Machine Type Communication Research Articles

Joint Resource and Power Allocation for Clustered Cognitive M2M Communications Underlaying Cellular Networks

The massive number of Machine-Type Communication Devices (MTCDs) coexisting with Cellular User Equipment (CUE), in addition to the diverse Quality-of-Service (QoS) requirements of M2M communications and cellular communications, present significant implementation challenges due to interference, congestion, and spectrum scarcity. This makes resource allocation an important but challenging problem. In this article, clustered Cognitive Machine-to-Machine (CM2M) communications underlaying cellular networks is proposed to solve this problem. In this system, MTCDs are grouped in clusters based on their spatial locations and communicate with the Base Station (BS) via a Machine-Type Communication Gateway (MTCG). Underlay Cognitive Radio (CR) is employed so that MTCDs within a cluster can share the spectrum of neighbouring CUE. A joint resource-power allocation problem is formulated and solved using a two-phase resource and power allocation scheme. The goal is to maximize the uplink sum-rate of the neighbouring CUE and clustered MTCDs while satisfying interference, power, and minimum data rate constraints. Simulation results are presented which show that the proposed scheme significantly improves the sum-rate of the network compared to other schemes while satisfying the constraints.

A Funnel Fukunaga–Koontz Transform for Robust Indoor–Outdoor Detection Using Channel-State Information in 5G IoT Context

The massive machine-type communication will be at the core of ambient connectivity, requiring for energy-efficient systems. Earlier studies highlighted the efficiency of positioning approaches based on channel-state information (CSI) in different environments. Many works limited the solution assessment to a single room in a fully indoor testbed. This article extends the application of CSI for indoor–outdoor detection on an unprecedented large area and considers mMTC-oriented long-term evolution and fifth-generation Internet of Things in the sub-GHz frequency band. Hinged on a novel long-term evolution protocol dedicated for machine-type communications, the results focus on a unique packet exchange with a single access point to save battery life and simplify deployment. The study evaluates different input features and investigates the target positioning accuracy for multiple unsupervised and supervised dimensionality reduction methods. We present a new dimension reduction scheme consisting of an unsupervised funnel on top of a supervised dimension reduction approach. Results show that the introduced Funnel Fukunaga–Koontz transform outperforms other dimension reduction approaches, regardless of the input features and the number of locations.

Guest Editorial: Artificial Intelligence Enabled Internet of UAVs for Emergency Communications

The unprecedented demands for high-quality and ubiquitous wireless services impose enormous challenges to the existing cellular networks to meet the challenges of connectivity, reliability, etc. The targeted applications in designing the beyond fifth generation (BSG) systems include enhanced mobile broadband (eMBB), ultra-reliable, low latency communications (URLLC), and massive machine-type communications (mMTC). Recently to meet those diverse requirements, the latest technologies like millimeter wave (mmWave) communications, ultra-dense networks (UDN), and massive multiple-input and multiple-output (MIMO) are under consideration. Moreover, we also require an evolution in an architectural platform that performs joint communications, sensing, localization, and computing while ensuring ultra-high throughput, ultra-low latency, and ultra-high reliability for real-time emergency communications.

Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Grant-free access is considered as a key enabler for massive machine-type communications (mMTC) as it promotes energy-efficiency and small signalling overhead. Due to the sporadic user activity in mMTC, joint user identification and channel estimation (JUICE) is a main challenge. This paper addresses the JUICE in single-cell mMTC with single-antenna users and a multi-antenna base station (BS) under spatially correlated fading channels. In particular, by leveraging the sporadic user activity, we solve the JUICE in a multi measurement vector compressed sensing (CS) framework under two different cases, with and without the knowledge of prior channel distribution information (CDI) at the BS. First, for the case without prior information, we formulate the JUICE as an iterative reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -norm minimization problem. Second, when the CDI is known to the BS, we exploit the available information and formulate the JUICE from a Bayesian estimation perspective as a maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> probability (MAP) estimation problem. For both JUICE formulations, we derive efficient iterative solutions based on the alternating direction method of multipliers (ADMM). The numerical experiments show that the proposed solutions achieve higher channel estimation quality and activity detection accuracy with shorter pilot sequences compared to existing algorithms.

