Joint sensing and transmission for cognitive amplify‐and‐forward two‐way relay networks

In this paper, we propose a cognitive transmission scheme for Amplify‐and‐Forward (AF) two‐way relay networks (TWRNs) and investigate its joint sensing and transmission performance. Specifically, we derive the overall false alarm probability, the overall detection probability, the outage probability of the cognitive TWRN over Rayleigh fading channels. Furthermore, based on these probabilities, the spectrum hole utilization efficiency of the cognitive TWRN is defined and evaluated. It is shown that smaller individual or overall false alarm probability can result in less outage probability and thus larger spectrum hole utilization efficiency for cognitive TWRN, and however produce more interference to the primary users. Interestingly, it is found that given data rate, more transmission power for the cognitive TWRN does not necessarily obtain higher spectrum hole utilization efficiency. Moreover, our results show that a maximum spectrum hole utilization efficiency can be achieved through an optimal allocation of the time slots between the spectrum sensing and data transmission phases. Finally, simulation results are provided to corroborate our proposed studies. Copyright © 2016 John Wiley & Sons, Ltd.     

An optimal cooperative spectrum sensing strategy with exponential primary link traffic

In this paper, we investigate the optimal sensing settings for a cognitive radio (CR) network consisting a number of CR users and a fusion center (FC). Our objective is to maximize the channel utilization under the constraint that the signals from the primary user (PU) are sufficiently protected. We focus on the utilization of the channel in which PUs dynamically enter the network with burst nature. Thus, we apply the average error probability (AEP) as the metric of channel utilization. Moreover, in order to protect the PU signal from being interfered, the missing detection probability is applied as the constraint function. Assuming that counting rules are utilized in the FC, we derive the false alarm probability, the missing detection probability, and the AEP in the maximum a posteriori (MAP) fashion. Then, after proving the monotonic properties of the objective function and the constraint function, we propose an efficient algorithm named Algorithm I that can derive the optimal settings for maximizing the channel utilization. Moreover, a simplified algorithm named Algorithm II is also proposed to minimize the AEP, supposing that perfect synchronization exists between the CR users and the PU. Finally, we show our numerical results and compare our optimal results with those found by exhaustive searches. We conclude that our Algorithm I produces optimal results very close to those found by exhaustive searches. Performance comparison between Algorithms I and II is also provided in terms of the AEP and the probability of missing detection. Copyright © 2012 John Wiley & Sons, Ltd.     

A novel approach for energy‐efficient resource allocation in double threshold‐based cognitive radio network

This paper mainly focuses on solving the energy efficiency (EE) maximization problem in double threshold‐based soft decision fusion (SDF) cooperative spectrum sensing (CSS) in the cognitive radio network (CRN). The solution to this objective problem starts with the selection of suitable secondary users (SUs) both for the spectrum sensing and data transmission. Here, energy efficiency is maximized under the constraints of interference to the primary user (PU), an acceptable outage of SUs, the transmission power of the SUs and the probability of false alarm. We propose a novel algorithm called iterative Dinkelbach method (IDM) which jointly optimizes the sensing time and transmission power allocation to the SUs. Further, Lagrangian duality theorem is employed to find the exact power assigned to the SUs. Finally, simulation results are carried out to validate the effectiveness of our proposed scheme by comparing with the other existing schemes. The performance is also analyzed for different system parameters.     

Power Allocation with Max–Min and Min–Max Fairness for Cognitive Radio Networks with Imperfect CSI

This paper consider the power allocation strategies in the cognitive radio (CR) system in the presence of channel estimation errors. As the user has different channel condition in CR systems, different amount of power resource is required to meets the QoS request. In order to guarantee the fairness of each CR user, ensure the interference from the primary user and other CR users meet the QoS requirement of the CR user and limit the interference that is caused by CR users on primary user within the range into the level that primary user can tolerate, we proposed some new power allocation schemes. The targets are to minimize the maximum power allocated to CR users, to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all CR users and to minimize the maximum outage probability over all CR users. The first power allocation scheme can be formulated using Geometric Programming (GP). Since GP problem is equivalent to the convex optimization problem, we can obtain the optimal solutions for the first scheme. The latter two power allocation schemes are not GP problems. We propose iterative algorithms to solve them. Simulation results show that proposed schemes can efficiently guarantee the fairness of CR users under the QoS constraint of the primary user and CR users.     

