Human-type Communication Research Articles

Dynamic priority-based bandwidth allocation scheme for machine type communications

The burgeoning realm of Machine Type Communications (MTC) has ushered in a paradigm shift in wireless communication, necessitating innovative strategies to efficiently manage diverse application requirements. This paper presents a pioneering Dynamic Priority-based Bandwidth Allocation (DPBA) scheme tailored specifically for MTC scenarios, encompassing both MTC and Human Type Communications (HTC) coexistence. DPBA employs an adaptive framework addressing dynamic MTC traffic, optimizing resource use while meeting latency constraints and sporadic data patterns. By dynamically prioritizing MTC applications based on requirements, the scheme allocates bandwidth for reliable data delivery. DPBA extends to MTC and HTC coexistence, managing diverse quality demands. This underlines its versatility and capacity to serve distinct needs in a shared spectrum. To substantiate its performance, the DPBA scheme is rigorously evaluated against a spectrum of related allocation methods like Proportional Fairness (PF), Static Priority Scheduling (SPS), and the Machine Learning-based Scheme (MLS), DPBA excels in simulations, minimizing packet loss, latency, enhancing throughput and ensuring fairness, surpassing benchmarks. This research underscores the critical importance of tailored bandwidth allocation strategies in advancing MTC and HTC coexistent environments. With its adaptability, efficiency, and remarkable performance, the DPBA scheme emerges as a cornerstone in the trajectory of wireless communication networks, catering to the distinct requirements of both MTC and HTC applications.

Q-Learning Based Load Balancing in Heterogeneous Networks with Human and Machine Type Communication Co-existence

Q-Learning Based Load Balancing in Heterogeneous Networks with Human and Machine Type Communication Co-existence

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

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

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.

Massive Access in 5G and Beyond Ultra-Dense Networks: An MARL-Based NORA Scheme

Power-domain Non-Orthogonal Multiple Access (NOMA) and Ultra-Dense Network (UDN) are promising candidates to cope with the massive access challenge of Machine-Type Communications (MTC). The power level pool is crucial for NOMA to bring performance gains. The existing related literatures rarely consider the power level pool design problem, or only resolve it in the single-cell scenario. However, this problem in multi-cell scenario is more complex and difficult to solve due to the presence of inter-cell interference. Towards this end, we propose a Non-Orthogonal Random Access (NORA) scheme to enable the coexistence of Human-Type Communications (HTC) and MTC for 5G and beyond UDN, where the power level pool design problem in multi-cell scenario is our focus. In order to deal with the complexity caused by multiple optimization objectives and inter-cell interference, we present a Multi-Agent Reinforcement Learning (MARL)-based solution to solve this problem, where each small base station acts as an agent to learn a suitable gap between adjacent power levels. Extensive numerical comparisons demonstrate the superior performances of our proposed scheme in multiple perspectives.

Joint Coherent and Non-Coherent Detection and Decoding Techniques for Heterogeneous Networks

Cellular networks that are traditionally designed for human-type communication (HTC) have the potential to provide cost effective connectivity to machine-type communication (MTC). However, MTC is characterized by unprecedented traffic in cellular networks, thus posing a challenge to its successful incorporation. In this work, we propose a unified framework for amicable coexistence of MTC and HTC. We consider a heterogeneous network where machine-type devices coexist with enhanced mobile broadband (eMBB) devices and propose transceiver techniques that promote efficient signal recovery from these devices. For this, we present an eMBB pilot and MTC data generation strategy that facilitates joint coherent decoding of eMBB data and non-coherent decoding of MTC data. Furthermore, we assess the feasibility of coexistence using receiver operating characteristics, outage probability, and normalized mean square error (NMSE). Our numerical results reveal that a harmonious coexistence of the heterogeneous services can be achieved with properly configured average signal-to-noise ratios and pilot length.

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.

