Idle Probability Research Articles

Wireless standard technology identification via signal temporal characteristics: a comprehensive 802.11b/g/n case study

ISM spectrum is becoming increasingly populated with various wireless technologies. Consequently, wireless coexistence suffers especially when heterogeneous communicating wireless devices are collocated and sharing the same spectrum. Technology awareness of the collocated devices provides a pathway to improving wireless coexistence. This paper presents a novel method for identifying wireless technologies through the use of simple energy detection techniques to measure channel temporal characteristics including activity and idle time probability distributions. Wireless technology identification enables situational awareness to improve coexistence and reduce interference among the devices. An intelligent wireless device is capable of detecting wireless technologies operating in the vicinity. In this work, a wireless technology identification algorithm was assessed experimentally. Temporal traffic pattern for 802.11b/g/n homogeneous and heterogeneous networks were measured and used as algorithm input. Identification accuracies of up to 96.83% and 85.9% were achieved for homogeneous and heterogeneous networks, respectively.

Opportunistic Channel Sharing in Stochastic Networks With Dynamic Traffic

In this paper, we consider the stochastic network with dynamic traffic. The spatial distribution of access points (APs) and users are modeled as mutually independent Poisson point processes. Different from most of the previous literature works, which assume that all the APs are fully loaded, we consider the fact that APs having no data to transmit do not generate interference to users. The APs opportunistically share the channel according to the existence of the packet to be transmitted and the proposed interference suppression strategy. In the interference suppression region, only one AP can be active at a time to transmit the packet on the channel and the other adjacent APs keep silent to reduce serious interference. The idle probability of any AP, influenced by the traffic load and availability of the channels, is analyzed. The density of simultaneously active APs in the network is obtained, and the packet loss rate is further elaborated. We reveal the impacts of network features (e.g., AP density, user density, and channel state) and service features (e.g., user request and packet size) on the network performance.

A New Method for Channel Availability Analysis and the Associated Policy Design for Selection of Channel Sensing Order in CRNs

We consider the problem of the selection of channel sensing order in cognitive radio networks (CRNs) where the primary users (PUs) use multiple channels for data transmission. We assume that the occupancy state of each channel is correlated with that of the other channels, and the spectrum occupancy state for all the channels follows the discrete-time Markov model. In this paper, we propose to characterize the channel availability of the primary network using the idle probability of consecutive time slots (IPCOTS). Under the discrete-time Markov model, we derive the IPCOTS of each particular channel, under the condition that the states of all the channels in the primary network are identically distributed. Moreover, we propose a suboptimal state-dependent policy, called IPCOTS policy, for the selection of channel sensing order. Finally, we extend the policy to three typical cases. Simulation results show that, in an M/M/m/m-modeled primary network, more secondary user (SU) rewards can be obtained using the IPCOTS policy in comparison with the existing approaches.

Multi-objective multi-layer congested facility location-allocation problem optimization with Pareto-based meta-heuristics

Facility location-allocation problems arise in many practical settings from emergency services to telecommunication networks. We propose a multi-objective multi-layer facility location-allocation (MLFLA) model with congested facilities using classical queuing systems. The goal is to determine the optimal number of facilities and the service allocation at each layer. We consider three objective functions aiming at: (1) minimizing the sum of aggregate travel and waiting times; (2) minimizing the cost of establishing the facilities; and (3) minimizing the maximum idle probability of the facilities. The problem is formulated as a multi-objective non-linear integer mathematical programming model. To find and analyze the Pareto optimal solutions, we propose a Pareto-based multi-objective meta-heuristic approach based on the multi-objective vibration damping optimization (MOVDO) and the multi-objective harmony search algorithm (MOHSA). We demonstrate the effectiveness of the proposed model and exhibit the efficacy of the procedures and algorithms by comparing MOVDO and MOHSA with two well-known evolutionary algorithms, namely, the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective simulated annealing (MOSA).

Energy-Efficient Channel Handoff for Sensor Network-Assisted Cognitive Radio Network.

