Machine Type Communication Research Articles

Device-Level Energy Efficient Strategies in Machine Type Communications: Power, Processing, Sensing, and RF Perspectives

Device-Level Energy Efficient Strategies in Machine Type Communications: Power, Processing, Sensing, and RF Perspectives

Grant Free MIMO-NOMA With Differential Modulation for Machine Type Communications

Grant Free MIMO-NOMA With Differential Modulation for Machine Type Communications

Adaptive Scheduling of Shared Grant-Free Resources for Heterogeneous Massive Machine type Communication in 5G and Beyond Networks

Adaptive Scheduling of Shared Grant-Free Resources for Heterogeneous Massive Machine type Communication in 5G and Beyond Networks

Evaluating the Performance of 5G NR in Indoor Environments: An Experimental Study

The 5G wireless standard has emerged as a trans-formative technology with the potential to revolutionize various industries by providing enhanced connectivity and communication capabilities. This advanced standard offers a diverse range of applications, including Ultra-Reliable Low-Latency Communication (URLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine Type Communication (mMTC). In this Scientific research paper, we present a comprehensive analysis of the performance and capabilities of a deployed indoor 5G network in a controlled laboratory environment. The experimental setup comprises an Amarisoft Callbox, serving as the 5G core, along with a Remote Radio Head (RRH) and user equipment (UEs). Our primary objective is to assess the network's performance by evaluating the downlink, uplink, and joint downlink/uplink transmissions for TCP and UDP protocols between the gNodeB and user equipment (UE). To achieve this, we carefully examine key performance metrics such as latency, power consumption, CPU utilization, and signal quality. Through various configurations that involve MIMO 2 technology and a 30 kHz sub-carrier spacing, we investigate the impact of different NR Bandwidth settings. By establishing TCP and UDP connections for both uplink and downlink scenarios, we meticulously measure and scrutinize the system's performance, thereby providing valuable insights into its efficiency and suitability for diverse application requirements.

Data-Centric Resource Allocation for Machine-Type Communications With Lossy Links Based on Compressive Sensing

Data-Centric Resource Allocation for Machine-Type Communications With Lossy Links Based on Compressive Sensing

Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

Learning Random Access Schemes for Massive Machine-Type Communication With MARL

Learning Random Access Schemes for Massive Machine-Type Communication With MARL

Multichannel Slotted ALOHA Simulator Design for Massive Machine-Type Communication (mMTC) on 5G Network

Massive Machine-type Communication (mMTC) is one of the main service scenarios in 5G. At the time of initializing the connection to the base station, the MTC machines will make a connection request via the random access procedure. One of the schemes of random access procedure for handling this connection request is similar to how multichannel slotted ALOHA works. Multichannel slotted ALOHA itself is a development of the slotted ALOHA scheme which originally has only a single channel. At the initial state of mMTC, there will be an explosion of the number of demands to the available channels. Given the number of machines that will be connected, the likelihood of a collision on the same channel increases. As a result, the probability of failure also increases. The system's configuration has an impact on the likelihood of success and the time it takes to achieve it. The number of channels influences the likelihood of collisions, the backoff window influences the transmission distribution in each slot, and the maximum transmission limits the ability of device retransmission. These three arrangements have an impact on one another. The simulator build in this research is expected to make it easier for researchers to optimize multichannel slotted ALOHA configurations in 5G to handle the surge in access demands from mMTC devices.

Retracted: Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications

Retracted: Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications

Market and Sharing Alternatives for the Provision of Massive Machine-Type and Ultra-Reliable Low-Latency Communications Services over a 5G Network

The objective of this paper is to analyze economic alternatives for the provision of ultra-reliable low-latency communication (URLLC) and massive machine-type communication (mMTC) services over a fifth-generation (5G) network. Two business models, a monopoly and a duopoly, are studied and two 5G network scenarios are analyzed: a 5G network where the network resources are shared between the two services without service priority, and a 5G network with network slicing that allows for URLLC traffic to have a higher priority. Microeconomics is used to model the behavior of users and operators, and game theory is used to model the strategic interaction between users and operators. The results show that a monopoly over a 5G network with network slicing is the most efficient way to provide both URLLC and mMTC services.

