Due to the growing number of interconnected Internet of Things (IoT) devices along with a greater prevalence of data-intensive 5G applications, it has become increasingly clear that we need efficient methods to share a finite bandwidth among our growing needs. In this work, we examine the coexistence of these two types of applications and propose a method through which to improve its efficiency. To this end, we model the performance of NarrowBand IoT (NB-IoT) latency and 5G New Radio (NR) throughput for IoT and 5G applications performance respectively. Using these models, we formulate two methods through which to statically divide bandwidth between the two networks. Further, we provide a Dynamic Spectrum Sharing (DSS) scheme, allowing NB-IoT users to opportunistically utilize unused 5G spectrum, minimizing unused spectrum. Finally, we reconsider the static bandwidth division methods with our DSS scheme enabled, showing that the addition of our scheme allows more bandwidth to be allocated to the 5G network, resulting in a higher tolerance for variance in the notoriously volatile 5G traffic distributions.

Due to the exponential growth of endpoints in the Internet of Things (IoT), new protocols have been proposed to utilize cellular infrastructures, allowing a large amount of IoT devices to communicate through them. These novel protocols make up the Cellular IoT (C-IoT). In C-IoT, the energy efficiency of endpoints is essential in order to reduce both operational cost and required maintenance. One method of energy reduction is discontinuous reception (DRX). DRX allows a device’s radio frequency (RF) circuitry to turn off for brief periods of time. While off, the device experiences a tradeoff between saving energy and an increase in expected latency, which can be tuned by how long the device spends asleep. In this article, we model DRX as a Markov decision process (MDP). This MDP is solved using a low-complexity “DRX-aware” value iteration algorithm, then verified through simulation and analytical analysis. Further, the energy-latency tradeoff is explored by varying the device’s priority on either energy or latency in addition to varying the traffic intensity. Finally, a method of traffic estimation is applied, and the model’s performance in an environment with time-varying traffic intensity is explored. This approach is compared with a reinforcement learning approach, showing that the traffic estimation approach is better suited to the problem of DRX optimization.

Due to the exponential growth of endpoints in the Internet of Things (IoT), new protocols have been proposed to utilize cellular infrastructures, allowing a large amount of IoT devices to communicate through them. These novel protocols make up the Cellular IoT (C-IoT). In C-IoT, the energy efficiency of endpoints is essential in order to reduce both operational cost and required maintenance. One method of energy reduction is Discontinuous Reception (DRX). DRX allows a device's Radio Frequency (RF) circuitry to turn off for brief periods of time. While off, the device experiences a tradeoff between saving energy and an increase in expected latency, which can be tuned by how long the device spends asleep. In this paper, we model DRX as a Markov Decision Process (MDP). This MDP is solved using a dynamic programming approach and verified through simulation. Further, the energy-latency tradeoff is explored by varying the device's priority on either energy or network performance in addition to varying the traffic intensity.

The coexistence of a wide variety of different applications with diverse Quality of Service (QoS) requirements calls for more sophisticated radio resource scheduling (RRS) in 5G networks compared to previous generations. To address this challenge, a growing body of research formulates the RRS problem as a Markov decision process (MDP) and aims to solve it using deep reinforcement learning (DRL). A key consideration when formulating an MDP is the choice of reward function, which determines the goal of the decision agent. Despite the reward function being a critical component of an MDP, there is currently no systematic study comparing how different reward functions affect network performance. To this end, we carry out a comparative study of the delay and overflow performance using several reward functions that aim to minimize packet delays. Through extensive simulations under different traffic and channel conditions, we identify a reward function that can achieve near optimal delay with up to 55 − 67% fewer packet drops than the other investigated options, and does not require any tuning.

