Machine Type Communication Devices Research Articles

A new subcarrier‐index modulation schemes for downlink NOMA systems

SummaryIn this paper, we propose two downlink multiple access architectures for networks where human‐type communication users (HTCUs) and machine‐type communication devices (MTCDs) coexist. The proposed schemes combine non‐orthogonal multiple access (NOMA), orthogonal frequency division multiplexing (OFDM), and index modulation (OFDM‐IM) concepts. In the first scheme, the base station (BS) transmits bits of HTCUs using modulated symbols and bits of MTCDs by the subcarrier activation pattern (SAP). This approach called IM‐NOMA with null subcarriers (IM‐NOMA‐NS) ensures that inactive subcarriers are always null, which improves the system bit error rate (BER) performance. To improve the spectral efficiency (SE), we propose a second approach, termed IM‐NOMA with dual‐mode modulation (IM‐NOMA‐DM), in which the HTCUs' bits are transmitted using two‐dimensional modulation and the MTCDs' bits are transmitted using one‐dimensional modulation and the SAP. For each proposed system, a near‐optimal low‐complexity detector, based on the energy‐detection (ED) and the log‐likelihood ratio (LLR) criterion, is provided to mitigate the detection burden of the optimal maximum‐likelihood (ML) detector. The BER performances and SE of the proposed schemes are investigated. The average BERs of IM‐NOMA‐NS and IM‐NOMA‐DM are derived in closed‐form expressions corroborated by the simulation results. We have proved numerically that the proposed schemes achieve a good trade‐off between BER performance, SE, and the number of supported users, making them more suitable for Internet of Things (IoT) applications.

A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks.

In the realm of the fifth-generation (5G) wireless cellular networks, renowned for their dense connectivity, there lies a substantial facilitation of a myriad of Internet of Things (IoT) applications, which can be supported by the massive machine-type communication (MTC) technique, a fundamental communication framework. In some scenarios, a large number of machine-type communication devices (MTCD) may simultaneously enter the communication coverage of a target base station. However, the current handover mechanism specified by the 3rd Generation Partnership Project (3GPP) Release 16 incurs high signaling overhead within the access and core networks, which may have negative impacts on network efficiency. Additionally, other existing solutions are vulnerable to malicious attacks such as Denial of Service (DoS), Distributed Denial of Service (DDoS) attacks, and the failure of Key Forward Secrecy (KFS). To address this challenge, this paper proposes an efficient and secure handover authentication protocol for a group of MTCDs supported by blockchain technology. This protocol leverages the decentralized nature of blockchain technology and combines it with certificateless aggregate signatures to mutually authenticate the identity of a base station and a group of MTCDs. This approach can reduce signaling overhead and avoid key escrow while significantly lowering the risk associated with single points of failure. Additionally, the protocol protects device anonymity by encrypting device identities with temporary anonymous identity markers with the Elliptic Curve Diffie-Hellman (ECDH) to abandon serial numbers to prevent linkage attacks. The resilience of the proposed protocol against predominant malicious attacks has been rigorously validated through the application of the BAN logic and Scyther tool, underscoring its robust security attributes. Furthermore, compared to the existing solutions, the proposed protocol significantly reduces the authentication cost for a group of MTCDs during handover, while ensuring security, demonstrating commendable efficiency.

Throughput and delay analysis of cognitive M2M communications

In this paper, we analyze throughput and delay performance of clustered Machine Type Communication (MTC) devices which access an eNodeB utilizing a primary spectrum in underlay mode. We assume that the MTC devices form two clusters and there is an optimal preamble allocation between the two clusters to maximize the throughput. We further investigate the impact of the tolerable interference threshold on throughput, successful preamble decoding probability, and delay. Then, the impact of the preamble partition factor and the access barring factor on throughput and delay is analyzed. Finally, we evaluate the impact of the number of devices, retransmission requests, and preamble partitions on the delay.

