Massive Machine Type Communication Research Articles

Gyre Precoding and T-Transformation-Based GFDM System for UAV-Aided mMTC Network

In this paper, an unmanned aerial vehicle (UAV)-aided multi-antenna configured downlink mmWave cooperative generalized frequency division multiplexing (GFDM) system is proposed. To provide physical layer security (PLS), a 3D controlled Lorenz mapping system is introduced. Furthermore, the combination of T-transformation spreading codes, walsh Hadamard transform, and discrete Fourier transform (DFT) techniques are integrated with a novel linear multi-user multiple-input multiple-output (MU-MIMO) gyre precoding (GP) for multi-user interference reduction. Furthermore, concatenated channel-coding with multi-user beamforming weighting-aided maximum-likelihood and zero forcing (ZF) signal detection schemes for an improved bit error rate (BER) are also used. The system is then simulated with a single base station (BS), eight massive machine-type communications (mMTC) users, and two UAV relay stations (RSs). Numerical results reveal the robustness of the proposed system in terms of PLS and an achievable ergodic rate with signal-to-interference-plus-noise ratio (SINR) under the implementation of T-transformation scheme. By incorporating the 3D mobility model, brownian perturbations of the UAVs are also analyzed. An out-of-band (OOB) reduction of 320 dB with an improved BER of 1×10−4 in 16-QAM for a signal-to-noise ratio, Eb/N0, of 20 dB is achieved.

MMTC Deployment over Sliceable Infrastructure: The Megasense Scenario

Massive Machine Type Communication (mMTC) has long been identified as a major vertical sector and enabler of the industry 4.0 technological evolution that will seamlessly ease the dynamics of machine-to-machine communications while leveraging 5G technology. To advance this concept, we have developed and tested an mMTC network slice called Megasense. Megasense is a complete framework that consists of multiple software modules, which is used for collecting and analyzing air pollution data that emanates from a massive amount of air pollution sensors. Taking advantage of 5G networks, Megasense will significantly benefit from an underlying communication network that is traditionally elastic and can accommodate the on-demand changes in requirements of such a use case. As a result, deploying the sensor nodes over a sliceable 5G system is deemed the most appropriate in satisfying the resource requirements of such a use case scenario. In this light, in order to verify how 5G-ready our Megasense solution is, we deployed it over a network slice that is totally composed of virtual resources. We have also evaluated the impact of the network slicing platform on Megasense in terms of bandwidth and resource utilization. We further tested the performances of the Megasense system and come up with different deployment recommendations based on which the Megasense system would function optimally.

Clustering-Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO

Future wireless networks need to support massive machine type communication (mMTC) where a massive number of devices accesses the network and massive MIMO is a promising enabling technology. Massive access schemes have been studied for co-located massive MIMO arrays. In this paper, we investigate the activity detection in grant-free random access for mMTC in cell-free massive MIMO networks using distributed arrays. Each active device transmits a non-orthogonal pilot sequence to the access points (APs) and the APs send the received signals to a central processing unit (CPU) for joint activity detection. The maximum likelihood device activity detection problem is formulated and algorithms for activity detection in cell-free massive MIMO are provided to solve it. The simulation results show that the macro-diversity gain provided by the cell-free architecture improves the activity detection performance compared to co-located architecture when the coverage area is large.

Experimental Demonstration of Dynamic Optical Beamforming for Beyond 5G Spatially Multiplexed Fronthaul Networks

This paper presents a beyond 5G fronthaul network with dynamic beamforming and -steering. The proposed fronthaul solution deploys optical beamforming (OBF) by combining space division multiplexing (SDM), analogue radio-over-fiber (ARoF), and the novel optical beam forming network (OBFN) technologies. From the service management and orchestration (MANO) point of view, the proposed fronthaul solution also deploys an advanced software defined networking (SDN) and Network Function Virtualization (NFV) control and orchestration architecture developed with the goal to optimally manage and reconfigure the physical layer resources (i.e., optical and radio) at the central office and cell sites (i.e., pool of baseband units (BBUs), remote radio heads (RRHs), ARoF transceivers and OBFNs). The proposed beyond 5G fronthaul architecture is primarily oriented to deploy massive machine-type communication (mMTC) services with high-bandwidth requirements, such as for industry 4.0. In this paper we experimentally validate the novel OBFN system, and the dynamic SDN/NFV MANO of the transport connectivity and network services for optical beamforming. The obtained experimental results show that the overall delay for the provisioning and removal of an OBF service, considering the contribution of the involved optical and radio systems and the SDN/NFV MANO layer, is 134s and 18s respectively. The reconfiguration of the OBF service to add or remove a beam can be performed in the range of 65-87s.

