Idle Probability Research Articles

A Hybrid Communication Scheme for Throughput Maximization in Backscatter-Aided Energy Harvesting Cognitive Radio Networks

Motivated by the benefits of cognitive radio (CR), energy harvesting (EH), and backscatter communication (BC) technologies to support Internet of Things (IoT) systems, we investigate the backscatter-aided energy harvesting cognitive radio networks (EH-CRNs) in a multi-channel scenario. To achieve high throughput on various channels, we propose a novel hybrid communication scheme that the secondary transmitter (ST) selects one channel for spectrum sensing, and performs multiple actions based on the sensing result. To be specific, if the selected channel is detected as busy, the ST potentially performs underlay mode transmission, ambient backscatter communication (AmBC), or radio frequency (RF) EH. Otherwise, the ST performs interweave mode transmission. Based on the ST’s knowledge of the channel availability and the amount of the available energy, the decisions of channel and specific action selections are made. Furthermore, the sequential decision problem is formulated as a mixed observability Markov decision process (MOMDP), and addressed by the classic value iteration algorithm. The proposed scheme could be flexibly adapted to the changes in energy and channel availabilities. Simulations demonstrate the superiority of this scheme in terms of throughput, and show that even without channel selection, the proposed scheme conducted on the channels with different idle probabilities always achieves high throughput.

Thompson Sampling-Based Dynamic Spectrum Access in Non-Stationary Environments

In dynamic spectrum access (DSA), unlicensed secondary users (SUs) estimate the idle probabilities of primary channels by using historical sensing results and opportunistically access the channel with the highest idle probability for transmission. Due to the rapid traffic changes and irregular user mobility, the primary channels can be highly dynamic and their idle probabilities are generally time-varying. In this paper, we investigate DSA in non-stationary environments. Specifically, we consider two channel state models, i.e., the non-stationary Bernoulli model with a time-varying mean and the non-stationary Markovian model with a time-varying transition matrix. For the single-SU scenario, we propose a Thompson sampling based method with a change detection technique, which is capable of detecting the variations of channel statistics and adjusting the channel access strategy accordingly. For the multi-SU scenario, we propose a Thompson sampling based collision alleviation method to coordinate the transmissions of SUs, which does not need any prior protocol or information exchange among SUs. Numerical results show that the proposed methods outperform the existing ones in terms of successful transmission ratio in various network settings.

An effective routing algorithm for spectrum allocations in cognitive radio based internet of things

SummaryThe Internet of Things (IoT) concept increases the spectrum demands of mobile users in wireless communications because of the intensive and heterogeneous structure of IoT. Various devices are joining IoT networks every day, and spectrum scarcity may be a crucial issue for IoT environments in the near future. Cognitive radio (CR) is capable of sensing and detecting spectrum holes. With the aim of CR, more powerful IoT devices will be constructed in such crowded wireless environments. Also, dynamic and ad‐hoc CR networks have not a fixed base station. Therefore, CR capable IoT (CR‐based IoT) device approach with routing capabilities will be a solution for future IoT environments. In this study, spectrum aware Ad hoc on‐demand distance vector routing protocol is proposed for CR‐based IoT devices in IoT environments. For the performance analysis of the proposed method, various network scenarios with different idle probability have been performed and throughput and delay results for different offered loads have been analyzed.

Genetic algorithm-based hybrid spectrum handoff strategy in cognitive radio-based internet of things

In Cognitive radio-based Internet of Things (CR-IoT) systems, the return of the primary user (PU) causes the secondary user (SU) that is communicating to face the spectrum handoff problem. In the process of spectrum handoff, the user terminal cant get the idle channels in time because of the unknown channel usage state.To solve this problem, a hybrid spectrum handoff algorithm based on genetic algorithm is proposed. The algorithm considers the regularity of PU activities in space and time, defines the idle probability of channels from the perspective of week attributes and time periods, obtains the optimal time period length using genetic algorithm,generates a channel idle probability table, and provides the target channel sequence for SUs in combination with the proposed channel ordering scheme. Simulation results show that when the total number of SUs is within 10 \(\sim \) 20, the proposed algorithm has a spectrum handoff outage probability of less than 7%, an average delivery time of less than 13s, a total packet error rate of less than 5.5%, a channel utilization of consistently above 70%, and an average detection times of less than 7 times.