Resource allocation in orthogonal frequency division multiple access‐based cognitive radio systems with minimum rate constraints

In this paper, resource allocation problem in orthogonal frequency division multiple access‐based cognitive radio (CR) systems to maintain minimum transmission rate constraints of CR users (CRUs) with the specified interference thresholds is investigated. Firstly, a single primary user (PU) CR system is considered, and a suboptimal resource allocation algorithm to maximize the sum transmission rate of all CRUs is proposed. Secondly, the single‐PU scenario is extended to multiple‐PU case, and an asymptotically optimal resource allocation algorithm is proposed using dual methods subject to constraints on both interference thresholds of PUs and total transmit power of all CRUs. Analysis and numerical results show that, in contrast to classical resource allocation algorithms, the proposed algorithm can achieve higher transmission rate and guarantee each CRU's minimum transmission rate in both scenarios. Copyright © 2012 John Wiley & Sons, Ltd.     

Joint spectrum sensing and resource allocation optimization using genetic algorithm for frequency hopping–based cognitive radio networks

In cognitive radio (CR) networks, secondary users should effectively use unused licensed spectrums, unless they cause any harmful interference to the primary users. Therefore, spectrum sensing and channel resource allocation are the 2 main functionalities of CR networks, which play important roles in the performance of a CR system. To maximize the CR system utility, we propose a joint out‐of‐band spectrum sensing and operating channel allocation scheme based on genetic algorithm for frequency hopping–based CR networks. In this paper, to effectively sense the primary signal on hopping channels at each hopping slot time, a set of member nodes sense the next hopping channel, which is called out‐of‐band sensing. To achieve collision‐free cooperative sensing reporting, the next channel detection notification mechanism is presented. Using genetic algorithm, the optimum sensing and data transmission schedules are derived. It selects a sensing node set that participate the spectrum sensing for the next expected hopping channel during the current channel hopping time and another set of nodes that take opportunity for transmitting data on the current hopping channel. The optimum channel allocation is performed in accordance with each node's individual traffic demand. Simulation results show that the proposed scheme can achieve reliable spectrum sensing and efficient channel allocation.     

Polyphase filter bank based multi‐band spectrum sensing in cognitive radio systems

Spectrum sensing has been identified as an essential enabling functionality for cognitive radio (CR) systems to guarantee that CR users could share the spectrum resource with licensed users on a non‐interfering basis. Recently, simultaneous sensing of multi‐band licensed user activity has been attracting more and more research interest. Generally, the multi‐band sensing is implemented through energy detection by estimating power spectral density. In this paper, we investigate a multi‐band energy detection architecture based on different polyphase filter banks (PFBs), which aim to reliably sense multiple active bands by exploiting the low power leakage property of PFB. We have theoretically derived the closed‐form expressions of detection probability and false alarm probability for PFBs and fast Fourier transform based detectors, respectively. Theoretical detection thresholds are therefore computed, which ensures a fair comparison for different detectors. Final experimental results are presented to verify our theoretical analysis and demonstrate that PFBs based sensing architecture exhibits better sensing performance than the conventional FFT.Copyright © 2014 John Wiley & Sons, Ltd.     

Optimal Resource Allocation for Spectrum Sensing Based Cognitive Radio Networks with Statistical QoS Guarantees

Resource allocation under spectrum sensing based dynamic spectrum sharing strategy is a critically important issue for cognitive radio networks (CRNs), because they need to not only satisfy the interference constraint caused to the primary users (PUs), but also meet the quality-of-service (QoS) requirements for the secondary users (SUs). In this paper, we develop the optimal spectrum sensing based resource allocation scheme for the delay QoS constrained CRNs. Specifically, we aim at maximizing the maximum constant arrival rate of the SU that can be supported by the time-varying service process subject to the given statistical delay QoS constraint. In our derived power allocation scheme, not only the average transmit and interference power constraints are considered, but also the impact of the PUs?? transmission to the CRNs and the PUs?? spectrum-occupancy probability are taken into consideration. Moreover, the spectrum sensing errors are also taken into consideration. Simulation results show that, (1) the effective capacity of the secondary link decreases when the statistical delay QoS constraint becomes stringent; (2) given the QoS constraint, the effective capacity of the secondary link varies with the interference power constraint and the SNR of the primary link.     

