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 con-sidered 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 men-tioned 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.

Orthogonal Codes-Based Dynamic Spectrum Access in Cognitive Radio Networks

Dynamic spectrum access(DSA) in cognitive radio(CR) networks became a challenging research area recently. In CR technology, the DSA between primary users(PUs) and secondary users(SUs) simultaneously can be achieved without degrading the performance of the PUs by SUs interference. This can be achieved by donating incentive power to the PUs in order to compensate the interference caused by the SUs. Consequently, PUs allow SUs to share the spectrum. In this paper, orthogonal codes-based dynamic spectrum access(OC-DSA) technique is proposed. OC-DSA technique employs orthogonality between PUs and SUs transmitted data symbols in addition to the incentive power donation. Compared to other techniques, the proposed technique uses a simple encoder at the SU network for the same PU network infrastructure. By applying orthogonal codes, the interference caused by SUs is canceled and hence the donated power to incentivize the PUs is reduced. Also, the SU packet rate is increased significantly. The simulation results show that the proposed technique provides effective improvements over other existing techniques in the signal strength and the bit error rate performance of both the PU network and the SU network at the receiver side. Moreover, the proposed technique requires less donated power to incentivize the PU and has higher packet rate.

Multi-Strategy Dynamic Spectrum Access in Cognitive Radio Networks: Modeling, Analysis and Optimization

Dynamic spectrum access(DSA) based on cognitive radios(CR) technique is an effective approach to address the "spectrum scarcity" issue. However, traditional CR-enabled DSA system employs only single DSA strategy, which might not be suited to the dynamic network environment. In this paper, we propose a multi-strategy DSA(MS-DSA) system, where the primary and the secondary system share spectrum resources with multiple DSA strategies simultaneously. To analyze the performance of the proposed MS-DSA system, we model it as a continuous-time Markov chain(CTMC) and derive the expressions to compute the corresponding performance metrics. Based on this, we define a utility function involving the concerns of effective throughput, interference quantity on primary users, and spectrum leasing cost. Two optimization schemes, named as spectrum allocation and false alarm probability selection, are proposed to maximize the utility function. Finally, numerical simulations are provided to validate our analysis and demonstrate that the performance can be significantly improved caused by virtues of the proposed MS-DSA system.

Dynamic Spectrum Access Scheme of Variable Service Rate and Optimal Buffer-Based in Cognitive Radio

Dynamic spectrum access (DSA) scheme in Cognitive Radio (CR) can solve the current problem of scarce spectrum resource effectively, in which the unlicensed users (i.e. Second Users, SUs) can access the licensed spectrum in opportunistic ways without interference to the licensed users (i.e. Primary Users, PUs). However, SUs have to vacate the spectrum because of PUs coming, in this case the spectrum switch occurs, and it leads to the increasing of SUs’ delay. In this paper, we proposed a Variable Service Rate (VSR) scheme with the switch buffer as to real-time traffic (such as VoIP, Video), in order to decrease the average switch delay of SUs and improve the other performance. Different from previous studies, the main characteristics of our studying of VSR in this paper as follows: 1) Our study is on the condition of real-time traffic and we establish three-dimension Markov model;2) Using the internal optimization strategy, including switching buffer, optimizing buffer and variable service rate;3) As to the real-time traffic, on the condition of meeting the Quality of Service(QoS) on dropping probability, the average switch delay is decreased as well as improving the other performance. By extensive simulation and numerical analysis, the performance of real-time traffic is improved greatly on the condition of ensuring its dropping probability. The result fully demonstrates the feasibility and effectiveness of the variable service rate scheme.

Centralized Dynamic Spectrum Allocation in Cognitive Radio Networks Based on Fuzzy Logic and Q-Learning

A novel centralized approach for Dynamic Spectrum Allocation (DSA) in the Cognitive Radio (CR) network is presented in this paper. Instead of giving the solution in terms of formulas modeling network environment such as linear programming or convex optimization, the new approach obtains the capability of iteratively on-line learning environment performance by using Reinforcement Learning (RL) algorithm after observing the variability and uncertainty of the heterogeneous wireless networks. Appropriate decision-making access actions can then be obtained by employing Fuzzy Inference System (FIS) which ensures the strategy being able to explore the possible status and exploit the experiences sufficiently. The new approach considers multi-objective such as spectrum efficiency and fairness between CR Access Points (AP) effectively. By interacting with the environment and accumulating comprehensive advantages, it can achieve the largest long-term reward expected on the desired objectives and implement the best action. Moreover, the present algorithm is relatively simple and does not require complex calculations. Simulation results show that the proposed approach can get better performance with respect to fixed frequency planning scheme or general dynamic spectrum allocation policy.