Intelligent Dynamic Spectrum Access Using Deep Reinforcement Learning for VANETs

In vehicular ad hoc networks (VANETs), vehicles can communicate with other vehicles or devices through vehicle-to-X communication. However, with the rise of the Internet of Things, it is a real challenge to make the limited spectrum resource meet the increasing communication demands, i.e., the channel contention problem. To solve this problem, this paper proposes a strategy combining multi-hop forwarding through vehicles and dynamic spectrum access. Firstly, a group-based multi-hop broadcast protocol, G-hop, is proposed. G-hop classifies vehicles with similar characteristics such as moving speed and communication distance into same groups using the depth-first-search algorithm. Messages are forwarded within a group in priority and then across groups, which limits both the range and the number of relay vehicles, i.e., channel contenders. Further, we adopt deep reinforcement learning techniques to achieve dynamic spectrum access. We design a Global Optimization algorithm based on Experience Accumulation (GOEA) using deep reinforcement learning. In GOEA, a network structure combined with recurrent neural network and deep Q-network is proposed for learning the time-varying process, and then a reward method is applied to optimize the global utility. Vehicles that need to transmit messages select channels dynamically following the guidance of GOEA. The experimental results demonstrate that the G-hop protocol reduces the packet loss rate from 0.8 to about 0.1. Meanwhile, compared with Slotted Aloha and DQN, our GOEA algorithm reduces the collision probability and channel idle probability by 60%. Moreover, as the number of vehicles increases, the transmission success rate can be improved by 20%.

RE-ORA: Residual Energy-Aware Online Random Access for Improving the Lifetime of Slotted ALOHA-Based Swarming Drone Networks

In this paper, to maximize the lifetime of drone swarms by resolving the battery exhaustion problems caused by undesired retransmissions, we propose a residual energy-aware online random access scheme (RE-ORA) by adjusting the packet transmission opportunities based on the residual energy of a drone in S-ALOHA-based swarming drone networks. We aim to improve the battery lifetime of the drone with the smallest energy among the drone swarms. In addition, we analyze the success, collision, and idle probabilities of the swarming drone networks. In particular, we analyze the intradrone swarm collision and interdrone swarm collision probabilities. Through intensive simulations, we show that the proposed RE-ORA scheme outperforms the conventional algorithm with respect to the average lifetime of drone swarms and successful packet transmission probability according to the number of drones and the residual energy.

Performance analysis of access class barring for next generation IoT devices

Massively dense deployment of Internet of Things (IoT) devices has put a stringent requirement on cellular networks to provide convenient service for not only human type traffic (HTC) but also for bursty traffic for IoT devices. Any bottleneck in the random access process means the bottleneck of the entire system because it is the first step before scheduled access. Access class barring (ACB) scheme is one of the key schemes in long term evolution advanced (LTE-A) to control the congestion in a random access process in which the access of some devices is barred based on a parameter, ACB factor, to relieve the congestion. In this paper, we analyze the ACB factor and criteria of its selection as the optimal selection of the ACB factor is essential for the maximum throughput of the system. The metrics used in the analysis of the ACB factor are total service time (TST), access delay and maximum collision, success, and idle probabilities with fixed and optimal ACB factor. Simulation results in MATLAB provide the complete picture of the behavior of the ACB factor and its control, used in the random access process.

QoS provisioning in energy-efficient cooperative networks with power assignment and relay deployment planning

The cooperative network is an energy-efficient technology to provide ubiquitous broadband access and quality-of-service (QoS). This paper investigates the tradeoffs among QoS, capacity, and power consumption in the cooperative networks. By adaptively selecting among the direct, relay, and cooperative transmission (TX) modes for users, we can enhance the capacity with lower transmission power. Furthermore, we determine the proper relay location and transmission powers of base station (BS) and relay station, so that more users can exploit the low-power two-hop TX modes to enhance the capacity and reduce the delay. In the real networks, if the packet queue is empty, the BS can be idle without sending (to save energy). Hence, we also properly increase the link capacity and idle probability to further reduce power consumption. We develop an analytical model to evaluate the delay, variance, capacity, idle probability, and actual power consumption in unsaturated traffic conditions. Then, we apply an optimization approach to analytically determine the optimal relay location and transmission powers, aiming to maximize the overall energy efficiency (EE) subject to the delay requirements. Numerical results show that the adaptive TX-mode selection scheme along with the joint power assignment and relay deployment planning can significantly enhance the capacity and reduce the power consumption. Then, the goal of QoS support can be achieved with higher EE.