Energy-Efficient Dynamic MAC Protocol for M2M Communication

In the Internet of Things, which is vital for smart city applications, a vast network of battery-operated Machine Type Communication Devices (MTCDs) requires Machine Type Communication. These MTCDs connect directly to a network with a base station, facilitating a wide range of smart city functionalities. The development of energy-efficient communication protocols is essential to support the deployment of energy-constrained battery-operated devices. These protocols aim to minimize energy consumption, thereby enhancing network lifetime. Additionally, they must address scalability, reduce collisions and delays, and ensure a high Quality of Service (QoS) to effectively manage the vast number of devices involved. Clustering is a viable strategy to enhance scalability and energy efficiency in network communications. However, within this framework, particularly in the proposed Power Efficient Dynamic MAC protocol for M2M Communication (PMAC), there's still a need for energy-efficient communication between the Machine Type Communication Devices (MTCDs). This requirement is crucial to optimize the overall performance and sustainability of the network. In this paper, we analyses the Energy Efficient CSMA/CA (E-CSMA/CA) protocol, specifically developed for facilitating communication between Machine Type Communication Devices (MTCDs) in short-range communications. This analysis is set within the context of smart city applications, with a particular focus on smart metering. This approach underscores the importance of efficient, reliable communication protocols in modern urban infrastructure, especially for applications like smart metering that are integral to the smart city ecosystem.

Low Latency Signature-Based 4-Step Random Access Procedure for Massive Machine-Type Communications

Massive machine-type communication (mMTC) is one of the major services in the 5G new radio (NR) system. In the mMTC application scenarios, many machine-type communication devices (MTCDs) communicate with each other or through an MTC server. However, how to solve the problem of excessive MTCDs accessing the network simultaneously is an important task. To overcome the large number of random access collisions and retransmission problems, this paper proposes a method to solve the mMTC random access problem. The proposed method consists of two parts, modified Contention Based Random Access (CBRA) with 4-step RA type and 4-step Signature RA type. With the modified CBRA with 4-step RA type, each MTCD will obtain the information to generate the signature. After that, the 4-step Signature RA type is used for MTCDs to proceed with the random access with the gNB through the generated signatures. In our proposed method, the average number of false positives and average process time are evaluated under different numbers of active MTCDs and different parameter values for the signature generation. Besides, the average number of preamble attempts and average process time are used as the metrics to compare the proposed scheme with the 5G NR 4-step RA type. Finally, simulation results show that the proposed scheme performs better than the 5G NR 4-step RA type in the mMTC scenarios.

Joint delay and energy aware dragonfly optimization‐based uplink resource allocation scheme for LTE‐A networks in a cross‐layer environment

AbstractThe exponential growth in data traffic from smart devices has led to a need for highly capable wireless networks with faster data transmission rates and improved spectral efficiency. Allocating resources efficiently in a 5G communication system with a huge number of machine type communication (MTC) devices is essential to ensure optimal performance and meet the diverse requirements of different applications. The LTE‐A network offers high‐speed mobile data services and caters to MTC devices and has relatively low data service requirements compared to human‐to‐human (H2H) communications. LTE‐A networks require advanced scheduling schemes to manage the limited spectrum and ensure efficient transmissions. This necessitates effective resource allocation schemes to minimize interference between cells in future networks. To address this issue, a joint delay and energy aware Levy flight Brownian movement‐based dragonfly optimization (DELFBDO)‐based uplink resource allocation scheme for LTE‐A Networks is proposed in this work to optimize energy efficiency, maximize the throughput and reduce the latency. The DELFDO algorithm efficiently organizes packets in both time and frequency domains for H2H and MTC devices, resulting in improved quality of service while minimizing energy consumption. The Simulation results demonstrate that the proposed method increases the energy efficiency by producing the appropriate channel and power assignment for UEs and MTC devices.

Fairness aware deep reinforcement learning for grant-free NOMA-IoT networks

Next generation networks have special areas related with the Internet of Things (IoT) to improve the performance of cellular networks in terms of throughput. Grant-free non-orthogonal multiple access (GF-NOMA) seems a feasible solution, letting machine type communication (MTC) devices transmits their packets when they ready to transmit. GF-NOMA increases the spectral efficiency by using the superimposing signals with different power levels over the same time and frequency resources. However, the main drawbacks of GF-NOMA are randomness and the management of power level selection of MTC devices. In 6G-IoT networks, the intelligence should be met to random access. It is time to design new access methods to solve the GF-NOMA issues that should be between the randomness and fully coordinated medium access. Deep-Q-Network (DQN) has become a very hot research topic in recent years that let the MTC devices to make a smart decision in an intelligent way to improve the throughput. Selfishness is an undesirable behavior of DQN for GF-NOMA system where the resources have different cost. In this study, we develop a novel learning framework for power domain GF-NOMA. The goal of our learning framework is to maximize the throughput considering fairness in power consumption which provides long-life to the IoT network. The learning algorithm push the MTC devices to exchange the resources between each other over time. The results show that the proposed method outperform the NOMA scheme with random selection in terms of throughput and increase the fairness index when the DQN with selfish behavior is employed.