Analysis and evaluation of the effectiveness of methods for ensuring the quality of service for software-defined networks of the standard 5G/IMT-2020

The quality of service (QoS) in networking is the process of managing network resources to reduce packet loss and to lower network jitter and latency. QoS has been widely used in traditional network and can also be implemented in the 5G standard based on a software-defined network (SDN). A traditional network carries several challenges, such as vendor dependency, the complexity of managing a large network, dynamically changing forwarding policies, and more. Software-defined networking is a new networking strategy designed to address the challenges of a traditional IP network, such as high levels of complexity and inability to adapt to the new quality of service requirements in a timely manner. The fundamental idea behind SDNs compared to the conventional networking paradigm is the creation of horizontally integrated systems through the separation of the control and the data plane while providing an increasingly sophisticated set of abstractions. Recently, various SDN-enabled QoS frameworks have emerged that offer many possibilities for network reconfiguration and high-level definition of policies. QoS requirements for 5G networks have been defined on the basis of three main categories of use cases: extreme mobile broadband (xMBB), massive machine type communications (mMTC) IoT/M2M devices, and highly reliable М2М-communication (ultra-reliable machine-type communications – uMTC). This paper analyzes and surveys the QoS based on the openflow protocol method and QoS based on open-source SDN controllers method in 5G network. In addition, we discuss various architectural issues of open-source SDN controllers network and examine their impact on the QoS. Furthermore, we outline the characteristics of the QoS parameters such as latency, availability, reliability, jitter, and bandwidth in the 5G network. Finally, the article discusses and compares parameters of the QoS in 5G determined by world’s leaders in 5G technology.

Signal power and energy-per-bit optimization problems in systems mMTC

Introduction: Currently, the issues of Internet of Things technology are being actively studied. The operation of a large number of various self-powered sensors is within the framework of a massive machine-type communication scenario, using random access methods. Topical issues in this type of communication are how to reduce the transmission signal power and to increase the device lifetime by reducing the consumed energy per bit. Purpose: Formulation and analysis of the problems of minimizing the transmission power and consumed energy per bit in systems with or without retransmissions in order to obtain the achievability bounds. Results: A model of the system is described, within which four problems are formulated and described, concerning the signal power minimization and energy consumption for given parameters (the number of information bits, the spectral efficiency of the system, and the Packet Delivery Ratio). The numerical results of solving these optimization problems are presented. They make it possible to obtain the achievability bounds for the considered characteristics in systems with or without losses. The lower bounds obtained by the Shannon formula are also presented, assuming that the message length is not limited. The obtained results showed that solving the minimization problem with respect to one of the parameters (signal power or consumed energy per bit) does not minimize the second parameter. This difference is most significant for information messages of a small length, which is common in IoT scenarios. Practical relevance: The results obtained allow you to assess the potential for minimizing the transmission signal power and consumed energy per bit in random multiple access systems with massive machine-type communication scenarios. Discussion: The presented problems were solved without taking into account the average delay of message transmission; the introduction of such a limitation should increase the transmitted signal power and consumed energy per bit.

5G NR system design: a concise survey of key features and capabilities

As we enter a new era of next-generation wireless systems represented by Fifth Generation (5G) New Radio (NR) technology, it is essential to grasp the recent progress in their standardization and development. This article offers a concise survey of the 5G NR system design that aims at introducing its features according to the relevant Third Generation Partnership (3GPP) specifications. Our focus is set on the flexibility of 5G NR, which refers to its capability to support novel services and technologies, such as enhanced Mobile Broadband (eMBB) and Internet of Things (IoT) for massive Machine Type Communications (mMTC) while satisfying the underlying quality requirements. The key enablers of the 5G NR operation are scalable numerology, ultra-lean and beam-centric design, support for low latency, spectrum extension, and forward compatibility. This work summarizes these important features by studying the overall 5G architecture and the user-/control-plane protocol stacks specified by 3GPP. Furthermore, the impact of scalable numerology on system performance is discussed. Finally, we also consider open challenges and future research directions.

Optimal UAV Deployment and Resource Management in UAV Relay Networks.

