Users In Cognitive Radio Networks Research Articles

On the Transmission Rate Strategies in Cognitive Radios

We investigate instantaneous transmission rate strategies for secondary users in cognitive radio networks by analyzing their effective capacity performance in different signal-to-noise ratio regimes with different quality-of-service constraints and transmission block sizes. Describing a channel model with one secondary transmitter and one secondary receiver with the potential presence of primary users, we present an interference power constraint that limits the transmission power of secondary users not only when a channel is sensed as busy but also when a channel is sensed as idle. Calling the existing transmission rate strategy Optimistic Policy , we introduce two other strategies, particularly Conservative Policy and Greedy Policy . Secondary users in Optimistic Policy set the instantaneous transmission rate to the instantaneous mutual information assuming the correctness of channel sensing results, whereas they set the instantaneous transmission rate to the instantaneous mutual information regarding possible transmission outages in Conservative Policy and disregarding possible transmission outages in Greedy Policy . We construct a state transition diagram and formulate the effective capacity employing these policies. We calculate the minimum energy-per-bit requirements and the high signal-to-noise ratio slope in order to explore performance variations in low and high signal-to-noise ratio regimes, respectively. Correspondingly, we show that Optimistic Policy is, in general, more favorable in secondary users when the quality-of-service constraints are loose, the transmission blocks are shorter, and the signal-to-noise ratio is low. On the other hand, Conservative Policy is better when the quality-of-service constraints are strict, the transmission blocks are longer, and the signal-to-noise ratio is high.

Virtualization-based Cognitive Radio Networks

The emerging network virtualization technique is considered as a promising technology that enables the deployment of multiple virtual networks over a single physical network. These virtual networks are allowed to share the set of available resources in order to provide different services to their intended users. While several previous studies have focused on wired network virtualization, the field of wireless network virtualization is not well investigated. One of the promising wireless technologies is the Cognitive Radio (CR) technology that aims to handle the spectrum scarcity problem through efficient Dynamic Spectrum Access (DSA). In this paper, we propose to incorporate virtualization concepts into CR Networks (CRNs) to improve their performance. We start by explaining how the concept of multilayer hypervisors can be used within a CRN cell to manage its resources more efficiently by allowing the CR Base Station (BS) to delegate some of its management responsibilities to the CR users. By reducing the CRN users’ reliance on the CRN BS, the amount of control messages can be decreased leading to reduced delay and improved throughput. Moreover, the proposed framework allows CRNs to better utilize its resources and support higher traffic loads which is in accordance with the recent technological advances that enable the Customer-Premises Equipments (CPEs) of potential CR users (such as smart phone users) to concurrently run multiple applications each generating its own traffic. We then show how our framework can be extended to handle multi-cell CRNs. Such an extension requires addressing the self-coexistence problem. To this end, we use a traffic load aware channel distribution algorithm. Through simulations, we show that our proposed framework can significantly enhance the CRN performance in terms of blocking probability and network throughput with different primary user level of activities.

Hybrid Relaying Protocol for Joint Power and Subcarrier Allocation for OFDM Based Cognitive Radio Networks

This paper aims to avoid the interference imposed by the secondary user on a primary user in Cognitive Radio Network (CRN). In CRN, the interference from secondary user enforced on primary user mainly depends on spectral interval between primary and secondary systems. Moreover, it also depends on the power allocated to the secondary user. In order to avoid interference imposed by secondary user on primary user, a Hybrid Relaying Protocol for Joint Power and Subcarrier Allocation for Orthogonal Frequency Division Multiplexing (OFDM) based Cognitive Radio Networks is proposed. In hybrid relaying protocol, a secondary user uses amplify and forward (AF) protocol and decode and forward (DF) protocol based on the requirement to maximize network throughput. A greedy algorithm is proposed for the selection of relay to get the optimal solution. Moreover, an efficient hybrid power and subcarrier algorithm is used by considering interference constraint imposed by cognitive network to the primary user.