An intelligent energy efficient handover mechanism with adaptive discontinuous reception in next generation telecommunication networks

Long Term Evolution (LTE) and LTE-Advanced (A) networks have been introduced to accommodate the User Equipment (UEs) that supports high complex technologies such as OFDM, SC-FDMA, MIMO. Recently, 3GPP presented 5G technology to meet the performance requirement of the International Mobile Telecommunication (IMT)-2020. It supports three specific services like Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), massive Machine Type Communication (mMTC). To increase the capacity of the network, 5G will deploy an ultra-dense network in the metro area. It fulfils the spectral efficiency requirement but at the same time increases handover rate. Due to handover, the power consumption of UEs may increase dramatically. To overcome this issue, LTE-A network adopts Discontinuous Reception (DRX) to conserve battery life of UE. 5G networks will also apply evolved variations of this scheme. In general, DRX is composed of some parameters and these need to be optimized during mobility. This paper presents a method for preservation of the battery life of UEs during mobility. The proposed mechanism comprises of three parts. In the first part, Part 1, the operation of normal DRX during mobility is explored. The next parts, Part 2 and Part 3, utilize the machine learning-based methods for the pre-prediction of Target eNB (TeNB) and provide ranks to the UEs during handover. Further, DRX parameters are adjusted according to the dynamic behaviour of the network. Handover latency, end-to-end delay is improved by 20% and 55%, respectively as compared to the conventional scheme. Further, machine learning based (Part 2) and dynamic adjustment of DRX parameters (Part 3) contribute to enhance the power saving factor by maximum up to 85%, and 33% as compared to state of art mechanism.

Optimization and design of a diffuse optical wireless sensor network.

Wireless sensor networks (WSNs) are currently being deployed in everyday objects to collect and transmit information related to humidity, temperature, heartbeat, motion, etc. Such networks are part of the massive machine-type communication (mMTC) scenario within the fifth/sixth generation of wireless networks. In this paper, we consider the optimization and design of an optical WSN composed of multiple battery-powered sensor nodes based on light-emitting diode transmitters. Extending our previous work, we take into account both line-of-sight and diffuse-light propagation, and show that in indoor scenarios, diffuse radiation can improve link availability under shadowing/blocking and extend battery life. In order to optimize the optical wireless link parameters, we use a machine-learning approach based on a genetic algorithm to ascertain the performance limits of the system. The presented results indicate that the proposed system is a viable wireless option for WSNs within the context of mMTC.

Asymptotic performance of FBMC-PAM systems in frequency-selective Rayleigh fading channels

This paper deals with the performance analysis of the recently introduced FBMC-PAM system, a multicarrier scheme that represents a promising alternative to OFDM to meet the requirements of cognitive radio or machine type communications. An analytical expression of the symbol error-rate (SER) of the single-tap sub-channel (SC) equalizer designed as the inverse of the complex gain that multiplies the symbol of interest at the output of the filter matched to each subcarrier, is derived. Specifically, an asymptotic (i.e., for a large number of subcarriers) analytical expression of the SER of the SC receiver operating in frequency-selective Rayleigh fading channel is obtained and compared via simulation results. Moreover, a simple approximate closed-form expression of the asymptotic SER of the SC receiver is obtained by taking into account, for each active subcarrier, only inter-symbol interference (ISI) terms due to the considered subcarrier and adjacent subcarriers, and, moreover, inter-carrier interference (ICI) terms due to adjacent subcarriers.

Permutation-Based Block Code for Short Packet Communication Systems.

This paper presents an approach to the construction of block error-correcting code for data transmission systems with short packets. The need for this is driven by the necessity of information interaction between objects of machine-type communication network with a dynamically changing structure and unique system of commands or alerts for each network object. The codewords of a code are permutations with a given minimum pairwise Hamming distance. The purpose of the study is to develop a statistical method for constructing a code, in contrast to known algebraic methods, and to investigate the code size. An algorithm for generating codewords has been developed. It can be implemented both by enumeration of the full set of permutations, and by enumeration of a given number of randomly selected permutations. We have experimentally determined the dependencies of the average and the maximum values of the code size on the size of a subset of permutations used for constructing the code. A technique for computing approximation quadratic polynomials for the determined code size dependencies has been developed. These polynomials and their corresponding curves estimate the size of a code generated from a subset of random permutations of such a size that a statistically significant experiment cannot be performed. The results of implementing the developed technique for constructing a code based on permutations of lengths 7 and 11 have been presented. The prediction relative error of the code size did not exceed the value of 0.72% for permutation length 11, code distance 9, random permutation subset size 50,000, and permutation statistical study range limited by 5040.