An Adaptive Multitaper-SVD Spectrum Sensing Method for OFDM-Based Cognitive Radio Systems

Cognitive radio (CR) has been proposed and widely investigated as an approach for increasing the spectrum efficiency. CR devices exploit so called white spaces in the spectrum allocated to the primary users (PU) by a process commonly referred to as the spectrum sensing. In this work, we use the singular value decomposition (SVD) for spectrum sensing in orthogonal frequency division multiplexing-based (OFDM) CR systems. A single input multiple output channel is assumed between the PU and the secondary user equipped with multiple antennas. At the CR side, the multitaper method (MTM) is used for the spectrum sensing in each antenna. As a first contribution, we aim at reducing the time necessary to perform spectrum sensing. To this end, we propose an adaptive MTM–SVD spectrum sensing method that decreases the sensing time. As a second contribution, we formulate a three dimensional SVD (referred to as 3-D SVD) scheme that efficiently processes signals and quantities related to multiple antenna traffic, OFDM multiple blocks and different tapers, simultaneously. Simulation results indicate that the proposed adaptive MTM–SVD decreases the sensing time by about 61–69 % for various proposed adaptive algorithms, compared to the conventional MTM–SVD method. Besides, performance improvement in probability of detection is achieved from 2–13 % for a predefined probability of false alarm by using adaptive MTM–SVD. In addition to further reduction of the sensing time, the proposed 3D-MTM–SVD outperforms conventional methods for the low probability of the false alarm.     

Energy harvesting,cooperative spectrum sensing,and data transmission improvement in energy‐efficient multichannel cognitive sensor networks

In this paper, we investigate the energy harvesting capability in a multichannel wireless cognitive sensor networks for energy‐efficient cooperative spectrum sensing and data transmission. Spectrum sensors can cooperatively scan the spectrum for available channels, whereas data sensors transmit data to the fusion center (FC) over those channels. We select the sensing, data transmission, and harvesting sensors by setting the sensing time, data transmission time, and also harvesting time to maximize the network data transmission rate and improve the total energy consumption in the multichannel network under global probability of false alarm and global probability of detection constraints. We formulate our optimization problem and employ the convex optimization method to obtain the optimal times and nodes for spectrum sensing, data transmission, and harvesting energy in each subchannel for multiband cognitive sensor networks. Simulation results show that in our proposed algorithm, the network data transmission rate is improved while more energy is saved compared with the baseline methods with equal sensing time in all subchannels.     

Joint impact of sensing time and IED parameter on the performance of an energy efficient cognitive radio system

Joint impact of sensing time and improved energy detector (IED) parameter is evaluated for an energy efficient cooperative cognitive radio (CR) system where the CR users use IED. The aim of this work is to design the CR system in such a way that it can achieve two objectives for a given level of protection on primary user: (i) optimization of sensing time to make balance between detection performance and throughput and (ii) appropriate allocation of energy between sensing time and transmission time so as to enhance the energy efficiency of the CR system. The key parameters such as sensing time and IED parameter are set appropriately to meet the objectives. The performance is assessed in terms of throughput and energy efficiency of the system. The effect of the sensing time and the IED parameter on the performance is evaluated under a collision constraint. Furthermore, the optimal sensing time and IED parameter are investigated jointly for which the higher throughput as well as maximum energy efficiency can be obtained, and at the same time, a desired detection probability can also be maintained by the CR system. Copyright © 2016 John Wiley & Sons, Ltd.     

