Machine Type Communication Research Articles

Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways.

LoRaWAN has imposed itself as a promising and suitable technology for massive machine-type communications. With the acceleration of deployment, improving the energy efficiency of LoRaWAN networks has become paramount, especially with the limitations of throughput and battery resources. However, LoRaWAN suffers from the Aloha access scheme, which leads to a high probability of collision at large scales, especially in dense environments such as cities. In this paper, we propose EE-LoRa, an algorithm to improve the energy efficiency of LoRaWAN networks with multiple gateways via spreading factor selection and power control. We proceed in two steps, where we first optimize the energy efficiency of the network, defined as the ratio between the throughput and consumed energy. Solving this problem involves determining the optimal node distribution among different spreading factors. Then, in the second step, power control is applied to minimize the transmission power at nodes without jeopardizing the reliability of communications. The simulation results show that our proposed algorithm greatly improves the energy efficiency of LoRaWAN networks compared to legacy LoRaWAN and relevant state-of-the-art algorithms.

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.

Time-Frequency Warped Waveforms for Well-Contained Massive Machine Type Communications

This paper proposes a novel time-frequency warped waveform for short symbols, massive machine-type communication (mMTC), and internet of things (IoT) applications. The waveform is composed of asymmetric raised cosine (RC) pulses to increase the signal containment in time and frequency domains. The waveform has low power tails in the time domain, hence better performance in the presence of delay spread and time offsets. The time-axis warping unitary transform is applied to control the waveform occupancy in time-frequency space and to compensate for the usage of high roll-off factor pulses at the symbol edges. The paper explains a step-by-step analysis for determining the roll-off factors profile and the warping functions. Gains are presented over the conventional Zero-tail Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing (ZT-DFT-s-OFDM), and Cyclic prefix (CP) DFT-s-OFDM schemes in the simulations section.

Multiple-Dimension Biohealth Data Acquisition System for 5G Telehealthcare Application

A 5G-enabled digital multiple-dimension biohealth data acquisition system prototype integrating an electrochemical dongle, a photochemical dongle and a fluorescence sensor for remote and smart healthcare application is proposed in this paper. The system evolves from the Internet of Medical Things (IoMT) and can automatically collect multiple biochemical indicators including chronic disease markers, renal function indicators, and inflammatory factors from patients' blood samples with rapid speed and high accuracy. Because the system is based on Massive Machine Type Communications (mMTC), it can interact reliably with hospital systems or personal mobile devices with ultra-low latency. To generate a platform for in-time interaction between patients and medical staff, an Application (App) terminal is developed to guarantee real-time medical data sharing and face-to-face remote consultation. This not only promotes the personal proactive health management, but also generates valuable records to hospitals for future prevention of diseases, which is highly practical for tele-health management.

Mobility Assisted Adaptive Clustering Hierarchy for IoT Based Sensor Networks in 5G and Beyond

One of the massive machine type communication (mMTC) applications for monitoring and sensing in 5G cellular network is the Internet of Things (IoT) based wireless sensor network (WSN). Non uniform battery usage by the nodes in these networks often results in creating energy holes or voids in the network making the network disconnected. An effective solution is to deploy multiple mobile nodes throughout the network, however finding optimal path for these mobile nodes is reported to be an NP hard problem. This paper proposes MAACH (Mobility Assisted Adaptive Clustering Hierarchy), an efficient mobility assisted clustering and routing framework for IoT based sensor network in 5G and beyond. Also, an elaborate method to calculate the exact path optimally for multiple mobile nodes is presented to alleviate non uniform energy dissipation of the sensing nodes. Simulation results show that our algorithm effectively finds the optimal trajectory of multiple mobile nodes in a distribute manner and also improves network stability period by 60-70% and the network lifetime by 70-90% across multiple network deployments.

Efficient Massive Machine Type Communication (mMTC) via AMP

We propose efficient and low-complexity multiuser detection (MUD) algorithms for Gaussian multiple access channel (G-MAC) for short-packet transmission in massive machine type communications. To do so, we first formulate the G-MAC MUD problem as a sparse signal recovery problem and obtain the exact and approximate joint prior distribution of the sparse vector to be recovered. Then, we employ the Bayesian approximate message passing (AMP) algorithms with the optimal separable and nonseparable minimum mean squared error (MMSE) denoisers for soft decoding of the sparse vector. The effectiveness of the proposed MUD algorithms for a large number of devices is supported by simulation results. For packets of 8 information bits, while the state-of-the-art AMP with soft-threshold denoising achieves 8/100 of the upper bound at Eb/N0 = 4 dB, the proposed algorithms reach 4/7 and 1/2 of the upper bound.

