The Stable Channel State Analysis for Multimedia Packets Allocation over Cognitive Radio Networks

In cognitive radio networks, secondary users (SUs) are considered as selfish spectrum users, thus how to maximize the spectrum efficiency by these selfish users becomes an endless research topic. Recent studies mostly focus on the competition analysis between SUs using economic mechanism, such as game theory and auction, but the spectrum owner can hardly increase the spectrum efficiency directly when SUs apply the distributed manner. In this paper, we consider the slotted uplink scenario where several SUs have data transmitted to secondary access point (AP) under distributed random access manner. The AP decides how to divide its spectrum which maximizes the whole throughput, then SUs select the channels which would maximize their own profit. Our results show that SUs' channel selection process leads to a Nash Equilibrium, and the AP derives the proper number of channels based on the properties of NE. Moreover, we derive a rule for AP to decide which SUs should access the spectrum and lead to the increment in the whole throughput.

Quality of experience (QoE) of a secondary spectrum user is mainly governed by its spectrum utilization, the energy consumption in spectrum sensing and the impact of channel switching in a cognitive radio network. It can be enhanced by prediction of spectrum availability of different channels in the form of their idle times through historical information of primary users’ activity. Based on a reliable prediction scheme, the secondary user chooses the channel with the longest idle time for transmission of its data. In contrast to the existing method of statistical prediction, the use and applicability of supervised learning based prediction in various traffic scenarios have been studied in this paper. Prediction accuracy is investigated for three machine learning techniques, artificial neural network based Multilayer Perceptron (MLP), Support Vector Machines (SVM) with Linear Kernel and SVM with Gaussian Kernel, among which, the best one is chosen for prediction based opportunistic spectrum access. The results highlight the analysis of the learning techniques with respect to the traffic intensity. Moreover, a significant improvement in spectrum utilization of the secondary user with reduction in sensing energy and channel switching has been found in case of predictive dynamic channel allocation as compared to random channel selection.

Recently, cognitive radio has been proposed to improve the spectrum resource utilisation. In cognitive radio networks, the secondary or unauthorised users SUs are allowed to sense, detect and utilise the frequency bands that are not currently being used. Once the primary user PU is active, the secondary users have to vacate the channel within certain amount of time. Thus, it is a challenging task to find the suitable sensing period for better spectrum utilisation. In this paper, we propose a selectable sensing period optimisation SSPO mechanism. In the algorithm, each secondary user use the different periods for channel detection according to the different channel states. so that we can deal with the tradeoff between spectrum utilisation and energy consumption more flexibly. We consider the constraint of interference to PU and the energy consumption to compute the optimal sensing periods. Our simulation results demonstrate that, with the constraint of energy consumption, the SSPO algorithm can gain the spectrum utilisation 22:8% more than the previous algorithm. Moreover, with the constraint of spectrum utilisation, the SSPO algorithm can save the energy consumption 20:6% more than the previous algorithm.

Turning foes to allies in cognitive radio networks

We investigate opportunistic cooperation between secondary (unlicensed) users and primary (licensed) users in cognitive radio networks. We consider two models for such cooperation. In the first model, called the Cooperative Relay Model, a secondary user cannot transmit its own data concurrently with a primary user. However, it can employ cooperative relaying in order to improve the effective transmission rate of the primary user. In the second model, called the Interference Model, a secondary user is allowed to transmit its data concurrently with a primary user. However, the secondary user can “cooperate” by deferring its transmissions when the primary user is busy. In both models, the secondary users must make intelligent cooperation decisions as they seek to maximize their own throughput subject to average power constraints. The decision options are different during idle and busy periods of the primary user, and the decisions in turn influence the durations of these periods according to a controllable infinite state Markov chain. Such problems can be formulated as constrained Markov decision problems, and conventional solution techniques require either extensive knowledge of the system dynamics or learning based approaches that suffer from large convergence times. However, using a generalized Lyapunov optimization technique, we design a novel greedy and online control algorithm that overcomes these challenges. Remarkably, this algorithm does not require any knowledge of the network arrival rates and is provably optimal.

Energy-efficient cooperative cognitive relaying schemes for cognitive radio networks

In a cognitive radio network (CRN), secondary users (SUs) utilize primary users (PUs) licensed spectrum in an opportunistic manner. Spectrum sensing is of the utmost importance in CRN to find and use the available spectrum without harmful interference to the PUs. Conventionally, to implement spectrum utilization, SUs are required to sense the primary spectrum first and then transmit data on the available spectrum. In this paper, we propose a dedicated wireless spectrum sensing network (WSSN), eliminating sensing overhead from SUs with the aim of improving achievable throughput. With WSSN assistance, we eliminate sensing time from the SUs frame, hence increasing the transmission time, which maximizes the achievable throughput. Additionally, the sensing duration is increased by deploying a dedicated WSSN, decreasing the probability of false alarm and achieving a targeted high probability of detection. A low probability of false alarm increases the spectrum utilization, improving the achievable throughput, while a high detection probability ensures PUs protection. Moreover, the proposed technique also addresses hidden and exposed terminal problems along with smooth spectrum mobility. Finally, we provide simulation results to demonstrate the proposed techniques, effectiveness. In the results, we have compared the achievable throughput of the proposed scheme with that of conventional CRN.