Guest Editorial: Massive Machine-Type Communications for IoT

Future wireless cellular networks are envisioned to not only enhance broadband access for human-centric applications, but also offer massive connectivity across tens of billions of devices for machine-centric applications empowered by Internet of Things (IoT) technologies (e.g., smart factory and smart city). To embrace the forthcoming era of IoT, the fifth-generation (5G) cellular communication standard has already identified massive machine-type communications (mMTC) as a key use case in future networks. Contrary to human-type high-speed communications, the focus of IoT communications is on providing connectivity to tens of billions of devices with high energy efficiency, low latency, and high reliability. Consequently, 5G and beyond cellular communication technologies call for radically innovative solutions to support mMTC. Motivated by the crucial role of mMTC in IoT and the dramatically new challenges arising from mMTC as compared to human-type communication, this Feature Topic aims to bring together researchers, industry practitioners, and individuals working on the related areas to share their new ideas, latest findings, and state-of-the-art results.

Adaptive DRX Method for MTC Device Energy Saving by Using a Machine Learning Algorithm in an MEC Framework

Machine-type communication (MTC) is recognized as an enabling building block for constructing the Internet of Things (IoT) through cellular networks. Since MTC devices (MTCDs) are usually powered by batteries, reducing the energy consumption of an MTCD is an important requirement in the design of an IoT system. The third-generation partnership project (3GPP) specified discontinuous reception (DRX) to save energy in user equipment (UE) by making a UE device sleep whenever it is not involved in data transmission or reception. However, UE is used for human-type communications (HTCs), and MTCs are different from HTCs in many ways, namely, in terms of traffic arrival patterns, mobility, and information priority. Thus, using the DRX developed for UE to save the energy of an MTCD leads to inefficiency in terms of the amount of power saved and the data transmission delay. In the literature, many efforts have been made to dynamically vary the DRX cycle based on traffic activities to reduce the energy consumption of an MTCD. These assume a specific distribution for the traffic arrival process and predict the next packet arrival time to control the DRX cycle. However, since the characteristics of the MTC traffic arrival pattern vary widely according to the types of applications and the network status, previous approaches are limited in the achievable energy efficiency and the range of MTC applications that they can support. To address these issues, we devise adaptive DRX (ADRX) by adopting a machine learning algorithm called bank-of-experts. Without making any assumption regarding the underlying traffic process, ADRX predicts the next packet arrival time using only the observed packet arrival times. Using the predicted next packet arrival time, ADRX dynamically adjusts the sleep interval. In terms of implementation, ADRX is executed in an eNB by following the Multi-access Edge Computing (MEC) framework. Thus, an MTCD avoids consuming the energy required to execute a sophisticated energy-saving algorithm while obtaining the benefits provided by ADRX. Since different IoT services have different requirements for the amount of saved energy and data transmission delay, we design a loss function by combining these two performance metrics so that ADRX can support a wide range of application services. We first analyze the bound of the cumulative regrets of ADRX, and then through simulations, we compare the performance of ADRX with the standard DRX with optimal sleep duration (OSDRX) and a generalized and autonomous DRX (GDRX) scheme. The results show that ADRX outperforms GDRX in terms of the loss function and has almost the same performance as OSDRX.

Distributed Algorithm for Base Station Assignment in 4G/5G Machine-Type Communication Scenarios with Backhaul Limited Conditions.

A progressive paradigm shift from centralized to distributed network architectures has been consolidated since the 4G communication standard, calling for novel decision-making mechanisms with distributed control to operate at the network edge. This situation implies that each base station (BS) must manage resources independently to meet the quality of service (QoS) of existing human-type communication devices (HTC), as well as the emerging machine type communication (MTC) devices from the internet of things (IoT). In this paper, we address the BS assignment problem, whose aim is to determine the most appropriate serving BS to each mobile device. This problem is formulated as an optimization problem for maximizing the system throughput and imposing constraints on the air interface and backhaul resources. The assignment problem is challenging to solve, so we present a simple yet valid reformulation of the original problem while using dual decomposition theory. Subsequently, we propose a distributed price-based BS assignment algorithm that performs at each BS the assignment process, where a novel pricing update scheme is presented. The simulation results show that our proposed solution outperforms traditional maximum signal to interference plus noise ratio (Max-SINR) and minimum path-loss (Min-PL) approaches in terms of system throughput.