Reinforcement Learning Based Dynamic Spectrum Access in Cognitive Internet of Vehicles

Cognitive Internet of Vehicles(CIoV)can improve spectrum utilization by accessing the spectrum licensed to primary user(PU)under the premise of not disturbing the PU’s transmissions.However,the traditional static spectrum access makes the CIoV unable to adapt to the various spectrum environments.In this paper,a reinforcement learning based dynamic spectrum access scheme is proposed to improve the transmission performance of the CIoV in the licensed spectrum,and avoid causing harmful interference to the PU.The frame structure of the CIoV is separated into sensing period and access period,whereby the CIoV can optimize the transmission parameters in the access period according to the spectrum decisions in the sensing period.Considering both detection probability and false alarm probability,a Q-learning based spectrum access algorithm is proposed for the CIoV to intelligently select the optimal channel,bandwidth and transmit power under the dynamic spectrum states and various spectrum sensing performance.The simulations have shown that compared with the traditional non-learning spectrum access algorithm,the proposed Q-learning algorithm can effectively improve the spectral efficiency and throughput of the CIoV as well as decrease the interference power to the PU.

Energy Aware Spectrum Access in Cognitive Radio Networks with Imperfect Channel Sensing

Due to the problem of spectrum underuti-lization and energy inefficiency in wireless commu-nications, the research on energy efficient Cogni-tive Radio Networks （CRNs） has received signifi-cant attention in both industry and academia. In this paper, we consider the problem of optimal spectrum selection and transmission parameters de-sign with the objective of minimizing energy con-sumption in CRNs. Since the system state cannot be directly observed due to miss detections and estimation errors, we formulate the optimal spec-trum access problem as a Partially Observable Markov Decision Process （POMDP）. In particular, the proposed scheme selects the optimal spectrum, modulation and coding scheme, transmission pow-er, and link layer frame size in each time slot ac-cording to the belief state, which captures all the history information of past actions and observa- tions. The optimal policy can be acquired by sol-ving POMDP problem with linear programming based algorithm Sinmlation results show that sig-nificant energy savings can be achieved by the proposed scheme.

Traffic sensitive spectrum access scheme for cognitive radio networks

A traffic sensitive spectrum access scheme is proposed to satisfy the traffic load requirement of secondary users （SUs）. In the proposed design, SU only accesses available channels which can meet the traffic demand. To achieve this, the expected transmission time （E3W） of the SU is calcu- lated first based on the delivery ratio. Then, the channel idle time is estimated based on the activity of primary users （PUs）. Therefore, available channels with estimated idle time longer than ETr could be chosen. With high probability, the SU can finish transmission on these channels without disruption, thereby satisfying the traffic load demand of the SU. Finally, our method is extended to the multi-channel scenario where each SU can access multiple channels simultaneously. Performance analysis shows that our method satisfies the requirement of SUs while effectively improving the throughput.

Space-Time Correlation Based Fast Regional Spectrum Sensing in Cognitive Radio

In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial correlation among them. In each group,some representative meshes are selected as detecting meshes(DMs)using a multi-center mesh(MCM)clustering algorithm,while other meshes(EMs)are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus,detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results,minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.

Dynamic Matching-Based Spectrum Detection in Cognitive Radio Networks

Cognitive Radio Network provides an opportunity to reduce the spectrum resource crisis by allowing secondary users to access the idle spectrum allocated to primary users. The precondition of spectrum sharing is to obtain the Spectrum Availability Information(SAI). Energy detection is a typical technology to get SAI. With the mobility of primary users, the energy received by secondary users varies greatly with the distance from the target primary users. Most of the existing energy detection algorithms that use fixed thresholds are not suitable. We propose a Dynamic Matching-Based Spectrum Detection(DMBSD) scheme which can detect sensing data, reduce the impact of malicious data and make final sensing results more accurate with dynamic threshold setting and data matching. Simulation results show that the proposed scheme can detect tempered data, and increase detection probability by decreasing false alarm probability and missed detection probability.