Active User Detection and Channel Estimation for Massive Machine-Type Communication: Deep Learning Approach

Recently, massive machine-type communications (mMTCs) have become one of key use cases for 5G. In order to support massive users transmitting small data packets at low rates, grant-free (GF) access and nonorthogonal multiple access (NOMA) have been suggested. Since each device transmits information without scheduling in the GF-NOMA systems, the device identification process, called active user detection (AUD), is required at the base station (BS). For the NOMA-based systems, the channel estimation (CE), an operation after the AUD, is a challenging task since multiple devices’ transmit signals and channels are superimposed in the same wireless resources. In this article, we propose a deep learning (DL)-based AUD and CE in the GF-NOMA systems. In our work, DL figures out the direct mapping between the received NOMA signal and the indices of active devices and associated channels using the long short-term memory (LSTM). From numerical experiments, we show that the proposed scheme is effective in handling the AUD and CE in the mMTC environments.

SNR Prediction with ANN for UAV Applications in IoT Networks Based on Measurements.

The 5G deployment brings forth the usage of Unmanned Aerial Vehicles (UAV) to assist wireless networks by providing improved signal coverage, acting as relays or base-stations. Another technology that could help achieve 5G massive machine-type communications (mMtc) goals is the Long Range Wide-Area Network (LoRaWAN) communication protocol. This paper studied these complementary technologies, LoRa and UAV, through measurement campaigns in suburban, densely forested environments. Downlink and uplink communication at different heights and spreading factors (SF) demonstrate distinct behavior through our analysis. Moreover, a neural network was trained to predict the measured signal-to-noise ratio (SNR) behavior and results compared with SNR regression models. For the downlink scenario, the neural network results show a root mean square error (RMSE) variation between 1.2322–1.6623 dB, with an error standard deviation (SD) less than 1.6730 dB. For the uplink, the RMSE variation was between 0.8714–1.3891 dB, with an error SD less than 1.1706 dB.

Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches

Massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) are two key services in fifth-generation (5G) mobile wireless networks. These networks have been developed with extremely high service quality requirements: scalability for mMTC and reliability with low latency for URLLC. Fifth-generation network slicing will play a key role in supporting the distinct requirements of various services. Software-defined networking (SDN), a promising technology for network softwarization, physically separates the network control plane from the data plane by centrally controlling switches with an SDN controller. However, control channel bottleneck and processing delays due to this centralized control may reduce the scalability, reliability, and security of SDN. This paper proposes an SDN framework with programming protocol–independent packet processor (P4) switches (SDNPS), and defines a packet format containing in-band network telemetry data to simultaneously support heavy Internet of Things and URLLC traffic in 5G network slices. The method both satisfies the requirements of mMTC and URLLC and alleviates the load on the SDN controller. P4 is an advanced switch interface technology that provides enhanced stateful forwarding and reveals a persistent state on the SDN data plane. To demonstrate the superiority of SDNPS, simulations are performed on conventional SDNs and SDNPS. SDNPS outperforms the other schemes in terms of average throughput, packet loss ratio, and packet delay for both the mMTC and URLLC network slices.

Buffer-Aided Relaying in NOMA-Based MTC Networks With Finite Blocklength and Statistical QoS Constraints

Machine-type communication (MTC) is one of the main enabling technologies to support various applications with diverse quality of service (QoS) requirements. Finite blocklength transmission has great potential in meeting the strict delay requirements of delay-sensitive MTC devices (MTCDs), while also causing loss of network capacity due to the decoding error probability. Aiming at this problem, we introduce uplink non-orthogonal multiple access (NOMA) and buffer-aided relaying to assist the finite blocklength transmission with delay requirements for improving the achievable effective capacity (EC), which is defined as the maximum short-packet constant arrival rate under specific statistical QoS constraints. To solve the EC maximization problem, we derive the closed-form expression of time allocation coefficient. Then we establish a concave lower bound of EC using successive convex approximation (SCA) for power allocation of MTCDs, and formulate a non-cooperative game based distributed power allocation algorithm for relay. Furthermore, a joint time and power allocation (JTPA) algorithm is proposed to implement joint resource allocation. Simulation results show that under finite blocklength and statistical QoS constraints, adopting buffer-aided relaying can improve EC by 41.82% compared with no-buffer relaying. Moreover, the achievable EC of JTPA algorithm is only 3.12% lower than that of exhaustive search while reducing complexity.

Data-Driven Random Access Optimization in Multi-Cell IoT Networks Using NOMA

Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks, where IoT devices contend for accessing the shared wireless channel using an adaptive p-persistent slotted Aloha protocol. To enable a capacity-optimal network, a novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity. It is shown that the network optimization objective is high dimensional and mathematically intractable, yet it admits favourable mathematical properties that enable the design of efficient data-driven algorithmic solutions which do not require a priori knowledge of the channel model or network topology. A centralized model-based algorithm and a scalable distributed model-free algorithm, are proposed to optimally tune the transmission probabilities of IoT devices and attain the maximum capacity. The convergence of the proposed algorithms to the optimal solution is further established based on convex optimization and game-theoretic analysis. Extensive simulations demonstrate the merits of the novel formulation and the efficacy of the proposed algorithms.