The visiting and less-privileged status of the secondary users (SUs) in a cognitive radio network obligates them to release the occupied channel instantly when it is reclaimed by the primary user. The SU has a choice to make: either wait for the channel to become free, thus conserving energy at the expense of delayed transmission and delivery, or find and switch to a vacant channel, thereby avoiding delay in transmission at the expense of increased energy consumption. An energy-efficient decision that considers the tradeoff between energy consumption and continuous transmission needs to be taken as to whether to switch the channels. In this work, we consider a sensor network-assisted cognitive radio network and propose a backup channel, which is sensed by the SU in parallel with the operating channel that is being sensed by the sensor nodes. Imperfect channel sensing and residual energy of the SU are considered in order to develop an energy-efficient handoff strategy using the partially observable Markov decision process (POMDP), which considers beliefs about the operating and backup channels and the remaining energy of the SU in order to take an optimal channel handoff decision on the question “Should we switch the channel?” The objective is to dynamically decide in each time slot whether the SU should switch the channel or not in order to maximize throughput by utilizing energy efficiently. Extensive simulations were performed to show the effectiveness of the proposed channel handoff strategy, which was demonstrated in the form of throughput with respect to various parameters, i.e., detection probability, the channel idle probabilities of the operating and backup channels, and the maximum energy of the SU.

Sequential Channel Sensing in Cognitive Small Cell Based on User Traffic

In this letter, we study the problem of sequential channel sensing in cognitive small cell (CSC), taking into account the effect of data traffics of offload users (OUs). To capture the characteristics of practical network traffic, we propose a general OU traffic model, which includes non-bursty Poisson process and bursty ON/OFF Pareto process. We do not assume that the OUs always have packets to transmit; instead, we use the actual data transmitted per second as the achievable traffic throughput. Furthermore, we formulate a sensing order optimization problem based on the traffic model. We jointly consider channel capacity, idle probability, heterogeneous sensing time and OU traffic in getting the optimal channel sensing order. Simulation results show that the proposed approach outperforms existing methods.

Dynamic Allocation of Random Access Opportunity for Machine-Type Communication in LTE-Advanced

With the rapid growth of machine-type communications (MTC) devices, the radio access network (RAN) will be overloaded when a large number of MTC devices try to access the radio networks in a short time. In this paper, a combination of a dynamic resource allocation and a random access check mechanism is proposed to solve the overload problems for MTC in LTE-Advanced. An analytical model is presented with the derivation of three metrics, the collision probability, the success probability, and the idle probability to evaluate the method. Simulation and analysis results show the superior performance of the method we proposed to solve the RAN overload problem.

An opportunistic channel access scheme for interweave cognitive radio systems

We propose a novel opportunistic access scheme for cognitive radios in an interweave cognitive system, that considers the channel gain as well as the predicted idle channel probability (primary user occupancy: Busy/idle). In contrast to previous work where a cognitive user vacates a channel only when that channel becomes busy, the proposed scheme requires the cognitive user to switch to the channel with the next highest idle probability if the current channel's gain is below a certain threshold. We derive the threshold values that maximize the long term throughput for various primary user transition probabilities and cognitive user's relative movement.

Waiting probability analysis for opportunistic spectrum access

Cognitive radio (CR) technology is regarded as a backbone for the future generation wireless systems. Using CR technology, secondary users (SUs), a.k.a. CR users, are allowed to access RF spectrum opportunistically provided that they are not causing harmful interference to primary users (PUs) and vacate the bands upon the arrival of licensed PUs. In this paper, we investigate waiting probability of SUs to get channel access based on PUs’ ON-OFF activities. Multi-user cognitive radio system is considered where SUs contend for spectrum access using time division multiple access over idle PU channels. Using queue dynamics as Poisson driven stochastic process, we characterise the waiting probability of secondary users. Generally speaking, in practical systems, secondary users of cognitive radio network would have no knowledge of activities of other users, thus the probability of being idle or contention probabilities of SUs’ in cognitive radio network have to be assigned according to the available local information. Our focus is on SUs’ waiting probability analysis, for which a systematic understanding is lacking. Simulation results show that the use of multiple channels and/or multiple slots leads to significant delay reduction and transmission fairness.

Optimal Throughput for Cognitive Radio with Energy Harvesting in Fading Wireless Channel

Energy resource management is a crucial problem of a device with a finite capacity battery. In this paper, cognitive radio is considered to be a device with an energy harvester that can harvest energy from a non-RF energy resource while performing other actions of cognitive radio. Harvested energy will be stored in a finite capacity battery. At the start of the time slot of cognitive radio, the radio needs to determine if it should remain silent or carry out spectrum sensing based on the idle probability of the primary user and the remaining energy in order to maximize the throughput of the cognitive radio system. In addition, optimal sensing energy and adaptive transmission power control are also investigated in this paper to effectively utilize the limited energy of cognitive radio. Finding an optimal approach is formulated as a partially observable Markov decision process. The simulation results show that the proposed optimal decision scheme outperforms the myopic scheme in which current throughput is only considered when making a decision.

