Massive Machine Type Communication Research Articles

Flexible architecture for deployment of edge computing applications

Recently, a great development has occurred in the communication networks, in which the trend of development is towards automation and flexible network structure. In addition, the stages of telecommunication development are conventionally divided from manual switching to flexible virtualized architecture, to which telecommunications operators have resorted. Furthermore, in the context of the 3rd generation partnership project (3GPP) standards framework, the network function virtualization (NFV) with might and main has been evaluated by telecom operators and shows that the efficiency of the topology can be significantly improved. However, it is difficult to organize ultra-reliable low-latency communication (URLLC) and massive machine type communications (mMTC) services within 5G, due to the topology centralization. Moreover, network monitoring, in particular of carrier-class equipment, reveals that there is no ideal static network topology in which the network and its elements have been uniformly loaded over a long service time. Therefore, in this study, a dynamic network topology and service placement is proposed to analyze and predict services, in which Genetic Algorithm (GA) is utilized. In addition, an efficient forecasting and live migration methods of service as an application to edge computing systems are introduced, where this approach can be used in the systems with an intelligent allocation of operator equipment resources for providing flexibility and high-quality topological organization. Finally, simulation results proved that the network equipment efficiency can significantly be increased by more than 30%.

Secure physical layer short-packet communication for 5G

Wireless short-packet communication is one of the key technologies for realizing 5G Internet of Things (IoT) applications such as massive machine type communication (mMTC) and ultra-reliable low latency communication (uRLLC). For applications such as the industrial IoT and the Internet of Vehicles, the security of wireless communication is of paramount importance. This paper investigates the secure transmission from the perspective of physical layer security for short-packet communication systems under slow fading channels. A secrecy outage probability (SOP) is defined based on the conclusion of secure short-packet communication information theory, while the reliable throughput with the SOP restriction is established as the secure communication performance metric of the short-packet communication system. On this basis, adaptive and non-adaptive encoder schemes are designed, reliable throughputs under these two encoder schemes can be maximized by designing coding rates and optimizing transmission thresholds with the explicit/partial instantaneous channel state information (CSI) of the legitimate channel, respectively. Whats more, the impacts of main system parameters on the optimal throughput are further analyzed. Numerical results verify the effectiveness of transmission schemes, and verify impacts of main system parameters on the secure performance of the transmission systems with SOP constraints.

Secure Short-Packet Communications at the Physical Layer for 5G and Beyond

Short-packet communication is a key technology to support two emerging application scenarios in 5G and beyond 5G, massive machine type communication (mMTC) and ultra-reliable low-latency communication (uRLLC), which are introduced to satisfy the broader communication requirements of potential applications such as the Internet of Vehicles and industrial Internet of Things (IoT). The sharp increase in privacy data in various IoT applications has made security issues more prominent. The typical upper-layer encryption mechanism cannot fully address the security challenge considering the resource restriction of IoT terminals. In this article, we investigate secure short-packet communication from the perspective of physical layer security (PLS), which can be regarded as a promising security solution in 6G. Specifically, the state-of-the-art development of fundamental information theory of secure short-packet communications and corresponding performance evaluation criteria in fading channels are summarized. Then we review recent works, which investigate short-packet communication systems in different communication scenarios or with different security strategies from the perspective of PLS. Finally, we give future research directions and challenges.

Toward Optimal Connection Management for Massive Machine-Type Communications in 5G System

The massive machine-type communications (mMTC) is one of the three generic services for 5G. With the connection-based random access (CBRA) scheme, each machine-type device (MTD) establishes a connection with the base station (BS) prior to its data transmission. Due to the explosive growth of the number of MTDs, many MTDs would establish connections with the BS, which necessitates the study on how to efficiently manage the massive connections with MTDs. To address this issue, in this article, we propose a unified utility-based analytical framework for the optimal connection management of mMTC in 5G networks, where the signaling overhead for connection establishment, access delay, and the connection resource utilization ratio is included. Specifically, we first derive key performance metrics, i.e., the mean time length of each connection and resource utilization ratio, as functions of traffic input rate and inactivity timer. By further considering the signaling overheads and access delay of each MTD, the network utility is formulated and maximized by optimally choosing the inactivity timer. We then present a detailed discussion on the effect of system parameters on the optimal inactivity timer and the corresponding maximum network utility. Finally, we extend the analytical framework to the scenario in which the CBRA scheme coexists with the packet-based random access scheme, i.e., transmitting packets in the random access channel without connection establishment. The critical threshold in terms of the traffic input rate is characterized, which sheds important light on the access scheme selection issue.