Proactive Traffic Offloading in Dynamic Integrated Multisatellite Terrestrial Networks

The integration between the satellite network and the terrestrial network will play a key role in the upcoming sixth-generation (6G) of mobile cellular networks thanks to the wide coverage and bandwidth offered by satellite networks. To leverage this integration, we propose a proactive traffic offloading scheme in an integrated multi-satellite terrestrial network (IMSTN) that considers the future networks’ heterogeneity and predicts their variability. Our proposed offloading scheme hinges on traffic prediction to answer the stringent requirements of data-rate, latency and reliability imposed by heterogeneous and coexisting services and traffic namely enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable low latency communication (URLLC). However, the fulfilment of these requirements during offloading in dynamic IMSTN comes at the expense of significant energy consumption and introduces inherently supplementary latency. Therefore, our offloading scheme aims to balance the fundamental trade-offs first between energy consumption and the achievable data-rate and second between energy consumption and latency while meeting the respective needs of the present traffic. Our findings prove the importance of the cooperation between the multi-satellite network and the terrestrial network conditioned by traffic prediction to enhance the performance of IMTSN in terms of latency and energy consumption.

Cluster-Aided Collision Resolution Random Access in Distributed Massive MIMO Systems

Massive machine-type communication (MTC) is expected to be one of the key concerns in future B5G and 6G. However, the explosive growth of MTC devices (MTDs) in Internet of Things (IoT) poses great challenges for the initial access procedure. To address this issue, we propose a cluster-aided collision resolution random access (CACRRA) protocol for grant-based random access (RA) in the distributed massive multiple input multiple output (mMIMO) systems, which takes full advantage of the spatial multiplexing provided by distributed mMIMO. By exploiting the difference of large-scale fading coefficients between different access points (APs) and MTDs, we further propose an exclusive clustering algorithm to cluster APs for each pilot. Moreover, the pilot collision resolution is performed distributedly in each cluster by leveraging the channel hardening and the favorable propagation of mMIMO channels, which achieves a large reuse factor of pilots. Simulation results show that the proposed CACRRA can efficiently resolve the pilot collision and significantly improve the access performance. Specifically, the proposed protocol can improve the RA throughput more than nine times compared to the conventional protocol in incredibly crowded scenarios.

Access Control for Ambient Backscatter Enhanced Wireless Internet of Things

Beyond fifth-generation (B5G) and future networks face the challenges of spectral, energy and cost efficiency for large-scale machine-type communications. Recently, emerging ambient backscatter communication (AmBC) technology provides a promising paradigm for the development of green Internet of Things (IoT) networks in B5G era. Unlike existing work on AmBC which mostly focuses on physical layer with relatively ideal model, i.e., classic three-nodes model composed of radio frequency (RF), backscatter device (BD) and IoT device, this paper studies the access control strategy, including coefficient design and device association, from the perspective of networking. Assuming whether channel information is available a-priori, we propose online and offline access control strategies respectively. For offline access control strategy, we leverage the difference of two convex functions approximation (DCA) and dual decomposition to transform the non-concave optimization problem into the concave one, and design a distributed access control strategy called DCA-S. Furthermore, for the case that channel information is assumed to be unknown in advance due to the dynamics of primary and backscatter networks, we design a combinatorial multi-armed bandit (CMAB) access control strategy (CMAB-S). Numerical results show that the proposed DCA-S and CMAB-S can achieve significant performance improvement of the system in both cases of available and unavailable channel information compared with benchmark schemes.

Compressed Random Access for Noncoherent Massive Machine-Type Communications With Energy Modulation

Massive machine-type communications (mMTC) for the Internet of Things (IoT) are expected to support a large number of devices/users for short packet transmissions with low complexity and low energy consumption. By utilizing the simple while efficient noncoherent energy-based transmission scheme, this work aims to jointly detect the user activity and the desired data for mMTC. First, by exploiting the sparse characteristics of the user activity, approximation message passing (AMP) algorithm is proposed to eliminate the multi-user interference, and a denoiser is designed to minimize the mean-squared error (MSE) of the transmitted signals. Then, maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">posteriori</i> (MAP) criterion is adopted to approximately detect the user activity and the desired data. By minimizing the symbol error probability of the above two-step algorithm, the power constellation for each user is designed, and it is shown to be asymptotically optimal as the number of the receiver antennas goes to infinity. Finally, simulation results reveal that the proposed noncoherent scheme outperforms the coherent one in the low SNR regime and for short packet transmissions.

