Critical Machine Type Communication Research Articles

Beyond 5G URLLC evolution: New service modes and practical considerations

Ultra-Reliable Low Latency Communications (URLLC) arose to serve Industrial IoT (IIoT) use cases within 5G. However, currently, it has inherent limitations in supporting future services. Therefore, in this article, based on state-of-the-art research and practical deployment experience, from two distinct test networks from Finland and South Korea, we introduce and advocate for three variants of critical Machine-Type Communications (MTC), namely, broadband, scalable and extreme URLLC. Moreover, we discuss use cases and key performance indicators and identify two critical technology enablers for each new service class. Finally, we bring practical considerations from the IIoT testbed and provide an outlook towards new research directions.

Performance Analysis of MIMO-NOMA Iterative Receivers for Massive Connectivity

The Fifth Generation (5G) of wireless networks introduced support to Machine-Type Communications (MTC), which is the wireless connectivity solution for Internet of Things (IoT) applications. MTC is split into two different categories: massive MTC (mMTC) and critical MTC (cMTC). Current 5G standards and technologies are not capable of fully satisfying the requirements of both mMTC and cMTC use cases, thus industry and academia have already started developing solutions for MTC in beyond-5G and 6G networks. In some mMTC use cases, receivers might not be equipped with a large number of antennas owing to cost, size or power limitations, thus the number of active devices in a time slot may surpass the number of antennas. Due to the limited spatial multiplexing capabilities, only multi-antenna techniques are not enough to provide connectivity to a massive number of devices in such scenarios. In this paper, we propose and evaluate the performance of iterative linear receivers that can address this issue. By combining Multiple-Input Multiple-Output (MIMO) techniques with Non-Orthogonal Multiple Access (NOMA) exploiting Successive Interference Cancellation (SIC) or Parallel Interference Cancellation (PIC) decoding, the proposed novel receivers are capable of performing dynamic ordering SIC/PIC decoding of multiple overlapping signals even when the number of active devices surpasses that of receive antennas. The performance of the receivers is studied in terms of outage probability and computational complexity. Simulation results show that, among all the receivers studied in this paper, the PIC-based Minimum Mean Square Error (MMSE) receiver presents the best performance while at the same time reducing the number of complex signal operations such as matrix inversions.

Deployment of Polar Codes for Mission-Critical Machine-Type Communication Over Wireless Networks

Mission critical Machine-type Communication, also referred to as Ultra-reliable Low Latency Communication is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected devices. While the reduction in PHY layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability, the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper layers. The mcMTC has a significant technical impact on the design of all layers of the communication protocol stack. In this paper, an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a bearing in mind short packet transmission system. To achieve this aim, we analyzed and compared the channel coding performance of convolutional codes, LDPC codes, and polar codes in wireless network on the condition of short data packet transmission. The Viterbi decoding algorithm, logarithmic belief propagation algorithm, and cyclic redundancy check - successive cancellation list decoding algorithm were adopted to CC, LDPC codes, and polar codes, respectively. Consequently, a new PHY layer for mcMTC has been proposed. The reliability of the proposed approach has been validated by simulation in terms of Bit error rate vs. SNR. The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER less 10e-5 with the implementation of polar codes. The results also show that the general-purpose wireless networks are prominent in providing short packet mcMTC with the modification needed.

Dynamic Preamble-Resource Partitioning for Critical MTC in Massive MIMO Systems

Preamble resources are scarce and precious in random access (RA), which need to be efficiently utilized to support critical machine-type communication (MTC) with stringent access requirements. In this article, we study a grant-free RA scenario for critical MTC in the context of massive multiple-input–multiple-output (MIMO). To enhance the access reliability of delay-sensitive devices within a predefined latency budget, two dynamic preamble-resource partitioning (DPP) schemes are proposed. Particularly, by leveraging massive MIMO, we first analytically investigate the feasibility of DPP in the considered RA scenario and demonstrate its performance superiority to the conventional scheme. Based on the analytical results, we then propose a greedy DPP scheme that performs locally optimal preamble-resource partitioning in each RA slot. To find the globally optimal DPP solution, we further propose a reinforcement learning (RL)-based scheme by modeling the considered RA scenario as a Markov decision process. Simulation results show the practicality and effectiveness of the proposed DPP schemes. In particular, to achieve a target access failure rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 10^{-2}$ </tex-math></inline-formula> , the proposed RL-based scheme is able to enhance the RA traffic load by 42% and improve the preamble resource utilization by over 25% compared to the conventional baseline scheme.

