mMTC Devices Research Articles

A Learning-Based Energy-Efficient Device Grouping Mechanism for Massive Machine-Type Communication in the Context of Beyond 5G Networks

With the increasing demand for high data rates, low delay, and extended battery life, managing massive machine-type communication (mMTC) in the beyond 5G (B5G) context is challenging. MMTC devices, which play a role in developing the Internet of Things (IoT) and smart cities, need to transmit short amounts of data periodically within a specific time frame. Although blockchain technology is utilized for secure data storage and transfer while digital twin technology provides real-time monitoring and management of the devices, issues such as constrained time delays and network congestion persist. Without a proper data transmission strategy, most devices would fail to transmit in time, thus defying their relevance and purpose. This work investigates the problem of massive random access channel (RACH) attempts while emphasizing the energy efficiency and access latency for mMTC devices with critical missions in B5G networks. Using machine learning techniques, we propose an attention-based reinforcement learning model that orchestrates the device grouping strategy to optimize device placement. Thus, the model guarantees a higher probability of success for the devices during data transmission access, eventually leading to more efficient energy consumption. Through thorough quantitative simulations, we demonstrate that the proposed learning-based approach significantly outperforms the other baseline grouping methods.

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.

Energy‐efficient machine type communication in HetNets under statistical QoS guarantees

AbstractThere has been an unprecedented rise in the Internet‐of‐Things (IoT) in the past decade. One of the underlying reasons for this exponential growth is that IoT allows for massive machine‐type communication (mMTC), which facilities machine‐to‐machine (M2M) communication with minimal human intervention. However, mMTC devices are characterized by resource and energy limitations, and thus require powerful and efficient power allocation methods that do not jeopardize quality‐of‐service (QoS) performance. In this work, a statistical QoS analysis of M2M communication in heterogeneous wireless networks is performed. We propose a communication mode selection (CMS) scheme that selects an uplink transmission link from the available candidate links based on pathloss criteria. The closed‐form expressions for the Effective Capacity and effective energy efficiency (Effective‐EE) of the M2M communication in the heterogeneous wireless network are derived and verified via simulations. The optimal fixed transmission rate that maximizes the Effective Capacity and Effective‐EE is also analytically analyzed. Furthermore, four scenarios are adopted to study the overall Effective Capacity and Effective‐EE of a two‐hop M2M communication link where the transmitter and access point operate under independent and identical QoS constraints. This article also investigates the impact of circuit power and the efficiency of the power amplifier on the achievable Effective‐EE of the communication link.

An Efficient Group-based Service Authentication and Session Key Negotiation Scheme for mMTC Devices in 5G

An Efficient Group-based Service Authentication and Session Key Negotiation Scheme for mMTC Devices in 5G

On the Security of a Lightweight and Secure Access Authentication Scheme for Both UE and mMTC Devices in 5G Networks

The Internet of Things (IoT) and 5G networks play important roles in the latest systems for managing and monitoring various types of data. These 5G based IoT environments collect various data in real-time using micro-sensors as IoT things devices and sends the collected data to a server for further processing. In this scenario, a secure authentication and key agreement scheme is needed to ensure privacy when exchanging data between IoT nodes and the server. Recently, Cao et al. in “LSAA: A lightweight and secure access authentication scheme for both UE and mMTC devices in 5G networks” presented a new authentication scheme to protect user privacy. They contend that their scheme not only prevents various protocol attacks, but also achieves mutual authentication, session key security, unlinkability, and perfect forward/backward secrecy. This paper demonstrates critical security weaknesses of their scheme using informal and formal (mathemati) analysis: it does not prevent a single point of failure and impersonation attacks. Further, their proposed scheme does not achieve mutual authentication and correctness of security assumptions, and we perform simulation analysis using a formal verification tool to its security flaws. To ensure attack resilience, we put forward some solutions that can assist constructing more secure and efficient access authentication scheme for 5G networks.

Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices

Efficient utilisation of adaptive modulation and coding ensures the quality transmission of information bits through the significant reduction in bit error rate (BER). Channel prediction using parametric estimation is not efficient for massive machine-type communication (mMTC) devices under the 5G New Radio (NR). In this paper, we have proposed a channel prediction scheme based on a deep learning (DL) algorithm possessed by parametric analysis. In deep learning, the pipeline methodology is used along with the image processing technique to predict the channel condition for optimal selection of the adaptive modulation and coding (AMC) profile. The deep learning-based pipelining approach utilises image restoration (IR) and image super-resolution (SR). The super-resolution method is used to de-noise the low-pixel 2-D image that is obtained from the parametric value of the beacon to predict the channel condition. The estimation results are compared with the conventional minimum mean square error (MMSE) and an approximation to the linear MMSE (ALMMSE) method, which is obtained through channel state information (CSI). The comparison results show that the parametric-enabled deep learning approach is superior, especially in poorer channel conditions. The performance of BER through parametric estimation along with the DL approach is ~66% more efficient as compared to the conventional MMSE method for BPSK mapping.

