The ever-increasing number of resource-constrained machine-type communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as enhanced mobile broadband (eMBB), massive machine type communications (mMTCs), and ultra-reliable and low latency communications (URLLCs), the mMTC brings the unique technical challenge of supporting a huge number of MTC devices in cellular networks, which is the main focus of this paper. The related challenges include quality of service (QoS) provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead, and radio access network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy random access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and narrowband IoT (NB-IoT). Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions toward addressing RAN congestion problem, and then identify potential advantages, challenges, and use cases for the applications of emerging machine learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity $Q$ -learning approach in the mMTC scenario along with the recent advances toward enhancing its learning performance and convergence. Finally, we discuss some open research challenges and promising future research directions.

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Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

In futuristic wireless communications, a massive number of devices need to access networks with diverse quality of service (QoS) requirements. It is estimated that the number of connected devices will exceed 20 billions in 2020, and machine-to-machine (M2M) devices will account for nearly half of total connected devices. However, existing cellular systems and wireless standards, designed primarily for human-to-human (H2H) communications focusing on reducing access latency, increasing data rate, and system throughput, are not well suited for M2M communications that require massive connections, diverse QoS requirements, and low energy consumption. Radio resource management (RRM) in conventional H2H communications aims at improving spectrum efficiency and energy efficiency. Similarly, RRM also plays a vital role in M2M communications. In this paper, we make a comprehensive survey on state-of-the-art research activities on RRM in M2M communications. First, we discuss the issues on RRM for machine-type communications in LTE/LTE-A cellular networks including access control, radio resource allocation, power management, and the latest 3GPP standards supporting M2M communications. Acknowledging the fact that a single technology can not support all M2M applications, we discuss RRM issues for unlicensed band radio access technologies in M2M capillary networks, including IEEE 802.11ah, Bluetooth low energy, ZigBee, and smart metering networks. We also survey M2M RRM methods in heterogeneous networks consisting of cellular networks, capillary networks, and ultra dense networks. Finally, we review recent standard activities and discuss the open issues and research challenges.

Radio Resource Management in Machine-to-Machine Communications—A Survey

This paper investigates the discontinuous reception (DRX) mechanism of universal mobile telecommunications system (UMTS). DRX is exercised between the network and a mobile station (MS) to save the power of the MS. The DRX mechanism is controlled by two parameters: the inactivity timer threshold and the DRX cycle. Analytic and simulation models are proposed to study the effects of these two parameters on output measures including the expected queue length, the expected packet waiting time, and the power saving factor. Our study quantitatively shows how to select appropriate inactivity timer and DRX cycle values for various traffic patterns.

Modeling UMTS discontinuous reception mechanism

IoTtalk is a platform for Internet of Things (IoT) device interaction, which nicely integrates a reconfigurable multi-sensor device called MorSensor with the proposed IoT management platform in the network domain. The sensors can be dynamically plugged in/out of a MorSensor device without being turned off, and IoTtalk automatically generates/reuses the application software for these sensors. We propose a dynamic ranging concept that automatically specifies the value range of a sensor so that it can send “meaningful” data to any connected output IoT device. Therefore, a MorSensor device can “talk” to other IoT devices, such as a light bulb or an electric fan. We also show that IoTtalk is a simple yet almost free solution for automatic sensor calibration. Finally, we illustrate that IoTtalk is a powerful tool for developing interactive science experiments.