A linear model for surface mining haul truck allocation incorporating shovel idle probabilities

We present models of trucks and shovels in oil sand surface mines. The models are formulated to minimize the number of trucks for a given set of shovels, subject to throughput and ore grade constraints. We quantify and validate the nonlinear relation between a shovel’s idle probability (which determines the shovel’s productivity) and the number of trucks assigned to the shovel via a simple approximation, based on the theory of finite source queues. We use linearization to incorporate this expression into linear integer programs. We assume in our integer programs that each shovel is assigned a single truck size but we outline how one could account for multiple truck sizes per shovel in an approximate fashion. The linearization of shovel idle probabilities allows us to formulate more accurate truck allocation models that are easily solvable for realistic-sized problems.

Distributed Cognitive Radio Spectrum Access with Imperfect Sensing Using CTMC

Cognitive radio (CR) has become an efficient approach for the utilization of scarce spectrum by enabling spectrum access in an opportunistic manner. With increasing wireless network service providers, users, and applications, it is indispensable to optimize usage of even parts of the spectrum that are licensed. Dynamic spectrum access (DSA) is an approach which facilitates the opportunistic use of licensed spectra, when they are idle. In this paper, a primary prioritized distributed DSA algorithm using continuous-time Markov chain (CTMC) model is proposed. In a distributed scheme, each secondary user needs to be aware of the statistics—arrival and service rates—of the other secondary users to optimize the throughput while maintaining fairness. A heuristics-based approach is formulated making use of the estimated spectrum idle probability and the interference each user experiences to iteratively update user statistics. This framework is also extended to incorporate the effects of imperfect spectrum sensing in the form of misdetection and false alarms. Simulation results show that the proposed algorithm attains an overall throughput that is better than CSMA when the primary user spectrum utilization is around 45%. The degradation in throughput caused by imperfect sensing over perfect sensing is also analyzed.

Optimization of the (MAP1, MAP2)/(PH1, PH2)/N retrial queue model of wireless cellular networks with channel allocation

We consider a queueing model with N channels and an orbit with infinite waiting positions to model a specific cell in wireless networks. It is assumed that handoff and new calls have different arrival rates of Markov Arrival Process (MAP) and their channel holding time distributions are different Phase-type (PH) distributions. We analyze the Markov chain underlying the queueing system and obtain its ergodicity condition. Using matrix analytic techniques, we derive many performance measures such as the blocking probability of new calls and the dropping probability of handoff calls, the idle probability of the target cell, the mean queue lengths of two types of calls. We give numerical results to show the impact of system parameters on performance measures.

A two-class discrete-time queueing model with two dedicated servers and global FCFS service discipline

This paper considers a simple discrete-time queueing model with two types (classes) of customers (types 1 and 2) each having their own dedicated server (servers A and B resp.). New customers enter the system according to a general independent arrival process, i.e., the total numbers of arrivals during consecutive time slots are i.i.d. random variables with arbitrary distribution. Service times are deterministically equal to 1 slot each. The system uses a “global FCFS” service discipline, i.e., all arriving customers are accommodated in one single FCFS queue, regardless of their types. As a consequence of the “global FCFS” rule, customers of one type may be blocked by customers of the other type, in that they may be unable to reach their dedicated server even at times when this server is idle, i.e., the system is basically non-workconserving. One major aim of the paper is to estimate the negative impact of this phenomenon on the queueing performance of the system, in terms of the achievable throughput, the system occupancy, the idle probability of each server and the delay. As it is clear that customers of different types hinder each other more as they tend to arrive in the system more clustered according to class, the degree of “class clustering” in the arrival process is explicitly modeled in the paper and its very direct impact on the performance measures is revealed. The motivation of our work are systems where this kind of blocking is encountered, such as input-queueing network switches or road splits.