An efficient quiet period management scheme for cognitive radio systems

In cognitive radio (CR) systems, the channel sensing scheme for detecting the presence of primary user directly affects the quality-of-service of CR users and primary user. In this letter, we propose a sensing scheme that consists of a series of consecutive energy detections followed by feature detection, where the energy detection time is much shorter than the feature detection time. With the proposed scheme, multiple energy detections decrease the feature detection due to false alarm and the overall channel sensing time. The performance evaluation using Markov analysis shows that the proposed scheme can heighten the maximum channel utilization of CR users, while maintaining the detection delay of primary user under a predefined value.     

How much time is needed for wideband spectrum sensing?

In this paper, we consider a wideband cognitive radio network (CRN) which can simultaneously sense multiple narrowband channels and thus aggregate the perceived available channels for transmission. We study the problem of designing the optimal spectrum sensing time and power allocation schemes so as to maximize the average achievable throughput of the CRN subject to the constraints of probability of detection and the total transmit power. The optimal sensing time and power allocation strategies are developed under two different total power constraints, namely, instantaneous power constraint and average power constraint. Finally, numerical results show that, under both cases, for a CRN with three 6MHz channels, if the frame duration is 100ms and the target probability of detection is 90% for the worst case signal-to-noise ratio of primary users being ?12dB, ?15dB and ?20dB, respectively, the optimal sensing time is around 6ms and it is almost insensitive to the total transmit power.     

基于中继协作的认知无线电性能研究

频谱感知是认知无线电技术的关键。协作频谱感知能够充分利用网络资源,提高网络中认知用户的检测概率,降低认知用户的虚警概率。基于两个认知用户之间的中继协作,研究了协作频谱感知的感知性能与吞吐量的折衷。仿真结果表明：中继协作的频谱感知方法能够使得在充分保证对授权用户不造成干扰即检测概率一定时,能够有效地降低认知用户感知授权用户的虚警概率,缩短＂弱＂认知用户获得最大吞吐量的最优感知时间,提高其最大吞吐量,并提高认知系统的最大吞吐量。     

Simultaneous spectrum sensing,data transmission and Energy harvesting in multi‐channel cognitive sensor Networks with imperfect signal cancellation

In multichannel cognitive sensor networks, the sensor users which have limited energy budgets sense the spectrum to determine the activity of the primary user. If the spectrum is idle, the sensor user can access the licensed spectrum. However, during the spectrum sensing, no data transmits. For improving the network throughput and saving more energy consumption, we propose the simultaneous spectrum sensing and data transmission scheme where the sensor receiver decodes the received signal, and from the remaining signal, the status of the channel (idle/busy) is determined. We also consider that the sensor users are powered by a radio‐frequency (RF) energy harvester. In this case, energy harvesting, data transmission, and spectrum sensing are done simultaneously. On the other hand, we select the proper sensor users for spectrum sensing and energy harvesting. We also allocate the best channels for data transmission simultaneously so that the network throughput maximizes and the constraints on the energy consumption and the detection performance are satisfied for each band. We formulate the problem and model it as a coalition game in which sensors act as game players and decide to make coalitions. Each coalition selects one of the channels to sense and transmit data, while the necessary detection probability and false alarm probability and also the energy consumption constraints are satisfied. The utility function of a coalition is proposed based on the energy consumption, false alarm probability, detection probability, and the network throughput. This paper proposes an efficient algorithm to reach a Nash‐stable coalition structure. It is demonstrated that the proposed method maximizes the network throughput and reduces the energy consumption while it provides sufficient detection quality, in comparison to other existent methods.     

Access Control Engine with Dynamic Priority Resource Allocation for Cognitive Radio Networks

An access control engine with dynamic priority resource allocation (ACE-DPRA) is proposed for unlicensed users to utilize free spectrum of wireless communication systems. A cognitive radio (CR) network with sensing and learning abilities is essential for unlicensed users to achieve ACE-DPRA. Three algorithms are included in ACE-DPRA to improve the spectral efficiency. While requesting to set up connection, unlicensed CR users generate excessive interferences to licensed users. The proposed ACE-DPRA with an admission control scheme allows the connection of unlicensed CR users without degrading the communication quality of licensed users. The priority algorithm for utilizing the unused spectrum is designed according to the location information of unlicensed users. A transmitted power control method is achieved by a fuzzy-learning mechanism. The spectral efficiency of wireless communication systems can be increased after adopting the proposed ACE-DPRA algorithm. Simulation results show that licensed users keep the advantages of high transmission data rate, low interference power, and low average outage probability after the connection of unlicensed CR users.     

