Primary User Activity Research Articles

An Intelligent Distributed Channel Selection Framework with Hybrid Mode Selection for Interference Mitigation in D2D based 5G Networks

Device-to-device (D2D) communication is an essential part of the 5G system used in cellular networks, which enables devices to communicate with each other without passing the base station BS. It is likely to aid in satisfying increasing capacity and effectively offloading traffic from the BS by distributing the transmission between D2D users from one side and cellular users and the BS from the other side. It results in improved throughput, energy efficiency, and other parameters. At the same time, the introduction of D2D communication in cellular networks creates new technical challenges to be addressed. One of the major issues is the mitigation of interference between device users (DUs) and primary users (PUs). This issue leads to performance degradation if it is not managed properly. In this paper, an intelligent distributed channel selection framework (IDCSF) in D2D communication for 5G networks with hybrid selection modes of D2D is proposed to help DUs select the best channel for transmission to mitigate interference. This channel selection framework classifies the available sensed channels using self-organizing map (SOM) learning technique into four classes considering channels with sensing errors false alarm (FalsA) and miss detection (MissD) to prevent using occupied channels. Furthermore, PU activity model is adjusted to aid in recovering from bad channel selection decisions. Moreover, fuzzy logic technique is utilized to determine the reliable channels which can help the DU along with channel selection algorithm to find channel’s weight values. The channel with highest weight value is selected as the best channel. Simulation results show that the proposed framework IDCSF enhances selecting the best channel, improves throughput and spectral utilization, and reduces average delay, interference and search time compared to other approaches.

Analysis of delays in medium access control devices in cognitive radio systems with correlation of time intervals in request flows

This article is about the development of a method for analyzing the average waiting time of a request in a queue with correlated inter-arrival and service times. The ON-OFF process is often used to model the primary user channel occupancy in different questions related to cognitive radio, such as resource allocation, medium access control protocols, spectrum handoff and others. It is assumed that the time intervals of the ON- and OFF- periods are not correlated. More realistic models of primary user activity require the presence of correlation not only between consequent periods, but also the presence of correlation between channel occupancy and other quantities interpreted as service time in models based on queuing systems. As a result, here is proposed a variant of constructing the probability density function of a random variable which is the difference between the service time of the i-th request and the time interval between the moments of arriving of the i-th and (i-1)-th requests with correlated inter-arrival and service times. Solution based on the use of a two-dimensional characteristic function, so in this case it is possible to more purposefully specify the components of the statistical connection of previously described sequences. These components are the cumulants of the described distribution. For a queuing system with hyperexponential distribution of inter-arrival and service intervals, an analytical expression for the average waiting time of a request in a queue is found.

Constructing Connected-Dominating-Set with Maximum Lifetime in Cognitive Radio Networks

Connected-dominating-set (CDS) is a representative technique for constructing virtual backbones of wireless networks and thus facilitates implementation of many tasks including broadcasting, routing, etc. Most of existing works on CDS aim at constructing the minimum CDS (MCDS), so as to reduce the communication overhead over the CDS. However, MCDS may not work well in cognitive radio networks (CRNs) where communication links are prone to failure due to stochastic activities of primary users (PUs). A MCDS without consideration of the stochastic activities of PUs easily becomes invalid when the PUs become active. This study addresses a new CDS construction problem by considering the PUs’ activities. Our problem is to maximize the lifetime of the CDS while minimizing the size of the CDS, where the lifetime of a CDS is defined as the expected duration that the CDS is maintained valid. We show that the problem is NP-hard and propose a three-phase centralized algorithm. Given a CRN, the centralized algorithm can compute a CDS such that the lifetime of the CDS is maximized (optimal), and the size of the CDS is upper-bounded. We further present a two-phase localized algorithm which requires 2-hop information. Extensive simulations are conducted to evaluate the proposed algorithms.