A Novel Multiple Access Scheme for 6G Assisted Massive Machine Type Communication

The diverse Internet-of-things (IoT) applications involve massive machine type communication (mMTC) with large number of communicating nodes. The energy and resource overhead owing to shorter battery lives and limited network resources are the main challenges of mMTC in IoT. To support this massive random access and to overcome these challenges, future wireless networks are envisioned with collision resolution capabilities, reduced latency and ultra-high reliability. This paper presents a novel scheme for 6G assisted massive machine type communication (mMTC) with collision resolution capabilities and reduced latency. A cell-free network model is proposed in which the communication of mMTC devices is assisted through access points (APs) cooperation. The performance of proposed network is evaluated for achieved signal-to-noise ratio (SNR) and accuracy of node detection for different node locations, fading parameters and cell-areas. With increase in cell area and shadow fading, the SNR achieved by active nodes decreases. Further, an algorithm is proposed in the paper that makes AP clusters for serving the communicating nodes. The tendency of network for successful node detection is determined for different cluster sizes with different activation probabilities. In the end, the proposed algorithm is compared with two other schemes, namely, random clustering scheme and nearest-neighbour clustering scheme. It is found that the proposed approach achieves best performance in the detection of active communicating nodes in the system model with 9.09% improvement as compared to random scheme and 1.1% as compared to nearest-neighbour scheme.

Distance-Aware Relay Selection in an Energy-Efficient Discovery Protocol for 5G D2D Communication

Massive machine-type communications (mMTC) is one of the main services delivered by the fifth Generation (5G) mobile network. The traditional cellular architecture where all devices connect to the base station is not energy efficient. For this reason, the use of device-to-device (D2D) communications is considered to reduce the energy consumption of mMTC devices. The main idea is to use nearby user equipment (UE) as a relay and establish with it D2D communication. However, the relay selection process also consumes energy, and this consumption can be significant compared to the energy consumed during the data transmission phase. In this paper, we propose a distributed energy-efficient D2D relaying mechanism for mMTC applications. This mechanism favors the selection of the UEs with low path loss with the mMTC device. Through mathematical analysis and simulations, we show that our mechanism allows a reduction of the total energy consumption of mMTC devices (up to 75% compared to direct transmission) when they have an unfavorable link budget. Moreover, our mechanism achieves almost constant energy consumption for a large range of UE densities and distances between the mMTC device and the base station.

LSAA: A Lightweight and Secure Access Authentication Scheme for Both UE and mMTC Devices in 5G Networks

As a development of the next generation of mobile communication networks and systems (5G), the Third-Generation Partnership Project (3GPP) committee has standardized a new 5G authentication and key-agreement (5G-AKA) protocol to ensure the access security of a mobile equipment. However, there are still some security vulnerabilities in the 5G-AKA protocol, and there is no authentication protocol proposed for massive device concurrent connection by the 3GPP working groups. In this article, we propose a novel lightweight and secure access authentication scheme named lightweight secure access authentication (LSAA) that contains two lightweight extended Chebyshev chaotic maps-based access authentication protocols for two types of 3GPP standard mobile devices: 1) common user equipment (UE) and 2) massive machine-type communication (mMTC) devices. Our proposed protocols can achieve several security functionalities, including mutual authentication, session-key establishment, identity privacy protection, and perfect forward/backward secrecy (PFS/PBS). In addition, the proposed protocols are lightweight in nature compared with the 5G-AKA. In order to comprehensively and accurately evaluate LSAA, we carry out formal security analysis by employing two formal verification tools Proverif and Scyther, and informal security analysis on the proposed protocols. We further evaluate the performance of the proposed protocols with regard to authentication signaling cost, authentication communication cost, authentication computational cost, and authentication storage cost. The security evaluation and performance analysis results show that our proposed protocols can provide advanced security and high efficiency.

Interference-Aware Subcarrier Allocation for Massive Machine-Type Communication in 5G-Enabled Internet of Things.