Modeling MTC and HTC Radio Access in a Sliced 5G Base Station

In this article, we develop a modeling framework to describe the uplink behavior of radio access in a sliced cell, including most features of the standard 3GPP multiple access procedures. Our model allows evaluating throughput and latency of each slice, as a function of cell parameters, when resources are in part dedicated to individual slices and in part shared. The availability of an accurate model is extremely important for the automated run time management of the cell and for the correct setting of its parameters. Indeed, our model considers most details of the behavior of sliced 5G cells, including Access Class Barring (ACB) and Random Access CHannel (RACH) procedures, preamble decoding, Random Access Response (RAR), and Radio Resource Control (RRC) procedures. To cope with a number of slices devoted to serve various co-deployed tenants, we derive a multi-class queueing model of the network processor. We then present ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) an accurate and computationally efficient technique to derive the performance measures of interest using continuous-time Markov chains, which scales up to a few slices only, and ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) tight performance bounds, which are useful to tackle the case of more than a fistful of slices. We prove the accuracy of the model by comparison against a detailed simulator. Eventually, with our performance evaluation study, we show that our model is very effective in providing insight and guidelines for allocation and management of resources in cells hosting slices for services with different characteristics and performance requirements, such as machine type communications and human type communications.

Cross‐layer resource allocation for critical MTC coexistent with human‐type communications in LTE: a two‐sided matching approach

Cellular systems present one of the most suitable wireless technologies to efficiently serve critical machine-type communications (MTC) that require strict quality-of-service (QoS) guarantees. Therefore, ultra-reliable and low-latency communications is a target use case in the design of the upcoming generations of cellular networks. From the radio resource management perspective, guaranteeing such stringent QoS requirements in long term evolution (LTE) networks is a challenging task, especially in the case of the coexistence of MTC with the human-type communications (HTC). In this study, the authors address the resource allocation and scheduling problem of critical MTC that coexist with HTC in LTE. The optimisation problem is formulated such that the overall system utility is maximised while fulfilling the different QoS demands of the two sets of users. Utilising the effective bandwidth and effective capacity theories, a cross-layer design is developed to guarantee the QoS requirements of the critical MTC. For a computationally-efficient solution of the problem, they formulate it as a two-sided matching process that can be used as a practical scheduling scheme. To this end, they analyse the convergence, stability, and computational complexity of the proposed methods. Results reveal the close-to-optimal performance of the matching-based scheduling scheme and its superiority to other existing techniques.

Massive Access for 5G and Beyond

Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. A typical application of massive access is the cellular Internet of Things (IoT). Different from conventional human-type communication, massive access aims at realizing efficient and reliable communications for a massive number of IoT devices. Hence, the main characteristics of massive access include low power, massive connectivity, and broad coverage, which require new concepts, theories, and paradigms for the design of next-generation cellular networks. This paper presents a comprehensive survey of massive access design for B5G wireless networks. Specifically, we provide a detailed review of massive access from the perspectives of theory, protocols, techniques, coverage, energy, and security. Furthermore, several future research directions and challenges are identified.

Multiple-Association Supporting HTC/MTC in Limited-Backhaul Capacity Ultra-Dense Networks

Coexistence of Human-Type Communications (HTCs) and Machine-Type Communications (MTCs) is inevitable. Ultra-Dense Networks (UDNs) will be efficacious in supporting both types of communications. In a UDN, a massive number of low-power and low-cost Small Cells (SCs) are deployed with density higher than that of the HTC users. In such a scenario, the backhaul capacities constitute an intrinsic bottleneck for the system. Hence, we propose a multiple association scheme where each HTC user associates to and activates multiple SCs to overcome the backhaul capacity constraints mainly encountered in the downlink. In addition, having more active cells allows for more MTC devices to be supported by the network. Using tools from stochastic geometry, we formulate a novel mathematical framework investigating the performance of HTC in both downlink and uplink as well as the uplink MTC. Stretched Exponential Path Loss (SEPL) model is considered to practically reflect the UDN environment. Extensive simulations were conducted to verify the accuracy of the mathematical analysis under different system parameters. Results show the existence of an optimum number of SCs to which an HTC user may connect under backhaul capacity constraints. Besides, the proposed multiple-association scheme improves the performance of MTC in terms of both ASE and density of supported devices.