Characterizing optimal LPWAN access delay in massive multi-RAT smart grid deployments

Massive machine-type communications services penetrating the market, such as smart grids, differ from conventional services characterized by stochastic arrival patterns. Requiring permanent end device (ED) connectivity, control centers poll EDs over regular time intervals, leading to batch message arrivals that affect the mean access delay at the air interface. In addition, the smart grid requires high reliability and network availability that can be achieved by utilizing multiple radio access technologies in EDs. Radio access technologies (RATs), i.e., narrowband internet of things (NB-IoT) and long-term evolution machine type communication (LTE-M), have been identified as promising solutions for these use-cases. This work first reports results of an extensive performance evaluation measurement campaign showing that LTE-M can be considered a preferred option and NB-IoT as a possible backup solution for smart grids. We also show that none of the selected technologies can fully meet the requirements of smart grid use cases. We then develop a theoretical model and corresponding association algorithm for balancing traffic load between two low-power wide area network (LPWAN) technologies to minimize the mean access delay. Our results demonstrate that the optimized usage of multi-RAT EDs considerably increases the number of supported EDs operating in polling-based mode. For 500EDs utilizing a single LTE-M technology, the mean access delay is over two seconds — contradicting the minimum requirements of smart grid applications. On the other hand, multi-RAT EDs running the developed algorithm increase the service capacity by up to six times (up to 3000EDs) while still satisfying the two-second latency budget.

An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning

Network slicing shows promise as a means to endow 5G networks with flexible and dynamic features. Network function virtualization (NFV) and software-defined networking (SDN) are the key methods for deploying network slicing, which will enable end-to-end (E2E) isolation services permitting each slice to be customized depending on service requirements. The goal of this investigation is to construct network slices through a machine learning algorithm and allocate resources for the newly created slices using dynamic programming in an efficient manner. A substrate network is constructed with a list of key performance indicators (KPIs) like CPU capacity, bandwidth, delay, link capacity, and security level. After that, network slices are produced by employing multi-layer perceptron (MLP) using the adaptive moment estimation (ADAM) optimization algorithm. For each requested service, the network slices are categorized as massive machine-type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low-latency communications (uRLLC). After network slicing, resources are provided to the services that have been requested. In order to maximize the total user access rate and resource efficiency, Dijkstra’s algorithm is adopted for resource allocation that determines the shortest path between nodes in the substrate network. The simulation output shows that the present model allocates optimum slices to the requested services with high resource efficiency and reduced total bandwidth utilization.

Design and Optimization of RSMA for Coexisting HTC/MTC in 6G and Future Networks

With the fast development of emerging Internet of Everything applications, human-type communications (HTC) and machine-type communications (MTC) will inevitably coexist in future 6G cellular networks. To support massive connectivity while fulfilling diverse requirements of both HTC and MTC, we present a device-to-device aided rate splitting multiple access (RSMA) scheme by encoding both the MTC devices’ messages and HTC users’ common messages into a general common data stream in each cell (or group). Nevertheless, such deploying strategy may bring challenges in complex resource allocation and transmission modes selection. In view of this, we introduce a simple received signal strength (RSS) based transmission modes selection scheme, through which the RSS-threshold selection problem is formulated to maximize the HTC and MTC sum rates. Considering the computational complexity and scalability, we employ a multi-agent reinforcement learning based algorithm for each small base station to choose the optimal RSS threshold thus to achieve maximum sum rate while maintaining massive connectivity. Simulation results reveal our proposed RSMA deploying strategy outperforms non-orthogonal multiple access (NOMA) in system coverage while maintaining high-level system rate. Besides, the proposed MARL based scheme can further improve the system sum rate and coverage for both the HTC and MTC.

