The fifth generation (5G) cellular network is an enabler of connectivity that promises to facilitate technical innovations in many industry verticals. In the healthcare sector, the rapidly evolving medical extended reality (MXR) applications can benefit from 5G connectivity to deliver virtual content while achieving seamless mobility. In this paper, we address the challenges of testing 5G connectivity in MXR applications by showcasing an example MXR application setup (i.e., a virtual clinical room) and incorporating it into a 5G network testbed. This paper provides a comprehensive evaluation of the connectivity requirements and the potential impact of 5G network configurations on MXR by collecting communication traffic between different network entities. Experimental results offer insight into the traffic behavior of the MXR application and its supporting connectivity infrastructure.

The fifth generation of cellular network (5G) can facilitate in-ambulance patient monitoring, diagnosis, and treatment by a remote specialist. However, 5G coverage and link quality can vary in time and location. The ambulance route selection can help meet the communication requirements of the in-ambulance applications. In this paper, we propose an innovative ambulance route selection framework which combines the communication requirements along with the network coverage and resources. The framework leverages the minimization of drive test (MDT) data to estimate the network coverage along the ambulance routes. To address the uneven distribution of location-based user-generated MDT data, we examine the performance and trustworthiness of several interpolation techniques to enrich the global MDT map for route selection. A simulated analysis shows that the proposed framework can dynamically adapt to varying application requirements as well as rapidly changing network conditions such as outages. Results also reveal that nearest neighbor and kriging interpolation techniques help complement the proposed framework by addressing the data sparsity problem.

For accurate characterization and evaluation of wireless mechatronic systems, effective modeling of wireless communication channels is of paramount importance, especially to simulation-oriented methods. Conventional simulation methods employ mathematical models to abstract details of prototype channels. Although such mathematical models often have rigorous theoretical underpinnings, they can be weak in capturing complex environmental characteristics and complex forms of diversity that are exhibited in industrial communication environments. To address this problem, we develop, in this paper, a new approach to deriving effective simulation models for industrial communication channels. Our approach involves field measurements from actual wireless mechatronic environments together with feature extraction from the measurements, and data-driven classification based on the extracted features. Our approach leads to a general framework for simulating wireless mechatronic systems in a way that realistically incorporates the complex channel characteristics of these systems.

Efficient and accurate simulation methods are of increasing importance in the design and evaluation of factory communication systems. Model-based simulation methods are based on formal models that govern the interactions between components and subsystems in the systems that are being simulated. The formal models facilitate systematic integration across the system, and enable powerful methods for analysis and optimization of system performance. However conventional simulation approaches utilize communication channel models that do not fully reflect the characteristics and diversity of industrial communication channels. To help bridge this gap, we develop in this paper new methods for channel model construction for link-layer simulation that systematically incorporate field measurements of wireless communication channels from industrial networks, and derive corresponding channel modeling library components. The generated library components capture channel characteristics in the form of lookup tables, which can be flexibly integrated into system-level simulators or co-simulation tools. We integrate our new table-generation methods into a model-based co-simulator that jointly simulates the interactions among process flows, physical layouts of workcells, and communication channels in factory systems that are integrated with wireless networks. Experimental results using our lookup-table-augmented co-simulator demonstrate the utility of the proposed methods for flexibly and accurately integrating realistic industrial network channel conditions into simulation processes.

The wireless devices in cyber-physical systems (CPS) play a primary role in transporting the information flows within such systems. Deploying wireless systems in industry has many advantages due to lower cost, ease of scale, and flexibility due to the absence of cabling. However, industrial wireless deployments in various industrial environments require having the proper models for industrial wireless channels. In this work, we propose and assess an algorithm for characterizing measured channel impulse response (CIR) of time-varying wireless industrial channels. The proposed algorithm performs data processing, clustering, and averaging for measured CIRs. We have deployed a dynamic time warping (DTW) distance metric to measure the similarity among CIRs. Then, an affinity propagation (AP) machine learning clustering algorithm is deployed for CIR grouping. Finally, we obtain the average CIR of various data clusters as a representation for the cluster. The algorithm is then assessed over industrial wireless channel measurements in various types of industrial environments. The goal of this work is to have a better industrial wireless channels representation that results in a better recognition to the nature of industrial wireless communications and allows for building more effective wireless devices and systems.