Traffic‐Adaptive Proactive Spectrum Handoff Strategy for Graded Secondary Users in Cognitive Radio Networks

In order to meet different delay requirements of various communication services in Cognitive radio (CR) networks, Secondary users (SUs) are divided into two classes according to the priority of accessing to spectrum in this paper. Based on the proactive spectrum handoff scheme, the Preemptive resume priority (PRP) M/G/1 queueing is used to characterize multiple spectrum handoffs under two different spectrum handoff strategies. The traffic-adaptive spectrum handoff strategy is proposed for graded SUs so as to minimize the average cumulative handoff delay. Simulation results not only verify that our theoretical analysis is valid, but also show that the strategy we proposed can reduce the average cumulative handoff delay evidently. The effect of service rate on the proposed spectrum switching point and the admissible access region are provided.

An Improved Power Control AFSA for Minimum Interference to Primary Users in Cognitive Radio Networks

In order to make primary users (PUs) receive minimum interference generated from all secondary user (SUs) in underlay cognitive radio networks (CRNs), while ensure the quality of services (QoS) of SUs, a strategy of standard distributed optimal power control based on artificial fish swarm algorithm (AFSA) is proposed. The strategy considers interference plus noise ratio of each SU under the minimum threshold and the transmit power of each SU below the maximum permitted power. To well adapt to the dynamic communication scenarios and enhance QoS for SUs, an improved artificial fish swarm algorithm (IAFSA) is also presented. Simulation-based performance analysis illustrate that, in comparison with the particle swarm optimization algorithm, chaos particle swarm optimization algorithm and shuffled frog leaping algorithm, both AFSA and IAFSA can lead SUs to transmit less power in order to reduce the interference to PUs, and simultaneously provide fast global convergence, stability, robustness to CRNs, and better communication performance.

Game-theoretic analysis of selfish secondary users in cognitive radio networks

In this paper, we study the problem of selfish behavior of secondary users (SUs) based on cognitive radio (CR) with the presence of primary users (PUs). SUs are assumed to contend on a channel using the carrier sense multiple access with collision avoidance (CSMA/CA) and PUs do not consider transmission of SUs, where CSMA/CA protocols rely on the random deference of packets. SUs are vulnerable to selfish attacks by which selfish users could pick short random deference to obtain a larger share of the available bandwidth at the expense of other SUs. In this paper, game theory is used to study the systematic cheating of SUs in the presence of PUs in multichannel CR networks. We study two cases: A single cheater and multiple cheaters acting without any restraint. We identify the Pareto-optimal point of operation of a network with multiple cheaters and also derive the Nash equilibrium of the network. We use cooperative game theory to drive the Pareto optimality of selfish SUs without interfering with the activity of PUs. We show the influence of the activity of PUs in the equilibrium of the whole network.

A novel protocol for transparent and simultaneous spectrum access between the secondary user and the primary user in cognitive radio networks

In this paper, we propose a novel protocol for cognitive radio networks, termed spectrum co-access protocol (SCAP), for secondary users to transparently and simultaneously access spectrum with primary users. The motivation is to eliminate the disruption to secondary user communications by the resurgence of primary user transmission. This protocol enables mutually beneficial coexistence between the primary user network and the secondary user network. Through spectrum co-access, SCAP creates a virtual secondary user control channel in licensed spectrum that is invisible to the primary user. As a result, SCAP is a medium access control protocol that allows for simultaneous spectrum access between the secondary user and the primary user networks. The performance evaluation indicates that SCAP provides significant performance improvement for the secondary user network over the existing opportunistic spectrum access scheme.