Cross‐layer resource allocation for critical MTC coexistent with human‐type communications in LTE: a two‐sided matching approach

Cellular systems present one of the most suitable wireless technologies to efficiently serve critical machine-type communications (MTC) that require strict quality-of-service (QoS) guarantees. Therefore, ultra-reliable and low-latency communications is a target use case in the design of the upcoming generations of cellular networks. From the radio resource management perspective, guaranteeing such stringent QoS requirements in long term evolution (LTE) networks is a challenging task, especially in the case of the coexistence of MTC with the human-type communications (HTC). In this study, the authors address the resource allocation and scheduling problem of critical MTC that coexist with HTC in LTE. The optimisation problem is formulated such that the overall system utility is maximised while fulfilling the different QoS demands of the two sets of users. Utilising the effective bandwidth and effective capacity theories, a cross-layer design is developed to guarantee the QoS requirements of the critical MTC. For a computationally-efficient solution of the problem, they formulate it as a two-sided matching process that can be used as a practical scheduling scheme. To this end, they analyse the convergence, stability, and computational complexity of the proposed methods. Results reveal the close-to-optimal performance of the matching-based scheduling scheme and its superiority to other existing techniques.

Learning-Based Autonomous Scheduling for AoI-Aware Industrial Wireless Networks

Due to the ever-increasing time-sensitive industrial applications, critical-machine type communication (C-MTC) is a promising technique for timely delivery services in industrial wireless networks (IWNs), where vicinal devices can benefit from device-to-device (D2D) communication for low power consumption and latency. For real-time applications, Age of Information (AoI) is an essential metric that represents the freshness of data from the perspective of destinations. Thus, an AoI orchestration agent for link scheduling in D2D-enabled IWNs is needed. Most existing works on AoI deal with this scheduling in a centralized manner, which cannot afford timely packet delivery requirements for numerous D2D devices. Different from the existing works, a learning-based autonomous AoI and power orchestration agent, namely, L-AoI, is proposed for D2D-enabled IWNs in this article, where D2D devices adaptively compete for wireless resources in a distributed manner. As a result, the global channel state information as well as the actions of other D2D devices are unknown. Hence, D2D devices deal with this uncertainty under the guidance of L-AoI so that AoI constraints can be respected. By leveraging from the belief-based Bayesian reinforcement learning, L-AoI learns the scheduling action profile with strategies of other D2D devices considered so that the spectrum sharing coalitions can be intelligently formed. Both theoretical analysis and simulation are provided to validate the performance of L-AoI in terms of AoI stability and violation ratio.

A Critical MTC Resource Allocation Approach for LTE Networks With Finite Blocklength Codes

Critical machine-type communications (cMTC) are targeted as a major use case in the design of the fifth generation (5G) cellular systems. In this regard, the third-generation partnership project (3GPP) has introduced several enhancements to evolve the LTE standard to meet the 5G requirements. Shortened transmission time intervals (sTTIs) are considered one of the most significant improvements proposed to satisfy the stringent latency requirements of cMTC. However, this entails several challenges to the resource allocation and scheduling process. In this paper, we address the resource allocation and scheduling of cMTC in LTE networks. The impact on the conventional human-type communications (HTC) is considered while adopting a puncturing scheduling technique. In addition, the reliability of the cMTC is ensured by utilizing the finite blocklength coding analysis to model the transmission errors and the effective bandwidth and effective capacity concepts to guarantee the queuing delay statistics of the cMTC packets. Moreover, we propose matching theory-based computationally efficient algorithms to solve the formulated optimal resource allocation problems with reduced complexity. The proposed methods are analyzed from a practical perspective. Extensive simulations show a close-to-optimal performance of the proposed schemes while outperforming other scheduling algorithms from the literature.