UAV equipped three-dimensional (3D) wireless networks can provide a solution for the requirements of 5G communications, such as enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC). Especially, the introduction of an unmanned aerial vehicle (UAV) as a relay node can improve the connectivity, extend the terrestrial base station (BS) coverage and enhance the throughput by taking advantage of a strong air-to-ground line of sight (LOS) channel. In this paper, we consider the deployment and resource allocation of UAV relay network (URN) to maximize the throughput of user equipment (UE) within a cell, while guaranteeing a reliable transmission to UE outside the coverage of BS. To this end, we formulate joint UAV deployment and resource allocation problems, whose analytical solutions can be hardly obtained, in general. We propose a fast and practical algorithm to provide the optimal solution for the number of transmit time slots and the UAV relay location in a sequential manner. The transmit power at BS and UAV is determined in advance based on the availability of channel state information (CSI). Simulation results demonstrate that the proposed algorithms can significantly reduce the computational effort and complexity to determine the optimal UAV location and transmit time slots over an exhaustive search.

6G Cognitive Information Theory: A Mailbox Perspective

With the rapid development of 5G communications, enhanced mobile broadband, massive machine type communications and ultra-reliable low latency communications are widely supported. However, a 5G communication system is still based on Shannon’s information theory, while the meaning and value of information itself are not taken into account in the process of transmission. Therefore, it is difficult to meet the requirements of intelligence, customization, and value transmission of 6G networks. In order to solve the above challenges, we propose a 6G mailbox theory, namely a cognitive information carrier to enable distributed algorithm embedding for intelligence networking. Based on Mailbox, a 6G network will form an intelligent agent with self-organization, self-learning, self-adaptation, and continuous evolution capabilities. With the intelligent agent, redundant transmission of data can be reduced while the value transmission of information can be improved. Then, the features of mailbox principle are introduced, including polarity, traceability, dynamics, convergence, figurability, and dependence. Furthermore, key technologies with which value transmission of information can be realized are introduced, including knowledge graph, distributed learning, and blockchain. Finally, we establish a cognitive communication system assisted by deep learning. The experimental results show that, compared with a traditional communication system, our communication system performs less data transmission quantity and error.

SCADA-Based Message Generator for Multi-Vendor Smart Grids: Distributed Integration and Verification of TASE.2.

Recent developments in massive machine-type communication (mMTC) scenarios have given rise to never-seen requirements, which triggered the Industry 4.0 revolution. The new scenarios bring even more pressure to comply with the reliability and communication security and enable flawless functionality of the critical infrastructure, e.g., smart grid infrastructure. We discuss typical network grid architecture, communication strategies, and methods for building scalable and high-speed data processing and storage platform. This paper focuses on the data transmissions using the sets of standards IEC 60870-6 (ICCP/TASE.2). The main goal is to introduce the TASE.2 traffic generator and the data collection back-end with the implemented load balancing functionality to understand the limits of current protocols used in the smart grids. To this end, the assessment framework enabling generating and collecting TASE.2 communication with long-term data storage providing high availability and load balancing capabilities was developed. The designed proof-of-concept supports complete cryptographic security and allows users to perform the complex testing and verification of the TASE.2 network nodes configuration. Implemented components were tested in a cloud-based Microsoft Azure environment in four geographically separated locations. The findings from the testing indicate the high performance and scalability of the proposed platform, allowing the proposed generator to be also used for high-speed load testing purposes. The load-balancing performance shows the CPU usage of the load-balancer below 15% while processing 5000 messages per second. This makes it possible to achieve up to a 7-fold improvement of performance resulting in processing up to 35,000 messages per second.

A service‐aware transport layer framework for SDN‐enabled cellular core networks

Since futuristic cellular core networks are envisioned to accommodate emerging vertical domain applications with diverse QoS requirements, software-defined networking (SDN) technology has played a vital role in improving the scalability and flexibility of network management. This paper presents the design and development of a service-aware transport layer framework in SDN-enabled cellular core networks. It aims to study and accommodate three typical traffic types: enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC), and massive machine-type communications (mMTC) in optimal path selection. The proposed framework is able to dynamically allocate network resources to corresponding traffic of above types for satisfying the requirements of each identified flow, using a multi-level queue management mechanism to alleviate the flow dropping problem when the network becomes congested. For experiment and verification, a testbed platform was built to evaluate the functionalities of developed modules, the performance of the diffserv-based optimal path selection, and the operation of multi-level queue management. The experiment results demonstrate that the proposed algorithms could obtain a higher accepting ratio than the other ones in different network conditions.

Minimum Length Scheduling for Multi-Cell Full Duplex Wireless Powered Communication Networks.

