Sensing tasks allocation in cellular-based cognitive radio network

In Cognitive Radio (CR) Networks, especially in Cellular-based CR networks, in order to obtain the precise spectrum state which is available in the whole coverage, the spectrum sensing function need to be accomplished by the cooperation of multi-sensing nodes, i.e., cooperative spectrum sensing. This paper mainly focuses on the key question of the cooperative spectrum sensing: How many sensing nodes are required and how to allocate the sensing tasks to these sensing nodes in an effective way? In this paper, we classify the spectrum bands into three categories by a pre-sensing mechanism, in order to firstly exclude unavailable channels and select candidate channels for fine sensing in the next stage. Then the required number of sensing node for each candidate channel is determined by the proposed scheme. Through the scheme proposed in this paper, the sensing tasks allocation in cellular-based CR network can be accomplished in a high effective way and the sensing overhead could keep a low level.     

A dynamic spectrum access network based on cognitive radio

Dynamic spectrum access technologies based on Cognitive Radio (CR) is under intensive research carried out by the wireless communication society and is expected to solve the problem of spectrum scarcity. However, most enabling technologies related to dynamic spectrum access are considered individually. In this paper, we consider these key technologies jointly and introduce a new implementation scheme for a Dynamic Spectrum Access Network Based on Cognitive Radio (DSAN-BCR). We start with a flexible hardware platform for DSAN-BCR, as well as a flexible protocol structure that dominates the operation of DSAN-BCR. We then focus on the state of the art of key technologies such as spectrum sensing, spectrum resources management, dynamic spectrum access, and routing that are below the network layer in DSAN-BCR, as well as the development of technologies related to higher layers. Last but not the least, we analyze the challenges confronted by these mentioned technologies in DSAN-BCR, and give the perspectives on the future development of these technologies. The DSAN-BCR introduced is expected to provide a system level guidance to alleviate the problem of spectrum scarcity.     

A MULTI-RESOLUTION SPECTRUM SENSING （MRSS） SCHEME UNDER MEASUREMENT-BASED CHANNEL MODELS IN COGNITIVE RADIO

Spectrum sensing is one of the key technologies in Cognitive Radios (CRs). Previous works are accomplished under simple channel models, which may lead to unreliable results when it applied to the over-the-air systems. In this paper, we investigate the performance of a Multi-Resolution Spectrum Sensing (MRSS) algorithm under measurement-based channel models in China. MRSS is a wavelet based algorithm which is suitable for non-stationary, wideband signal analysis. Using statistical modeling, measurement-based channel models are presented under typical urban and suburban scenarios in Shanghai, China. Then, the performance of the MRSS algorithm is evaluated under the measurement-based channel models. Simulation results show that, using MRSS, the performance is always better in the scenarios where Line-Of-Sight (LOS) path exist; also, in LOS scenarios, rich scattering effect helps to increase the performance.     

Novel spectrum sensing method based on the spatial spectrum for cognitive radio systems

Spectrum sensing is the key problem for Cognitive Radio (CR) systems. A method based on the Peak-to-Average Amplitude-Ratio (PAAR) of the Spatial Spectrum (SS) of the received signals is proposed to sense the available spectrum for the cognitive users with the help of the multiple antennas at the receiver of the cognitive users. The greatest advantage of the new method is that it requires no information of the noise power and is free of the noise power uncertainty. Both the simulation and the analytical results show that the proposed method is robust to noise uncertainty, and greatly outperform the classical Energy Detector (ED) method.     

Spectrum handoff in cognitive radio with fuzzy logic control

The secondary usage of spectrum has been investigated in Cognitive Radio (CR) network to resolving the spectrum scarcity issue in wireless communication. When Primary Users (PU) who own the spectrum appear, spectrum handoff is needed to maintain the communications of Secondary Users. But the decision making of spectrum handoff is a challenge issue for CR network, because the input of decision making, which obtain through spectrum sensing, is heterogeneous and inexact. In this paper we will use fuzzy logic control theory to solve this issue and make use of new information for handoff operation: the probability of PU’s occupancy at a certain channel. Our new algorithm can make more intelligent decision compared to simple traditional spectrum handoff decision making and reduce the probability of spectrum handoff, also the performance of SU’s communication can be enhanced.     