Multichannel spectrum access based on reinforcement learning in cognitive internet of things

With the development of Internet of Things(IoT), the demands for communication spectrum have increased rapidly, resulting in the shortage of limited spectrum resources. Cognitive IoT (CIoT) based on cognitive radio (CR) can improve the spectrum utilization by accessing the idle spectrum licensed to a primary user (PU). In this paper, a multichannel spectrum access scheme based on reinforcement learning (RL) is proposed to improve the spectrum access of CIoT, wherein the CIoT can use multiple channels for transmissions to reduce the communication interruptions. The channels are ranked in the decreasing order of their predicted idle probabilities, which can make the CIoT find enough idle channels quickly via decreasing the number of sensing operations and spectrum handoffs. The simulation results show that our proposed scheme is superior to the single-channel spectrum access scheme in terms of throughput, communication interruption, average collision probability and average spectrum switching frequency.

Reinforcement Learning-Based Multislot Double-Threshold Spectrum Sensing With Bayesian Fusion for Industrial Big Spectrum Data

With the rapid increase of industrial systems, industrial spectrum is stepping into the era of big data, and at the same time spectrum resources are facing serious shortage. Cognitive industrial system (CIS) based on cognitive radio can improve spectrum utilization by accessing the idle spectrum licensed to primary user. However, the CIS must find enough idle channels by performing spectrum sensing. In this article, a reinforcement learning-based multislot double-threshold spectrum sensing with Bayesian fusion is proposed to sense industrial big spectrum data, which can find required idle channels faster while guaranteeing spectrum sensing performance. Double thresholds are set to guarantee both high detection probability and spectrum access probability, and weighed energy detection is proposed to maximize detection probability when the energy statistic falls into the confusion area between the double thresholds. Bayesian fusion is proposed to get a final decision on the channel availability by combining the local sensing decisions of all the time slots. A prediction and selection algorithm for idle channels is proposed to predict the idle probability of each channel and find required idle channels from the sorted channel set. From simulation results, the proposed spectrum sensing scheme outperforms cooperative spectrum sensing and energy detection, which can predict idle channels accurately and get needed idle channels with fewer sensing operations.

Balancing herding and congestion in service systems: a queueing perspective

In service industries such as restaurants and tourism, empirical findings show that uninformed customers may consider queues as a signal of service quality and choose to join a longer queue. Service managers become aware of this phenomenon and stimulate customer purchase by maintaining a queue. In this paper, we explore issues related to the balance between herding and congestion for service systems using a state-dependent queue. In our model, the herding effect is represented by system idle probability (as opposed to system busy probability) and the congestion is represented by a non-decreasing function of queue length. An optimization problem with the objective of minimizing the long-run average cost and constraints on traffic intensities is formulated, and the structure of its optimal solution is characterized. Further, we find closed-form solutions of the optimal state-dependent traffic intensity and the optimal service rate switching state, and characterize the relationship between the optimal solution and system parameters. Through a series of propositions and numerical examples, we gain insight into the balance between stimulation of herding effect and reduction of customer waiting, and propose that service managers should intentionally slow down when the queue is short and operate at their full speed when the queue is long.

Performance analysis of multilayer multicast MANET CRN based on steiner minimal tree algorithm

In this study, the multicast mobile ad hoc (MANET) CRN has been developed, which involves multi-hop and multilayer consideration and Steiner minimal tree (SMT) algorithm is employed as the router protocol. To enhance the network performance with regards to throughput and packet delivery rate (PDR), as channel assignment scheme, the probability of success (POS) is employed that accounts for the channel availability and the time needed for transmission when selecting the best channel from the numerous available channels for data transmission from the source to all destinations nodes effectively. Within Rayleigh fading channels under various network parameters, a comparison is done for the performance of SMT multicast (MANET) CRN with POS scheme versus maximum data rate (MDR), maximum average spectrum availability (MASA) and random channel assignment schemes. Based on the simulation results, the SMT multicast (MANET) CRN with POS scheme was seen to demonstrate the best performance versus other schemes. Also the results proved that the throughput and PDR performance are improved as the number the primary channels and the channel’s bandwidth increased while dropped as the value of packet size D increased. The network’s performance grew with rise in the value of idle probability (P_I) since the primary user’s (PU) traffic load is low when the value of P_I is high.