Statistical Device Activity Detection for OFDM-Based Massive Grant-Free Access

Existing works on grant-free access, proposed to support massive machine-type communication (mMTC) for the Internet of things (IoT), mainly concentrate on narrow band systems under flat fading. In contrast, this paper investigates massive grant-free access in a wideband system under frequency-selective fading. First, we present an orthogonal frequency division multiplexing (OFDM)-based massive grant-free access scheme. Then, we propose two different but equivalent models for the received pilot signal. Specifically, one directly models the received signal for actual devices, whereas the other can be interpreted as a signal model for virtual devices. The two signal models are insightful and essential for designing various device activity detection and channel estimation methods for OFDM-based massive grant-free access. Next, we systematically investigate statistical device activity detection under frequency-selective Rayleigh fading based on the two signal models. In particular, in the case without prior knowledge of device activities, we model device activities as deterministic but unknown binary constants and propose three maximum likelihood (ML) estimation-based device activity detection methods with different detection accuracies and computation times. In the case with prior knowledge of device activities, we model device activities as realizations of Bernoulli random variables with a known joint distribution, which appropriately incorporates the prior knowledge, and propose three maximum a posterior probability (MAP) estimation-based device activity methods, which further enhance the accuracies of the corresponding ML estimation-based methods at the cost of increased computational complexities. The proposed methods can meet diverse practical needs for OFDM-based massive grant-free access.

Variational Bayesian Inference Clustering-Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC

Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates sharing of access resources to enhance access efficiency. However, some challenges remain to be addressed in the receiver design, such as unknown identity of active users and multi-user interference (MUI) on shared access resources. In this work, we deal with the problem of joint user activity and data detection for grant-free random access. Specifically, the approximate message passing (AMP) algorithm is first employed to mitigate MUI and decouple the signals of different users. Then, we extend the data symbol alphabet to incorporate the null symbols from inactive users. In this way, the joint user activity and data detection problem is formulated as a clustering problem under the Gaussian mixture model. Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference based clustering (VBIC) algorithm is developed to solve this clustering problem. Simulation results show that, compared with state-of-art solutions, the proposed AMP-combined VBIC (AMP-VBIC) algorithm achieves a significant performance gain in detection accuracy.

Random Multi-access Scheme Based on Group Testing Theory for Massive Machine-type Communication

Considering emerging massive machine-type communication with very few devices simultaneously transmitting short messages, random multiple access has currently become a research hot spot since the traditional multiple access protocol struggles to work properly. Inspired by the superimposed code, a channel assignment scheme based on non-adaptive group testing theory is proposed to allow multiple users to share a single channel at the same time. A superimposed code with threshold value d, which can recover the original code through the Boolean sum of up to d codes, is constructed by studying the problem of group testing. When the number of active users with a message to be transmitted is no more than the threshold value, active users encode their messages by the superimposed code and multiple active users transmit their messages together by sharing a single channel. The receiver can decode the mixed messages by the disjunction of the superimposed code. We focus on the design of the superimposed code by group testing theory, formulate this problem as the construction of a transversal design, and use the Latin square to form a useful transversal design. Theoretical proof shows that our design works well under limited circumstances where the number of active users does not exceed the threshold value. Additionally, our design can recover the original codes by the mixture of codes, which are more easily than the algorithms based on compressive sensing. Simulation analysis presents that our design performs better than the traditional competitive access protocols in terms of successful probability, throughput, and packet loss rate.

Radio Resource Allocation for 5g Network using Deep Reinforcement Learning

Abstract: 5G is poised to support new emerging service types that help in the realization of futuristic applications. These services include enhanced Mobile BroadBand (eMBB), ultra-Reliable Low Latency Communication (uRLLC), and massive MachineType Communication (mMTC). 5G New Radio (NR) is envisioned to efficiently support ultra-reliable low-latency communication (URLLC) for new services and application with high reliability, availability and low latency such as factory automation and autonomous vehicles. 5G promises massive increases in traffic volume and data rates. Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and, radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless networks. The paper provides a detailed analysis of the impact of various parameters on the system performance, including the number of users, the signal-tointerference-plus-noise ratio, and. The proposed approach has the potential to significantly improve the performance of 5G networks and enable new applications and services that require high data rates, low latency, and reliable communication. We propose an algorithm for data bearers in millimeter wave (mmWave) frequency band.