A distributed method for relay selection in cooperative cognitive radio networks

We analyze the performance of a fully decentralized relay-selection method for the relay-assisted Cognitive Radio (CR) systems. The method requires no explicit communication among relay, assumes no prior knowledge of geometry. In particular, we assume that the frequencies chosen by the relay-assisted CR network overlapped with an active primary link. The proposed relay-selection scheme decreases the outage probability by considering both the channel scenario and the interference to the primary link compared with the non-cooperative transmission. Benefits of cooperative diversity are increased with the increased number of relays.     

A collaborative spectrum sensing scheme using fuzzy comprehensive evaluation in cognitive radio

Cognitive Radio (CR) is a promising technology to solve the challenging spectrum scarcity problem.However,to implement CR,spectrum sensing is the groundwork and the precondition.In this paper,a collaborative spectrum sensing scheme using fuzzy comprehensive evaluation is proposed.The final sensing decision of the proposed scheme is based on the combination of distributed sensing results of different Secondary Users (SUs).To improve the reliability of the sensing decision,the combination procedure takes into account the credibility of each SU,which is evaluated using fuzzy comprehensive evaluation.The effect of the presence of malicious SUs and malfunctioning SUs on the performance of the proposed scheme is also investigated.The efficiency of the scheme is validated through analysis and simulation.     

Null-space projection and waterfilling resource allocation in multi-antenna cognitive radio networks

Cognitive Radio (CR) is a promising technique for the next generation mobile communication system for its capability to solve the conflicts between the scarcity and underutilization of spectrum. In this paper, aiming at maximizing the system capacity of a multi-antenna CR system on the premise that avoid interference to the primary system in the same band simultaneously, a resource allocation method which is able to avoid interference between PRimary (PR) and CR users by projecting the transmit signals of CR users on the null space of the PR users’ channels is proposed. CR users with better channel condition are selected, and the interference from CR system to PR users can be removed completely by projecting the transmit signals of CR system on the null-space of PR users’ channels. Parallel sub-channels are constructed for CR users through Singular Value Decomposition (SVD). At last, waterfilling is also adopted to increase the CR users’ capacity. Simulation result demonstrates that compared with existing methods, our method can improve the achievable sum rate of CR users as well as reduce the outage probability of PR users.     

Low-complexity joint beamforming and power allocation for SINR balancing in cognitive radio networks

We consider the Signal-to-Interference plus Noise Ratio (SINR) balancing problem involving joint beamfoming and power allocation in the Cognitive Radio (CR) network, wherein the Single-Input Multi-Output Multiple Access Channels (SIMO-MAC) are assumed. Subject to two sets of constraints: the interference temperature constraints of Primary Users (PUs) and the peak power constraints of Cognitive Users (CUs), a low-complexity joint beamforming and power allocation algorithm called Semi-Decoupled Multi-Constraint Power Allocation with Constraints Preselection (SDMCPA-CP) for SINR balancing is proposed. Compared with the existing algorithm, the proposed SDMCPA-CP can reduce the number of matrix inversions and matrix eigen decompositions significantly, especially when large numbers of PUs and CUs are active, while still providing the optimal balanced SINR level for all the CUs.     

Modeling radio resource management in cognitive radio system based on OFDMA using colored Petri Net

Cognitive Radio (CR) system based on Orthogonal Frequency Division Multiple Access (OFDMA), such as Wireless Regional Area Networks (WRAN) and Worldwide Interoperability for Microwave Access (WiMAX), often attempt to improve performance via dynamic radio resource management, which is characterized as concurrent processing of different traffic and nondeterministic system capacity. It is essential to design and evaluate such complex system using proper modeling and analysis tools. In the previous work, most of the communication systems were modeled as Markov Chain (MC) and Stochastic Petri Nets (SPN), which have the explicit limitation in evaluating adaptive OFDMA CR system with wide area traffic. In this paper, we develop an executable top-down hierarchical Colored Petri Net (CPN) model for adaptive OFDMA CR system, and analyze its performance using CPN tools. The results demonstrate that the CPN can model different radio resource management algorithms in CR Systems, and the CPN tools require less computational effort than Markov model using Matlab, with its flexibility and adaptability to the traffics which arrival interval and processing time are not exponentially distributed.     