Towards trustworthy collaboration in spectrum sensing for ad hoc cognitive radio networks

Cognitive radio networks (CRN) make use of dynamic spectrum access to communicate opportunistically in frequency bands otherwise licensed to incumbent primary users such as TV broadcast. To prevent interference to primary users it is vital for secondary users in CRNs to conduct accurate spectrum sensing, which is especially challenging when the transmission range of primary users is shorter compared to the size of the CRN. This task becomes even more challenging in the presence of malicious secondary users that launch spectrum sensing data falsification (SSDF) attacks by providing false spectrum reports. Existing solutions to detect such malicious behaviors cannot be utilized in scenarios where the transmission range of primary users is limited within a small sub-region of the CRN. In this paper, we present a framework for trustworthy collaboration in spectrum sensing for ad hoc CRNs. This framework incorporates a semi-supervised spatio-spectral anomaly/outlier detection system and a reputation system, both designed to detect byzantine attacks in the form of SSDF from malicious nodes within the CRN. The framework guarantees protection of incumbent primary users' communication rights while at the same time making optimal use of the spectrum when it is not used by primary users. Simulation carried out under typical network conditions and attack scenarios shows that our proposed framework can achieve spectrum decision accuracy up to 99.3 % and detect malicious nodes up to 98 % of the time.

Energy-efficient broadcast in multihop cognitive radio networks

Broadcast is an important operation in Cognitive Radio Networks (CRNs). How to achieve broadcast in an energy-efficient way is important since users in CRNs are usually battery-powered. Existing single-channel broadcast mechanisms are not suitable for CRNs because multiple channels can be used in a CRN. Most existing multi-channel broadcast schemes rely on a common control channel (CCC) to support broadcast. However, finding such a CCC in a CRN is difficult because an unlicensed user’s available channels are changed over time and space. In this paper, we focus on an environment where no broadcast structure or CCC is established in advance while a node is equipped with one CR transceiver and does not aware of the locations and distances to other nodes. We have defined the one-hop Minimum Multichannel Set Cover (MMSC) problem and the multi-hop Minimum Cost Broadcast (MCB) problem in such an environment. Both problems are NP-hard and we have proposed a heuristic solution, the Energy-Efficient Broadcast (EEB) protocol, to solve them. Considering the uncertain primary user occupancy issue, the EEB protocol features a distributed solution that uses only local information. Simulation results verify that EEB effectively reduces energy consumption and achieves high packet delivery ratio.

Performance Evaluation of Joint Admission and Eviction Controls of Secondary Users in Cognitive Radio Networks

In recent years, cognitive radio (CR) has been proposed to promote the efficient utilization of the spectrum by exploiting the spectrum holes. The fundamental issue in CR networks is the efficient utilization of radio resources. Call admission control (CAC), which controls the number of calls based on the available resources and bandwidth, is an important functionality to ensure the quality of service (QoS). Thus, in this paper, a new call admission and eviction control of secondary users (CAEC) is proposed. The proposed CAEC is a combination of three variant schemes. The main objective of this proposed scheme is to make admission and evictions decisions jointly in order to improve the spectrum utilization efficiency and ensure the QoS for secondary users (SUs) and to provide better balance between the required SU’s QoS and primary users’ protection. Performance evaluation of the proposed scheme with its variants is studied and compared with other two previous schemes, known as basic CAC (BCAC) and queue-based CAC (QCAC), in terms of the QoS requirements, such as useful utilization and loss probability. Results show that the proposed CAEC achieves higher admitted traffic for SUs than BCAC and QCAC. In addition, it ensures higher system utilization without degrading the primary users’ performance.

Genetic Algorithm Based QoS Aware Adaptive Subcarrier Allocation in Cognitive Radio Networks

In this paper, an adaptive subcarrier allocation scheme with reconfiguration of operating parameters for Cognitive Radio Networks (CRN) is presented. A QoS-conscious spectrum decision frame work is projected, where spectrum bands are determined by considering the application requirements as well as the dynamic nature of the spectrum bands. The novel subcarrier allocation algorithm is developed to fulfill different performance objective as a solution for subcarrier allocation and power allocation problem for Cognitive Radio (CR) users in CRNs. It employs operating frequency parameter modification using Proportional Resource Algorithm and Genetic Algorithm (GA). The multi objective optimization problem with equality and inequality constraint is considered. Moreover, a dynamic subcarrier allocations scheme is developed based on GA to decide on the spectrum bands adaptively dependent on the time-varying CR network capacity. The proposed algorithm targets to achieve maximum data rate for each subcarrier, maximize the overall network throughput and maximize the number of satisfied user under the constraints of bandwidth and guarantee Quality of Service (QoS) requirement from dynamic spectrum management (DSM) perspective. Moreover, it determines the best available channel.