Coexistence of Human-Type and Machine-Type Communications in Uplink Massive MIMO

In this article, we study the receiver design for the uplink transmission of a human-type communications (HTC) and machine-type communications (MTC) (H&M) coexisted massive MIMO system. We first establish a probability model to characterize the crucial system features including channel sparsity of massive MIMO and signal sparsity of MTC packets. With the probability model, we propose to conduct joint device activity identification, channel estimation, and signal detection. We develop a message-passing-based statistical interference framework to systematically and efficiently solve the joint estimation problem for the H&M coexisted massive MIMO system. Specifically, we propose two receiver schemes based on time-slotted and non-time-slotted grant-free random access for massive machine-type device connectivity. We show that, by exploiting the channel and signal sparsity, our proposed message-passing-based algorithms significantly outperform the conventional training-based approaches in which the device activity state and the channel are estimated by sending pilots prior to data transmission, and are able to approach the genie bound with known signal support in the relatively high signal-to-noise (SNR) regime. Last but not least, we show that there exists a significant gain in terms of the number of admissible devices in the system by allowing H&M coexistence, as compared to orthogonal transmission approaches in which different time/frequency slots are assigned to HTC and MTC services.

A Critical MTC Resource Allocation Approach for LTE Networks With Finite Blocklength Codes

Critical machine-type communications (cMTC) are targeted as a major use case in the design of the fifth generation (5G) cellular systems. In this regard, the third-generation partnership project (3GPP) has introduced several enhancements to evolve the LTE standard to meet the 5G requirements. Shortened transmission time intervals (sTTIs) are considered one of the most significant improvements proposed to satisfy the stringent latency requirements of cMTC. However, this entails several challenges to the resource allocation and scheduling process. In this paper, we address the resource allocation and scheduling of cMTC in LTE networks. The impact on the conventional human-type communications (HTC) is considered while adopting a puncturing scheduling technique. In addition, the reliability of the cMTC is ensured by utilizing the finite blocklength coding analysis to model the transmission errors and the effective bandwidth and effective capacity concepts to guarantee the queuing delay statistics of the cMTC packets. Moreover, we propose matching theory-based computationally efficient algorithms to solve the formulated optimal resource allocation problems with reduced complexity. The proposed methods are analyzed from a practical perspective. Extensive simulations show a close-to-optimal performance of the proposed schemes while outperforming other scheduling algorithms from the literature.

Traffic modelling of smart city internet of things architecture

Internet of things (IoT) is one of the modern technologies, developing existing cellular communication networks to the emerging fifth-generation technology. The traffic modelling of IoT applications is different from the recommended traffic modelling of a Human-type communication. Numerous unstructured data arise from rapid growth of IoT heterogeneous networks and services. One of challenges occurs by diverse IoT unstructured data is a variety of IoT data characteristics, which increase traffic modelling influences. The study of IoT characteristics has a crucial role in modelling data bursts of IoT use cases. This study proposes an enhanced ON/OFF traffic modelling technique to model IoT data characteristics of diverse applications, especially the IoT smart city. A novel modelling technique categorises and analyses characteristics of IoT smart city use case into five major traffic patterns. In this study, realistic smart home networks were built as a part of IoT smart city in an experimental model. Massive traffic profiles are generated from a pilot according to a proposed IoT smart city architecture model. Various traffic profiles of a pilot are modelled into theoretical models by Easy-Fit tool. Traffic modelling concept of a novel technique and their five traffic patterns are proven in the pilot results.