The optimal admission control for secondary users (SUs) using blockchain technology in a cognitive radio (CR) network is investigated in this paper. When an SU comes to the system, it will decide to either enter the system, or give up its service and then leave the system based on the current number of PU and SU users in the network. SUs are linked as a block in the waiting area to access the spectrum in an opportunistic way without interfering the operations of PUs. When a PU arrives, if the band is occupied by SU(s), the SU service on the band is interrupted and SU(s) is then placed back to the waiting queue. Admitting an SU would bring a reward to the system provider but holding an SU in the system would also incur an expense to the provider. We concentrate on the optimization problem of when to admit or reject an arriving SU in order to achieve the maximum total discounted expected reward for any initial state. By establishing a discounted Continuous Time Markov Decision Process (CTMDP) model, we verify that the optimal policies for admitting SUs are state-related control limit policies. An extensive numerical analysis is implemented to show how to determine the value of optimal threshold and of the corresponding maximum objective function. Especially, a simulation by using the idea of the neural network and machine learning is implemented to show how to determine the optimal threshold value for any new input parameters through learning process for a given input parameters. The idea and the corresponding result of this paper can be applied in designing optimal cognitive radio networks.

Cognitive radio networks (CRNs) have emerged as a promising solution to enhance spectrum utilization by using unused or less used spectrum in radio environments. The basic idea of CRNs is to allow secondary users (SUs) access to licensed spectrum, under the condition that the interference perceived by the primary users (PUs) is minimal. In CRNs, the channel availability is uncertainty due to the existence of PUs, resulting in intermittent communication. Transmission control protocol (TCP) performance may significantly degrade in such conditions. To address the challenges, some transport protocols have been proposed for reliable transmission in CRNs. In this paper we survey the state-of-the-art transport protocols for CRNs. We firstly highlight the unique aspects of CRNs, and describe the challenges of transport protocols in terms of PU behavior, spectrum sensing, spectrum changing and TCP mechanism itself over CRNs. Then, we provide a summary and comparison of existing transport protocols for CRNs. Finally, we discuss several open issues and research challenges. To the best of our knowledge, our work is the first survey on transport protocols for CRNs.

Motivated by the low utilization of the licensed spectrum across many frequency bands, sensing-based oppor- tunistic spectrum access has recently emerged as an alternative to the outdated exclusive spectrum access policy. Under this new paradigm, a secondary (unlicensed) user monitors a primary (licensed) frequency band for a given and opportunistically transmits if it does not detect any ongoing licensed operations. Evidently, selection of the parameters involves balanc- ing a tradeoff between the speed and the quality with which the secondary user senses the licensed band. With the average throughput as the performance criterion, we obtain the parameters so as to optimize the performance of the secondary user while providing the primary user with its desired level of interference protection. I. INTRODUCTION As evidenced by recent measurements, many frequency bands across the licensed spectrum are significantly under- utilized (1), (2). This finding suggests that the spectrum scarcity, as perceived today, is largely due to the inefficient fixed frequency allocations rather than the physical shortage of the spectrum and has led the regulatory bodies to consider the opportunistic access to the temporally/spatially unused licensed bands (a.k.a. the white spaces) as a means to improve the efficiency of spectrum usage. In the absence of cooperation or signalling between the primary licensee and the secondary users, spectrum availability for the opportunistic access may be determined by direct spectrum where the secondary user monitors a licensed band for a given sensing time and opportunistically transmits if it does not detect any ongoing licensed operations. This approach is particularly appealing due to its low deployment cost and its compatibility with legacy primary users and is being considered for inclusion in the upcoming IEEE 802.22 standard for opportunistic access to the TV spectrum (3). Due to their ability to autonomously detect and to react to the changes in the spectrum usage, secondary users equipped with the spectrum capability may be considered as a primitive form of the cognitive radio (4). Design of any scheme involves balancing a tradeoff between the quality and the speed of through an appropriate selection of the time. As we shall illustrate, in the context of spectrum sensing, may be fine- tuned to enhance the secondary users' perceived quality-of- service (QoS) as long as the regulatory constraint for the protection of the primary users against harmful interference is satisfied. In particular, we will obtain the optimum times at different stages of the spectrum to maximize the average throughput of the secondary user. In this paper, simple energy detection (a.k.a. radiometry) (5) is chosen as the underlying detection scheme. In general, when some information about the structure of the primary signal is available, ad hoc feature-detectors offer a better performance (6). We note, however, that the methodology employed in this paper may be applied to optimize different spectrum sensors once the quality is characterized in terms of the time. The remainder of this paper is organized as follows. The regulatory constraints on spectrum are described in the following section. Section 3 provides an overview of the energy-based spectrum sensing. The optimum times for different stages of the spectrum are derived in Section 4. Finally, this paper is concluded in Section 5.