Random Access Based on Maximum Average Distance Code for Massive MTC in Cellular IoT Networks

Code-expanded Random Access (CeRA) is a promising technique for supporting massive machine-type communications in cellular IoT networks. However, its potentiality is limited by code ambiguity, which results from the inference of a larger number of codewords than those actually transmitted. In this letter, we propose a random access (RA) scheme to alleviate this problem by allowing devices to select the preambles to be transmitted considering a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${q}$ </tex-math></inline-formula> -ary code with maximum average distance. Moreover, a CeRA decoding approach based on hypergraphs is proposed and an analytical model is derived. Numerical results show that the proposed scheme significantly increases the probability of successful channel access as well as resource utilization.

A CNN Architecture for Learning Device Activity From MMV

Device activity detection has been extensively investigated for grant-free massive machine-type communications. Instead of using deep Multi-Layer Perception (MLP) networks, this letter proposes a novel convolutional neural network (CNN) architecture for learning device activity from multiple-measurement vectors (MMV). With the use of 1×1 convolutional layers, the proposed CNN could exploit the full potential of shared sparsity among multiple measurements. Extensive simulations show that the proposed CNN outperforms the existing deep MLP network in both performance and computational complexity, especially when the number of measurements increases.

5G Security Threat Assessment in Real Networks

Advances in mobile communication networks from 2G to 5G have brought unprecedented traffic growth, and 5G mobile communication networks are expected to be used in a variety of industries based on innovative technologies, fast not only in terms of extremely low latency but massive access devices. Various types of services, such as enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable and low latency communication (uRLLC), represent an increase in the number of attacks on users’ personal information, confidential information, and privacy information. Therefore, security assessments are essential to verify and cope with these various attacks. In this research, we (1) looked at 5G mobile communication network backgrounds and problems to investigate existing vulnerabilities and (2) assessed the current situation through evaluation of 5G security threats in real-world mobile networks in service.

Intelligent Stretch Optimization in Information Centric Networking-Based Tactile Internet Applications

The fifth-generation (5G) mobile network services are currently being made available for different use case scenarios like enhanced mobile broadband, ultra-reliable and low latency communication, and massive machine-type communication. The ever-increasing data requests from the users have shifted the communication paradigm to be based on the type of the requested data content or the so-called information-centric networking (ICN). The ICN primarily aims to enhance the performance of the network infrastructure in terms of the stretch to opt for the best routing path. Reduction in stretch merely reduces the end-to-end (E2E) latency to ensure the requirements of the 5G-enabled tactile internet (TI) services. The foremost challenge tackled by the ICN-based system is to minimize the stretch while selecting an optimal routing path. Therefore, in this work, a reinforcement learning-based intelligent stretch optimization (ISO) strategy has been proposed to reduce stretch and obtain an optimal routing path in ICN-based systems for the realization of 5G-enabled TI services. A Q-learning algorithm is utilized to explore and exploit the different routing paths within the ICN infrastructure. The problem is designed as a Markov decision process and solved with the help of the Q-learning algorithm. The simulation results indicate that the proposed strategy finds the optimal routing path for the delay-sensitive haptic-driven services of 5G-enabled TI based upon their stretch profile over ICN, such as the augmented reality /virtual reality applications. Moreover, we compare and evaluate the simulation results of propsoed ISO strategy with random routing strategy and history aware routing protocol (HARP). The proposed ISO strategy reduces 33.33% and 33.69% delay as compared to random routing and HARP, respectively. Thus, the proposed strategy suggests an optimal routing path with lesser stretch to minimize the E2E latency.

Efficient Channel Feedback Scheme for Multi-User MIMO Hybrid Beamforming Systems.

This paper proposes an efficient channel information feedback scheme to reduce the feedback overhead of multi-user multiple-input multiple-output (MU-MIMO) hybrid beamforming systems. As massive machine type communication (mMTC) was considered in the deployments of 5G, a transmitter of the hybrid beamforming system should communicate with multiple devices at the same time. To communicate with multiple devices in the same time and frequency slot, high-dimensional channel information should be used to control interferences between the receivers. Therefore, the feedback overhead for the channels of the devices is impractically high. To reduce the overhead, this paper uses common sparsity of channel and nonlinear quantization. To find a common sparse part of a wide frequency band, the proposed system uses minimum mean squared error orthogonal matching pursuit (MMSE-OMP). After the search of the common sparse basis, sparse vectors of subcarriers are searched by using the basis. The sparse vectors are quantized by a nonlinear codebook that is generated by conditional random vector quantization (RVQ). For the conditional RVQ, the Linde–Buzo–Gray (LBG) algorithm is used in conditional vector space. Typically, elements of sparse vectors are sorted according to magnitude by the OMP algorithm. The proposed quantization scheme considers the property for the conditional RVQ. For feedback, indices of the common sparse basis and the quantized sparse vectors are delivered and the channel is recovered at a transmitter for precoding of MU-MIMO. The simulation results show that the proposed scheme achieves lower MMSE for the recovered channel than that of the linear quantization scheme. Furthermore, the transmitter can adopt analog and digital precoding matrix freely by the recovered channel and achieve higher sum rate than that of conventional codebook-based MU-MIMO precoding schemes.