Performance comparison of multicast MANET CRN system’s based on POS scheme with different types of router protocol algorithms

<p>A multi-layer multi-hop mechanism, with one of three different types of router protocol algorithms (Steiner minimal tree (SMT), shortest path tree (SPT) and minimal spanning tree (MST)) was employed for the construction of multi-cast MANET CRNs. The probability of success (POS) used as the channel assignment scheme, is dependent on the availability of the channel, and the required transmission time. It was applied to the network after the conversion of the network’s random topology into one of the three algorithms (SMT, SPT or MST). This facilitated the selection of an efficient channel for the transmission of the CR user’s data, under the effect of the Rayleigh fading channel. Three idle probability circumstances (i.e., =0.1, 0.5, 0.9), and different network parameters, were used to compare the performance of the CRN with three routing protocols in terms of throughput, and packet delivery rate (PDR). According to the simulation results, for a high traffic load of PUs ( 1) at different network parameters , the CRN with the SPT algorithm performed better than the CRN with the SMT or MST algorithm. </p>

QoSRP: A Cross-layer QoS Channel-Aware Routing Protocol for the Internet of Underwater Acoustic Sensor Networks.

Quality of service (QoS)-aware data gathering in static-channel based underwater wireless sensor networks (UWSNs) is severely limited due to location and time-dependent acoustic channel communication characteristics. This paper proposes a novel cross-layer QoS-aware multichannel routing protocol called QoSRP for the internet of UWSNs-based time-critical marine monitoring applications. The proposed QoSRP scheme considers the unique characteristics of the acoustic communication in highly dynamic network topology during gathering and relaying events data towards the sink. The proposed QoSRP scheme during the time-critical events data-gathering process employs three basic mechanisms, namely underwater channel detection (UWCD), underwater channel assignment (UWCA) and underwater packets forwarding (UWPF). The UWCD mechanism finds the vacant channels with a high probability of detection and low probability of missed detection and false alarms. The UWCA scheme assigns high data rates channels to acoustic sensor nodes (ASNs) with longer idle probability in a robust manner. Lastly, the UWPF mechanism during conveying information avoids congestion, data path loops and balances the data traffic load in UWSNs. The QoSRP scheme is validated through extensive simulations conducted by NS2 and AquaSim 2.0 in underwater environments (UWEs). The simulation results reveal that the QoSRP protocol performs better compared to existing routing schemes in UWSNs.

Mathematical modelling of a tollbooth system with two parallel skill-based servers and two vehicle types

This paper considers a tollbooth system with two parallel heterogeneous servers and two vehicle types (say, cars and trucks), where one server collects tolls from vehicles of both types and the other only serves one type of them. With such characteristic, the system is referred to as “skill-based servers” for brevity in this paper. Meanwhile, vehicles are accommodated in a single common lane and get served on the “global First-Come-First-Served” basis. In fact, such a system and its variants are commonly encountered and their performance measures are of great significance to managers. We first develop a Quasi-Birth-Death process to explicitly model this tollbooth system. Then by applying spectral expansion technique, we derive the stationary probabilities for the computation of the system’s performance measures, such as mean queue size and the idle probability of each server. The Laplace–Stieltjes transforms of an arbitrary vehicle’s sojourn time is derived as well. Finally, numerical results are presented to show the impact of parameters’ selection upon performance measures, and they have intriguing managerial implications. The results also reveal that the tollbooth system with skill-based servers is much more efficient compared to the system with dedicated servers, which has been studied by Melange et al. (Comput Oper Res 71:23–33, 2016), especially in rush hours.

An approximation method for 2-chain flexible queues with preemptive priority

Chaining configurations (e.g. 2-chain) have been widely applied in service systems to improve responsiveness to customer demand. In this paper, we propose an approximation method for the analysis of symmetric parallel queueing systems with 2-chain configuration and preemptive priority. In the queueing system of interest, the arrival processes of different types of customers follow independent Poisson processes with identical arrival rate λ, and service times of different types of customers are exponentially distributed with identical service rate μ. Each server serves its primary customers with preemptive priority. When there are no primary customers in the system, the server serves secondary customers. We first derive the system stability condition. Then, we analyse the system's various performance measures. Our approach relies on the matrix-analytic method coupled with the idle probability of each server, which is proved to be . The average queue length of each queue and the rate that each server serves its secondary customers (i.e. the helping rate) can be estimated using our proposed method. Finally, we conduct numerical studies to demonstrate the accuracy of our proposed approximation method.

Target Channel Selection Algorithm for Cognitive Radio Network

Cognitive radio network has attained more popularity because of giving prominent solution for spectrum inefficiency due to static spectrum allocation. This research proposed target channel selection algorithm which have been employed to improving the throughput and reducing number of handoffs. The proposed target channel selection algorithm is select the channel based on the idle probability and efficiency reward of channel and these are calculated from the surveillance of earlier usage statistics. When the channel selection is based on the above factor, the Secondary User gets chance to utilize the Primary user channel in maximum time to achieve optimal goal. The results show that proposed algorithm-based channel selection shows the better performance than existing algorithms in factors such as throughput, number of handoffs and collision rate.