User-Based Discrete-Time Queuing Analysis for Opportunistic Spectrum Access in Cognitive Radio Networks

In cognitive radio networks, the spectrum utilization can be improved by cognitive users opportunistically using the idle channels licensed to the primary users. However, the new arrived cognitive users may not be able to use the channel immediately since the channel usage state is random. This will impose additional time delay for the cognitive users. Excessive waiting delay can make cognitive users miss the spectrum access chances. In this paper, a discrete-time Markov queuing model from a macro point of view is provided. Through the matrix-geometric solution theory, the average sojourn time for cognitive users in the steady state before accessing the spectrum is obtained. Given the tolerant delay of cognitive users, the macro-based throughput is derived and an access control mechanism is proposed. The numerical results show the effects of service completion probability on average sojourn time and throughput. It is confirmed that the throughput can be obviously improved by using the proposed access control mechanism. Finally, the performance evaluations based on users are compared to that based on data packets.

A Novel System Model based on Cost-prediction for Spectrum Leasing in Cognitive Radio

In current researches on spectrum leasing, Common model and Property-right model are two main approaches to dynamic spectrum sharing. However, Common model does not consider the obligation of Primary System (PS) and is unfair to Secondary System (SS), while the cooperation based on Property-rights model has problems on its feasibility. This paper proposes a novel system model, in which a Cost-Prediction scheme for Spectrum Leasing (CPSL scheme) is designed to forecast the cost that PS would pay for leasing spectrum. Cost Function is introduced as a criterion to evaluate the potential cost of spectrum leasing for PS. The simulation results show that compared with Common model based scheme, CPSL scheme substantially improves the QoS of the delay-sensitive traffic in SS at the cost of a small degradation of PS performance.

Performance Analysis of Wireless Network with Opportunistic Spectrum Sharing via Cognitive Radio Nodes

Cognitive radio （CR） is found to be an emerging key for efficient spectrum utilization. In this paper, spectrum sharing among service providers with the help of cognitive radio has been investigated. The technique of spectrum sharing among service providers to share the licensed spectrum of licensed service providers in a dynamic manner is considered. The performance of the wireless network with opportunistic spectrum sharing techniques is analyzed. Thus, the spectral utilization and efficiency of sensing is increased, the interference is minimized, and the call blockage is reduced.

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios is proposedin this paper. The key idea of the algorithm is to pursue the maximum total spectrum utilization of thesystem by adopting a parallel technique in every spectrum allocation, and to ensure the statistical fairnessrule by deploying a particular scheme during a series of allocations. The simulation results show that theproposed algorithm not only achieves a fairer and more efficient allocation of spectrum resources, but alsohas much shorter allocation duration than the color sensitive graph coloring (CSGC) algorithm.

On opportunistic spectrum efficiency in cognitive radio systems

The performance bound of cognitive radio systems is analyzed.We use opportunistic spectrum effi-ciency(OSE)as the performance metric,and point out that the maximum achievable OSE can be used toevaluate the maximum value that the introduction of secondary users can add to the conventional wirelesssystems.Based on assumptions of the PU s traffic model and some reasonable approximations,the ex-pression of OSE is obtained and the maximum achievable OSE is derived by solving an optimization prob-lem.The results are verified by computer simulation.

A Novel Cooperative Spectrum Sharing Algorithm Based on Optimal Cognitive Radio User Selection

In this paper, we consider the problem of cognitive radio (CR) user selection to maximize overall CR network (CRN) throughput when the available spectrum bandwidth is less than the demand by all CR users. We formulate optimal CR user selection problem. Then, based on approximation of the average received signal to interference plus noise ratio (SINR) and adaptive modulation and coding (AMC), we estimate the required bandwidth of CR users with different required quality of services (QoSs). Using the principle of optimality, we propose a novel cooperative spectrum sharing algorithm for a CRN. The proposed algorithm not only achieves exhaustive search performance but also its complexity is in the order of N × M versus 2N for exhaustive search, where N is the number of CR users, and M is the spectrum pool size. Extensive simulation results illustrate that the proposed algorithm significantly outperforms the existing algorithms that ignore optimal CR user selection. Also, these results illustrate a better fairness criterion than those of previous works.