Numerous Factors Affecting Performance of NOMA for Massive Machine Type Communications in B5G Systems

Massive machine type communications (mMTC) are one of the critical requirements for beyond fifth generation (B5G) communication systems. Services for a huge number of user terminals should be provided simultaneously due to the explosive development of mMTC. It is proved that non-orthogonal multiple access (NOMA) is effective in satisfying such a requirement. In this paper we evaluate the impacts of numerous factors, such as channel encoding, channel decoding, repetition number, multi-user detector, and number of receiver antennas, on the performance of NOMA. It is surprised to find that some conclusions drawn from orthogonal multiple access system may do not hold anymore for NOMA systems. The factors which have significant impact on the performance of NOMA should be paid more attention to in the system design. The analysis and evaluation results shine more light on how to design an effect NOMA scheme by considering both transmitter and receiver to fulfill the requirements of mMTC for B5G systems.

Filtered orthogonal frequency division multiplexing for improved 5G systems

Wireless communications became an integrated part of the human life. Fifth generation (5G) is the modern communication which provides enhanced mobile broadband (eMBB), ultra reliable low latency communications (URLLC), and massive machine type communication (mMTC). Thus, 5G have to provide coverage to multi-numerology devices, therefore, modulation and access schemes are suggested in the literature such as cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and filtered OFDM (f-OFDM). CP-OFDM suffers from the high out of band emission which limited the multi-numerology applications. In f-OFDM, the out of band emission can be suppressed to an accepted extent such that different numerologies can be coexisting. On the other hand, f-OFDM can be more improved by using a proper filtering approach. In this paper three different filters are suggested based windowed-sinc function; Hanning, Hamming, and Blackman. Simulation results show that the proposed filters are promising for high spectral efficiency and out of band emission rejection. Furthermore, the bit error rate, error vector magnitude, and power spectral density are further improved with respect to CP-OFDM scheme but some trade-off is present. Overall, the suggested windowed-sinc filters are outperforming the traditional CP-OFDM. As a conclusion, the suggested windnowed-sinc filters have no limitations on the modulation order or the number of subcarriers utilized in the system.

Deep Learning-Based Spreading Sequence Design and Active User Detection for Massive Machine-Type Communications

In this letter, we propose a deep learning-based spreading sequence design and active user detection (AUD) to support massive machine-type communications (mMTC) where a large number of devices access the base station using non-orthogonal spreading sequences. To design the whole communications system minimizing AUD error, we employ an end-to-end deep neural network (DNN) where the spreading network models the transmitter side and the AUD network estimates active devices. By using the AUD error as a loss function, network parameters including the spreading sequences are learned to minimize the AUD error. Numerical results reveal that the spreading sequences obtained from the proposed approach achieve higher AUD performance than the conventional spreading sequences in the compressive sensing-based AUD schemes, as well as in the proposed AUD scheme.

Guest Editorial: Massive Machine-Type Communications for IoT

Future wireless cellular networks are envisioned to not only enhance broadband access for human-centric applications, but also offer massive connectivity across tens of billions of devices for machine-centric applications empowered by Internet of Things (IoT) technologies (e.g., smart factory and smart city). To embrace the forthcoming era of IoT, the fifth-generation (5G) cellular communication standard has already identified massive machine-type communications (mMTC) as a key use case in future networks. Contrary to human-type high-speed communications, the focus of IoT communications is on providing connectivity to tens of billions of devices with high energy efficiency, low latency, and high reliability. Consequently, 5G and beyond cellular communication technologies call for radically innovative solutions to support mMTC. Motivated by the crucial role of mMTC in IoT and the dramatically new challenges arising from mMTC as compared to human-type communication, this Feature Topic aims to bring together researchers, industry practitioners, and individuals working on the related areas to share their new ideas, latest findings, and state-of-the-art results.