Multi‐objective spectrum assignment in heterogeneous cognitive radio networks for internet of things

SummaryInternet of Things (IoT) applications have massively increased in the last few years. Future‐generation IoTs are going to be massive in number, heterogeneous in nature, and extremely demanding in terms of computation and communication requirements. Therefore, effective techniques are needed to optimize the use of scarce communication resources. This work proposes a novel multi‐objective framework to investigate the channel assignment problem in cognitive radio network (CRN)‐based IoT applications. The multi‐objective optimization framework comprises three objective functions. The first objective aims to maximize the overall system throughput fairness, the second objective aims to maximize the overall system residual energy, and the third objective aims to increase the user satisfaction level by maximizing the overall priority index. The problem of channel assignment is modeled and solved using Mixed Integer Linear Programming (MILP) and ‐constraint technique. This technique generates a set of Pareto efficient solutions, and an optimal solution among these solutions is selected using a fuzzy logic decision mechanism. Moreover, power level selection is incorporated by solving the problem multiple times for different power levels and obtaining a preferred solution for each power level. The simulations are conducted to unveil an interesting trade‐off between the conflicting system design objectives, that is, the overall throughput and the overall residual energy while incorporating the significance of primary user (PU) activity when preforming channel assignment in CRN.

A multi-user cognitive radio full-duplex channel under imperfect spectrum sensing: Achievable rates and energy efficiency

In this work, the achievable rates and energy efficiency of a multi-user cognitive radio (CR) uplink channel with full-duplex (FD) are studied. For this multi-user CR channel, secondary users can work in FD mode to simultaneously transmit and listen to the channel. We consider a realistic scenario in which both FD operation and spectrum sensing are imperfect. At first, we examine the sensing performance and the rate region through the evaluation of the probability of miss-detection and the probability of false alarm when the primary user (PU) activity is assumed to be non-time-slotted. Due to an imperfect sensing operation, the cognitive radio channel is modeled as a non-Gaussian channel impacted by a Gaussian-mixture (GM) noise plus PU interference. Under this practical assumption, it is not possible to calculate the achievable rates and the rate region directly. Instead, we derive simple approximations of these metrics which can be easily calculated with arbitrarily small errors. In the second part of the paper, we further evaluate the derivatives of these achievable rates at low-power regimes. This calculation helps us evaluate the energy efficiency and obtain the minimum energy per bit Eb/N0min when Gaussian and discrete input signals are used.

Enhanced Wireless Network Usage and Loading on Network Delay using Cognitive Radio Network

Wireless communication is one of the fastest-growing fields of communication. Because the user can access and share information anywhere at any time. The communication process will suffer from an overload of traffic. If the node gets overloaded because of heavy traffic, then the performance of the whole network is degraded. So, the usage of the network will be delayed. The proposed work uses the intellectual load balancing approach for Cognitive Radio Network to focus on automatic monitoring of network activity, and the node will continuously balance among numerous nodes. In Cognitive Radio Network the Dynamic Spectrum Access techniques are used to avoid spectrum scarcity and underutilization. The CR base station collect the surrounding about the active primary user and waiting secondary user. To determine the principal user's on/off status of availability, the Markov chain model is employed. The waiting secondary user utilize the spectrum when the primary user is not using it. The secondary user leaves the spectrum and uses the spectrum of another primary user if the primary user is on status. It will assist with dynamic spectrum access method and it will help increase the overall performance of the network. The suggested Load balancing Cognitive Radio Network (LBCRN) sorts the nodes and uses spectrum identification before allocating the node to the secondary user. So that minimise transaction delays, prevent overcrowding of a specific node, automatically lower the level of packet drops, and enhance packet delivery radio levels. As a result, this strategy has helped to lessen the issue of spectrum shortages in the wireless communication industry.