Industrial Internet of Things (IIoT) applications, featured with data-centric innovations, are leveraging the observability, control, and analytics, as well as the safety of industrial operations. In IIoT deployments, wireless links are increasingly used in improving the operational connectivity for industrial data services, such as collecting massive process data, communicating with industrial robots, and tracking machines/parts/products on the factory floor and beyond. The wireless system design for IIoT applications is inherently a joint effort between operational technology (OT) engineers, information technology (IT) system architects, and wireless network planners. In this paper, we propose a new reference framework for the wireless system design in IIoT use cases. The framework presents a generic design process and identifies the key questions and tools of individual procedures. Specifically, we extract impact factors from distinct domains including industrial operations and environments, data service dynamics, and the IT infrastructure. We then map these factors into function clusters and discuss their respective impact on performance metrics and resource utilization strategies. Finally, discussions take place in four exemplary IIoT applications where we use the framework to identify the wireless network issues and deployment features in the continuous process monitoring, discrete system control, mobile applications, and spectrum harmonization, respectively. The goals of this work are twofold: 1) to assist OT engineers to better recognize wireless communication demands and challenges in their plants, 2) to help industrial IT specialists to come up with operative and efficient end-to-end wireless solutions to meet demanding needs in factory environments.

Factory and process automation systems are increasingly employing information and communications technologies to facilitate data sharing and analysis in integrated control operations. Wireless connections provide flexible access to a variety of field instruments and reduce network installation and maintenance costs. This serves as an incentive for the adoption of industrial wireless networks based on standards such as the WirelessHART and ISA100.11a in factory control systems. However, process control systems vary greatly and have diverse wireless networking requirements in different applications. These requirements include deterministic transmissions in the shared wireless bandwidth, low-cost operation, long-term durability, and high reliability in the harsh radio propagation environment. It is an open question whether a generic wireless technology would meet the requirements of industrial process control. In this paper, we propose a novel simulation framework for performance evaluation of wireless networks in factory and process automation systems. We select a typical process control plant model, specifically the Tennessee Eastman Challenge (TE) Model, and define the interfaces between the process simulator and the wireless network simulator. We develop a model of the protocol stack of the WirelessHART specification in the OMNET++ simulation engine as a typical industrial wireless network. We present simulation results that validate the prospect of using WirelessHART in the TE plant, and we evaluate the impact of various wireless network configurations on the plant operation. Given its modular design, the proposed simulation framework can be easily used to evaluate the performance of other industrial wireless networks in conjunction with a variety of process control systems.

Abstract In this paper, a cluster‐based two‐phase coordination scheme for cooperative cognitive radio networks is proposed considering both spectrum efficiency and network fairness. Specifically, candidate secondary users (SUs) are first selected by a partner selection algorithm to enter the two‐phase cooperation with primary users (PUs). In phase I, the selected SUs cooperate with PUs to acquire a fraction of time slot as a reward. In phase II, all SUs including the unselected ones share the available spectrum resources in local clusters; each of which is managed by a cluster head who participated in the cooperation in phase I. To improve the total network utility of both PUs and SUs, the maximum weighted bipartite matching is adopted in partner selection. To further improve the network performance and communication reliability, network coding is exploited during the spectrum sharing within the cluster. Simulation results demonstrate that, with the proposed cluster‐based coordination scheme, not only the PUs' transmission performance is improved, but also SUs achieve spectrum access opportunities. Copyright © 2015 John Wiley & Sons, Ltd.

There is a growing interest in the use of renewable energy sources to power wireless networks in order to mitigate the detrimental effects of conventional energy production or to enable deployment in off-grid locations. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity. The dynamics of energy supply hence impose new challenges for network planning and resource management. In this paper, the sustainable performance of a wireless mesh network powered by renewable energy sources is studied. To address the intermittently available capacity of the energy supply, adaptive resource management and admission control schemes are proposed. Specifically, the goal is to maximize the energy sustainability of the network, or equivalently, to minimize the failure probability that the mesh access points (APs) deplete their energy and go out of service due to the unreliable energy supply. To this end, the energy buffer of a mesh AP is modeled as a G/G/1(/N) queue with arbitrary patterns of energy charging and discharging. Diffusion approximation is applied to analyze the transient evolution of the queue length and the energy depletion duration. Based on the analysis, an adaptive resource management scheme is proposed to balance traffic loads across the mesh network according to the energy adequacy at different mesh APs. A distributed admission control strategy to guarantee high resource utilization and to improve energy sustainability is presented. By considering the first and second order statistics of the energy charging and discharging processes at each mesh AP, it is demonstrated that the proposed schemes outperform some existing state-of-the-art solutions.

