Intelligent selection of threshold in covariance-based spectrum sensing for cognitive radio networks

Wireless Networks - The radio spectrum sensing has been an important issue of research in cognitive radio networks over the last decade and the appropriate selection of threshold plays a crucial...     

Iterative optimization of detection threshold and throughput for secondary users in multiband cognitive radio systems

In cognitive radio(CR) systems,efficient spectrum sensing ensures the secondary user(SU) to successfully access the spectrum hole.Typically,the detection problem has been considered separately from the optimization of transmission strategy.However,in practice,due to non-zero probabilities of miss detection and false alarm,the sensing phase has an impact on the throughput of SUs as well as on the transmission of primary user(PU).In this paper,using energy detection,we maximize the total throughput of SUs by ...     

Effective throughput maximization for in-band sensing and transmission in cognitive radio networks

In cognitive radio networks, a secondary user is expected to utilize idle periods in a spectrum band but avoid interfering with busy periods occupied by primary users in the same band. To achieve the above goal, usually a secondary user periodically senses a spectrum band, and once an idle period is detected, the secondary user sends data in a transmission time. Due to (i) miss-detection of busy periods or (ii) unpredictable arrivals of busy periods, a secondary user may send data in busy periods, which causes useless data transmission. A secondary user usually cares about effective throughput which excludes the useless transmitted data. In order to alleviate the useless data transmission and enhance effective throughput, we consider dividing one long data transmission into two or more smaller data transmissions. Analyses, which are verified by simulations, are developed in this paper to calculate effective throughput in a periodic sensing structure with sensing errors. We use the analyses to select a set of parameters of sensing and transmission such that effective throughput is maximized at a certain load while the interference is below a pre-determined level. Besides, we study two policies, namely, fixed parameter policy and dynamic parameter policy, to maximize effective throughput in a spectrum band with variable loads; the former policy selects and applies one fixed set of parameters to different loads, but the latter policy uses different sets of parameters in different loads respectively. Numerical results show that the dynamic parameter policy outperforms the fixed parameter policy.     

A unified spectrum sensing and throughput analysis model in cognitive radio networks

Cognitive radio (CR) is a newly developed technology for increasing spectral efficiency in wireless communication systems. In the CR networks, there exist two traditional spectrum‐sharing technologies called spectrum overlay and spectrum underlay. A new hybrid overlay/underlay paradigm has also been discussed in the literature. In this work, we create a unified spectrum sensing and throughput analysis model, which is suitable for overlay, underlay, and hybrid overlay/underlay paradigms in the CR networks. In the proposed model, the energy detection scheme is employed for the spectrum sensing in the network in which the co‐channel interference is present among primary users and secondary users (SUs). The SUs' throughput in the proposed CR system model is then analyzed. The simulations are also carried out for demonstrating the performance of overlay, underlay, and hybrid overlay/underlay paradigms. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive power control and beam-forming joint optimization in cognitive radio networks

A novel adaptive power control and beam-forming joint optimization algorithm is proposed in cognitive radio(CR) underlay networks,where cognitive network share spectrum with primary network which spectrum is licensed.In this paper,both primary base station(PBS) and cognitive base station(CBS) are all equipped with multi antennas,while each primary user(PU) and cognitive user(CU) has only one antenna.Different from traditional algorithms,an adaptive weight factor generating solution is supplied to different access users(both PUs and CUs) in this paper,and the different priority of users is also considered,because PUs have higher priority,the weight factor of PUs is fixed as constant and signal-to-interference and noise ratio(SINR) threshold is unchanged,while for CUs,it is set adaptively and SINR threshold is also changed accordingly.Using this algorithm,the transmit power is decreased,which relax the strict requirements for power amplifier in communication systems.And moreover,owing to PUS has fixed SINR threshold,the calculated SINR at receiver is nearly unchanged,but for CUs,the SINR is changing with the adaptive weight factor.Under the assurance of quality of service(QoS) of PUs,the solution in this paper can enable CRs access to the CR network according to adaptive SINR threshold,therefore which supplies higher spectrum utilization efficiency.     

Jointly optimizing sensing time and resource allocation in multichannel cognitive radio networks

Spectrum sensing is a key technique for determining the spectrum available in cognitive radio (CR) networks. In this paper, we study how to jointly optimize sensing time and resource allocation to maximize the sum transmission rate of all CR users of a multichannel CR network. We take into consideration the transmission power and interference constraints to protect primary users from harmful interference, as well as constraints of detection probability and false alarm probability. Under these constraints, we propose an asymptotically optimal resource allocation algorithm. The optimal sensing time can be obtained using the traditional one‐dimensional exhaustive search. However, owing to the high complexity of searching for the sensing time, we propose a simplified method to get the optimal sensing time under the assumption that false alarm probability is small. Simulation results show that the simplified method can obtain the optimal sensing time efficiently under strict constraint of false alarm probability. Copyright © 2012 John Wiley & Sons, Ltd.     