Group-Based Data Offloading Techniques Assisted by D2D Communication in 5G Mobile Network

Machine type communication devices proposed as one of the substantial data collections in the 5G of wireless networks. However, the existing mobile communication network is not designed to handle massive access from the MTC devices instead of human type communication. In this context, we propose the device-to-device communication assisted a mobile terminal (smartphone) on data computing, focusing on data generated from a correlated source of machine type communication devices. We consider the scenario that the MTC devices after collecting the data will transmit to a smartphone for computing. With the limitation of computing resources at the smartphone, some data are offloaded to the nearby mobile edge-computing server. By adopting the sensing capability on MTC devices, we use a power exponential function to compute a correlation coefficient existing between the devices. Then we propose two grouping techniques K-Means and hierarchical clustering to combine only the MTC devices, which are spatially correlated. Based on this framework, we compare the energy consumption when all data processed locally at a smartphone or remotely at mobile edge computing server with optimal solution obtained by exhaustive search method. The results illustrated that; the proposed grouping technique reduce the energy consumption at a smartphone while satisfying a required completion time.

Technologies Trend in the Integration of 5G Networks and IoT in Smart Cities: A Survey

This paper presents a survey of 5G and suitable concerns in the planning and development of communication model infrastructure for the deployment of Smart Cities, also called Intelligent Cities or Digital Cities. The paper first provides an overview of 5G technology, its evolution, and how it revolutionised worldwide communication. The drivers of 5G, such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC), will enable 5G to deliver higher speeds compared to the previous 4G and 3G generations and enable service providers to provide massive bandwidth catering to data-intensive applications such as Smart Cities. Besides human-computer interaction (HCI), smart cities include machine type communication (MTC), in which massive communication takes place between devices and the information infrastructure through gateways. A 5G network can successfully integrate the diversified service requirements and demands of smart city applications. Smart cities require massive IoT communication built into 5G. Multiple technologies are deployed in smart cities to enhance the standard of living for people. Citizens are provided with better health services, improved transportation, efficient energy, quality education, and safety and security to public. In this survey paper we present the contribution of 5G in the deployment of smart city applications and its pillars that play a very important role in the construction of smart cities. Better healthcare systems have been developed that integrate new communication technologies and conventional health care procedures. One of the vital segments of a smart city is its transportation system, which affects the environs and health of people. The energy efficiency of smart cities can be greatly improved by 5G by using smart appliances. Smart metres deliver real-time data about the energy consumed. Users can save energy and money. Similarly, it can assist the water board team by giving real-time statistics regarding water consumption to the consumers. Almost all the smart city infrastructure mentioned above has different requirements for delay, movement, dependability, and resilience of the network. Incorporating diverse applications into a communication network is a difficult chore. 5G has been designed for forward compatibility to support future amenities not known today. The paper outlines diverse requirements for smart cities based on application and technology requirements. Lastly, the supporting technologies are highlighted for the forthcoming integration of smart cities and communication infrastructure.

A layered grouping random access scheme based on dynamic preamble selection for massive machine type communications

Massive machine type communication (mMTC) has been identified as an important use case in Beyond 5G networks and future massive Internet of Things (IoT). However, for the massive multiple access in mMTC, there is a serious access preamble collision problem if the conventional 4-step random access (RA) scheme is employed. Consequently, a range of grantfree (GF) RA schemes were proposed. Nevertheless, if the number of cellular users (devices) significantly increases, both the energy and spectrum efficiency of the existing GF schemes still rapidly degrade owing to the much longer preambles required. In order to overcome this dilemma, a layered grouping strategy is proposed, where the cellular users are firstly divided into clusters based on their geographical locations, and then the users of the same cluster autonomously join in different groups by using optimum energy consumption (Opt-EC) based K-means algorithm. With this new layered cellular architecture, the RA process is divided into cluster load estimation phase and active group detection phase. Based on the state evolution theory of approximated message passing algorithm, a tight lower bound on the minimum preamble length for achieving a certain detection accuracy is derived. Benefiting from the cluster load estimation, a dynamic preamble selection (DPS) strategy is invoked in the second phase, resulting the required preambles with minimum length. As evidenced in our simulation results, this two-phase DPS aided RA strategy results in a significant performance improvement