This paper proposes a novel base station (BS) coordination approach for intercell interference mitigation in the orthogonal frequency-division multiple access based cellular networks. Specifically, we first propose a new performance metric for evaluating end user's quality of experience (QoE), which jointly considers spectrum efficiency, user fairness, and service satisfaction. Interference graph is applied here to capture and analyze the interactions between BSs. Then, a QoE-oriented resource allocation problem is formulated among BSs as a local cooperation game, where BSs are encouraged to cooperate with their peer nodes in the adjacent cells in user scheduling and power allocation. The existence of the joint-strategy Nash equilibrium (NE) has been proved, in which no BS player would unilaterally change its own strategy in user scheduling or power allocation. Furthermore, the NE in the formulated game is proved to lead to the global optimality of the network utility. Accordingly, we design an iterative searching algorithm to obtain the global optimum (i.e., the best NE) with an arbitrarily high probability in a decentralized manner, in which only local information exchange is needed. Theoretical analysis and simulation results both validate the convergence and optimality of the proposed algorithm with fairness improvement.

Dynamic spectrum access (DSA) can be leveraged by introducing external spectrum sensing for secondary users (SUs) to overcome the hidden primary users (PUs) problem and improve spectrum utilization. In this paper, we investigate the DSA networks with external sensors, i.e., external sensing agents, to utilize spectrum access opportunities located in cellular frequency bands. Considering the diversity of SUs' demands and the secondary bandwidths discovered by external sensors, it is critical to manage the detected spectrum resources in an efficient way. To this end, we formulate the resource management problem in the DSA networks as a dynamic resource demand-supply matching problem, and propose a cooperative matching solution. Specifically, spectrum access opportunities are classified into two types by the resource block size: massive sized blocks and small sized blocks. For the former type, SUs are encouraged to share the whole time-frequency block via forming coalitional groups with a "wholesale" sharing approach. For the latter type, the resource "aggregation" sharing approach is proposed to meet the time-frequency demand of individual SUs. To further reduce the delay in the spectrum allocation and compress the matching process, we develop a distributed fast spectrum sharing (DFSS) algorithm, which can deal with both two aforementioned types of resource sharing cases. Simulation results show that the DFSS algorithm can adapt to the dynamic spectrum variations in the DSA networks and the average utilization of detected spectrum access opportunities reaches nearly 90%.

In this paper, a spectrum sharing strategy in cooperative cognitive radio network (CCRN) is investigated. Specifically, a multi-phase cooperation architecture is proposed including cooperation partner selection and spectrum sharing among secondary users (SUs). The cooperation partners who are selected from SUs forward the data of primary users (PUs), and then acquire the spectrum access opportunities for their own transmissions as a reward. To improve the utility for the PU-SU cooperation pairs, the partner selection is modeled as an optimally weighted bipartite matching problem to maximize the total utility where energy efficiency is also considered. The partner SU further improves spectrum utilization by sharing the acquired spectrum with the surrounding SUs via cooperative network coding. Simulation results demonstrate that the proposed partner selection and spectrum sharing approach adapts well to the dynamic traffic loads in CCRN.

In cognitive radio networks, channel information is desired by unlicensed secondary users (SUs) to perform effective power control so as to avoid undue interference to licensed primary users (PUs). However, in general, there is no regular information exchange between PUs and SUs, which implies that SUs are unable to obtain up-to-date channel information at the PU side. Besides, the small-scale fading, in addition to shadowing, brings great uncertainty in SUs' channel estimation. In this paper, we consider limited information exchange between SUs and PUs, and study the impact of channel uncertainty on SUs' throughput performance with power control. We model the uncertain channel gain to be a random variable following a state-dependent probability distribution function, and design a power control method that is robust against the channel uncertainty. We formulate the robust power control problem as a chance constrained robust optimization and solve it by an iterative algorithm. Numerical results show that the proposed power control can provide better protection for PUs than existing methods that overlook the uncertainty in channel measurement.

In cognitive radio networks, it is often impossible to have regular information exchange between PUs and SUs. This implies that SUs are unable to obtain up-to-date channel information at the PU side, and will face technical challenges in accurately controlling their interference to PUs through power control. In this paper, we assume that SUs can estimate the channel information in the reciprocal channel, and study the channel uncertainty due to estimation errors and its impact on SUs' performance and PUs' protection. Specifically, we model the uncertain channel gain to be a random variable following a state-dependent distribution function, and propose a power control mechanism that is robust against the channel uncertainty. We study the robust power control in two cases. In the first case, all SU transmitters (e.g., secondary base stations) transmit with the same power, while in the second case each SU transmitter may choose distinct transmit power based on its own preference. In either case, we formulate the power control problem as a chance constrained robust optimization problem and design an iterative algorithm, respectively. Numerical results show that our robust power control mechanism can provide better protection for PUs than existing methods that overlook the uncertainty in channel measurement, and the second-case power control generally provides better Quality of Service (QoS) for SUs than that in the first case.