Review of 5G Wireless Cellular Network on Covid-19 Pandemic: Digital Healthcare & Challenges

Since the coronavirus disease (COVID-19) began in 2020, it has changed the way people live such as social life and healthcare. One of the simplest ways to avoid wide spread of the virus is to minimize physical contact and avoid going to a crowded place. Besides that, it also has prompted countries across the world to employ digital technologies such as wireless communication systems to combat this global crisis. Digital healthcare is one of the solutions that play a crucial role to support the healthcare sector in order to prevent and minimize physical contact through telehealth and telemedicine such as monitoring, diagnosis and patient care. 5G network has the potential to advance digital healthcare along with its key technology such as enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency Communication (URLLC), and massive Machine Type Communication (mMTC). Despite the benefits of digital healthcare by leveraging the 5G technology, there are still challenges to be overcome such as privacy protection issues, 5G deployment and limited connectivity. In this review, it highlights the relevance and challenges of 5G wireless cellular networks for digital healthcare during the COVID-19 pandemic. It also provides potential solutions and future research areas for researchers on 5G to reduce COVID-19 related health risks.

Cooperative-Aware Radio Resource Allocation Scheme for 5G Network Slicing in Cloud Radio Access Networks.

The 5G network is designed to serve three main use cases: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low-latency communications (uRLLC). There are many new technological enablers, including the cloud radio access network (C-RAN) and network slicing, that can support 5G and meet its requirements. The C-RAN combines both network virtualization and based band unit (BBU) centralization. Using the network slicing concept, the C-RAN BBU pool can be virtually sliced into three different slices. 5G slices require a number of Quality of service (QoS) metrics, such as average response time and resource utilization. In order to enhance the C-RAN BBUs utilization while protecting the minimum QoS of the coexisting three slices, a priority-based resource allocation with queuing model is proposed. The uRLLC is given the highest priority, while eMBB has a higher priority than mMTC services. The proposed model allows the eMBB and mMTC to be queued and the interrupted mMTC to be restored in its queue to increase its chance to reattempt the service later. The proposed model's performance measures are defined and derived using a continuous-time Markov chain (CTMC) model and evaluated and compared using different methodologies. Based on the results, the proposed scheme can increase C-RAN resource utilization without degrading the QoS of the highest-priority uRLLC slice. Additionally, it can reduce the forced termination priority of the interrupted mMTC slice by allowing it to re-join its queue. Therefore, the comparison of the results shows that the proposed scheme outperforms the other states of the art in terms of improving the C-RAN utilization and enhancing the QoS of eMBB and mMTC slices without degrading the QoS of the highest priority use case.

New sets of non-orthogonal spreading sequences with low correlation and low PAPR using extended Boolean functions

Extended Boolean functions (EBFs) are one of the most important tools in cryptography and spreading sequence design in communication systems. In this paper, we use EBFs to design new sets of spreading sequences for non-orthogonal multiple access (NOMA), which is an emerging technique capable of supporting massive machine-type communications (mMTC) in 5 G and beyond. In this work, first p-ary complementary sequences are constructed using EBFs and then, these sequences are used to design new sets of non-orthogonal spreading sequence sets having very low coherence and peak to average power ratio (PAPR). The proposed spreading sequence sets are capable of supporting a large number of active devices simultaneously. In fact, for a p-ary spreading sequence set, we theoretically achieve an overloading factor of 2p, where p is an odd prime. Specifically, for $$p=3$$ , we achieve an overloading factor of 6, which cannot be achieved through the existing constructions till date.

Selected Aspects of Non orthogonal Multiple Access for Future Wireless Communications

In this paper overview of recent selected works that deal with novel directions in which Non orthogonal multiple access (NOMA) research is progressing is presented. These include the cell-free NOMA, deep learning extensions and optimizations of NOMA, energy optimization and task offloading with mobile-edge computing, NOMA and physical layer security, as well as virtualization, centralized-RAN aspects. All these are hot issues towards deployments of NOMA in the designs of beyond 5G and 6th generation (6G) wireless communication networks. Even though 3rd Generation Partnership Project (3GPP) has not yet made the decision regarding which NOMA techniques should be adopted, it seems like researchers already indicate clearly that NOMA has important place in the future network deployments based on ultra-density, novel 5G use-cases (massive machine type communications, ultra-reliable low latency communications). This paper highlights the most promising directions for NOMA research. The paper is summarized with necessary steps that are required to get NOMA into practical usage.