A QoS-aware framework for spectrum characterization and switching decision in cognitive radio networks

The increasing demand for seamless wireless services coupled with underutilization of limited spectrum due to fixed channel allocation policy have posed numerous challenges to wireless communications. There is need to design appropriate channel allocation strategies for continuous communication by mobile users for higher spectral efficiency. Cognitive radio network (CRN) offers a solution to the spectrum scarcity problem inherent in 4G and other networks through dynamic spectrum access, by allowing unlicensed secondary users (SUs) with cognitive devices to opportunistically access the spectrum holes when the licensed primary users (PUs) are not occupying them. Often times, measurements taken by SUs during the sensing process are uncertain due to multipath fading, shadowing and varying channel conditions. This results in imprecise spectrum detection and selection by SUs during switching decisions causing incessant spectrum handoff and undesirable ping-pong effect. In this paper, a support vector machine (SVM) classifier is used to categorize spectrum into two classes of busy and idle. Then, based on heterogeneous quality of service (QoS) requirements of the SUs, dynamic activities of PUs, and the fluctuating channel state information, a QoS-aware Adaptive Neuro-Fuzzy Inference System (ANFIS) framework is developed for spectrum switching decision using underlay spectrum access model. SVM predicted spectrum holes with 98.8% accuracy. ANFIS model yielded a 91.62% accuracy in the task of allocating spectrum holes to SUs for coexistent with PUs. Results further indicate that the intelligent framework can ensure fairness among SUs, reduce interference, improve throughput, and spectral efficiency. It can be deployed in disaster relief and emergency, public safety, and battlefield environments.

Effectiveness of behavioural change interventions on physical activity, diet and body mass index of public primary healthcare users in Kosovo: the KOSCO cohort

BackgroundPrevalent physical inactivity and poor nutrition contribute to high non-communicable disease (NCD) morbidity and mortality in Kosovo. To improve health services for patients with NCD the Accessible Quality Healthcare project...

Severe hypertriglyceridemia prevalence at a primary care setting in Catalonia, Spain

Hypertriglyceridemia (HTG) increases the risk of cardiovascular disease and pancreatitis, and its prevalence varies across populations. To determine the prevalence of moderate-to-severe hypertriglyceridemia (msHTG, 500-879mg/dl) and severe hypertriglyceridemia (sHTG, ≥ 880mg/dl) in a primary care population in Catalonia, Spain, and to categorize them according to presence/absence of factors potentially causing HTG. Retrospective analysis of clinical and laboratory data in SIDIAP (Information System for the Development of Primary Care Research) from 2010, 2013, 2016, and 2019. We considered medications with hypolipidemic effects and those potentially increasing TG levels. We developed logistic regression models adjusted by age and sex to calculate the probability of having ms/sHTG according to covariates of interest. In the study years, 36.2‒42.0% of the >3.5 million active primary care users had ≥1 TG determination. Prevalence for msHTG was 0.7% and for sHTG 0.2% among those with recorded TG. In 2019, 54.7% were female; median (IQR) age was 62.5 (49.4‒73.7) years. Prevalence was higher in 36‒50-year-old persons (1.3% msHTG, 0.4% sHTG) and men (1.1% msHTG, 0.3% sHTG). Most cases were associated with secondary and <20% with non-secondary causes, the latter being most prevalent in young patients. The secondary causes more strongly associated with msHTG/sHTG were obesity, uncontrolled diabetes mellitus (DM) and gamma-glutamyl transferase >100U/L. The prevalence of msHTG was 0.7% and that of sHTG was 0.2% between 2010 and 2019 among individuals with recorded TG. msHTG/sHTG most often affected men around their fifties and people with obesity and uncontrolled DM. Most msHTG and sHTG cases were associated with the presence of secondary causes.