A Theoretical Discussion and Survey of Network Automation for IoT: Challenges and Opportunity

The introduction of the Internet of Things (IoT) and massive machine-type communications has implied an increase in network size and complexity. In particular, there is already a huge number of IoT devices in the market in various sectors, such as smart agriculture, smart city, smart home, smart transportation, etc. The IoT interconnectivity technologies are also increasing. Therefore, these are increasingly overwhelming the efforts of network administrators as they try to design, reconfigure and manage such networks. Relying on humans to manage such complex and dynamic networks is becoming unsustainable. Network automation promises to reduce the cost of administration and maintenance of network infrastructure, by offering networks the capability to manage themselves. Network automation is the ability of the network to manage itself. Various standardization organizations are taking the initiative in introducing network automation, such as European Telecommunication Standardization Institute (ETSI). ETSI is leading the standardization activities for network automation. It has provided different versions of reference architecture called generic autonomic network architecture (GANA), which describes a four-level abstraction for network-management decision elements (DEs), protocol level, function level, node level, and network level. In this article, we review and survey the existing works before and after the introduction of software-defined networking (SDN) and network-function-virtualization (NFV). We relate the main trending paradigms being followed, such as SDN, NFV, machine learning (ML), microservices, multiagent system (MAS), containerization, and cloudification, as a pivotal enabler of full network automation. We also discuss the autonomic architectures proposed in the literature. Finally, we presented possible future research directions and challenges that need to be tackled to progress in achieving full network automation.

Massive Machine-Type Communication and Satellite Integration for Remote Areas

Despite immense progress along different tracks, wireless connectivity for machine applications in remote areas is still very challenging. To address this vital need, in this article, we discuss and advocate the convergence between low-pow-er wide area network (LPWAN) grade massive machine-type communication (mMTC) wireless technologies and satellite (especially low Earth orbit, LEO) systems. In the article, we discuss the alternative implementation approaches allowing such convergence and highlight some of their pros, cons, and challenges. Furthermore, we obtain more in-depth insight into the matter by simulating and analyzing the LoRaWAN LPWAN sensors' performance served by a LEO satellite-based gateway. Our results demonstrate the feasibility of such a system and illustrate some of the relevant trade-offs between the network configurations and communication performance. These results motivate us to take a more profound look at such systems and the challenges they introduce. We highlight some of them and the potential directions for further studies in the final sections.

Deep-Learning-Enhanced NOMA Transceiver Design for Massive MTC: Challenges, State of the Art, and Future Directions

Non-orthogonal multiple access (NOMA) is a promising evolution path to meet the requirements of massive machine type communications (mMTC) in 5G and beyond. However, the deployment of NOMA is hindered by the non-unified signal processing architectures of various NOMA schemes and the inflexibility resulting from the offline design paradigm. The block-wise optimized transceivers make its performance far from the limit. The recent breakthrough of deep learning and its positive applications to wireless communications have paved the way to tackle these challenges. This article studies the effectiveness and efficiency of deep learning in enhancing NOMA performance. Specifically, we first present the deep neural network (DNN), which is constructed via a uniform signal processing architecture, and use it as the unified multiuser receiver in both data and model-driven approaches. This enables the end-to-end optimization of NOMA transceivers due to the universal function approximation property of DNN. On the other hand, with DNN we can automatically extract the user access behaviors out of the time-series signals and optimize the transceivers to match these cross-lay-er behaviors. We further analyze the integration of non-orthogonal communication and neural computation to accomplish high-efficiency data transmission at low cost. Finally, we identify some essential future directions of deep-learning-enhanced NOMA from the perspectives of online reconfigurability and adaptability toward the ever changing environment in future mMTC.