Medium Access Control Techniques for Massive Machine-Type Communications in Cellular IoT Networks

A key component of the Internet of things (IoT) ecosystem is wide-area network connectivity, for which cellular network technologies are a promising option through their support of massive machine-type communications (mMTC). However, numerous devices transmitting sporadically small data packets in a highly synchronized way can generate overload on the radio access network. This situation leads to a shortage of resources, especially those associated with the random access procedure for contention, control, and data transmission, causing preamble collision, control message blocking, and data collision. As a result, mMTC traffic can jeopardize the provisioning of quality of service (QoS) to end-devices, decrease the network efficiency, and increase access latency and device energy consumption. This paper proposes medium access control (MAC) techniques for addressing problems related to the support of mMTC in cellularnetworks. First, a solution for allocating control resources with QoS provisioning and access differentiation is provided, including a resource management model, a scheduling algorithm, and a message prioritization policy. Second, a mechanism for reducing data collisions is also proposed. This solution comprises a protocol in which every device with scheduled retransmission uses a probabilistic policy to decide whether to retransmit, a novel method at the device to estimate the number of nodes trying random access, and two different retransmission policies employing this estimation. Results show that the proposals support QoS, decrease access latency, decrease the device energy consumption, and increase resource utilization under massive random access.

Sensing and Secure NOMA-Assisted mMTC Wireless Networks

Throughout this study, a novel network model for massive machine-type communications (mMTC) is proposed using a compressive sensing (CS) algorithm and a non-orthogonal multiple access (NOMA) scheme. Further, physical-layer security (PLS) is applied in this network to provide secure communication. We first assume that all the legitimate nodes operate in full-duplex mode; then, an artificial noise (AN) signal is emitted while receiving the signal from the head node to confuse eavesdroppers (Eve). A convex optimization tool is used to detect the active number of nodes in the proposed network using a sparsity-aware maximum a posteriori (S-MAP) detection algorithm. The sensing-aided secrecy sum rate of the proposed network is analyzed and compared with the sum rate of the network without sensing, and the closed-form expression of the secrecy outage probability of the proposed mMTC network is derived. Finally, our numerical results demonstrate the impact of an active sensing algorithm in the proposed mMTC network; improvement in the secrecy outage of the proposed network is achieved through increasing the distance of the Eve node.

From 5G to beyond 5G: A Comprehensive Survey of Wireless Network Evolution, Challenges, and Promising Technologies

The histrionic growth of mobile subscribers, disruptive ecosystems such as IoT-based applications, and astounding channel capacity requirements to connect trillions of devices are massive challenges of the earlier mobile generations, 5G turned up the key solution. The prime objective of the 5G network is not only to maintain a 1000-fold capacity gain and 10 Giga Bits per second delivered to a single user, but it also assured quality-of-service, higher spectral efficiency, the ultra-reliable and improved battery lifetime of devices and massive machine-type communication (mMTC). The huge traffic load and high amount of resource consumption in 5G applications, augmented reality and virtual reality for magnificent virtual experience, and wireless body area networks will seriously affect the channel capacity of cellular cells and interrupt the admission and service of other users which makes compulsory new means of channel capacity and spectral efficiency enhancement techniques. In this research, we review several key emerging wireless technologies to increase channel capacity and spectral efficiency that will not only lead to improve network performance but also meets the ever-increasing user demands. We investigate various benefits and current research challenges of using these technologies. We analyze massive multi-input multi-output technology (mMIMO) an efficient technique and promising solution for the 5G and Beyond 5G (B5G) networks with several benefits and features. Moreover, this paper will be of vast help to the researchers who will involve advance investigation and also to the wireless network operator industry that is in the search for smooth development of state-of-the-art 5G and B5G networks.

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.

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.

Subterranean mMTC in Remote Areas: Underground-to-Satellite Connectivity Approach

Wireless underground sensor networks (WUSNs) promise to deliver substantial social and economic benefits across different verticals. However, many of the relevant application scenarios are located in remote areas with no supporting infrastructure available. To address this challenge, we conceptualize in this study the underground direct-to-satellite (U-DtS) connectivity approach, implying the reception of the signals sent by the underground massive machine-type communication (mMTC) sensors by the gateways operating on the low Earth orbit satellites. We start by discussing the two alternative architectures for enabling such networks and underline their features and limitations. Then, we employ simulations to model a smart-farming application scenario for studying the feasibility and performance of UDtS mMTC based on LoRaWAN technology, with the focus on the effect of the parameters including the burial depths, soils characteristics, and communication settings. Our results reveal that the LoRa modulation fails to compete with the underground propagation losses and the high packets collision rate. However, the long range-frequency hopping spread spectrum (LR-FHSS) modulation is perspective for U-DtS networks and may enable connectivity for the subsurface nodes located at a depth of several dozens centimeters. Finally, we pinpoint the potential challenges and the research directions for future U-DtS studies.