Snake Optimization Technique for Spectrum Handoff in Cluster based Cognitive Radio Network

In Cognitive Radio (CR) networks, the use of Secondary Users (SU) in the spectrum has an undesirable effect on spectrum handoff, which causes a handoff delay. The handoff procedure can result in service outages and considerable transmission quality delays, making it a regular source of concern for the SU. An effective spectrum handoff strategy that utilizes the Spectrum Binary Snake Optimization (SBSO) algorithm and the M/G/1 queuing model has been proposed in this study. The use of Cluster Based Cooperative Spectrum Sensing improves SU performance and reduces channel congestion. In order to report the active and inactive channels in the spectrum, the cluster head is connected to the SU base station, and a decision report is subsequently generated by the fusion center. With the use of a bitwise and mutation operator format, SBSO reduces the overall service time required for handoff in the approach that is being proposed. The proposed methodology also provides a framework for observing how primary user activity and spectrum handoff delays behave in the presence of potential interruptions in a CR network. The simulation model of the proposed work optimizes the packet delivery ratio with the three benchmark functions, and provides optimal handoff, and is compared to SBSO and other models that offer a better trade off over delay achievement.

Reinforcement Learning for Link Adaptation and Channel Selection in LEO Satellite Cognitive Communications

In this letter, we solve the link adaptation and channel selection problem in next generation satellite cognitive networks under dynamically varying channel availability and time-varying channel statistics. Primary user (PU) activity in Low Earth Orbit (LEO) satellite cognitive communications forces the set of available transmission channels for a secondary user (SU) to vary dynamically over time. We consider the scenario where the channel state varies in a piecewise-stationary mode, referred to as quasi-static (block-fading) channels. We formalize the problem as a reinforcement learning problem, and propose Discounted Structured and Sleeping Thompson Sampling (dSTS), which maximizes the SU’s throughput by selecting the optimum modulation and coding scheme (MCS) and the transmission channel under volatile and piecewise-stationary settings. When channel characteristics are unknown as well as piecewise-stationary, the proposed algorithm adapts the SU’s link-rate by exploiting the structure of the transmission success probability in transmission rates over the selected available channel. Furthermore, channel state information (CSI) is absent and feedback is limited to 1-bit (success/failure).

Using User-Generated YouTube Videos to Understand Unguided Interactions with Robots in Public Places

Professional service robots are increasingly being deployed in public places, which thus increases user exposure. However, we lack an empirical understanding of complex encounters taking place in dynamic and often crowded environments as well as how people overcome breakdowns during unguided interaction with a robot in a real-world scenario. In this paper, we conducted a covert, digital ethnographic study analyzing 104 user-generated YouTube videos focusing on people’s unguided interactions with robots in several public places. We identified several types of interaction breakdowns pertaining to someone (person-initiated interaction breakdown, IB) or something (environmental disturbances, ED) having a direct, negative effect on an ongoing unguided interaction. Our findings have implications for the design and development of service robots facing multi-user scenarios entertaining active (primary and secondary) users, inactive (commentators and observers) ‘users’, and Incidentally Co-present Persons (InCoPs) . Furthermore, we contribute to and built on the limited prior use of YouTube videos and digital ethnography in HRI research, thereby demonstrating its effectiveness in studying unguided interactions in public places, while supplementing and adding to the existing knowledge base of service robots in public places.

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Cognitive Radio Networks (CRNs) have become a successful platform in recent years for a diverse range of future systems, in particularly, industrial internet of things (IIoT) applications. In order to provide an efficient connection among IIoT devices, CRNs enhance spectrum utilization by using licensed spectrum. However, the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection. Specifically, the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User (PU) activity and create a robust routing path. This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain. Thus, a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method, namely, Channel Availability Probability. Moreover, a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique. This protocol combines lower layer (Physical layer and Data Link layer) sensing that is derived from the channel estimation model. Also, it periodically updates and stores the routing table for optimal route decision-making. Moreover, in order to achieve higher throughput and lower delay, a new routing metric is presented. To evaluate the performance of the proposed protocol, network simulations have been conducted and also compared to the widely used routing protocols, as a benchmark. The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio (with an improved margin of around 5–20% approximately) under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks (MCRNs). Moreover, the cross-layer routing protocol successfully achieves high routing performance in finding a robust route, selecting the high channel stability, and reducing the probability of PU interference for continued communication.