Internet of Things and Next Generation Wireless Communication Systems

Next generation wireless communication systems will connect billions of Internet of Thing (IoT) devices and user elements along with billions of people, enable machine-to-ma-chine communications across heterogeneous and dynamic environment, and provide low-latency computing and storage resources on demand at the deep edge as well as in the cloud. Tremendous research and development efforts have already been emphasized on the enabling technologies toward next generation wireless systems to support a variety of critical and personalized services in multiple application domains, including industry use cases, environment, transportation, education, public health and safety, and defense. Wireless communication networks have been greatly advanced by exploiting technologies such as millimeter wave, dynamic spectrum sharing, massive MIMO, beam forming, network slicing and virtualization, and artificial intelligence and machine learning. These technology breakthroughs can empower wireless networks to support extreme high capacity, massive connectivity, and ultra-low latency communications for new use cases identified for next generation wireless systems. In order to design and deploy wireless communication systems with IoT devices across heterogeneous and dynamic environments to provide next generation wireless services, however, there are many critical technical challenges yet to be addressed. In this issue of IEEE Wireless Communications, we are pleased to present two feature topics to bring together researchers, industry practitioners, and individuals working on the related areas to address some of these technical challenges for IoT and next generation wireless systems. One feature topic on “Massive Machine-Type Communications for IoT” includes four articles, and was organized by guest editors W. Ji, T. Ebrahimi, Z. Li, J. Yuan, D. O. Wu, and Y. Xin; the other feature topic on “Emerging Visual IoT Technologies for Future Communications and Networks” includes six articles, and was organized by guest editors L. Liu, E. G. Larsson, P. Popovski, G. Caire, X. Chen, and S. R. Khosravirad. Thanks to the two guest editor teams who did an excellent job in editing these two feature topics for our readers. Please stay tuned for new developments in the research area of IoT and next generation wireless communications and read the editorials and the papers in the feature topics.

Massive Wireless Energy Transfer With Statistical CSI Beamforming

Wireless energy transfer (WET) is a promising solution to enable massive machine-type communications (mMTC) with low-complexity and low-powered wireless devices. Given the energy restrictions of the devices, instant channel state information at the transmitter (CSIT) is not expected to be available in practical WET-enabled mMTC. However, because it is common that the terminals appear spatially clustered, some degree of spatial correlation between their channels to the base station (BS) is expected to occur. The paper considers a massive antenna array at the BS for WET that only has access to i) the first and second order statistics of the Rician channel component of the multiple-input multiple-output (MIMO) channel and also to ii) the line-of-sight MIMO component. The optimal precoding scheme that maximizes the total energy available to the single-antenna devices is derived considering a continuous alphabet for the precoders, permitting any modulated or deterministic waveform. This may lead to some devices in the clusters being assigned a low fraction of the total available power in the cluster, creating a rather uneven situation among them. Consequently, a fairness criterion is introduced, imposing a minimum amount of power allocated to the terminals. A piece-wise linear harvesting circuit is considered at the terminals, with both saturation and a minimum sensitivity, and a constrained version of the precoder is also proposed by solving a non-linear programming problem. A paramount benefit of the constrained precoder is the encompassment of fairness in the power allocation to the different clusters. Moreover, given the polynomial complexity increase of the proposed unconstrained precoder, and the observed linear gain of the system's available sum-power with an increasing number of antennas at the ULA, the use of massive antenna arrays is desirable.

Overview of the challenges and solutions for 5G channel coding schemes

ABSTRACT 5G is the next generation of mobile communications networks which will use cutting-edge network technologies to deliver enhanced mobile connectivity. 5G has introduced new requirements for channel coding in its three different service classes which are enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC) and massive machine-type communications (mMTC). eMBB is expected to keep up with consumer’s insatiable demand for high mobile data rates to support data-extensive applications. mMTC will provide connectivity to a massive number of connected devices sending short data packets simultaneously to support applications such as Internet of Things (IoT). Finally, URLLC will ensure reliable low latency connectivity to support mission-critical latency-sensitive applications such as telesurgery. To address these new requirements, several new channel coding schemes are being developed. This review article provides a detailed analysis of the new channel coding challenges set out by 5G. A detailed review of existing and emerging solutions is provided. Moreover, simulation are performed to assess the performances of Low-Density Parity-Check (LDPC) codes and Polar codes used in 5G’s eMBB. Directions for future works and new solutions for 5G channel coding are also discussed.

NB-IoT Random Access: Data-Driven Analysis and ML-Based Enhancements

In the context of massive machine-type communications (mMTCs), the narrowband Internet-of-Things (NB-IoT) technology is envisioned to efficiently and reliably deal with massive device connectivity. Hence, it relies on a tailored random access (RA) procedure, for which theoretical and empirical analyses are needed for a better understanding and further improvements. This article presents the first data-driven analysis of NB-IoT RA, exploiting a large-scale measurement campaign. We show how the RA procedure and performance are affected by network deployment, radio coverage, and operators' configurations, thus complementing simulation-based investigations, mostly focused on massive connectivity aspects. A comparison with the performance requirements reveals the need for procedure enhancements. Hence, we propose a machine learning (ML) approach and show that RA outcomes are predictable with good accuracy by observing radio conditions. We embed the outcome prediction in an RA-enhanced scheme and show that optimized configurations enable power consumption reduction of at least 50%. We also make our data set available for further exploration, toward the discovery of new insights and research perspectives.