A Bayesian Approach to Service Selection for Secondary Users in Cognitive Radio Networks

In cognitive radio networks where secondary users (SUs) use the time-frequency gaps of primary users' (PUs) licensed spectrum opportunistically, the experienced throughput of SUs depend not only on the traffic load of the PUs but also on the PUs' service type. Each service has its own pattern of channel usage, and if the SUs know the dominant pattern of primary channel usage, then they can make a better decision on choosing which service is better to be used at a specific time to get the best advantage of the primary channel, in terms of higher achievable throughput. However, it is difficult to inform directly SUs of PUs' dominant used services in each area, for practical reasons. This paper proposes a learning mechanism embedded in SUs to sense the primary channel for a specific length of time. This algorithm recommends the SUs upon sensing a free primary channel, to choose the best service in order to get the best performance, in terms of maximum achieved throughput and the minimum experienced delay. The proposed learning mechanism is based on a Bayesian approach that can predict the performance of a requested service for a given SU. Simulation results show that this service selection method outperforms the blind opportunistic SU service selection, significantly.

인벤토리모델을 이용한 스펙트럼풀링 최적화

ABSTRACT Recently, many research efforts have paid attention to spectrum pooling meshanism that provides efficient way to manage transient spectrum requests of secondary users in cognitive radio networks. Spectrum pooling is maintained by WSP (wireless service provider). WSP leases the spectrums stored in her spectrum pool to secondary users with being paid for it. It is natural that WSP tries to get profits as much as possible, which implies the WSP tries to minimize the cost required for maintaining her spectrum pool. In this paper, we model the spectrum pool into a probabilistic inventory model. Assuming secondary users’ spectrum requests follow normal distribution, we give a strategic way that minimizes the maintenance cost of the spectrum pool. By a series of simulations, we show that WSP can reduce the total maintenance cost through our inventory model-based spectrum pooling than maintaining fixed inventory level. 키워드 : 인지무선, 스펙트럼 풀링, 인벤토리 모델, WSP Key word : Cognitive Radio, Spectrum Pooling, Inventory Model, Wireless Service ProviderJournal of the Korea Institute of Information andCommunication Engineering

Robust Power Control Algorithm for Multiple Primary Users and Secondary Users in Cognitive Radio Networks

Conventional distributed power control algorithms in cognitive radio (CR) are based on the assumption of perfect channel state information (CSI) which may lead to performance degradation in practical systems. In this paper, the robust distributed power control problem is investigated in CR systems by considering the uncertainty of channel gain. The objective is to maximize the sum rate of secondary users (SUs) subject to the primary users (PUs) interference power constraints and the transmission power constraint of SUs. The channel gain fluctuation is described using ellipsoid sets and the interference power constraints can be converted into robust interference power constraints. Lagrangian duality method is applied to solve the robust power control problem in a distributed way. Simulation results show that the effectiveness of the proposed robust algorithm.

A Spatio-Stochastic Framework for Cross-Layer Design in Cognitive Radio Networks

In this paper, we address the problem of distributed resource management for secondary users in Cognitive Radio Networks (CRNs), through a topology aware and frequency agile cross-layer approach. We exploit the theory of spatial processes and propose a Markov Random Field (MRF) based framework, which enables secondary CRN users to achieve efficient and viable mechanisms in the lower protocol stack layers by exchanging local only information. Specifically, through Gibbs sampling secondary users can optimize in a distributed and parallel manner their channel allocation, medium access and routing without resolving to otherwise computationally demanding optimization approaches. Through analysis and simulation we exhibit the efficacy of the proposed framework and show that a semi-parallel implementation can significantly reduce the required overhead cost compared to the sequential Gibbs sampling approach, while retaining a very close performance to the latter. We also study the emerging trade-offs by demonstrating the performance benefits in terms of channel assignment, medium access and data flow.