Random Access Protocol for Massive Internet of Things Connectivity in Space–Air–Ground-Integrated Networks

Space-air-ground integrated networks (SAGIN) are receiving a lot of attention as a candidate for an extension to non-terrestrial networks beyond the limit of terrestrial networks. SAGIN can be a means to satisfy various quality-of-service (QoS) by providing several communication links in different characteristics. To fully utilize the advantage of SAGIN, a protocol for informing user equipment (UE) of an appropriate base station layer of SAGIN is needed according to QoS requirements. In addition, the development of the protocol should consider the characteristics of the UE. In particular, when the UE is a machine-type communication device (MTCD) constituting a massive internet of things network, random access congestion issue should be considered. To solve the problems, we propose a random access protocol. We propose an indicator named virtual deadline indicator (VDI) to inform MTCD which layer to try for random access. MTCD attempts random access to one layer of SAGIN by comparing the remaining deadlines of traffic and the point of the VDI. To minimize the deadline expiration rate, we propose an algorithm to shift the VDI position. The base station estimates the number of the MTCDs and remaining deadline distribution by using the number of the idle preambles to find the VDI location. Finally, we compare the performance of the protocol through simulation with three benchmark schemes. We confirm that the proposed protocol reduces the deadline expiration rate without compromising the age of information and energy efficiency.

Low complexity physical layer security approach for 5G internet of things

<span lang="EN-US">Fifth-generation (5G) massive machine-type communication (mMTC) is expected to support the cellular adaptation of internet of things (IoT) applications for massive connectivity. Due to the massive access nature, IoT is prone to high interception probability and the use of conventional cryptographic techniques in these scenarios is not practical considering the limited computational capabilities of the IoT devices and their power budget. This calls for a lightweight physical layer security scheme which will provide security without much computational overhead and/or strengthen the existing security measures. Here a shift based physical layer security approach is proposed which will provide a low complexity security without much changes in baseline orthogonal frequency division multiple access (OFDMA) architecture as per the low power requirements of IoT by systematically rearranging the subcarriers. While the scheme is compatible with most fast Fourier transform (FFT) based waveform contenders which are being proposed in 5G especially in mMTC and ultra-reliable low latency communication (URLLC), it can also add an additional layer of security at physical layer to enhanced mobile broadband (eMBB).</span>

Massive Connectivity and Low-Latency for Next-Generation Internet of Things: A Filtered OFDM-based Deep Learning Approach

Abstract The Internet of Things (IoT) is one of the numerous services offered by sixth-generation (6G) mobile communications. It is necessary to meet heterogeneous criteria for large connectivity and low latency to serve the various kinds of IoT applications. In order to concurrently provide enormous connectivity and meet the low-latency conditions in the uplink IoT network, we proposed filtered orthogonal frequency division multiplexing (FOFDM) based on a service group adopting the deep learning (DL) technique. The proposed FOFDM-DL platform focuses on two key areas: first, it works for the concurrence of different time-frequency granularity appropriate for distinct service-based grouping, and secondly, it facilitates low latency and massive connectivity to deliver dependable communications. The suggested FOFDM-DL architecture may accommodate the needs of massive machine-type communication referred as mMTC and ultra-reliable and low-latency communications known as uRLLC concurrently for the next-generation communication systems. However, the uRLLC and mMTC requirements can only be supported separately by the new radio (NR)-5G, beyond 5G (or 6G). In comparison to the traditional scheme, simulation results demonstrate that the suggested FOFDM-DL platform works surprisingly well in the next-generation IoT network.

Parity Check Codes for Second Order Diversity

Short block length “block” codes are typically not used in wireless standards as soft decision decoding is computationally intensive and hard decision decoding results in performance loss. However, short block lengths are of interest to massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC). In this paper, we propose a diversity preserving enhanced hard-decision decoding scheme for parity check codes (PCC) over Rayleigh fading channels. The proposed flip decoding scheme has linear complexity in the block length. Theoretical analysis and simulation results verify the correctness of the proposed detection scheme.

Path Loss Characterization in an Outdoor Corridor Environment for IoT-5G in a Smart Campus University at 850 MHz and 3.5 GHz Frequency Bands.

