and 200% overloading factors.

Priority Enabled Grant-Free Access With Dynamic Slot Allocation for Heterogeneous mMTC Traffic in 5G NR Networks

Although grant-based mechanisms have been a predominant approach for wireless access for years, the additional latency required for initial handshake message exchange and the extra control overhead for packet transmissions have stimulated the emergence of grant-free (GF) transmission. GF access provides a promising mechanism for carrying low and moderate traffic with small data and fits especially well for massive machine type communications (mMTC) applications. Despite a surge of interest in GF access, how to handle heterogeneous mMTC traffic based on GF mechanisms has not been investigated in depth. In this paper, we propose a priority enabled GF access scheme which performs dynamic slot allocation in each 5G new radio subframe to devices with different priority levels on a subframe-by-subframe basis. While high priority traffic has access privilege for slot occupancy, the remaining slots in the same subframe will be allocated to low priority traffic. To evaluate the performance of the proposed scheme, we develop a two-dimensional Markov chain model which integrates these two types of traffic via a pseudo-aggregated process. Furthermore, the model is validated through simulations and the performance of the scheme is evaluated both analytically and by simulations and compared with two other GF access schemes.

UL-DMRS Based NB-IoT Uplink System and its Performance Analysis Toward 5G Machine Type Communications

As we know, world is moving into the era of modern digital technology and looking forward tomassive machine type communications (mMTC), whichis an integral part of Internet of Things (IoT). The current technologysupporting mMTC market are not standardized; therefore, there are many short comings from physical layer which includes complexity in deployment, poor reliability, lesser flexibility, security threats and high maintenance cost. To address all these challenges in 5G machine type communication (MTC), the 3rdGeneration Partnership Project (3GPP) in release 13has standardizedNarrowband Internet of Things (NB-IoT) as a better choice in deployment of 5G MTC. NB-IoT has been recommended by ITU as a 5G standard and this recognition of NB-IoT as a core technology in massive machine type communication will impact the telecommunication industry. NB-IoT mainly works on low power wide area networks (LPWAN), which isconsidered as a major technology driver in 5G wireless technologies. Initially,we have compared a spectrum power of NB-IoT with W-Fi ac considering their own bandwidthand specificationsas per 3GPP and IEEE 802.11,respectively.As per analysis, we found many advantages of deploying NB-IoT in 5thgeneration wireless technology including ubiquitous coverage, low power consumption, less transmission power and better interference rejection. Considering thisfact of NB-IoT, we proposedand design a NB-IoT uplink systemusing NPUSCH, UL-SCH and UL-DMRS as per 3GPP 5G specificationsand performance analysis has been carried out