DRX Mechanism for Machine Type Communication (MTC) in Long Term Evolution Network (LTE): Potentials and challenges

Discontinuous Reception (DRX) Mechanism is the power saving technique employed by the Long Time Evolutional (LTE) Network to reduce the power saving. The scheme has seen numerous improvements and tuning to enhance its performance. The Machine Type Communication (MTC) devices have characters that widely differ from the normal devices (Human Type Devices). Therefore, DRX schemes needs to be adjusted to fully accommodate MTC. In this work, a survey of MTC DRX scheme literatures is adapted depicting challenges and potential.

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

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.

Reinforcement Learning Based RSS-Threshold Optimization for D2D-Aided HTC/MTC in Dense NOMA Systems

To fulfill the stringent requirements brought by human-type communication (HTC) along with massive machine-type communication (MTC), device-to-device (D2D) and non-orthogonal multiple access (NOMA) techniques will inevitably be incorporated into dense cellular networks to cater massive connectivity and maintain high spectral efficiency. However, such combination may lead to very complex network topologies and bring challenge in resource allocation, interference management and transmission mode selection. Note the received signal strength (RSS) is an important factor for cellular and D2D mode selection, it can affect multi-access mode determination in D2D-aided HTC/MTC dense NOMA systems. Therefore, the RSS threshold of each cell has great impact on system performance and should be carefully tuned. To this end, we formulate the RSS-threshold selection problem as a decentralized partially observable Markov decision process to maximize the performance for downlink and uplink communications. Accordingly, we employ a multi-agent reinforcement learning based scheme wherein each small base station acts as an agent and chooses the optimal RSS threshold to achieve maximum sum rate by interacting with the environment continuously. Extensive simulation results reveal our proposed scheme can improve the system sum rate and coverage by enhancing the connectivity of massive HTC and MTC devices via D2D and NOMA techniques.

Adaptive Packet Length Adjustment for Minimizing Age of Information Over Fading Channels

Motivated by the high information freshness requirement in the Internet of Things (IoT), this paper investigates adaptive packet length adjustment schemes to minimize the average age of information (AoI) for status update systems, where a machine type communication device adaptively adjusts the packet length in real time by exploiting the channel state information and AoI. Since the status packets in the IoT are often short, a significant packet error rate is introduced. Due to the instability of channel fading and the high packet error rate, optimizing the AoI performance is tricky. Under a power consumption constraint, the AoI minimization problem is modeled as a constrained Markov decision process (CMDP), and the structure of the optimal scheme is revealed. Then, under the CMDP framework, this paper transforms the AoI minimization problem into a linear programming problem and proposes a probabilistic packet length adjustment scheme, which can lead to the optimal solution. When the power consumption constraint is loose, a low-complexity suboptimal scheme is further proposed, where the expected average AoI of one period length is minimized. Simulation results verify the superiority of the proposed optimal scheme and show that the proposed low-complexity scheme can reach near-optimal performance.

LS-AKA: A lightweight and secure authentication and key agreement scheme for enhanced machine type communication devices in 5G smart environment

The 3rd Generation Partnership Project (3GPP) has implemented the Authentication and Key Agreement (AKA) protocol in 5G communication networks to ensure user equipment privacy. Due to the high density and concurrent communication, the primary goal is to provide an efficient authentication for massive enhanced Machine Type Communication (eMTC) devices. However, certain security flaws have been discovered in the 5G-AKA protocol, and no scheme has yet been developed that meets the needs of a group of eMTC devices, such as signaling congestion avoidance, key forward/backward secrecy (KFS/KBS) establishment, resistance against malicious attacks, and session key secrecy. Furthermore, the current group-based 5G communication network techniques do not require the group membership update mechanism at each device joining or leaving the group. To address these issues, we present a lightweight and secure technique for assembling a secure ecosystem of eMTC devices in a 5G network. The protocol employs the incremental hash mechanism to complete group member joining/leaving activities. For a thorough assessment of LS-AKA, the Random Oracle Model (ROM) is used to do formal security proofs, and informal security analysis shows that it is resistant to malicious attacks. In addition, the performance and simulation results of the existing and suggested protocols in terms of signaling, communication, and computing overhead are analyzed. According to the assessment results, the LS-AKA protocol improves the privacy and confidentiality of the 5G-enabled smart environment.