基于组合投资理论与主用户QoS保证的认知系统资源分配算法

受组合投资理论的启发,提出了一种既利用历史信道状态信息,同时又有效保证主用户不受次系统传输所产生干扰的新的资源分配算法.该算法以系统速率的方差作为优化目标,同时通过引入用户间干扰门限来衡量次系统对主用户造成的干扰,并利用二次规划的方法对该问题进行求解.最后,给出经典算法与本算法的性能比较,仿真结果表明该算法在保证次系统传输速率保持在一定的期望速率的条件下,使其方差最小,同时又使主用户所受的干扰限定在所能承受的范围内.     

Analysis of optimal threshold selection for spectrum sensing in a cognitive radio network: an energy detection approach

Wireless Networks - The spectrum sensing is a key process of the cognitive radio technology in which the cognitive users identify the unutilized/underutilized primary users (PUs)/licensed users...     

面向认知无线电网络能量高效协作感知的在线节点选择算法

面向认知无线电网络中能量高效协作感知任务需求,提出了面向单次协作感知过程的能量最小化节点选择问题EMNS和面向在线协作感知的能量高效节点选择问题OENS。证明了两问题均为NP-hard难题。针对EMNS问题,提出采用分枝定界算法BAB求最优解和贪婪节点选择算法GS求近似解。针对OENS问题,提出为每个节点引入考虑能量消耗负载均衡的动态权重系数,基于BAB和GS算法设计了启发式的在线节点选择算法OBAB、OGS1。仿真实验结果表明,提出的算法可显著增加网络完成的协作感知过程次数,可有效延长网络"生存期"。     

Throughput optimization using simultaneous sensing and transmission in energy harvesting cognitive radio networks

A three‐dimensional continuous‐time Markov model is proposed for an energy harvesting cognitive radio system, where each secondary user (SU) harvests energy from the ambient environment and attempts to transmit data packets on spectrum holes in an infinite queuing buffer. Unlike most previous works, the SU can perform spectrum sensing, data transmission, and energy harvesting simultaneously. We determine active probability of the SU transmitter, where the average energy consumption for both spectrum sensing and data transmission should not exceed the amount of harvested energy. Then, we formulate achievable throughput of secondary network as a convex optimization problem under average transmit and interference energy constraints. The optimal pair of controlled energy harvesting rate and data packet rate is derived for proposed model. Results indicate that no trade‐off is available among harvesting, sensing/receiving, and transmitting. The SU capability for self‐interference cancelation affects the maximum throughput. We develop this work under hybrid channels including overlay and underlay cases and propose a hybrid solution to achieve the maximum throughput. Simulation results verify that our proposed strategy outperforms the efficiency of the secondary network compared to the previous works.     

Distributed joint spectrum detection and power allocation in multi-band cognitive radio networks

This article proposes a novel dynamic spectrum sharing scheme in distributed multi-band cognitive radio networks. A non-cooperative game has been utilized to model the spectrum sharing among secondary base stations (SBSs). A distributed joint spectrum detection and power allocation algorithm is designed for maximizing the downlink throughput of secondary networks. Simulation results demonstrate that the proposed algorithm converges fast and achieves a better throughput performance than uniform threshold case. Meanwhile, the convergence of algorithm is proved by Nikaido-Isoda (N-I) function method.     

Stochastic approach for channel selection in cognitive radio networks using optimization techniques

Telecommunication Systems - Secondary Users (SU) are guided by Cognitive Radio Device in identifying a channel licensed to Primary Users (PU) when it is free. Whenever PU arrives, SU shall vacate...     

Toward secure distributed spectrum sensing in cognitive radio networks [cognitive radio communications and networks]

Cognitive radio is a revolutionary technology that promises to alleviate the spectrum shortage problem and to bring about remarkable improvement in spectrum utilization. Spectrum sensing is one of the essential mechanisms of CR and is an active area of research. Although the operational aspects of spectrum sensing are being studied actively, its security aspects have attracted very little attention. In this paper, we discuss security issues that may pose a serious threat to spectrum sensing. Specifically, we focus on two security threats - incumbent emulation and spectrum sensing data falsification - that may wreak havoc in distributed spectrum sensing. We also discuss methods for countering these threats and the technical hurdles that must be overcome to implement such countermeasures.     

Parameters optimization for cooperative sensing in multi‐channel cognitive radio networks

In this paper, we propose a relatively complete and robust optimization model under the scenario where multisecondary users cooperatively sense multi‐channels. The objective of this model is to maximize the system throughput, meanwhile aims to jointly optimize the parameters including the sensing time and the weight coefficients of the sampling results. Because this model is a nonlinear optimization model, we instead adopt a heuristic sequential parameters optimization method (SPO) to solve the model. The method begins with deriving the lower bound of the objective function of the optimization model. Then, it maximizes this lower bound by optimizing the weight coefficients through solving a series of suboptimal problems using Lagrange method. Given that the weight coefficients are found, it finally transforms the problem into another monotonic programming problem and exploits a fast‐convergent polyblock algorithm to find an optimized sensing time parameter. We finally conduct extensive experiments by simulations. The results demonstrate that, in terms of the throughput gained by the system, SPO can deliver a solution that is up to 99.3% of the optimal on average, which indicates that SPO can solve the proposed optimization model effectively. In addition, we also show the performance advantage of the proposed model on improving the system throughput by comparing with other state‐of‐the‐art optimization models. Wireless Communications and Mobile Computing. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimal multiband joint detection in cognitive radio networks with the Taguchi method

