Capacity Upper Bound of Channel Assembling in Cognitive Radio Networks with Quasistationary Primary User Activities

In cognitive radio networks (CRNs) with multiple channels, various channel-assembling (ChA) strategies may be applied to secondary users (SUs), resulting in different achieved capacity. However, there is no previous work on determining the capacity upper bound (UB) of ChA for SUs under given system configurations. In this paper, we derive the maximum capacity for CRNs with ChA through Markov chain modeling, considering that primary user (PU) activities are relatively static, compared with SU services. We first deduce a closed-form expression for the maximum capacity in a dynamic ChA strategy and then demonstrate that no other ChA strategy can provide higher capacity than that achieved by this dynamic strategy. © 2012 IEEE.The work of L. Jiao and F. Y. Li was supported in part by the Research Council of Norway through the ECO-boat MOL project under Grant 210426. The work of E. Song was supported in part by the National Natural Science Foundation of China under Grant 60901037 and in part by the Foundation for Basic Research of Sichuan University for Distinguished Young Scholars under Grant 0082604132188. The work of V. Pla was supported by the Spanish Ministerio de Ciencia e Innovacion through Project TIN2010-21378-C02-02. The review of this paper was coordinated by Prof. B. Hamdaoui.Jiao, L.; Song, E.; Pla, V.; Li, FY. (2013). Capacity Upper Bound of Channel Assembling in Cognitive Radio Networks with Quasistationary Primary User Activities. IEEE Transactions on Vehicular Technology. 62(4):1849-1855. https://doi.org/10.1109/TVT.2012.2236115S1849185562

Spectrum Migration Approach Based on Pre-decision Aid and Interval Mamdani Fuzzy Inference in Cognitive Radio Networks

This study intends to improve the QoS of SUs and CRNs performance. A novel spectrum migration approach based on pre-decision aid and interval Mamdani fuzzy inference is presented. we first define spectrum migration factors as spectrum characteristic metrics for spectrum migration decision. In addition, we use predecision aid to reduce system complexity and improve spectrum migration efficiency. To shorten spectrum migration decision time and seek the optimal spectrum holes, interval Mamdani fuzzy inference is put forward. Finally, simulation results show the proposed approach can inhibit the upward trend of service retransmission probability and average migration times effectively, and improve the effective utilization of CRNs spectrum resource significantly

Sensing or Transmission: Causal Cognitive Radio Strategies with Censorship

This paper introduces a novel opportunistic transmission strategy for cognitive radios (CRs). The primary user (PU) is assumed to transmit in a time-slotted manner according to a two-state Markov model, and the CR is either sensing, that is, obtaining a causal, noisy observation of a primary user (PU) state, or transmitting, but not both at the same time. In other words, the CR observations of the PU are censored whenever the CR is transmitting. The objective of the CR transmission strategy is to maximize the utilization ratio (UR), i.e., the relative number of the PU-idle slots that are used by the CR, subject to that the interference ratio (IR), i.e., the relative number of the PU-active slots that are used by the CR, is below a certain level. We introduce an a-posteriori LLR-based CR transmission strategy, called CLAPP, and evaluate this strategy in terms of the achievable UR for different PU model parameters and received signal-to-noise ratios (SNRs). The performance of CLAPP is compared with a simple censored energy detection scheme. Simulation results show that CLAPP has 52% gain in UR over the best censored energy detection scheme for a maximum IR level of 10% and an SNR of -2dB. \ua9 2002-2012 IEEE