An effective suboptimal power loading scheme in OFDM-based cognitive radio systems

Orthogonal frequency division multiplexing (OFDM) is an attractive modulation candidate for Cognitive Radio (CR) networks. Effective and reliable subcarrier power allocation in OFDM-based Cognitive Radio (CR) networks is a challenging problem. This paper focuses on the power allocation for OFDM-based Cognitive Radio (CR) networks. Our objective is to maximize the total transmission rates of Secondary Users (SU) by adjusting the power of subcarrier while the interference introduced to the Primary User (PU) is within a certain range and the total power of subcarrier is not beyond the total power constraint. We investigate the optimal power allocation algorithm for OFDM-based Cognitive Radio (CR) based on convex optimization theory. Then, because of high complexity of the optimal power allocation algorithm, we propose an effective suboptimal power loading scheme. Theory analysis and simulation results show that the performance of the suboptimal power allocation algorithm is close to the performance of the optimal power allocation algorithm, while the complexity of the suboptimal power allocation algorithm is much lower.     

S-transformation based integrated approach for spectrum estimation,storage, and sensing in cognitive radio

Cognitive Radio (CR) uses the principle of dynamic spectrum allocation to improve the utilization of spectrum bands. The estimation of missing data is essential for maintaining an uninterrupted quality of service in the CR. However, the existing methods are not suitable for interpolating missing data in high frequency signals. The storage of spectrum occupancy information is crucial for learning the spectrum usage and prediction. The existing techniques for wideband spectrum sensing suffer from poor edge detection capabilities. This paper proposes an S-Transformation (ST) based approach to solve these problems. For missing samples, the proposed method improves the accuracy of estimation. The ST can also be used to store the spectrum occupancy information. The simulation results show that the proposed scheme outperforms others by improving the accuracy of edge detection. Further, the simple implementation of the ST in the frequency domain is an advantage for the real time application.     

Joint angular and spectral estimation technique using nonlinear Kalman filters for cognitive radio

The joint estimation of direction of arrivals (DOA) and carrier frequencies of band-limited source signals is considered in this paper. A novel technique based on nonlinear Kalman filters is proposed for this joint angular and spectral estimation problem for cognitive radio (CR). Since sampling a wideband spectrum at Nyquist rate increases the analog-to-digital converter (ADC) requirements, we propose executing Kalman filter algorithm over a spatial state space model. Thus, one time sample is required and hardware complexity is reduced. Two types of nonlinear Kalman filters, extended Kalman filter (EKF) and unscented Kalman filter (UKF), are proposed. We consider their sub-optimal performance and show how to control their convergence. However, the proposed algorithms can detect a number of source signals limited to the number of elements in employing arrays.     

On co-channel and adjacent channel interference mitigation in cognitive radio networks

Cognitive radio (CR) is an emerging wireless communications paradigm of sharing spectrum among licensed (or, primary) and unlicensed (or, CR) users. In CR networks, interference mitigation is crucial not only for primary user protection, but also for the quality of service of CR user themselves. In this paper, we consider the problem of interference mitigation via channel assignment and power allocation for CR users. A cross-layer optimization framework for minimizing both co-channel and adjacent channel interference is developed; the latter has been shown to have considerable impact in practical systems. Cooperative spectrum sensing, opportunistic spectrum access, channel assignment, and power allocation are considered in the problem formulation. We propose a reformulation–linearization technique (RLT) based centralized algorithm, as well as a distributed greedy algorithm that uses local information for near-optimal solutions. Both algorithms are evaluated with simulations and are shown quite effective for mitigating both types of interference and achieving high CR network capacity.     

Effects of imperfect channel sensing and estimation on the performance of cognitive radio systems

In realistic scenarios of cognitive radio (CR) systems, imperfect channel sensing may occur due to false alarms and miss detections. Channel estimation between the secondary user transmitter and another secondary user receiver is another challenge in CR systems, especially for frequency‐selective fading channels. In this context, this paper presents a study of the effects of imperfect channel sensing and channel estimation on the performance of CR systems. In particular, different methods of channel estimation are analyzed under channel sensing imperfections. Initially, a CR system model with channel sensing errors is described. Then, the expectation maximization (EM) algorithm is implemented in order to learn the channel fading coefficients. By exploiting the pilot symbols and the detected symbols at the secondary user receiver, we can estimate the channel coefficients. We further compare the proposed EM estimation algorithm with different estimation algorithms such as the least squares (LS) and linear minimum mean square error (LMMSE). The expressions of channel estimates and mean squared errors (MSE) are determined, and their dependencies on channel sensing uncertainty are investigated. Finally, to reduce the complexity of EM algorithm, a sub‐optimal algorithm is also proposed. The obtained results show that the proposed sub‐optimal algorithm provides a comparable bit error rate (BER) performance with that of the optimal one yet with less computational complexity.     