Spectrum sensing is performed by secondary users (SUs) to detect primary users (PUs) in cognitive radio networks. The limited time duration allocated for spectrum sensing may result in high false alarm probability, which always leads to low spectrum utilization and degrades the SU throughput. To overcome this problem, we propose a novel joint sensingtime adaption and data transmission (JSTA-DT) scheme. In our proposed JSTA-DT scheme, two adjacent sensing periods are bundled to form a sensing block. In each sensing block, the SU performs partial time spectrum sensing in the first sensing period. If the sensing result indicates that the PU is absent, the SU transmits its data; otherwise, the SU performs full time spectrum sensing adaptively. Both theoretical analyses and simulation results show that compared to the conventional partial time spectrum sensing and data transmission scheme, the spectrum utilization and achievable SU throughput are improved significantly by using our proposed JSTA-DT scheme.

Cognitive Radio Networks (CRNs) is a formidable evolution in wireless technology owing to its potentiality to resolve spectrum scarcity crisis. In CRNs, cognitive radio user’s sense idle spectrum as well as opportunistically entrée sensed spectrum devoid of approved users. The best spectrum is said to be selected opportunistically, shared with other users with no severity to licensed users. Many CRN devices that are fueled by battery possessing constrained energy capacity. Prevailing battery mechanisms yet couldn’t contribute CRN devices works for a long time. Therefore, conservation of energy is mandatory as far as routing in CRN is concerned. In this work a machine learning based method called, Regression Cooperative Spectrum Sensing and Gradient Flying Fox Route Optimization (RCSS-GFFRO) for energy and bandwidth aware data routing with spectrum utilization in CRNs is proposed. Protection of primary users (PUs) as well as optimization of secondary user (SU) transmission included in proposed method. In the proposed method a cooperative spectrum sensing using machine learning (ML) model called, Median Regression function is applied. The Median Regression Cooperative Spectrum Sensing in our work with the bandwidth and energy efficiency ensures acceptable probability of interference to primary users due to secondary user’s access by measuring the relationship between bandwidth and energy efficiency simultaneously. Next, optimal route identification is performed using Gradient Flying Fox Route Optimization algorithm. By applying gradient optimization function minimizes the interference with primary users and increases secondary user performance via sensing time and transmission time parameters with higher routing performance. Simulation estimated with parameters.

The cooperation among the secondary users or among the secondary users and the primary users have proven to bring many benefits to both primary and secondary users in cognitive radio networks. In this paper, cooperative relaying in cognitive radio networks is studied for rateless coding error-control mechanism. For a pair of primary transmitter and receiver, we find the region in which any secondary or cognitive user can cooperate with the primary user in a constructive way. A queueing theoretical model is employed to describe the underlying system parameters and to create a mathematical framework for the analysis. The throughput performance of the secondary users is maximized based on the input traffic of the primary users. Using the unique properties of rateless codes, a low-complexity relay selection mechanism is also introduced. Through the experimental analysis, we show that the proposed rateless-coding-based cooperative mechanism can perform similar to the more complicated and more costly selective-repeat automatic repeat request algorithm. The numerical analysis using the Rapid Analysis of Queueing Systems (RAQS) software package is provided to support the findings.

In this paper Comparative analysis of hard and soft fusion techniques for Energy Efficient cooperative spectrum sensing (CSS) is presented. CSS is the efficient way of detecting spectrum holes in cognitive radio (CR) network by combining sensing information of various CR users also known as secondary users (SUs). In CSS, energy resources become precious when the SUs in CR network are battery operated. So it becomes important to use their energy efficiently. In this paper, Hard fusion techniques like AND, OR and Majority and soft fusion techniques like square law selection (SLS) and square law combine (SLC) fusion are explained and fusion rule threshold that maximizes the EE is calculated. Results show that for Energy Efficient CSS, Majority fusion outperforms other fusion techniques. For majority fusion EE is 4.4% more compared to SLS fusion and maximum EE is 3.8 Mbits/Hz/joule at sensing time equals to 2ms.

Channel accessibility by a secondary user (SU) in cognitive radio networks (CRNs) depends on the availability of the spectrum based on primary user and other SU activities. A new SU request may be blocked and an ongoing SU service may also be discarded if no sufficient spectrum is available. So far, little work has been done to analyze the reliability and availability aspects of CRNs from the perspective of the dependability theory. In this paper, we introduce the concept of availability for spectrum access in multi-channel CRNs, which is defined as the fraction of time that a CRN can allocate at least the minimum number of required channels for a new SU request. Through a proposed continuous-time discrete-state Markov model, we analyze the performance of CRN channel access schemes in terms of a few reliability-relevant metrics such as mean channel available time and mean time to first channel unavailability. Furthermore numerical results are also presented in this paper and the correctness of the derived theoretical models are verified through simulations.