The usage scenarios defined in the ITU-M2150-1 recommendation for IMT-2020 systems, including enhanced Mobile Broadband (eMBB), Ultra-reliable Low-latency Communication (URLLC), and massive Machine Type Communication (mMTC), allow the possibility of accessing different services through the set of Radio Interface Technologies (RITs), Long-term Evolution (LTE), and New Radio (NR), which are components of RIT. The potential of the low and medium frequency bands allocated by the Federal Communications Commission (FCC) for the fifth generation of mobile communications (5G) is described. In addition, in the Internet of Things (IoT) applications that will be covered by the case of use of the mMTC are framed. In this sense, a propagation channel measurement campaign was carried out at 850 MHz and 5.9 GHz in a covered corridor environment, located in an open space within the facilities of the Pedagogical and Technological University of Colombia campus. The measurements were carried out in the time domain using a channel sounder based on a Universal Software Radio Peripheral (USRP) to obtain the received signal power levels over a range of separation distances between the transmitter and receiver from 2.00 m to 67.5 m. Then, a link budget was proposed to describe the path loss behavior as a function of these distances to obtain the parameters for the close-in free space reference distance (CI) and the floating intercept (FI) path loss prediction models. These parameters were estimated from the measurements made using the Minimum Mean Square Error (MMSE) approach. The estimated path loss exponent (PLE) values for both the CI and FI path loss models at 850 MHz and 3.5 GHz are in the range of 2.21 to 2.41, respectively. This shows that the multipath effect causes a lack of constructive interference to the received power signal for this type of outdoor corridor scenario. These results can be used in simulation tools to evaluate the path loss behavior and optimize the deployment of device and sensor network infrastructure to enable 5G-IoT connectivity in smart university campus scenarios.

Tandem Spreading Multiple Access With MIMO

With the massive deployment of 5G commercial applications, the Internet of Everything promotes the transformation and upgrading of the society production mode. The Internet of Things (IoT) is supported by the massive machine-type communications (mMTC), which is one of the three major application scenarios of 5G. Recently, a novel spreading based non-orthogonal multiple access (NOMA) scheme named tandem spreading multiple access (TSMA) has been proposed for grant-free random access in mMTC. However, TSMA only considers the case of single antenna, the connectivity expansion on spatial domain has not been considered. In this article, a multi-antenna system scheme of TSMA (MIMO-TSMA) is proposed to scale up user connections. In this scheme, spectrum efficiency can be promoted by sharing the time frequency resources in different beams. In the meantime, scheme against channel deep fading is considered in this work. The simulation results show that MIMO-TSMA can effectively take advantage of MIMO and TSMA to enhance the mMTC system performance.

A Comprehensive Study on the Role of Machine Learning in 5G Security: Challenges, Technologies, and Solutions

Fifth-generation (5G) mobile networks have already marked their presence globally, revolutionizing entertainment, business, healthcare, and other domains. While this leap forward brings numerous advantages in speed and connectivity, it also poses new challenges for security protocols. Machine learning (ML) and deep learning (DL) have been employed to augment traditional security measures, promising to mitigate risks and vulnerabilities. This paper conducts an exhaustive study to assess ML and DL algorithms’ role and effectiveness within the 5G security landscape. Also, it offers a profound dissection of the 5G network’s security paradigm, particularly emphasizing the transformative role of ML and DL as enabling security tools. This study starts by examining the unique architecture of 5G and its inherent vulnerabilities, contrasting them with emerging threat vectors. Next, we conduct a detailed analysis of the network’s underlying segments, such as network slicing, Massive Machine-Type Communications (mMTC), and edge computing, revealing their associated security challenges. By scrutinizing current security protocols and international regulatory impositions, this paper delineates the existing 5G security landscape. Finally, we outline the capabilities of ML and DL in redefining 5G security. We detail their application in enhancing anomaly detection, fortifying predictive security measures, and strengthening intrusion prevention strategies. This research sheds light on the present-day 5G security challenges and offers a visionary perspective, highlighting the intersection of advanced computational methods and future 5G security.