Spectrum-energy-efficient sensing with novel frame structure in cognitive radio networks

Cooperative spectrum sensing (CSS) is a promising technology in spectrum sensing with an admirable performance. In this paper, we define a utility function which jointly considers the spectrum-efficiency and the energy-efficiency. In a single-user sensing scenario, by maximizing the utility function, a rigorous analytical expression for the optimal threshold of the energy detector is derived. In CSS, the general frame structure is inefficient since the time consumed by reporting contributes little to the sensing performance. In this paper, we propose a novel CSS frame structure, in which one secondary user's (SU's) reporting time is also used for other SUs’ sensing. For time varying channels, collecting the sensing results at different time points is expected to achieve a time diversity gain for a SU, then the novel multi-minislot CSS scheme is proposed. In CSS, the optimal randomized rule and the optimal final decision threshold are derived. Simulation results show a significant improvement of the utility by using the proposed multi-minislot CSS scheme. It is also shown that there exists an optimal number of cooperating SUs that maximizes the utility, and the optimal number decreases as the price of the sensing energy increases.     

A low EVM zero-IF RF transmitter for cognitive radio application

A zero-IF transmitter for Cognitive Radio (CR) application is presented. To effectively reduce the interference between Power Amplifier (PA) and Voltage Controlled Oscillator (VCO), two VCOs are adopted, one is 450 MHz and the other is from 1148 MHz to 1252 MHz with an 8 MHz step, so the frequency of them are different from the operational frequency of PA. The Local Oscillator (LO) of the modulator generated by mixing the signals of the two VCOs has a low phase noise of −82 dBc/Hz with an offset of 1 kHz. The measurement result of the transmitter shows that the Adjacent Channel Power Ratio (ACPR) is less than −47.5 dBc at 27 dBm output, and the Error Vector Magnitude (EVM) is less than 1.7%.     

认知无线电技术在电磁频谱管理中的应用研究

通过对认知无线电技术主要特点的分析,研究认知无线电技术带来频谱管理的巨大变化。分析了目前频谱管理的2种基本模式,并对传统频谱管理网系进行了研究,给出了它们的主要缺陷。通过详细分析了认知无线电网络频谱管理的基本原理与过程,并对比与传统频谱管理的异同,得出认知无线电技术应用在频谱管理中的优点。认知无线电技术应用在频谱管理中能够带来频谱管理的灵活性,突破传统频谱授权指配的方式,实现频谱管理的自适应性和智能化。     

Distributed cooperative spectrum sensing based on reinforcement learning in cognitive radio networks

Spectrum sensing is an initial task for the successful operation of cognitive radio networks (CRN). During cooperative spectrum sensing, malicious secondary user (SU) may report false sensing data which would degrade the final aggregated sensing outcome. In this paper, we propose a distributed cooperative spectrum sensing (CSS) method based on reinforcement learning (RL) to remove data fusion between users with different reputations in CRN. This method regards each SU as an agent, which is selected from the adjacent nodes of CRN participating in CSS. The reputation value is used as reward to ensure that the agent tends to merge with high reputation nodes. The conformance fusion is adopted to promote consensus of the whole network, while it’s also compared with the decision threshold to complete CSS. Simulation results show that the proposed method can identify malicious users effectively. As a result, the whole CRN based on RL is more intelligent and stable.     

认知无线电中基于截断序贯检测的频谱感知技术

序贯检测的检测时间是随实际接收信号采样点的变化而不同的随机变量,在平均意义上,序贯检测有较高的检测速度,但是个别情况下可能需要很长的检测时间,为了避免这种现象的发生,同时提高认知无线电中频谱感知的速度,该文提出了一种截断序贯检测算法。首先分析了截断对传统序贯检测性能的影响,给出了虚警概率和漏检概率的上限,然后基于该性能上限得到了截断序贯检测的检测门限,最后给出了截断序贯检测算法的流程。仿真结果表明,该算法在有限的检测时间内,能够满足系统的性能要求,且其平均检测时间小于传统的能量检测。