This paper studies multiband joint detection in cognitive radio networks with the Taguchi method. At the fusion center, linear fusion rule is adopted to collect the observations from distributed secondary users. We aim to achieve maximum aggregate throughput with limited aggregate interference. The problem is challenging due to its nonconvexity and high computational complexity to find the global optimum. Existing works attempt to convert the problem into convex optimization problem. In this paper, the Taguchi method is employed to estimate the gradient of the aggregate throughput, determine the optimal thresholds of the energy detectors and combination weights of the linear fusion rule regardless of convexity. To optimize thresholds and linear weights simultaneously, we employ newly defined variables to represent the changing ranges of detector thresholds when we search for optimal linear weights. In addition, the sensing duration is another factor to be optimized in the Taguchi method. The simulation results show that the proposed method is efficient and applicable for all classes of cognitive radio without considering convexity. The optimization performance is considerably improved. Moreover, the Taguchi method is insensitive to parameter initialization, which provides a relatively robust output. Copyright © 2013 John Wiley & Sons, Ltd.     

Intelligent threshold selection in fading environment of cognitive radio network: Advances in throughput and total error probability

One of the main challenges of spectrum sensing (SS) in the cognitive radio network (CRN) is selection of threshold for captivating the sensing decision, and such an issue becomes more challenging especially at low signal‐to‐noise ratio (SNR) because it affects the sensing performance and throughput of cognitive user (CU). However, the sensing decision of individual CU may not be accurate due to fading effects in the sensing environment, and significant improvement is accomplished with the cooperation of users. Therefore, in this paper, we have investigated the performance of energy detector SS (EDSS) technique in the CRN under fading environment with cooperative and noncooperative SS scenario. Further, we have introduced the concept of critical SNR ( γ _c ) to select the appropriate threshold and based on which the algorithms are proposed to enhance the throughput and reduce the total error (sensing error) probability of CU at different SNR (γ). From the results, we have accomplished that at the low SNR region (γ ≤ γ _c ), throughput is maximum with constant false‐alarm rate (CFAR) threshold selection approach in the cooperative scenario while in the high SNR region (γ >   γ _c ), its value is maximum with minimizing the error probability (MEP) threshold selection approach in the noncooperative scenario. Moreover, the sensing error is reduced with cooperation, and threshold selection with MEP approach outperforms CFAR approach for γ ≤ γ _c . Further, the proposed approach outperforms the other approaches in terms of throughput and sensing error (total error) probability in the Rayleigh and Nakagami‐m fading environments.     

Cooperative multiband joint detection in cognitive radio networks using artificial immune system

In cognitive radio technology, spectrum sensing enables users to sense the environment and find spectrum holes. Cooperative sensing is a good idea for reliable detection of primary users in shadowed environments. In this study, spatial spectral joint detection with some constraints that keep the interference at the primary user below a suitable level is considered as the optimization problem for collaborative sensing. Convex optimization is able to obtain near-optimal solutions because of the non-convexity nature of the optimization problem. In this paper, we use artificial immune system (based on the clonal selection theory) to obtain the optimal solutions without any reformulations or mathematical costs. Numerical results show that our proposed algorithm outperforms the genetic algorithm used in the previous works.     

Fuzzy logic for cross-layer optimization in cognitive radio networks [cognitive radio communications and networks]

The search for the ultimate architecture for cross-layer optimization in cognitive radio networks is characterized by challenges such as modularity, interpretability, imprecision, scalability, and complexity constraints. In this article we propose fuzzy logic as an effective means of meeting these challenges, as far as both knowledge representation and control implementation are concerned.     

On the transmission opportunity and TCP throughput in cognitive radio networks

The characteristics of cognitive radio networks have huge impacts on the end‐to‐end performance of the transmission control protocol (TCP) for secondary users. Thus, the existing TCP throughput expression, widely used in wired and wireless networks, is no longer suitable for cognitive radio networks. In this paper, we derive the transmission opportunity of secondary users, taking into account the dynamics of spectrum availability, the overhead and errors of spectrum sensing, as well as the interaction between TCP and lower layers. The amount of transmission opportunity is expressed in terms of effective data transmission time. On the basis of the analysis of the transmission opportunity, an expression of an effective TCP throughput is then derived. To evaluate this effective TCP throughput expression, two cross‐layer optimization problems are formulated as application examples to maximize the transport layer effective throughput and energy utility, respectively. Simulation results show that our analysis on transmission opportunity is accurate, and the derived effective TCP throughput expression is more precise than existing ones. Copyright © 2012 John Wiley & Sons, Ltd.