Energy Efficient MAC for M2M Communication Using Dynamic TDMA in IoT Network

In the Internet of Things (IoT) context, the relevance of M2M communication increased, creating the need for practical solutions. MTCDs, or Machine Type Communication Devices, frequently encounter issues such as collisions and delays while transmitting data, ensuring network scalability and maintaining the quality of service (QoS) for all devices participating in the communication. To address these challenges, this paper presents the protocol for switching MAC that optimizes energy usage with Dynamic Time Division Multiple Access (EMAC-DTDMA). TheEMAC-DTDMA protocol involves grouping, dynamic MAC switching, and timeslot allocation. The Harris Hawks Optimization (HHO) algorithm is applied to choose the cluster head of the machine type communication devices (MTCH) while the CSMA/CA and CSMA/CARP protocols with different backoff times are used to minimize collisions based on device density, backlog, and active nodes. The timeslots are allocated based on data size and QoS requirements using Dynamic TDMA. The Markov chain model is employed to overcome synchronization issues with traditional TDMA. The EMAC-DTDMA's performance is evaluated through simulation using a network simulator tool, considering access delay, energy usage, collision probability, and throughput.

Multi-agent deep reinforcement learning based resource management in SWIPT enabled cellular networks with H2H/M2M co-existence

Machine-to-Machine (M2M) communication is crucial in developing Internet of Things (IoT). As it is well known that cellular networks have been considered as the primary infrastructure for M2M communications, there are several key issues to be addressed in order to deploy M2M communications over cellular networks. Notably, the rapid growth of M2M traffic dramatically increases energy consumption, as well as degrades the performance of existing Human-to-Human (H2H) traffic. Sustainable operation technology and resource management are efficacious ways for solving these issues. In this paper, we investigate a resource management problem in cellular networks with H2H/M2M coexistence. First, considering the energy-constrained nature of machine type communication devices (MTCDs), we propose a novel network model enabled by simultaneous wireless information and power transfer (SWIPT), which empowers MTCDs with the ability to simultaneously perform energy harvesting (EH) and information decoding. Given the diverse characteristics of IoT devices, we subdivide MTCDs into critical and tolerable types, further formulating the resource management problem as an energy efficiency (EE) maximization problem under divers Quality-of-Service (QoS) constraints. Then, we develop a multi-agent deep reinforcement learning (DRL) based scheme to solve this problem. It provides optimal spectrum, transmit power and power splitting (PS) ratio allocation policies, along with efficient model training under designed behavior-tracking based state space and common reward function. Finally, we verify that with a reasonable training mechanism, multiple M2M agents successfully work cooperatively in a distributed way, resulting in network performance that outperforms other intelligence approaches in terms of convergence speed and meeting the EE and QoS requirements.

RIS-Enhanced Secure Transmission in MTC Networks With Finite Blocklength

In this paper, we propose a reconfigurable intelligent surface (RIS) assisted secure finite blocklength transmission framework in machine-type communications (MTC) networks, where the integration of millimeter-wave (mmWave) communication and non-orthogonal multiple access (NOMA) technology is considered to alleviate the problem of insufficient spectrum resources caused by massive MTC devices (MTCDs). For improving the ability of anti-eavesdropping, we aim to maximize the achievable sum secrecy capacity (SC) by jointly optimize the MTCDs’ transmission power, RIS phase coefficient and receive beamforming design. To handle the nonconvexity of the proposed optimization problem, we decouple it into three sub-problems, where the first two are solved by successive convex approximation (SCA) method. A minimum mean squared error successive interference cancellation (MMSE-SIC) scheme is proposed to tackle the receive beamforming problem for uplink NOMA networks. Furthermore, an alternating optimization based joint power, phase, and beamforming allocation (AO-JPPBA) algorithm is developed to implement joint optimization. Simulation results show that: 1) the security performance of the proposed AO-JPPBA is improved by 612.26% than the baseline scheme; 2) the proposed MMSE-SIC beamforming scheme is more effective in improving sum-SC of uplink NOMA networks; 3) RIS’s location has an obvious impact on sum-SC when considering eavesdroppers with strong wiretapping ability.