Wireless powered communication networks (WPCNs) will be a major enabler of massive machine type communications (MTCs), which is a major service domain for 5G and beyond systems. These MTC networks will be deployed by using low-power transceivers and a very limited set of transmission configurations. We investigate a novel minimum length scheduling problem for multi-cell full-duplex wireless powered communication networks to determine the optimal power control and scheduling for constant rate transmission model. The formulated optimization problem is combinatorial in nature and, thus, difficult to solve for the global optimum. As a solution strategy, first, we decompose the problem into the power control problem (PCP) and scheduling problem. For the PCP, we propose the optimal polynomial time algorithm based on the evaluation of Perron–Frobenius conditions. For the scheduling problem, we propose a heuristic algorithm that aims to maximize the number of concurrently transmitting users by maximizing the allowable interference on each user without violating the signal-to-noise-ratio (SNR) requirements. Through extensive simulations, we demonstrate a 50% reduction in the schedule length by using the proposed algorithm in comparison to unscheduled concurrent transmissions.

Detection of Hijacking DDoS Attack Based on Air Interface Traffic

Massive machine type communication (mMTC) network contains massive access devices, which increases the risk of distributed denial of service (DDoS) attacks. Especially, the DDoS attack launched by hijacked devices cannot be detected through traditional cryptography-based authentication because that the hijacked devices have legitimate identities. In this letter, we propose to exploit the characteristics of access traffic at the air interface to detect this kind of DDoS attack, and then identify the suspicious devices. We analyze both the characteristics of access traffic based on MTC traffic arrival model and the random access mechanism proposed by 3GPP to construct test statistics, and propose a sequential change point detection (SCPD) scheme to test whether there are anomalies as quickly as possible. After that, we identify the hijacked devices based on the abnormal traffic. Simulation experiments confirm the effectiveness of our detection scheme.

Outage Performance Analysis of Widely Linear Receivers in Uplink Multi-User MIMO Systems

This paper considers the application of widely linear (WL) receivers in an uplink multi-user system using real-valued modulation schemes, where the cellular base station (BS) with multiple antennas provides connectivity for randomly deployed single-antenna users. The targeted use case is massive machine type communication (mMTC) with grant-free access in the uplink, where the network is required to host a large number of low data rate devices transmitting in an uncoordinated fashion. Four types of WL receivers are investigated, namely the WL zero-forcing (ZF) and the WL minimum mean-squared error (MMSE) receivers, along with their enhanced versions employing successive interference cancellation (SIC) with channel-dependent ordering, i.e., the WL-ZF-SIC and WL-MMSE-SIC receivers. The outage performances of these receivers are analytically characterized in the high signal-to-noise ratio (SNR) regime and compared to those of conventional linear (CL) receivers using complex-valued modulation schemes. For the non-SIC receivers, we show that, when compared to the CL counterparts, the WL receivers yield a higher diversity gain when decoding the same number of users and have the same diversity gain but a decreased coding gain when the number of users is nearly doubled. The outage performance analysis of WL-SIC receivers is facilitated by the marginal distribution of ordered eigenvalues of a real-valued Wishart matrix. It is shown that the SIC operation with channel-dependent ordering brings no additional diversity gain to the WL receivers but instead increases the coding gain. Moreover, the coding gain of WL-SIC receivers grows as the number of users increases and even exceeds that of CL-SIC receivers under suitable conditions. For the mMTC scenario with grant-free transmission, it is demonstrated that the WL receivers outperform their CL counterparts in terms of offering a lower outage (and packet drop) probability and a higher system throughput for a given packet drop probability.

A Comprehensive Overview on 5G-and-Beyond Networks With UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.

Catalyzing random access at physical layer for Internet of Things: An intelligence enabled user signature code acquisition approach

Exploiting random access for the underlying connectivity provisioning has great potential to incorporate massive machine-type communication (MTC) devices in an Internet of Things (IoT) network. However, massive access attempts from versatile MTC devices may bring congestion to the IIoT network, thereby hindering service increasing of IIoT applications. In this paper, an intelligence enabled physical (PHY-)layer user signature code acquisition (USCA) algorithm is proposed to overcome the random access congestion problem with reduced signaling and control overhead. In the proposed scheme, the detector aims at approximating the optimal observation on both active user detection and user data reception by iteratively learning and predicting the convergence of the user signature codes that are in active. The cross-entropy based low-complexity iterative updating rule is present to guarantee that the proposed USCA algorithm is computational feasible. A closed-form bit error rate (BER) performance analysis is carried out to show the efficiency of the proposed intelligence USCA algorithm. Simulation results confirm that the proposed USCA algorithm provides an inherent tradeoff between performance and complexity and allows the detector achieves an approximate optimal performance with a reasonable computational complexity.