Transient Analysis for Multihop Wireless Networks Under Static Routing

In this article, we investigate the transient behavior of a sequence of packets/bits traversing a multi-hop wireless network under static routing. Our work is motivated by novel applications from the domain of process automation, Machine-Type Communication (MTC) and cyber-physical systems, where short messages are communicated and statistical guarantees need to be provided on a per-message level. In order to optimize such a network, apart from understanding the stationary system dynamics, an understanding of the short-term dynamics (i.e. transient behavior) is also required. To this end, we derive novel Wireless Transient Bounds (WTB) for end-to-end delay and backlog in a multi-hop wireless network using stochastic network calculus approach. We start by analyzing a single end-to-end path, i.e. a line topology, and then we show how the obtained results can be applied to a mesh network with static routing using a concept called ’ leftover service ’. WTB depends on the initial backlog at each node as well as the instantaneous channel states. We numerically compare WTB with Kernel-Based-Transient Bound (KBTB), which can be obtained by adapting existing stationary bound, as well as simulated end-to-end delay of the investigated network. While KBTB and stationary bounds are not able to capture the short-term system dynamics well, WTB provides relatively tight upper bound and has a decay rate that closely matches the simulation. This is achieved by WTB only with a slight increase in the computational complexity, by a factor of $O(T+N)$ , where $T$ is the duration of the arriving sequence and $N$ is the number of hops in the network. We believe that the presented analysis and the bounds are necessary tools for future work on transient network optimization for many important emerging applications, e.g., massive MTC, critical MTC, edge computing and autonomous vehicle.

Qos-Aware Memetic-Based Optimal Cross-Layer Resource Allocation in Mixed Lte Networks

With the recent advancements and due to the rapid growth of LTE networks, Machine Type Communication (MTC) plays a vital role in the characterization of Internet of Things (IOT).Human-to-Human (H2H) communication and MTC are the two different types of communication handled by LTE-A networks. Due to the co-existence of H2H communication and MTC in LTE-A networks, a serious challenge may arise for scheduling critical MTC with H2H communication networks. To maintain the Quality of Service (QoS) requirements for H2H communication and to provide data traffic for MTC networks LTE networks faces a serious challenge for allocating the resources blocks to the users. In this paper we propose a resource allocation algorithm for optimizing the problems faced by critical MTC and H2H communication networks by maintaining the QoS requirements from a cross-layer design perspective. A novel cross layer memtic based resource allocation algorithm is presented in this paper by investigating the resource allocation problem for different combinations of Channel Quality Indicator (CQI) modes for critical MTCDs and H2H UEs. The Performance and computational complexity of the proposed algorithm in different cases of CQI is measured in terms of cell throughput and probability of delay bound violation (PBDV) is analyzed and the simulations results shows that the proposed system is more efficient compared to other resource allocation algorithms.

Optimal Cross-Layer Resource Allocation for Critical MTC Traffic in Mixed LTE Networks

Machine-type communications (MTC) play an important role in implementing and enabling the Internet of Things. Long -term evolution (LTE) is a strong candidate technology for the interconnection of the MTC devices. However, to optimize LTE for MTC purposes, several issues need to be addressed. This is due to the different and diverse quality of service (QoS) requirements of MTC compared to those of human-to-human (H2H) communications. In particular, critical-MTC pose many challenges to radio resource management in LTE. They have stringent QoS requirements that need to be considered without sacrificing the QoS of the H2H traffic. In this paper, we formulate the resource allocation optimization problem from a cross-layer design perspective to consider both the QoS requirements of critical-MTC and those of H2H communications. We propose methods to handle the optimization problem to reduce the computational complexity of the optimal solution. Additionally, the performance of the proposed resource allocation algorithms is evaluated analytically. Moreover, more computationally efficient algorithms are proposed to practically implement the resource allocation process in several operational cases. Finally, the computational complexity of the proposed algorithms is analyzed. The simulations results show the superiority of the proposed algorithms and methods compared to other techniques from the literature.

Tactile Internet for Smart Communities in 5G: An Insight for NOMA-Based Solutions

In the last few years, there has been an exponential increase in the deployment of 5G-based test beds across the globe with an aim to reduce the latency for accessing various applications. The integration of generic services such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), critical machine-type communication (cMTC), and ultra-reliable low-latency communications (URLLC) can improve the performance of 5G-based applications. This service heterogeneity can be achieved by network slicing for an optimized resource allocation and an emerging technology, Tactile Internet, to achieve low latency, high bandwidth, service availability, and end-to-end security. In this paper, we discuss the application-specific nonorthogonal multiple access (NOMA)-based communication architecture for Tactile Internet which allows nonorthogonal resource sharing from a pool of eMBB, mMTC, cMTC, and URLLC devices to a shared base station. We summarize various variants of NOMA and their suitability for future low latency Tactile-Internet-based applications.