Strategic Service Logistics Games with Customer-Induced Competition

We study Emergency Medical Services (EMS) in emerging economies, which are mostly still decentralized. Randomly arriving customers can request service from multiple service providers (duplicate orders) and choose the one that provides quicker service. Such customer behavior plays off providers deliberately against each other; and they, in turn, strategically define their service regions. To our knowledge, this setting, where customers pit service providers against each other dynamically and temporally, has not been studied before, and we present the first paper on this topic. We consider a stylized non-cooperative game with two service providers, each with one server, located at either end of a Hoteling line. Demand is temporally Poisson and spatially uniform. Providers' strategically choose service regions to maximize their utility accounting for stochasticity and associated opportunity costs. %This simplified model is quite complex with interesting and novel properties. We observe that provider behaviors are coupled, that is, the joint idle probability component is {\em not independent}. We analyze this using renewal theory, and find that the dependence between the servers is `limited'. This leads us to a significantly more tractable approximation of the joint idle probability, which is close to the exact joint probability. We then show that the approximate utility functions of a provider, given a fixed strategy of the competitor, are unimodal. We prove this game admits a symmetric \emph{pure strategy} Nash Equilibrium, and that this approximate equilibrium is an $\epsilon-$Nash equilibrium of the exact system. Providers, thus, can choose service regions that overlap and compete for customers, as is seen in reality. We compare this setting with two single order systems. We benchmark this system against a fully centralized EMS, and compute the Price of Anarchy (PoA), and show it is bounded by a small constant depending on the game parameters. As centralization is not achievable, we next compare with a decentralized single-order system (where EMSs still compete). Providers in aggregate are worse off under duplicate orders, in terms of utilities and number of customers served. Customers being strategic leads to similarly strategic decisions on the part of providers, whose service ranges (mostly) turn out to be shorter under duplicate orders. The customer perspective is more nuanced -- customers if served may experience shorter response times under duplicate orders; but ultimately, they have fewer provider options; and because of some placing duplicate orders, many others are dropped. Duplicate orders often deteriorate service equity along the line, with some regimes as exceptions. Policymakers should carefuly consider equity vs access tradeoffs for these exceptions. We also show that the gap between the duplicate order and single order systems can (only) be partially bridged using bargaining mechanisms that improve both provider and customer performance metrics.

Bounds on the rate of convergence for one class of inhomogeneous Markovian queueing models with possible batch arrivals and services

Abstract In this paper we present a method for the computation of convergence bounds for four classes of multiserver queueing systems, described by inhomogeneous Markov chains. Specifically, we consider an inhomogeneous M/M/S queueing system with possible state-dependent arrival and service intensities, and additionally possible batch arrivals and batch service. A unified approach based on a logarithmic norm of linear operators for obtaining sharp upper and lower bounds on the rate of convergence and corresponding sharp perturbation bounds is described. As a side effect, we show, by virtue of numerical examples, that the approach based on a logarithmic norm can also be used to approximate limiting characteristics (the idle probability and the mean number of customers in the system) of the systems considered with a given approximation error.

Coverage Analysis of Multiuser Visible Light Communication Networks

In this paper, a new mathematical framework for the coverage probability analysis of multiuser visible light communication (VLC) networks is presented. It takes into account the idle probability of access points (APs) that are not associated with any users and hence do not function as the source of interference. The idle probability of APs is evident especially in underloaded networks as well as general networks that operate with an AP sleep strategy to save energy and/or minimize the co-channel interference. Due to the absence of the “multipath fading” effect, the evaluation of the distribution function of the signal-to-interference-plus-noise ratio (SINR) is more challenging in VLC networks than in radio frequency-based cellular networks. By using the statistical-equivalent transformation of the SINR, analytical expressions for the coverage probability are derived and given in tractable forms. Comparing the derived results with extensive Monte Carlo simulations, we show that assuming a thinned homogeneous Poisson point process for modeling active APs is valid in general, and it gives close results to the exact ones when the density of users is no less than the density of APs in the network. Both analytical and simulation results show that, for typical receiver noise levels (~−117 dBm), approximating the SINR by the signal-to-interference ratio is sufficiently accurate for the coverage analysis in VLC networks.