Zero-Touch Security Management for mMTC Network Slices: DDoS Attack Detection and Mitigation

massive Machine Type Communications (mMTC) network slices in 5G aim to connect a massive number of MTC devices, opening the door for a widened attack surface. Network slices are well isolated, resulting in a low impact on other running slices when attackers control IoT devices belonging to a mMTC network slice (i.e., in-slice attack). However, the impact of the in-slice attacks on the shared infrastructure components with other slices, such as the 5G Core Network (CN), can be harmful, considering the massive number that can be part of mMTC slice. In this paper, we propose a zero-touch security management solution that uses Machine Learning (ML) to detect and mitigate in-slice attacks on 5G CN components, focusing on Distributed Denial of Service (DDoS) attacks. To this aim, we propose: (1) a novel closed-control loop that assists the 5G CN in detecting and mitigating attacks; (2) a ML algorithm that predicts the upper bound of expected MTC devices Attach Requests during a time interval (or an event); (3) a detection algorithm that analyzes an event and uses the ML output to compute a probability that a specific device has participated to an attack; (4) a mitigation algorithm that disconnects and blocks MTC devices suspected to be part of an attack; (5) a Proof of concept implementation on top of a 5G facility.

Preamble slice orderly queue access scheme in cell-free dense communication systems

High reliability applications in dense access scenarios have become one of the main goals of 6G environments. To solve the access collision of dense Machine Type Communication (MTC) devices in cell-free communication systems, an intelligent cooperative secure access scheme based on multi-agent reinforcement learning and federated learning is proposed, that is, the Preamble Slice Orderly Queue Access (PSOQA) scheme. In this scheme, the preamble arrangement is combined with the access control. The preamble arrangement is realized by preamble slices which is from the virtual preamble pool. The access devices learn to queue orderly by deep reinforcement learning. The orderly queue weakens the random and avoids collision. A preamble slice is assigned to an orderly access queue at each access time. The orderly queue is determined by interaction information among multiple agents. With the federated reinforcement learning framework, the PSOQA scheme is implemented to guarantee the privacy and security of agents. Finally, the access performance of PSOQA is compared with other random contention schemes in different load scenarios. Simulation results show that PSOQA can not only improve the access success rate but also guarantee low-latency tolerant performances.

Short Packet-Based Status Updates in Cognitive Internet of Things: Analysis and Optimization

In the cellular Internet of Things (IoT), some machine type communication devices (MTCDs) are used to sample and send status information of monitored targets to help base station (BS) perform monitoring or controlling tasks. However, due to limited spectrum resources, the MTCDs often cannot occupy separate spectrum resources. This paper studies a cognitive IoT where the MTCDs exploit the spectrum resource vacated by cellular users to send status packets to the BS. The timeliness of status updates is characterized by the recently proposed metric, age of information (AoI). In the IoT, the packets sent by the MTCDs are often short, which means it is necessary to analyze the status updates under short packet communication theory (SPC). In this case, we adopt the automatic repeat request protocol to retransmit the erroneous packets, and the preemption policy is exploited to improve the status update performance. We first carefully analyze the evolution of AoI in the cognitive IoT, and adopt a recursive method to obtain the closed-form expression of the average peak AoI. Then, we analyze age-energy tradeoff, and propose a scheme of jointly optimizing transmit power, packet length, and spectrum sharing to reduce the average peak AoI of the network. The simulation results verify the correctness of the theoretical analysis, and show that the proposed scheme outperforms the benchmark schemes.

An Optimized LTE-Based Technique for Drone Base Station Dynamic 3D Placement and Resource Allocation in Delay-Sensitive M2M Networks

Drones are expected to facilitate extending wireless networks’ access for both human users and smart machine-type-communication devices (MTCDs) with strict and diverse quality of service (QoS) requirements. In this paper, we propose an optimal solution for the dynamic placement of an LTE drone-mounted base station to maximize the coverage of the MTCDs deployed over a large geographical area. The resulting technique jointly optimizes the drone's 3D positioning to maximize coverage and allocates the network resources in such a way that gives high priority to the delay-sensitive machine-to-machine (M2M) traffic. This optimization algorithm determines the optimal bound of the solution. Since it cannot be used in real-time operations due to its computational complexity, we also introduce a heuristic technique that offers a near-optimal solution with much reduced complexity. We conduct several simulation evaluations to assess the proposed techniques. These results are compared to those of other drone placement approaches. The comparisons show that the proposed techniques offer significantly better results in the communication coverage while fulfilling the diverse QoS requirements of the deployed M2M network. Moreover, the heuristic-based technique is shown to succeed in finding solutions close to the optimal bound with a considerable reduction of complexity over the exact algorithm.