5G-Based Smart Healthcare Network: Architecture, Taxonomy, Challenges and Future Research Directions

Healthcare is undergoing a rapid transformation from traditional hospital and specialist focused approach to a distributed patient-centric approach. Advances in several technologies fuel this rapid transformation of healthcare vertical. Among various technologies, communication technologies have enabled to deliver personalized and remote healthcare services. At present, healthcare widely uses the existing 4G network and other communication technologies for smart healthcare applications and are continually evolving to accommodate the needs of future intelligent healthcare applications. As the smart healthcare market expands the number of applications connecting to the network will generate data that will vary in size and formats. This will place complex demands on the network in terms of bandwidth, data rate, and latency, among other factors. As this smart healthcare market matures, the connectivity needs for a large number of devices and machines with sensor-based applications in hospitals will necessitate the need to implement Massive-Machine Type Communication. Further use cases such as remote surgeries and Tactile Internet will spur the need for Ultra Reliability and Low Latency Communications or Critical Machine Type Communication. The existing communication technologies are unable to fulfill the complex and dynamic need that is put on the communication networks by the diverse smart healthcare applications. Therefore, the emerging 5G network is expected to support smart healthcare applications, which can fulfill most of the requirements such as ultra-low latency, high bandwidth, ultra-high reliability, high density, and high energy efficiency. The future smart healthcare networks are expected to be a combination of the 5G and IoT devices which are expected to increase cellular coverage, network performance and address security-related concerns. This paper provides a state-of-the-art review of the 5G and IoT enabled smart healthcare, Taxonomy, research trends, challenges, and future research directions.

Matching-Based Resource Allocation for Critical MTC in Massive MIMO LTE Networks

Supporting critical Machine-Type Communications (MTC) in addition to Human-Type Communications (HTC) is a major target for LTE networks to fulfill the 5G requirements. However, guaranteeing a stringent Quality-of-Service (QoS) for MTC, in terms of latency and reliability, while not sacrificing that of HTC is a challenging task from the radio resource management perspective. In this paper, we optimize the resource allocation process through exploiting the additional degrees of freedom introduced by massive Multiple-Input Multiple-Output (MIMO) techniques. We utilize the effective bandwidth and effective capacity concepts to provide statistical guarantees for the QoS, in terms of probability of delay-bound violation, of critical MTC in a cross-layer design manner. In addition, we employ the matching theory to solve the formulated combinatorial problem with much lower computational complexity compared to that of the global optimal solution so that the proposed scheme can be used in practice. In this regard, we analyze the computational complexity of the proposed algorithms and prove their convergence, stability and optimality. The results of extensive simulations that we performed show the ability of the proposed matching-based scheme to satisfy the strict QoS requirements of critical MTC with no impact on those of HTC. In addition, the results show a close-to-global optimal performance while outperforming other algorithms that belong to different scheduling strategies in terms of the adopted performance indicators.

On the Road to 5G: Comparative Study of Physical Layer in MTC Context

During the past few years, we are witnessing the emergence of 5G and its high-level performance targets. Waveform (WF) design is one of the important aspects for 5G that received considerable attention from the research community in recent years. To find an alternative to the classical orthogonal frequency division multiplexing (OFDM), several multicarrier approaches addressing different 5G technical challenges, have been proposed. In this paper, we focus on critical machine-type communications (C-MTC), which is one of the key features of the foreseen 5G system. We provide a comparative performance study of the most promising multicarrier WFs. We consider several C-MTC key performance indicators: out-of-band radiations, spectral efficiency, end-to-end physical layer latency, robustness to time and frequency synchronization errors, power fluctuation, and transceiver complexity. The investigated multicarrier WFs are classified into three groups based on their ability to keep the orthogonality: in the complex domain, e.g., most of the OFDM-inspired WFs, in the real domain like offset-quadrature amplitude modulation (QAM)-based techniques, and non-orthogonal WFs like generalized frequency division multiplexing and filter bank-based multicarrier-QAM. Finally, the performances of these WFs are thoroughly discussed in order to highlight their pros and cons and permit a better understanding of their capabilities in the context of C-MTC.

5G internet of things [Editor's Note

Many of the applications and use cases that drive the requirements and capabilities of 5G are about end-to-end communication between devices. In order to distinguish them from the more human-centric wireless applications such as mobile telephony and mobile broadband, these applications are often labeled machine-type communication (MTC) or more broadly as the Internet of Things (IoT). Improved provisioning, device management, and service enablement will bring a wider range of potential IoT applications. Some predictions forecast that there will be 15 billion connected MTC devices by 2021, a nearly 40 fold increase over the number of currently deployed MTC devices. Although spanning a wide range of different applications, IoT can be divided into two main categories, massive MTC and critical MTC, depending on their characteristics and requirements. Each brings its own challenges and requirements for standardization. To create a truly networked society requires market driven standardization and the deployment of standardized technology for 5G IoT devices, networks, and software.