Resource Allocation Algorithm for Multi‐cell Cognitive Radio Networks with Imperfect Spectrum Sensing and Proportional Fairness

This paper addresses the resource allocation (RA) problem in multi‐cell cognitive radio networks. Besides the interference power threshold to limit the interference on primary users PUs caused by cognitive users CUs, a proportional fairness constraint is used to guarantee fairness among multiple cognitive cells and the impact of imperfect spectrum sensing is taken into account. Additional constraints in typical real communication scenarios are also considered—such as a transmission power constraint of the cognitive base stations, unique subcarrier allocation to at most one CU, and others. The resulting RA problem belongs to the class of NP‐hard problems. A computationally efficient optimal algorithm cannot therefore be found. Consequently, we propose a suboptimal RA algorithm composed of two modules: a subcarrier allocation module implemented by the immune algorithm, and a power control module using an improved sub‐gradient method. To further enhance algorithm performance, these two modules are executed successively, and the sequence is repeated twice. We conduct extensive simulation experiments, which demonstrate that our proposed algorithm outperforms existing algorithms.     

Sensing‐throughput tradeoff for cooperative multiple‐input single‐output cognitive radio

In this paper, we propose a new cooperative multiple‐input single‐output (MISO) cognitive radio (CR) system, which can use some of the antennas to transmit its data and the others to help to transmit the data of the primary user (PU) by performing cooperative communication if the presence of the PU is detected through the cooperative spectrum sensing. A new cooperative sensing‐throughput tradeoff model is proposed, which maximizes the aggregate rate of the CR by jointly optimizing sensing time and spatial sub‐channel power, subject to the constraints of the aggregate rate of the PU, the false alarm and detection probabilities, the aggregate interference to the PU and the aggregate power of the CR. Simulation results show that compared with the conventional scheme, the proposed cooperative scheme can achieve the larger aggregate rate of the CR, while keeping the aggregate rate of the PU invariable with the increasing of the interference. Copyright © 2013 John Wiley & Sons, Ltd.     

Cognitive Engine with Dynamic Priority Resource Allocation for Wireless Networks

A cognitive engine with dynamic priority resource allocation (CE-DPRA) is proposed for wireless networks by utilizing a maximum likelihood estimation method. The receiving signal strength (RSS) from an unlicensed cognitive radio (CR) user can be measured through estimating the unknown position of a licensed mobile user. The priority algorithm for access control enables the selection of a proper CR user waiting for transmission. Both data rate and spectral efficiency can be increased after adapting CE-DPRA. Also the power constraint method can avoid excessive interference caused by signal transmitting from CR users so as to improve the communication quality of mobile users. Simulation results show that the proposed CE-DPRA achieves the performance of high transmission data rate, less interference power, and low average outage probability.     

Novel distributed algorithm for coalition formation in cognitive radio networks for throughput enhancement using matching theory

In this paper, a novel algorithm is proposed for increasing the throughput in cognitive radio networks by forming coalitions among cognitive radio users in AWGN and fading channel environment. Although there have been numerous studies exploring the benefits of coalition formation in cognitive radio networks from the game theory perspective, there are several limitations in their application. To overcome the limitations of game theory, concepts from matching theory are used. Specifically, the stable marriage problem is used to formulate the interactions among the cognitive radio users as a matching game for collaborative distributed spectrum sensing under target detection probability constraint. The utility function is defined as the average probability of false alarm per cognitive radio user. The advantage of stable marriage is that it always converges to a stable matching and is Pareto optimal when the preferences of cognitive radios are strict. In the proposed model, the stable matching problem is extended to propose a novel algorithm to form coalitions of varying sizes for improving the utility of CR (false alarm and throughput). The coalitions formed using the algorithm are stable and do not deviate from the final matching. Using simulations and mathematical analysis, it is shown that the proposed algorithm leads to stable coalitions and returns significant improvement in terms of reduced probability of false alarm and improved throughput per cognitive radio user as compared to the noncooperative scenario.