IoTtalk: A Management Platform for Reconfigurable Sensor Devices

In future mobile networks, more and more machine-type communication (MTC) devices with different service requirements will be deployed. To meet the massive access needs of MTC, this paper develops a cluster-based congestion-mitigating access scheme (CCAS), with aim to mitigate the severe collision of MTC devices (MTCDs) that access to the base station (BS) concurrently. To this end, we first design a modified spectral clustering algorithm to group MTCDs into different clusters based on their locations and service requirements. Then, a device called MTC gateway (MTCG) is chosen by two steps to assist transmitting data for MTCDs in each cluster. In the data transmission process, MTCG is in charge of aggregating packets generated by MTCDs in a cluster and forwarding them to BS when the number of buffered packets reaches a certain threshold. To model the aggregation and forwarding process of each MTCG, we use queuing theory to analyze the access performance in terms of collision probability and access delay. In addition, we also implement simulations to further validate the accuracy of our analytical model and the effectiveness of CCAS. Numerical results, which are consistent with the theoretical values, show that the proposed CCAS can significantly decrease collision probability, and increase the number of successfully received packets of the system without increasing average access delay.

A Cluster-Based Congestion-Mitigating Access Scheme for Massive M2M Communications in Internet of Things

The increasing adoption of Machine-Type Communication (MTC) applications over the Long Term Evolution-Advanced (LTE-Advanced) network brings new challenges for the traditional resources allocation. Particularly, we focus on the smart metering traffic, which is less time-sensitive and transmitting small data at infrequent periods. In this scenario, many MTC devices transmit access requests to the eNodeBat the same time. This situation causes a serious problem of access congestion. Unfortunately, existing scheduling algorithms are not be able to efficiently handle smart metering traffic at a heavily loaded eNodeB. Specifically, there may not be sufficient resource blocks (RBs) available at an eNodeB for smart metering traffic. In this paper, we utilize a combination of Machine-Type Communications and Device-to-Device (D2D)communications to design our group-based uplink scheduling algorithm. In addition, we also take into account the fairness of inter-group and intra-group. Our simulation results demonstrate the benefits of our proposed scheme compared to conventional approaches on resource allocation.

Group-Based Uplink Scheduling for Machine-Type Communications in LTE-Advanced Networks

In Long Term Evolution-Advanced (LTE-A) network, Machine Type Communications (MTC) allows machines to transfer data with almost no human intervention. In MTC, power saving is one of the most important issues. In the meantime, the 3rd Generation Partnership Project (3GPP) proposes Discontinuous Reception (DRX) mechanism to allow user equipments (UEs) to receive data only at specified time slots and turn off the radio module at other time slots. Unfortunately, current DRX mechanism is designed for normal usage of mobile users, not for MTC. In this paper, we proposes Optimistic DRX (ODRX) mechanism to allow more sleep periods for MTC devices. To allow more sleep periods, ODRX considers to release the RRC connection and re-establish the connection when the MTC device is paged. We propose analytical and simulation models for ODRX, and compare with the standard DRX through simulation experiments. The results show that, compared to standard DRX, significant extra power can be saved by sacrificing little extra wake up latency.

Optimistic DRX for machine-type communications

As mobile devices and mobile applications largelygrow, mobile data traffic increases at an unprecedented rate. How to alleviate mobile traffic congestion becomes a widelydiscussed issue. Because that most mobile data is not necessarilyused immediately (e.x. update applications, download a largesize video, etc.), these data are allowed to complete transmissionbefore delay-tolerant deadline. In this paper, we propose residualsize-based mobile data offloading for file transmission on mobiledevices. Our method periodically checks the residual sizeof the file. According to the residual size and delay-tolerantdeadline, our method decides how to use cellular networkand WLAN(Wireless Local Area Network) for file transmission. Besides, we also propose a cost function to investigatetrade-off between deadline and the fraction of the transferredsize using available WLAN. We compare our method withWifflers prediction-based offloading and On-the-spot offloading. The results show that, by the cost function, the trade-off isalways worth in our method compared to Wifflers predictionbasedoffloading and On-the-spot offloading. Our method is morelikely to guarantee file transmission completion before delay-tolerant deadline.

Tsung-Hui Chuang

Papers

Group-Based Uplink Scheduling for Machine-Type Communications in LTE-Advanced Networks

Optimistic DRX for machine-type communications

Residual Size-Based Mobile Data Offloading for File Transmission on Mobile Devices