Massive machine-type communication (mMTC) is investigated as one of three typical scenes of the 5th-generation (5G) network. In this paper, we propose a 5G-enabled internet of things (IoT) in which some enhanced mobile broadband devices transmit video stream to a centralized controller and some mMTC devices exchange short packet data with adjacent devices via D2D communication to promote inter-device cooperation. Since massive MTC devices have data transmission requirements in 5G-enabled IoT with limited spectrum resources, the subcarrier allocation problem is investigated to maximize the connectivity of mMTC devices subject to the quality of service (QoS) requirement of enhanced Mobile Broadband (eMBB) devices and mMTC devices. To solve the formulated mixed-integer non-linear programming (MINLP) problem, which is NP-hard, an interference-aware subcarrier allocation algorithm for mMTC communication (IASA) is developed to maximize the number of active mMTC devices. Finally, the performance of the proposed algorithm is evaluated by simulation. Numerical results demonstrate that the proposed algorithm outperforms the three traditional benchmark methods, which significantly improves the utilization of the uplink spectrum. This indicates that the proposed IASA algorithm provides a better solution for IoT application.

Low PAPR Filter Bank Single Carrier for 5G mMTC

Asynchronous transmission for 5G massive machine type communication (mMTC) reduces power consumption and provides longer battery life of mMTC devices compared to synchronous transmission because of less signaling interaction between the base station and mMTC devices. To reduce frequency leakage from other user devices, several waveforms based on modification on orthogonal frequency division multiple (OFDM) with low out-of-band (OOB) emission property has been proposed in 5G new radio. However, those waveforms are typically of high peak to average power ratio (PAPR) and it has been shown for those waveforms there is OOB emission regrowth for mMTC devices caused by nonlinear power amplify (PA). We propose a low PAPR filter bank single carrier frequency division multiple access (SC-FDMA) waveform, called LP-FBSC, which helps to resist OOB emission regrowth caused by nonlinear PA. Two filters are applied in one stream to achieve low PAPR and low OOB emission performance, where one filter is poly phase network (PPN) structure in time domain for low OOB emission purpose, and the other is applied at frequency domain with element-wise multiplication for low PAPR purpose. In addition, the proposed method introduces multiple streams for purpose of even further reduced PAPR, where the modulated data and filter coefficients of the other streams are determined from those of first stream. For Pi/2-BPSK and Pi/4-QPSK modulation, the PAPR for LP-FBSC can be as low as 2.6 dB and 4.2 dB while low OOB emission property being maintained.

Low Complexity Interference Cancellation Method for mMTC Devices

For the massive Machine Type Communications (mMTC) service, a large number of devices can be connected simultaneously and the repetition signals that contain the same data can be transmitted to increase the received signal strength. Therefore, a multi-user transmission is required because the BS supports many devices by using limited radio resources. To manage inter-user interference (IUI) by multi-user transmission, a low-cost receiver that can cancel IUI is required because conventional MTC devices are low cost and low complexity. In this paper, a simple IUI cancellation method for the mMTC devices is proposed. The proposed method can measure IUI by using blank resources that are the parts of repetition signals, and it cancels IUI by subtracting the measured IUI at a signal-level. After canceling IUI, the mMTC device combines the signals to increase the signal-to-noise ratio (SNR). The block error rate (BLER) performances are investigated in a time-invariant channel and a time-varying channel, and the simulation results show that the proposed method can manage IUI successfully.

Cluster-Based Group Paging for Massive Machine Type Communications Under 5G Networks

Machine-type communications (MTCs) have been envisaged to play a key role within the future 5G cellular network. To handle the massive MTCs (mMTCs) and alleviate the congestion of the radio access network, the group paging (GP) scheme was proposed by the third-generation partnership project. However, its performance quickly decreases in the face of massive simultaneous channel accesses. In this paper, we propose a two-phase cluster-based GP (CBGP) scheme. First, owing to the advantages of low-cost, high-access capacity and handy deployment, IEEE 802.11ah is introduced to increase the capability of coping with massive access attempts. The separation of inner cluster data collection and header-based data transmission phases greatly alleviates the access congestion of cellular networks, reducing the access delay and increasing the successful access probability for mMTC devices. Besides, we also derive mathematical models of the CBGP scheme in terms of the successful access probability and average access delay. Moreover, effects from different numbers of clusters on the performance of the CBGP scheme are investigated and the optimal number of clusters is also derived, adaptive to different access scales. At last, numerical results are presented to validate the accuracy of our analytical models, demonstrate the effectiveness of the proposed CBGP scheme, and verify the optimal number of clusters, providing insights for the coming 5G cellular system design.