The coexistence of a wide variety of different applications with diverse Quality of Service (QoS) and Quality of Experience (QoE) requirements calls for more sophisticated radio resource scheduling in 5G and beyond (5GB) networks compared to previous generations. To address this challenge, a growing body of research has explored deep reinforcement learning (DRL) to solve the radio resource scheduling problem. In this paper, we review representative literature on the topic of downlink scheduling for 5GB networks using DRL, with emphasis on fine-grained approaches that directly allocate resource blocks (RBs) to user equipments (UEs). We conclude by discussing four ways to improve upon this early-stage research and identify some open problems that must be solved to make DRL a viable solution to the downlink scheduling problem in 5GB networks.

Micro-phasor measurement units$(\mu $-PMUs) are devices that permit monitoring voltage and current in the distribution grid with high accuracy, thus enabling a wide range of smart grid applications, such as state estimation, protection, and control. These devices need to transmit the synchronous measurements of voltage and current, also known as synchrophasors, to the power utility control center at a high rate. The use of wireless networks, such as LTE, to transmit synchrophasor data is becoming increasingly popular. However, synchrophasors are included in small frames and it would be more efficient to use low-power cellular solutions such as LTE cat-M. In this work, we present experimental research on the deployment of a$\mu $-PMU with the ability to connect over a commercial LTE cat-M network. The deployed$\mu $-PMU is built with off-the-shelf hardware, such as Arduino microcontrollers, and is used to transmit data—compliant with the IEEE C37.118.2 standard—at a variable rate from 1 to 80 frames/s. A detailed network performance analysis is carried out to show the suitability of LTE cat-M to support$\mu $-PMU communications. Experimental results on performance indicators, such as delay and jitter, are reported. The effect of the LTE cat-M access mechanism on the time distribution of frame arrivals is also thoroughly analyzed.

5G aims to provide ultra-low latency communication and very high Giga-bit data transmissions. The so called mmWave frequencies are used to achieve such high data rates, thanks to the larger bandwidth availability compared to the frequency spectrum currently used for cellular networks up to LTE. The mmWave spectrum, used for the first time in cellular networks, introduces a number of challenges and imposes a new paradigm in terms of architectures and connection protocols. In particular, additional latency is introduced in the initial attach procedure, which requires extra steps with respect to previous technologies. It is therefore important to thoroughly analyze all components of the initial access (IA) delay in 5G mmWave networks. Among the 5G IA delay components, this work mainly focuses on carrier frequency scanning, cell search, beamforming procedures, derivation of system information, and random access (RACH) procedure. We provide details and a methodology to estimate the delay in these steps of the initial access procedure for 5G mmWave networks.

When using parallel computing to run large-scale simulations, the parts of the system being simulated in different cores or threads often interact and exchange information, constraining the threads to be synchronized. Simulating wireless networks with mobility, when a user equipment (UE) ceases to be served by one Base Station (BS), to be served by a new one, a synchronization point may be required, if the new BS is being simulated in another thread. In a large-scale distributed wireless network with high mobility, the simulation speed-up obtained from multi-threading could be lost to the overhead burden for synchronizing the threads. We propose a heuristic approach to assign BSs to threads in such a way as to minimize the number of synchronization points. In a time interval of the simulation, accumulated interactions are interpreted as growing graphs. Advancing through the simulation time until the number of disconnected graphs is equal to the number of desired threads, showed to be a good strategy to determine the longest intervals that can be simulated without synchronization points while taking advantage of multi-threading. By means of simulation tests we show decrements of up to 100.0 %, in the number of synchronization points, in comparison to those required for the same simulation times when assigning BSs to threads in a random and balanced way.

Recent growth in the Internet of Things (IoT) has been remarkable. Among the solutions to accommodate such a growth is Cellular IoT (C-IoT), comprising a group of technologies extended from legacy cellular infrastructures. One of the key goals of C-IoT technologies is to extend the battery life of UEs (User Equipment) in the network. However, this often comes at the cost of degrading network performance. This work attempts to identify, categorize, and analyze the available literature on this problem. The literature is broadly categorized into three sections: scheduling, data processing, and sleep modes. In each of these sections, the literature is further sub categorized. Finally, a direction for future research is identified and discussed.

