Centralized Cognitive Radio Networks Research Articles

Multiuser Scheduling in Centralized Cognitive Radio Networks: A Multi-Armed Bandit Approach

In wireless communication networks, the network provider serves certain licensed primary users who pay for a dedicated use of the frequency channels. However, not all the channels are occupied by the primary users at all times. For efficient spectrum utilization, in centralized cognitive radio networks (CRNs), a cognitive base station (CBS) dynamically identifies the spectrum holes and allocates the frequency channels to the on-demand unlicensed secondary users known as cognitive radios (CRs). Although existing literature has developed various dynamic spectrum access mechanisms for CBS, there is still a dearth of studies due to the wide range of assumptions made in the solutions. Most of the existing works study the CBS scheduling problem scheme by adopting optimization-based methods and rely on the prior knowledge of the network parameters such as primary users’ activity. Moreover, the impact of channel switching costs on the network throughput has not been well studied. In this paper, we aim to maximize the CRNs total throughput, and we formulate the CBS scheduling problem as a non-stochastic (i.e., adversarial) combinatorial multi-armed bandit problem with semi-bandit feedback and arm switching costs. We propose two novel online learning algorithms for CBS scheduling with and without channel switching costs, where their regret performances are proved sublinear order-optimal in time as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{1/2}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2/3}$ </tex-math></inline-formula> , respectively, offering throughput-optimal scheduling for CRNs. Experiments on the synthetic and real-world spectrum measurement data complement and validate our theoretical findings.

Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized cognitive radio network (CRN) accessing the frequency band of a primary user (PU) in an underlay cognitive scenario with a designed PU protection specification. The main idea is that the CRN probes the PU, and subsequently, eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback indicates whether the probing-induced interference is harmful or not and can be used to learn the PU interference constraint. The cognitive part of this sequential probing process is the selection of the power levels of the secondary users that aims to learn the PU interference constraint with a minimum number of probing attempts while setting a limit on the number of harmful probing-induced interference events or equivalently of NACK packet observations over a time window. This constrained design problem is studied within the active learning (AL) framework and an optimal solution is derived and implemented with a sophisticated, accurate, and fast Bayesian learning method, the expectation propagation. The performance of this solution is also demonstrated through numerical simulations and compared with modified versions of AL techniques we developed in earlier work.

Interference Constraint Active Learning with Uncertain Feedback for Cognitive Radio Networks

In this paper, an intelligent probing method for interference constraint learning is proposed to allow a centralized cognitive radio network (CRN) to access the frequency band of a primary user (PU) in an underlay cognitive communication scenario. The main idea is that the CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback is implicit channel state information of the PU link, indicating whether the probing-induced interference is harmful or not. The intelligence of this sequential probing process lies in the selection of the power levels of the secondary users, which aims to minimize the number of probing attempts, a clearly active learning (AL) procedure, and expectantly the overall PU QoS degradation. The enhancement introduced in this paper is that we incorporate the probability of each feedback being correct into this intelligent probing mechanism by using a multivariate Bayesian AL method. This technique is inspired by the probabilistic bisection algorithm and the deterministic cutting plane methods (CPMs). The optimality of this multivariate Bayesian AL method is proven and its effectiveness is demonstrated through numerical simulations. Computationally cheap CPM adaptations are also presented, which outperform existing AL methods.

Comprehensive Reputation-Based Security Mechanism against Dynamic SSDF Attack in Cognitive Radio Networks

Collaborative spectrum sensing (CSS) was envisioned to improve the reliability of spectrum sensing in centralized cognitive radio networks (CRNs). However, secondary users (SUs)’ changeable environment and ease of compromise make CSS vulnerable to security threats, which further mislead the global decision making and degrade the overall performance. A popular attack in CSS is the called spectrum sensing data falsification (SSDF) attack. In the SSDF attack, malicious cognitive users (MUs) send false sensing results to the fusion center, which significantly degrades detection accuracy. In this paper, a comprehensive reputation-based security mechanism against dynamic SSDF attack for CRNs is proposed. In the mechanism, the reliability of SUs in collaborative sensing is measured with comprehensive reputation values in accordance with the SUs’ current and historical sensing behaviors. Meanwhile a punishment strategy is presented to revise the reputation, in which a reward factor and a penalty factor are introduced to encourage SUs to engage in positive and honest sensing activities. The whole mechanism focuses on ensuring the correctness of the global decision continuously. Specifically, the proposed security scheme can effectively alleviate the effect of users’ malicious behaviors on network decision making, which contributes greatly to enhancing the fairness and robustness of CRNs. Considering that the attack strategy adopted by MUs has been gradually transforming from simplicity, fixedness and singleness into complexity, dynamic and crypticity, we introduce two dynamic behavior patterns (true to false and then to true (TFT) and false to true and then to false (FTF)) to further validate the effectiveness of our proposed defense mechanism. Abundant simulation results verify the rationality and validity of our proposed mechanism.