Analysis of cumulative handoff delay for graded secondary users in cognitive radio networks

According to the fact that the secondary users＇ delay requirements for data transmission are not unitary in cognitive radio networks, the secondary users are divided into two classes, denoted by SU1 and SU2, respectively. It is assumed that SU1 has a higher priority to occupy the primary users＇ unutilized channels than SU2. A preemptive resume priority M/G/1 queuing network is used to model the multiple spectrum handoffs processing. By using a state transition probability matrix and a cost matrix, the average cumulative delays of SU1 and SU2 are calculated, respectively. Numerical results show that the more the primary user＇s traffic load, the more rapidly the SU2＇s cumulative handoff delay grows. Compared with the networks where secondary users are unitary, the lower the SUI＇s arrival rate, the more obviously both SUI＇s and SU2＇s handoff delays decrease. The admission access regions limited by the maximum tolerable delay can also facilitate the design of admission control rules for graded secondary users.

Cooperative Spectrum Sensing Deployment for Cognitive Radio Networks for Internet of Things 5G Wireless Communication

Recently,Cooperative Spectrum Sensing(CSS)for Cognitive Radio Networks(CRN)plays a significant role in efficient 5G wireless communication.Spectrum sensing is a significant technology in CRN to identify underutilized spectrums.The CSS technique is highly applicable due to its fast and efficient performance.5G wireless communication is widely employed for the continuous development of efficient and accurate Internet of Things(IoT)networks.5G wireless communication will potentially lead the way for next generation IoT communication.CSS has established significant consideration as a feasible resource to improve identification performance by developing spatial diversity in receiving signal strength in IoT.In this paper,an optimal CSS for CRN is performed using Offset Quadrature Amplitude Modulation Universal Filtered Multi-Carrier Non-Orthogonal Multiple Access(OQAM/UFMC/NOMA)methodologies.Availability of spectrum and bandwidth utilization is a key challenge in CRN for IoT 5G wireless communication.The optimal solution for CRN in IoT-based 5G communication should be able to provide optimal bandwidth and CSS,low latency,Signal Noise Ratio(SNR)improvement,maximum capacity,offset synchronization,and Peak Average Power Ratio(PAPR)reduction.The Energy Efficient All-Pass Filter(EEAPF)algorithm is used to eliminate PAPR.The deployment approach improves Quality of Service(QoS)in terms of system reliability,throughput,and energy efficiency.Our in-depth experimental results show that the proposed methodology provides an optimal solution when directly compares against current existing methodologies.

Performance analysis of cognitive radio networks using channel-prediction-probabilities and improved frame structure

This paper focuses on potential issues related to the random selection of a sensing channel that occurs after the prediction phase in a Cognitive Radio Network (CRN).A novel approach (Approach-l)for improved selection is proposed,which relies on the probabilities of channels by which they are predicted idle.Further,closed-form expressions are derived for the throughput of Cognitive Users (CUs)in the conventional and proposed approaches. In addition to this,a fimdamental approach for computing the prediction probabilities is also proposed.Moreover, a new challenging issue named "sense and stuck"was observed in the conventional approach.The proposed approach is validated by comparing the results achieved with the results of the conventional approach.However, to achieve the prediction probabilities,the pre-channel-state-information is a prerequisite,but it may be unavailable for particular scenarios;therefore,a modified selection method is introduced to avoid the sense and stuck problem.An algorithm to evaluate the throughput using the random,improved,and modified selection methods is presented with its space and time complexities.Furthermore,for additional improvement in the throughput of the CU,a new frame structure is introduced,in which the spectrum prediction and sensing periods are exploited for simultaneous transmission of data via the underlay spectrtun access technique (Approach-2).The simulated results of Approach-2 are compared with our pre-obtalned results of Approach-I,which confirm significant improvement in the throughput.

Markov Model Based Jamming and Anti-Jamming Performance Analysis for Cognitive Radio Networks

In this paper, we conduct a cross-layer analysis of both the jamming capability of the cognitiveradio-based jammers and the anti-jamming capability of the cognitive radio networks (CRN). We use a Markov chain to model the CRN operations in spectrum sensing, channel access and channel switching under jamming. With various jamming models, the jamming probabilities and the throughputs of the CRN are obtained in closed-form expressions. Furthermore, the models and expressions are simplified to determine the minimum and the maximum CRN throughput expressions under jamming, and to optimize important anti-jamming parameters. The results are helpful for the optimal anti-jamming CRN design. The model and the analysis results are verified by simulations.