Channel Estimation and User Identification With Deep Learning for Massive Machine-Type Communications

In this paper, we investigate the detection problem for a massive machine-type communication (mMTC) system that has correlated user activities. Two deep learning assisted algorithms are proposed to exploit the user activity correlation to facilitate channel estimation and user identification. Due to the dependency among user activities, conventional element-wise minimum mean square error (MMSE) denoiser used in the orthogonal approximate message passing (OAMP) algorithm cannot achieve satisfying performance during the two-step iterative process. Therefore, we propose a deep learning modified OAMP (DL-mOAMP) algorithm, which iteratively modifies the user activity ratio via exploiting the user activity correlation in the MMSE denoiser based on the estimated sequence during each OAMP iteration. Moreover, given a specific false alarm probability, a constant threshold employed in the conventional user identification is not optimal in the presence of user activity correlation. Thus, we propose a neural network framework that is dedicated to the user identification (DL-mOAMP-UI algorithm), which minimizes the missed detection probability under a pre-determined false alarm probability. Numerical results show that the proposed DL-mOAMP algorithm provides a substantial mean squared error performance gain compared to the conventional OAMP algorithm and the DL-mOAMP-UI algorithm can further improve the user identification accuracy of an mMTC system.

An Empirical Modeling for the Baseline Energy Consumption of an NB-IoT Radio Transceiver

NarrowBand Internet of Things (NB-IoT) is an emerging cellular IoT technology that offers attractive features for deploying low-power wide-area networks suitable for implementing massive machine-type communications. NB-IoT features include, e.g., extended coverage and deep penetration for massive connectivity, longer battery-life, appropriate throughput, and desired latency at lower bandwidth. Regarding the device energy consumption, NB-IoT is mostly underestimated for its control and signaling overheads, which calls for a better understanding of the energy consumption profiling of an NB-IoT radio transceiver. With this aim, this work presents a thorough investigation of the energy consumption profiling of the radio resource control (RRC) communication protocol between an NB-IoT radio transceiver and a cellular base station. Using two different commercial off the shelf NB-IoT boards and two mobile network operators (MNOs) NB-IoT test networks operational at Tallinn University of Technology, Estonia, we propose an empirical baseline energy consumption model. Based on comprehensive analyses of the profile traces from the widely used BG96 NB-IoT module operating in various states of the RRC protocol, our results indicate that the proposed model accurately depicts the baseline energy consumption of an NB-IoT radio transceiver while operating at different coverage class levels. The evaluation errors of our proposed model vary between 0.33% and 15.38%.

UAV-Aided Backscatter Communications: Performance Analysis and Trajectory Optimization

In 5G massive machine-type communication (mMTC), power-limited or battery-free parasite devices such as radio frequency identification (RFID) tags, can use the transmitted signals from host devices as ambient signals for backscatter communications to send information to a base station (BS). Unmanned aerial vehicles (UAVs) can be employed as host devices to help transmissions of parasite devices due to the advantages of high mobility and low operating cost. In this paper, we propose a signal detection approach based on the central limit theorem to detect the presence of parasite devices and separate parasite signals from host signals. Then, closed-form expressions for the probability of error detection and the bit error rate (BER) are derived. Moreover, the trajectory planning of multiple UAVs is optimized with the consideration of minimizing the energy consumption of UAV swarms to serve parasite devices. Theoretical and simulation results show that our proposed method provides good detection performance for parasite devices. It also shows that the trajectory planning of multiple UAVs is optimized.

Robust transmission using channel encoding towards 5G New Radio: A telemetry approach

This paper presents a robust channel encoding scheme under adaptive modulation and coding for a massive machine type communication device in 5G new radio. For the very first time, mode-selection and distance statistics algorithms have been simultaneously evaluated, in which together it provides the closest approximation of efficient adaptive modulation and coding with robust transmission. The prediction of optimum adaptive modulation and coding is based on the analysis of uplink packet using distance statistics, and downlink packet using mode-selection mechanism. The performance of 5G new radio by incorporating OFDM subcarrier has been evaluated using analytical as well as simulation approach. Mode-selection algorithm has been considered to predict the environmental condition under a fading channel while the distance statistics provide feedback of the previously transmitted channel condition. The result of both the approaches provide a better bit error rate for adaptive modulation & coding profile under 1/4, 1/18, 1/16 and 1/32 cyclic prefix.