In this paper, cognitive routing coupled with spectrum sensing and sharing in a multi-channel multi-hop cognitive radio network (CRN) is investigated. Recognizing the spectrum dynamics in CRN, we propose an opportunistic cognitive routing (OCR) protocol that allows users to exploit the geographic location information and discover the local spectrum access opportunities to improve the transmission performance over each hop. Specifically, based on location information and channel usage statistics, a secondary user (SU) distributedly selects the next hop relay and adapts its transmission to the dynamic spectrum access opportunities in its neighborhood. In addition, we introduce a novel metric, namely, cognitive transport throughput (CTT), to capture the unique properties of CRN and evaluate the potential relay gain of each relay candidate. A heuristic algorithm is proposed to reduce the searching complexity of the optimal selection of channel and relay. Simulation results are given to demonstrate that our proposed OCR well adapts to the spectrum dynamics and outperforms existing routing protocols in CRN.

Next generation communication networks are anticipated to make use of renewable energy sources, e.g., solar and wind power, to reduce carbon footprints and achieve an environmentally sustainable system. However, renewable energy sources have the limitation of unstable availability and capacity, which introduces new challenges for network planning and resource management. In this paper, adaptive resource management is introduced for wireless mesh networks that are powered by sustainable energy sources. The objective is to address the unreliability of the energy supply and to maximize the energy sustainability of the network, or equivalently, minimize the probability that mesh access points (APs) deplete their energy and go out of service. Specifically, the energy buffer of a mesh AP is modeled as a G/G/1 queue and a diffusion approximation is applied to analyze the transient evolution of the queue length and energy depletion duration. Based on the analysis, a resource management scheme is proposed to adaptively distribute traffic over various relay paths across the network and a distributed admission control strategy is applied to further guarantee high resource utilization under the energy sustainability constraint. By considering the first and second order statistics of the energy charging and discharging processes, it is demonstrated that the proposed scheme outperforms some existing state-of- the-art solutions.

The performance of opportunistic forwarding in the multi-hop cognitive radio networks (CRN) is dependent on the efficient detection of spectrum opportunities and the diversity of relay links. To find appropriate spectrum opportunities, effective spectrum sensing is essential in a CRN, especially under highly dynamic channel conditions. In this paper, a novel channel metric is proposed to determine the sensing sequence at the node based on its history channel observations. This metric is then applied in the design of node residence strategy for relay nodes over the channels. Each node distributively calculates the metric and determines the sensing and residence channel to exploit better spectrum opportunities for packet forwarding. By allowing relay nodes to aggregate in the channel with better transmission opportunity, a larger multi-user diversity gain can be exploited and a lower access delay can be achieved. Simulation results demonstrate that the proposed approach well adapts to the channel dynamics and outperforms other solutions, especially in a CRN with low node density.

In this paper, we exploit the heterogeneity of wireless channels and propose an efficient opportunistic cognitive routing (OCR) scheme for dynamic spectrum access (DSA) networks. We first introduce a novel routing metric by jointly considering physical characteristics of spectrum bands and diverse activities of primary users (PU) in each band. To effectively explore the spectrum opportunities, a proper channel sensing sequence for fast and reliable message delivery is determined by secondary users (SU) in a distributed way. We then develop a greedy forwarding scheme that SUs can select the next hop relay based on the geometry information and channel access opportunity of their one hop neighbors. For the proposed OCR, as routing control messages are locally exchanged, SUs can efficiently make the routing decision and opportunistically access the available channels. We further evaluate the performance of OCR via extensive simulations. It is shown that our proposed scheme outperforms existing opportunistic routing schemes in DSA networks by exploiting the heterogeneity of spectrum bands for opportunistic channel access.

We propose a distributed quality of service (QoS)-aware MAC protocol for multi-channel cognitive radio networks supporting multimedia applications. Specifically, based on the channel usage patterns of primary users (PUs), secondary users (SUs) determine a set of channels for channel sensing and data transmissions to satisfy their QoS requirements. We further enhance the QoS provisioning of the proposed cognitive MAC by applying differentiated arbitrary sensing periods for various types of traffic. An analytical model is developed to study the performance of the proposed MAC, taking the activities of both PUs and SUs into consideration. Extensive simulations validate our analysis and demonstrate that our proposed MAC can achieve multiple levels of QoS provisioning for various types of multimedia applications in cognitive radio networks.