The massive introduction of traffic from the Internet of Things (IoT), particularly in smart-city scenarios, needs to be supported by a steady, pervasive and reliable communication infrastructure. Cellular networks, such as Long Term Evolution (LTE) and 5G, are considered a popular solution to support the increasing amount of traffic from IoT, especially in smart cities. However, a massive deployment of IoT devices in existing cellular infrastructures can jeopardize the communication of human users and the overall network performance. In this study, the coexistence of IoT traffic and human users in a smart-city LTE infrastructure was studied through simulation using the SimuLTE software. Real geographical data were employed on the position of LTE base stations and IoT devices, retrieved from publicly available sources. Key network indicators, such as user throughput and cell utilization, were adopted to analyze both network and user performance. Simulation results showed a considerable performance degradation when IoT traffic is introduced into the network.

We consider an LTE downlink scheduling system where a base station allocates resource blocks (RBs) to users running delay-sensitive applications. We aim to find a scheduling policy that minimizes the queuing delay experienced by the users. We formulate this problem as a Markov Decision Process (MDP) that integrates the channel quality indicator (CQI) of each user in each RB, and queue status of each user. To solve this complex problem involving high dimensional state and action spaces, we propose a Deep Reinforcement Learning based scheduling framework that utilizes the Deep Deterministic Policy Gradient (DDPG) algorithm to minimize the queuing delay experienced by the users. Our extensive experiments demonstrate that our approach outperforms state-of-the-art benchmarks in terms of average throughput, queuing delay, and fairness, achieving up to 55% lower queuing delay than the best benchmark.

Distributed energy resources (DERs) often rely on renewable sources whose random power fluctuations bring about voltage disturbances in distribution networks. Under such circumstances, voltage regulation through centralized control of DERs requires reliable detection and coordination mechanisms. This article proposes a new data-driven approach for event-triggered and coordinated control of DERs based on features of voltage disturbances. Synchrophasor datasets are processed to construct disturbance matrices that quantify spatio-temporal features of voltage disturbances. The estimated features are employed in clustering and control of DERs to suppress incipient events before exceeding a critical time. The proposed approach is tested in the IEEE 123-bus network, which has 15 solar photovoltaic sources with battery energy storage systems. The simulation results validate reliability and efficiency of the proposed method, and confirm that feature extraction combined with coordination of DERs can improve reliable and economic operation of distribution grids with renewables.

Electrical distribution network operators require measurements from phasor measurement units (PMUs), micro-PMUs ( μPMUs), and smart meters (SMs) in order to develop efficient distributed management system (DMS) applications. The high data-rate transmission of those measurements is a burden to the underlying communication system and its feasibility needs to be investigated. In this paper, we propose a method to characterize the traffic generated by a DMS application in a smart city scenario and an analysis of its impact on a realistic LTE infrastructure. Real geographic data on the position of SMs, PMUs, and μPMUs are employed to accurately model this DMS application and its generated traffic. A realistic LTE infrastructure is used to measure the load of DMS traffic at each eNodeB. The impact of synchronous and asynchronous DMS traffic on the LTE access is discussed, and bottlenecks in the LTE communication network are identified.

A new mathematical model and methodology are proposed to evaluate the performance of large-scale RF-mesh networks that use time-slotted ALOHA with frequency hopping spread spectrum. This type of architecture is quite usual in advanced metering infrastructures. An analytic formulation for the delay, based on Markov-modulated modeling of the system, is derived. The formula can be extended to evaluate other important performance metrics. The proposed methodology is applied to a large scale network of several thousands of nodes, and numerical results are reported to show the wide variety of performance evaluations that are enabled. The usefulness of the assessment of the feasibility of different types of applications (e.g., smart-metering and sensor networks) is shown. An analysis of the scalability of this methodology and a comparison with simulation results are also presented.