Adjustable admission control with threshold in centralized CR networks: Analysis and optimization

In order to enhance the Quality of Service (QoS) for the secondary users (SUs) in Cognitive Radio (CR) networks reasonably, in this paper, we propose an adjustable admission control scheme considering an access threshold under a centralized architecture. We assume that a buffer is set for all the SUs. On the arrival instant of an SU packet, if the number of SU packets already in the buffer is equal to or greater than the access threshold that is set in advance, this SU packet will be admitted to join the system with an adjustable access probability, which is inversely proportional to the total number of packets in the system. Based on the adjustable admission control scheme proposed in this paper, considering the priority of the primary users (PUs) in CR networks, we build a preemptive priority queueing model. Aiming to comply with the digital nature of modern networks, we establish a two-dimensional discrete-time Markov chain (DTMC) and construct the transition probability matrix of the Markov chain. Accordingly, we provide the formulas for several performance measures, such as the blocking rate, the throughput and the average latency of the SU packets. With numerical results, we show the influence of the access threshold on different performance measures for the SU packets. Finally, taking into account the trade-off between different performance measures, we build a net benefit function to find the optimal access threshold with an optimization algorithm.

A Novel Evolutionary-Based Cooperative Spectrum Sensing Mechanism for Cognitive Radio Networks

Cognitive radio (CR) is considered as a feasible intelligent technology for 4G wireless networks or self-organization networks and envisioned as a promising paradigm of exploiting intelligence for enhancing efficiency of underutilized spectrum bands. In CR, one of the main concerns is to reliably sense the presence of primary users, to attain protection against harmful interference caused by the potential spectrum access of secondary users (SUs). In this paper, evolutionary algorithms, namely, genetic algorithm (GA) and particle swarm optimization (PSO) are investigated. An imperialistic competitive algorithm (ICA) is proposed to minimize error detection at the common soft data fusion (SDF) center for structurally centralized cognitive radio network (CRN). By using these techniques, evolutionary operations are invoked to optimize the weighting coefficients applied on the sensing measurement components received from multiple cooperative SUs. The proposed method is compared with other evolutionary algorithms, as well as other conventional deterministic, such as maximal ratio combining- (MRC-), modified deflection coefficient- (MDC-), normal deflection coefficient- (NDC-) based SDF schemes and OR-rule HDF based. MATLAB simulations confirm the superiority of the ICA-based scheme over the PSO-, GA-based and other conventional schemes in terms of detection performance. In addition, the ICA-based scheme also shows promising convergence and time running performance as compared to other iterative-based schemes. This makes ICA an adequate solution to meet real-time requirements.

QoS-Guaranteed Capacity of Centralized Cognitive Radio Networks with Interference Averaging Techniques

It is widely believed that cognitive radio (CR) networks have an opportunistic nature and therefore can only support best-effort traffics without quality-of-service (QoS) guarantees. In this paper, we propose a centralized CR network that adopts interference averaging techniques to support QoS guaranteed traffics under interference outage constraints. In such a CR network, a CR user adaptively adjusts its transmit power to compensate for the channel loss, thereby keeping the receive signal power at the CR base station (BS) at a constant level. The closed-form system capacity of such a CR network is analyzed and derived for a single cell with one CR BS and multiple CR users, taking into account various key factors such as interference outage constraints, channel fading, cell radius, and locations of primary users. The accuracy of the theoretical results is validated by Monte Carlo simulations. Numerical and simulation results show promising capacity potential for deploying QoS-guaranteed CR networks in frequency bands with fixed primary receivers. Our work can provide theoretical guidelines for the strategic planning of centralized CR networks.