Rate-Region Characterization and Channel Estimation for Cell-Free Symbiotic Radio Communications

Cell-free massive MIMO and symbiotic radio communication have been recently proposed as the promising beyond fifth-generation (B5G) networking architecture and transmission technology, respectively. To reap the benefits of both, this paper studies cell-free symbiotic radio communication systems, where a number of cell-free access points (APs) cooperatively send primary information to a receiver, and simultaneously support the passive backscattering communication of the secondary backscatter device (BD). We first derive the achievable communication rates of the active primary user and passive secondary user under the assumption of perfect channel state information (CSI), based on which the transmit beamforming of the cell-free APs is optimized to characterize the achievable rate-region of cell-free symbiotic communication systems. Furthermore, to practically acquire the CSI of the active and passive channels, we propose an efficient channel estimation method based on two-phase uplink-training, and the achievable rate-region taking into account CSI estimation errors is further characterized. Simulation results are provided to show the effectiveness of our proposed beamforming and channel estimation methods.

Tree-based Multicast Routing and Channel Assignment for Enhanced Throughout in Emerging Cognitive Radio Networks

In future multi-hop wireless networks like 5G and B5G, efficient large-scale video sharing and data dissemination are expected to rely heavily on multicast routing and Cognitive Radio (CR) technology. While multicast routing is efficient when the network always has access to the spectrum, the dynamic nature of Primary User (PU) activities, heterogeneous spectrum across the CR Network (CRN), and PU access priority make it challenging to implement efficient multicast routing protocols in CRNs. This paper proposes a hierarchical multicast routing mechanism for multi-hop CRNs that exploits the Shortest Path Tree (SPT) and Minimum Spanning Tree (MST) concepts. The proposed multicast routing mechanism consists of tree construction and channel assignment algorithms. The tree-construction algorithm models the network topology as a multicast tree rooted at the CR source and spanning all the CR nodes. Based on the constructed tree, the channel assignment algorithm employs the Probability Of Success (POS) metric to assign channels to the various layers defined by the constructed SPT or MST, ensuring that the most reliable channel is used for the multi-hop multicast transmissions. Simulation experiments are conducted to evaluate the mechanism’s effectiveness, revealing significant improvements in throughput and Packet Delivery Rate (PDR) compared to state-of-the-art protocols under different network conditions. The simulations also show that the SPT-based mechanism outperforms the MST-based mechanism in terms of throughput but has a higher tree construction complexity.

A Comprehensive Analysis of Energy Efficiency Using Cooperative Spectrum Sensing Network

The proposed cooperative spectrum sensing network presented in this paper has potential properties that could improve the detection probability. The proposed network consists of multiple cognitive radio (CR) nodes. Each CR node is equipped with several antennas, and selection combining is used to identify the highest value of sensing data associated with each antenna. Further, this sensing data is passed to fusion center and primary user activity is identified at the fusion centre using the fusion rules. This study mainly focuses on identifying unknown signals in a Rayleigh fading environment using an improved energy detector. Initially, the novel analytical closed-form of expressions for false alarm and missing detection probabilities was provided under Rayleigh fading with multiple antennas at each CR. Further, the performance of the total error rate is studied under single and multiple antenna scenarios, and a quantitative evaluation is offered to determine average throughput and energy efficiency performance. Later, to simulate the performance with a solid foundation of mathematical analysis, a simulation test-bed is created in MATLAB. Finally, the performance is improved with the proposed scheme compared to conventional scheme.

Jamming resilient multi-channel transmission for cognitive radio IoT-based medical networks