A fair scheduling scheme based on collision statistics for cognitive radio networks

SUMMARYIn cognitive radio networks (CRNs), considering the randomness of primary users' (PUs) arrival and the nonideality of spectrum sensing performed by secondary users (SUs), the collisions between PUs and SUs are unavoidable. Frequent occurrences of collisions will strongly degrade PUs' and SUs' QoS. Therefore, collision statistics, such as the average number of collisions, is a very important performance metric in CRNs. If collision statistics are not considered in the scheduling scheme of the CRNs, some SUs will meet more collisions than the others, which cause unfair experiences among the SUs. Therefore, a fair scheduling scheme based on collision statistics is proposed in this paper, which can improve fairness across all SUs. First, the number of collisions is defined as an important fairness metric for each SU, and the scheduler dynamically adjusts the priorities of the SUs by periodically counting each SU's collision number. Then, a prediction algorithm, based on the continuous‐time Markov chain model, is proposed to predict the idle probabilities of the available channels in the next slot. Considering both the priorities of the SUs and the idle probabilities of the available channels, a rational scheduling scheme will be achieved finally. Assuming the ordered hunt scheduling scheme applied by the primary system, the simulation results show that the proposed scheme can significantly improve the fairness across all SUs with little impact on spectrum utilization. Copyright © 2012 John Wiley & Sons, Ltd.

Queueing Analysis of Spectrum Trading for Wireless Cognitive Radio Networks

Spectrum trading is an important approach for the secondary users (SU, the unlicensed user) to share the spectrum resource with the primary users (PU, the licensed user). Such economic method can improve not only the efficiency but also the quality of spectrum sharing. For getting the most profit from the spectrum trading, we considered a spectrum trading model based on queueing theory. We analyzed the relationship between the spectrum server and the spectrum demander and discussed the problem of server uncertainty. Simulation results demonstrated the analysis results in terms of the idle probability and average queue size.

Optimization of sensing time in multichannel sequential sensing for cognitive radio

SUMMARYThis paper studies the design of sensing time in multichannel sequential sensing for cognitive radio under the multichannel sensing slots and one transmission slot scheme. Firstly, we consider the case where the channel idle probability is known. In this case, when the channels’ signal‐to‐noise ratios (SNRs) are equal, we prove that there exist optimal sensing times that maximize the achievable throughput for secondary user, while primary user is sufficiently protected. When the channels’ SNRs are different, brute‐force search is used to find the optimal sensing order that maximizes the achievable throughput of secondary user, and the corresponding optimal sensing times will be obtained. Then, a suboptimal and low‐complexity approach of one‐slot sensing time without knowing the channel idle probability is proposed. The proposed approach solves the problem of sensing order by selecting the channel with the minimum sensing time in the next sensing slot when the channels’ SNRs are different. Numerical results show that the proposed approach achieves a performance close to that of the optimal sensing time under different conditions and has significant performance improvement over the approach of fixed sensing time. Copyright © 2011 John Wiley & Sons, Ltd.

Available Bandwidth Measurement Method of High-Speed Network Based on Link Idle Probability

Available bandwidth measurement of network is an important step in network traffic design and assignment as well as network planning. The previous packet pair and packet train technologies based on network bottleneck segment principle can not be used for available bandwidth measurement of high-speed network. Based on analysis of shortcomings of bandwidth measurement method accordance with network bottleneck segment, the paper presented a kind of high-speed network available bandwidth measurement method based on link idle probability and gave specific steps. The method took router as main body and do not need to consider complicated interference of data flow in the network. Simulation test based on OPNET indicates the feasibility of this method.

An N-Component Series Repairable System with Repairman Doing Other Work and Priority in Repair

In this paper, we discussed the reliability of an N-unit series repairable system with the repairman doing other work and priority in repair. In this system, it is assumed that the working time distributions of the n components and the arrival interval time distributions of the customers which are out of the system are both exponential and the components in system is given priority in repair. It is also assumed that the repair time distributions of the n components and the service time distributions of the customers are both general continuous distributions. After repair, components are “as good as new”. Under these assumptions, using a supplementary variable technique and Laplace transform technique, some important reliability indices such as the system availability, the idle probability of the repairman and the rate of service for customers are derived. Our problem is to determine whether or not given the priority to components in repairing such that the benefit of the system is maximized.

Transient solution of a non-empty chemical queueing system

In this paper, we illustrate that a power series technique can be used to derive explicit expressions for the transient state distribution of a queueing problem having “chemical” rules with an arbitrary number of customers present initially in the system. Based on generating function and Laplace techniques Conolly et al. (in Math Sci 22:83–91, 1997) have obtained the distributions for a non-empty chemical queue. Their solution enables us only to recover the idle probability of the system in explicit form. Here, we extend not only the model of Conolly et al. but also get a new and simple solution for this model. The derived formula for the transient state is free of Bessel function or any integral forms. The transient solution of the standard M/M/1/∞ queue with λ = μ is a special case of our result. Furthermore, the probability density function of the virtual waiting time in a chemical queue is studied. Finally, the theory is underpinned by numerical results.