Uplink Performance of NOMA-Based Combined HTC and MTC in Ultradense Networks

In this article, we study the uplink coverage and the uplink network throughput of an ultradense network (UDN) where human-type communication (HTC) users and machine-type communication (MTC) devices coexist. We employ non-orthogonal multiple access (NOMA) radio access to address the massive connectivity requirements of MTC while satisfying the high data rate requirements of HTC. To this end, stochastic geometry is utilized to model the network exploiting the inherent randomness of the proposed scenario. The enormous number of small cells in UDN along with NOMA can provide a common ground to satisfy the diverse requirements of both MTC and HTC. The distinguishing features of both UDNs and MTC are considered in modeling the network where stretched exponential path-loss (SEPL) is used to capture short-link distances. Moreover, the truncated channel inversion power control is employed in both HTC users (HTCUs) and MTC devices (MTCDs) to mitigate the uplink intercell interference. The results show the significant impact of various system parameters on the network performance. Closed-form and easy-computable expressions are derived for the considered performance metrics, and are assessed by Monte Carlo simulations.

A Grant-Free Method for Massive Machine-Type Communication With Backward Activity Level Estimation

Massive machine type communications (mMTC) is one of the three major scenarios of the fifth generation (5G) communication system, and raises new challenges for the development of new radio access technology. Unlike human type communications (HTC), mMTC is typically characterised by a massive number of devices, small-sized packets, low or no mobility, low energy consumption and sporadic transmission, which requires novel solutions. In this paper, we propose the 2-step random access with early data transmission (2-step-EDT) framework. To solve the optimization problem proposed in the framework, we introduce an algorithm, namely, Backward Sparsity Adaptive Matching Pursuit with Checking and Projecting (BSAMP-CP), which jointly conducts the sparsity level estimation, active device detection, channel estimation and data recovery in two phases. Specifically, in the first phase, BSAMP-CP conducts the sparsity level estimation in a backward manner exploiting the data length diversity information. In the second phase, BSAMP-CP jointly conducts activity detection, channel estimation and data recovery, taking the joint sparsity information of pilot and data symbols, the error checking information and the modulation constellation information into account. Furthermore, we provide a theoretical analysis on the convergence of the proposed BSAMP-CP in the noiseless case and the rationale for the improvement yielded by exploiting data length diversity. Simulation results demonstrate the superiority of the proposed solution in comparison to other existing methods.

Grant-Free Non-Orthogonal Multiple Access for IoT: A Survey

Massive machine-type communications (mMTC) is one of the main three focus areas in the 5th generation (5G) of wireless communications technologies to enable connectivity of a massive number of Internet of things (IoT) devices with little or no human intervention. In conventional human-type communications (HTC), due to the limited number of available channel resources and orthogonal resource allocation techniques, users get a transmission slot by making scheduling/connection requests. The involved control channel signaling, negligible with respect to the huge transmit data, is not a major issue. However, this may turn into a potential performance bottleneck in mMTC, where huge number of devices transmit short packet data in a sporadic way. To tackle the limited radio resources and massive connectivity challenges, non-orthogonal multiple access (NOMA) has emerged as a promising technology that allows multiple users to simultaneously transmit their data over the same channel resource. This is achieved by employing user-specific signature sequences at the transmitting devices, which are exploited by the receiver for multi-user data detection. Due to its massive connectivity potential, NOMA has also been considered to enable grant-free transmissions especially in mMTC, where devices can transmit their data whenever they need without the scheduling requests. The existing surveys majorly discuss different NOMA schemes, and exploit their potential, in typical grant-based HTC scenarios, where users are connected with the base station, and various system parameters are pre-defined in the scheduling phase. Different from these works, this survey provides a comprehensive review of the recent advances in NOMA from a grant-free connectivity perspective. Various grant-free NOMA schemes are presented, their potential and related practical challenges are highlighted, and possible future directions are thoroughly discussed at the end.