Capacity Analysis of Centralized Cognitive Radio Networks for Best-effort Traffics

A centralized cognitive radio (CR) network is proposed and its system capacity is studied. The CR network is designed with power control and multi-user scheduling schemes to support best-effort traffics under peak interference power constraints. We provide an analytical framework to quantify its system capacity, taking into account various key factors such as interference constraints, density of primary users, cell radius, the number of CR users, and propagations effects. Furthermore, closed-form formulas are derived for its capacities when only path loss is considered in the channel model. Semi-analytical expressions for the capacities are also given when more realistic channel models that include path loss, shadowing, and small-scale fading are used. The accuracy of the proposed analytical framework is validated by Monte Carlo simulations. Illustrated with a practical example, the provided analytical framework is shown to be useful for the strategic planning of centralized CR networks.

A Spectrum Switching Delay-Aware Scheduling Algorithm for Centralized Cognitive Radio Networks

We formulate a scheduling problem that takes into account different hardware delays experienced by the secondary users (SUs) in a centralized cognitive radio network (CRN) while switching to different frequency bands. We propose a polynomial-time suboptimal algorithm to address our formulated scheduling problem. We evaluate the impact of varying switching delay, number of frequencies, and number of SUs. Our simulation results indicate that our proposed algorithm is robust to changes in the hardware spectrum switching delay and its performance is very close to its upper bound. We also compare our proposed method with the corresponding constant switching delay-based algorithm and demonstrate that our suggestion of taking into account the different hardware delays while switching to different frequency bands is essential for scheduling in CRNs.

A sensing and etiquette reputation-based trust management for centralized cognitive radio networks

In centralized cognitive radio networks (CRNs), the secondary user base station (SUBS), as a fusion center for sensing data, makes spectrum allocation decisions based on the sensing information from secondary users (SUs) around. However, secondary users’ changeable environment and easy compromise make the SUBS vulnerable to sensing data falsification and primary user emulation attack, which will mislead its global decision making. In addition, after SUs acquire the right to use spectrum, they may behave maliciously out of selfishness or for other reasons. In this paper, we propose a novel trust management mechanism for CRNs. In the mechanism, we measure the trustworthiness of SUs in spectrum sensing and spectrum use with sensing reputation and etiquette reputation respectively in accordance with the two roles of SUs during the cognition cycle. Spectrum allocation schemes are built based on the two reputations. Specifically, the sensing reputation-based data fusion can effectively reduce the effect of users’ malicious behaviors on network decision making. And sensing reputation combined with etiquette reputation to act as the basis for spectrum allocation will encourage SUs to engage in positive and truthful sensing activities and obey spectrum use etiquette. Meanwhile, the probability of spectrum bands being allocated to malicious users will be reduced, which is of great benefit to the enhancement of the fairness and robustness of CRNs. Simulation results show that the sensing reputation-based data fusion can significantly reduce the SUBS’ error times in decision making and has strong anti-attack ability. The sensing and etiquette reputation-based multiple-factor resource allocation enables CRNs to adjust the resource allocation strategy flexibly according to the network's requirements on security and profits.

Modeling Channel Allocation for Multimedia Transmission Over Infrastructure Based Cognitive Radio Networks

Multimedia applications have being widely supported by cognitive radio networks in which high bandwidth can be provided by fully utilizing radio spectrum. However, the research communities lack a theoretical modeling and analysis for multimedia system performance over cognitive radio networks. In this paper, a novel Markov-Chain based model of centralized cognitive radio network (CCRN) consisting of APs and user equipments (UEs) equipped with several antennas (one antenna for channel sensing, the others for data communication) is proposed. In the proposed model, AP will scan the idle primary channels (PCs) that are not used by primary users (PUs), and then transmit data through selected idle channels. Each selected channel in CCRN will be divided into many sub-time slots and some of them are used as the signalling control and the other slots are used only for data transmissions. In addition, three types of communications are considered and an appropriate number of time slots are pre-assigned to ensure QoS of multiple types of multimedia traffic. We used a multi-dimension Markov chain to model time slot allocation and proposed a general channel allocation model for multimedia traffic over CCRN. We validated our model through both theoretical analysis and simulations.

An Auction Theory Based Algorithm for Throughput Maximizing Scheduling in Centralized Cognitive Radio Networks

In this letter we propose an auction theory based algorithm for throughput maximizing scheduling in centralized cognitive radio networks (CRN). In the considered CRN scheme, a centralized base station coordinates the assignment of frequencies and time slots to cognitive users with multiple antennas. Our proposed algorithm uses first-price sealed bid auction mechanism in which frequency and time slot pairs are considered as the auctioned resources and cognitive users are the bidders. The experimental results show that our computationally efficient algorithm yields very close throughput performance to the optimization software CPLEX values.