Analysis of Sensing Efficiency for Cooperative Spectrum Sensing With Malicious Users in Cognitive Radio Networks

In this letter, we consider cooperative spectrum sensing with the presence of malicious users. The detailed detection performance and sensing efficiency are formulated to analyze the impact of the incorrect information on the sensing system. To cope with the misleading effect of the malicious users, we further propose to implement the authentication mechanism to filter out the incorrect information. We also optimize the authentication code length to reduce the system overhead. Simulation results demonstrate that we can reap comparable performance with the authentication mechanism and achieve a good tradeoff between sensing efficiency and communication overhead.

ANALYSIS AND OPTIMIZATION OF BER FOR SECONDARY USERS IN COGNITIVE RADIO USING BBO AND PSO METHODS

Cognitive radio has been recognized as an answer to problem of limited spectrum in wireless communication. In this paper, the Bit-error-rate (BER) of secondary users in Cognitive radio network has been reduced using the Biography-based optimization (BBO) and Particle swarm optimization (PSO) techniques by maximizing the channel gains. For each secondary transmitter antenna, the channel gain for secondary to secondary link and secondary to primary link is optimized and the data is sent from an antenna using constrained difference selection method, showing maximum gain. Other than selection of antenna, another constraint for the optimization is the transmit power for secondary user. The BER has been improved for various values of Signalto-noise ratio (SNR) over which the transmission took place. It is proved from the simulation results that the average BER has been reduced by 8.571% and 5.229% with BBO and PSO respectively. It has been shown that BBO and PSO have a trade-off between reduction of average SNR and Time elapsed. BBO takes 0.048169 seconds for calculation whereas PSO does the same in 0.036195 seconds. At last, practical applications of both techniques are discussed.

Channel Reservation Strategies for Multiple Secondary Users in Cognitive Radio Networks

In cognitive radio networks (CRNs), improving the performance of SUs is the main aspect of present study. In this paper, we focus on the optimal access strategy to provide QoS guarantees for multi-services. The contribution of this paper is as follows. Firstly, we formulate and solve the optimal spectrum access problem with different channel reservation scheme. Secondly, we take account of buffer for handoff secondary users and propose a handoff scheme for multi-services. In addition, the computation complexity of solving the optimal channel reservation problem is simplified by a binary search. The system is analyzed using a continuous-time Markov chain model. The numerical results confirm the validity of the proposed scheme.

Effective Monitoring of Freeloading User in the Presence of Active User in Cognitive Radio Networks

In cognitive networks, primary and secondary users (PUs and SUs) share the radio resource according to fair regulations. The goal of this paper is to introduce a new method to detect an unfair user (freeloader) that hides its radio signal under the noise floor in the presence of an active (either primary or secondary) user. The performance of the proposed technique is evaluated by theoretical analysis and computer simulations. The probability of detection is expressed, for constant false-alarm rates, as a function of both the active signal power and the background noise variance. The results show the effectiveness of the new monitoring algorithm in several operating cases.

Multiple-level power allocation strategy for secondary users in cognitive radio networks

Abstract In this paper, we propose a new multiple-level power allocation strategy for the secondary user (SU) in cognitive radio (CR) networks. Different from the conventional strategies, where SU either stays silent or transmit with a constant/binary power depending on the busy/idle status of the primary user (PU), the proposed strategy allows SU to choose different power levels according to a carefully designed function of the receiving energy. The way of the power level selection at SU side is optimized to maximize the achievable rate of SU under the constraints of average transmit power at SU and average interference temperature to PU. Simulation results demonstrate that the proposed strategy can significantly improve the throughput of SU compared to the conventional strategies.