Dynamic multichannel access with imperfect channel state detection

Abstract—A restless multi-armed bandit problem that arises in multichannel opportunistic communications is considered, where channels are modeled as independent and identical Gilbert–Elliot channels and channel state detection is subject to errors. A simple structure of the myopic policy is established under a certain condition on the false alarm probability of the channel state detector. It is shown that myopic actions can be obtained by maintaining a simple channel ordering without knowing the underlying Markovian model. The optimality of the myopic policy is proved for the case of two channels and conjectured for general cases. Lower and upper bounds on the performance of the myopic policy are obtained in closed-form, which characterize the scaling behavior of the achievable throughput of the multichannel opportunistic system. The approximation factor of the myopic policy is also analyzed to bound its worst-case performance loss with respect to the optimal performance. Index Terms—Cognitive radio, dynamic multichannel access, myopic policy, restless multi-armed bandit

Cooperative Spectrum Sensing in Cognitive Radio Networks Using Multidimensional Correlations

In this paper, a multidimensional-correlation-based sensing scheduling algorithm, (CORN)2, is developed for cognitive radio networks to minimize energy consumption. A sensing quality metric is defined as a measure of the correctness of spectral availability information based on the fact that spectrum sensing information at a given space and time can represent spectrum information at a different point in space and time. The scheduling algorithm is shown to achieve a cost of sensing (e.g., energy consumption, sensing duration) arbitrarily close to the possible minimum, while meeting the sensing quality requirements. To this end, (CORN)2 utilizes a novel sensing deficiency virtual queue concept and exploits the correlation between spectrum measurements of a particular secondary user and its collaborating neighbors. The proposed algorithm is proved to achieve a distributed and arbitrarily close to optimal solution under certain, easily satisfied assumptions. Furthermore, a distributed Selective-(CORN)2 (S-(CORN)2) is introduced by extending the distributed algorithm to allow secondary users to select collaboration neighbors in densely populated cognitive radio networks. In addition to the theoretically proved performance guarantees, the algorithms are evaluated through simulations

Coalition Formation Games for Collaborative Spectrum Sensing

Collaborative Spectrum Sensing (CSS) between secondary users (SUs) in cognitive networks exhibits an inherent tradeoff between minimizing the probability of missing the detection of the primary user (PU) and maintaining a reasonable false alarm probability (e.g., for maintaining a good spectrum utilization). In this paper, we study the impact of this tradeoff on the network structure and the cooperative incentives of the SUs that seek to cooperate for improving their detection performance. We model the CSS problem as a non-transferable coalitional game, and we propose distributed algorithms for coalition formation. First, we construct a distributed coalition formation (CF) algorithm that allows the SUs to self-organize into disjoint coalitions while accounting for the CSS tradeoff. Then, the CF algorithm is complemented with a coalitional voting game for enabling distributed coalition formation with detection probability guarantees (CF-PD) when required by the PU. The CF-PD algorithm allows the SUs to form minimal winning coalitions (MWCs), i.e., coalitions that achieve the target detection probability with minimal costs. For both algorithms, we study and prove various properties pertaining to network structure, adaptation to mobility and stability. Simulation results show that CF reduces the average probability of miss per SU up to 88.45% relative to the non-cooperative case, while maintaining a desired false alarm. For CF-PD, the results show that up to 87.25% of the SUs achieve the required detection probability through MWCComment: IEEE Transactions on Vehicular Technology, to appea

A survey on MAC protocols for complex self-organizing cognitive radio networks

Complex self-organizing cognitive radio (CR) networks serve as a framework for accessing the spectrum allocation dynamically where the vacant channels can be used by CR nodes opportunistically. CR devices must be capable of exploiting spectrum opportunities and exchanging control information over a control channel. Moreover, CR nodes should intelligently coordinate their access between different cognitive radios to avoid collisions on the available spectrum channels and to vacate the channel for the licensed user in timely manner. Since inception of CR technology, several MAC protocols have been designed and developed. This paper surveys the state of the art on tools, technologies and taxonomy of complex self-organizing CR networks. A detailed analysis on CR MAC protocols form part of this paper. We group existing approaches for development of CR MAC protocols and classify them into different categories and provide performance analysis and comparison of different protocols. With our categorization, an easy and concise view of underlying models for development of a CR MAC protocol is provided