The era of the Internet-of-things (IoT) comes with tremendous burdens on pre-existing network infrastructures and protocols due to spectrum scarcity and reliability concerns. Cognitive radio (CR) technology is proposed for IoT applications to alleviate the spectrum scarcity paradigm. In CR-IoT-based networks, the IoT devices/nodes share the spectrum with primary users (PUs). However, in order not to interfere with PUs communications and to conform with the elevating throughput requirements, efficient multi-radio/multi-channel assignment algorithms are required. Additionally, in order to ensure reliable transmission, algorithms need to be resilient to jamming attacks, which have detrimental impacts on network performance. In this paper, parallel-channel security-aware medium access control (PCS-MAC) is proposed as a probabilistic-based jamming resilient multi-channel assignment algorithm proposed for medical networks. PCS-MAC considers primary user activity, channel conditions, jamming attack levels, and data-rate requirements to provide spectrally efficient data transmission between CR-IoT nodes subject to delay constraints under jamming attacks to assure the delivery of time-critical patient data. The performance of PCS-MAC is practically validated using the open large-scale future Internet-of-things (FIT) IoT-LAB testbed. Practical results show that our proposed algorithm significantly enhances network performance, yielding throughput rates that supersedes the state-of-the-art algorithms presented in literature.

Distributed robust channel allocation for clustered cognitive radio-based IoT networks using graph theory

The exponential growth in the Internet of Things (IoT) devices for the Internet of Everything (IoE) services demands more operating spectrum. Utilizing the unlicensed spectrum by a large number of IoT networks leads to congestion in the unlicensed spectrum. To mitigate the scarcity of radio spectrum for IoT networks, integration of the cognitive radio technology with IoT networks allows IoT devices to operate and share the licensed spectrum with primary users (PUs). For efficient licensed-spectrum sharing, a cognitive radio-based spectrum assignment algorithm is proposed for IoT networks, which minimizes network interference and ensures connectivity against the PUs activity. For interference reduction, a conflict graph is used to determine the potential interfering links in the network, and channels are accordingly assigned to the radio interfaces of each IoT device. To ensure connectivity in the network, an ordered pair of channels is assigned to the radios of the IoT devices such that the network topology is robust to the presence of the PUs on multiple channels. The robustness of the network topology avoids frequent channel switching and improves the energy efficiency of the network. Simulation results show that the proposed algorithm minimizes overall network interference, and achieves 100% successful packet transmission, compared to other channel assignment algorithms. The algorithm shows that the network is not partitioned due to the PUs’ presence on up to half of the available licensed channels, which significantly reduces the amount of channel switching required, and conserves energy in the IoT devices.

A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach.

With the significant rise in demand for network utilization, such as data transmission and device-to-device (D2D) communication, fifth-generation (5G) networks have been proposed to fill the demand. Deploying 5G enhances the utilization of network channels and allows users to exploit licensed channels in the absence of primary users (PUs). In this paper, a hybrid route selection mechanism is proposed, and it allows the central controller (CC) to evaluate the route map proactively in a centralized manner for source nodes. In contrast, source nodes are enabled to make their own decisions reactively and select a route in a distributed manner. D2D communication is preferred, which helps networks to offload traffic from the control plane to the data plane. In addition to the theoretical analysis, a real testbed was set up for the proof of concept; it was composed of eleven nodes with independent processing units. Experiment results showed improvements in traffic offloading, higher utilization of network channels, and a lower interference level between primary and secondary users. Packet delivery ratio and end-to-end delay were affected due to a higher number of intermediate nodes and the dynamicity of PU activities.

Energy-Efficient Transmission Range and Duration for Cognitive Radio Sensor Networks

Cognitive Radio (CR) promises an efficient utilization of radio spectrum resources by enabling dynamic spectrum access to overcome the spectrum scarcity problem. Cognitive Radio Sensor Networks (CRSNs) are one type of Wireless Sensor Networks (WSNs) equipped with CR capabilities. CRSN nodes need to operate energy-efficiently to extend network lifetime due to their limited battery capacity. In this paper, for the first time in literature, we formulate the problem of finding a common energy-efficient transmission range and transmission duration for all CRSN nodes and network deployment that would minimize the energy consumed per goodput per meter toward the sink in a greedy forwarding scenario. Results reveal non-trivial relations for energy-efficient CRSN transmission range and duration as a function of nine critical network parameters such as primary user activity levels. These relations provide valuable insights for detailed CRSN designs prior to deployment.