There are two major challenges in wide-band spectrum sensing in a heterogenous spectrum environment. One is the spectrum acquisition in the wide-band scenario due to limited sampling capability; the other is how to collaborate in a heterogenous spectrum environment. Compressed spectrum sensing is a promising technology for wide-band signal acquisition but it requires effective collaboration to combat noise. However, most collaboration methods assume that all the secondary users share the same occupancy of primary users, which is invalid in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose an automatic clustering collaborative compressed spectrum sensing (ACCSS) algorithm. A hierarchy probabilistic model is proposed to represent the compressed reconstruction procedure, and Dirichlet process mixed model is introduced to cluster the compressed measurements. Cluster membership estimation and compressed spectrum reconstruction are jointly implemented in the fusion center. Based on the probabilistic model, the compressed measurements from the same cluster can be effectively fused and used to jointly reconstruct the corresponding primary user's spectrum signal. Consequently, the spectrum occupancy status of each primary user can be attained. Numerical simulation results demonstrate that the proposed ACCSS algorithm can effectively estimate the cluster membership of each secondary user and improve compressed spectrum sensing performance under low signal-to-noise ratio.

In multiuser cognitive radio (CR) networks, we address the problem of joint transmit beamforming (BF) and power control (PC) for secondary users (SUs) when they are allowed to transmit simultaneously with primary users (PUs). The objective is to optimize the network sum rate under the interference constraints of PUs, which is a nonconvex problem. Iterative dual subgradient (IDuSuG) algorithm is proposed by iteratively performing BF and PC to optimize the sum rate, among which minimum mean square error (MMSE) or virtual power-weighed projection (VIP2) is used to design beamformers and subgradient method is used to control the power. VIP2 algorithm is devised for the case in which the interference caused by MMSE beamformer exceeds the threshold. Moreover, channel uncertainty due to lack of cooperation is considered. A closed-form worst-case expression is derived, with which the uncertainty optimization problem is transformed into a certain one. A robust algorithm based on IDuSuG is provided by modifying updates in iterative process. Furthermore, second-order cone programming approximation (SOCPA) method is proposed as another robust algorithm. Typical network models are approximated to SOCP problems and solved by interior-point method. Finally the network sum rates for different PU and SU numbers are assessed for both certainty and uncertainty channel models by simulation.

In the underlay decode-and-forward (DaF) cooperative cognitive radio (CR) network, an optimal relay can be selected by the conventional max-min selection on the condition of not violating the interference temperature (IT) constraint. However, the max-min selection may cause some extra amount of interference to the primary system (PS) such that the so-called transfer ratio (TR) may be lower. Note that TR is newly defined as the ratio of the secondary system's (SS's) capacity gain to the PS's capacity loss due to the activities of SS. In order to improve the TR value, we are motivated by the pricing function in game theory to propose a novel low-interference relay selection by taking the impacts of the interference from SS to PS into consideration. Using the low-interference selection, however, it will not always allow the optimal relay to be picked. To clarify this phenomenon, the still optimal probability is defined as the probability of selecting the optimal relay by the proposed scheme. In addition, the impacts of the low-interference selection on the SS's capacity and outage probability are also analyzed. The simulation results prove that compared with the max-min selection, the proposed scheme can achieve higher TR values as well as the total capacity which also indicates that a higher spectrum efficiency can be achieved. It is believed that the results of this paper can provide an alternative viewpoint of evaluating the spectrum efficiency and inspire more interesting and important research topics in the future.

Cognitive radio is a promising approach to ensuring the coexistence of heterogeneous wireless networks since it can perceive wireless conditions and freely switch among different network modes. When there are many network opportunities, how to decide the appropriate network selection for CR user's current service is the main problem we study in this paper. We make full use of the intelligent characteristic of CR user and propose a fuzzy learning based network selection scheme, in which the network selection choice is made based on the estimated evaluations of available networks. Multiple factors are considered when estimating these evaluations. Both the outer environment factors directly sensed by CR user (signal strength of the available network and network mode), and also the factor that cannot be determined beforehand and is learnt by our scheme (the bandwidth allocated by the optional network) are considered. From several interactions with the wireless environment, the experience of network selection behavior is accumulated which will direct our scheme to make a proper decision of the network. Two simulations verify that our scheme could not only guarantee a better bandwidth requirement of CR user compared with other three network selection methods, but also shows it to be a reasonable scheme for utilizing the available resources of these networks.

Dynamic spectrum allocation (DSA) based on secondary spectrum market is considered a promising technology to improve spectrum utilization efficiency and to relieve the wireless spectrum shortage problem. We propose a dynamic spectrum allocation algorithm named market equilibrium and game (MEG), and construct a complete secondary spectrum market. The market based on the MEG algorithm consists of two submarkets: multiple primary services providers (PSPs) and a dynamic spectrum allocation server (DSAS) form the high submarket, while the low submarket is composed of the DSAS and a number of secondary users. In the low submarket, the MEG algorithm provides a game type selection strategy. By this strategy, the DSAS can win more payoffs with lower unit spectrum price, which encourages secondary users to use more spectrum. A secondary user can also choose its preferable game type between dynamic game and Nash bargaining flexibly. On the other hand, a bargaining procedure in the high submarket is designed in the MEG algorithm to ensure that market equilibrium is quickly reached. A performance analysis shows that the strategy of game type selection is fair and feasible for both the DSAS and the secondary users. Moreover, the bargaining procedure is better than the existing algorithm which adjusts price step by step in the high submarket. Simulation results also demonstrate that the market fluctuation in the low submarket is passed to the high submarket by way of the DSAS. The MEG algorithm can effectively satisfy the highly-fluctuating demands from the secondary users. In addition, the MEG algorithm can improve the payoffs of all players and increase spectrum utilization efficiency.

Concomitantly with the progress of wireless communications, cognitive radio has attracted attention as a solution for depleted frequency bands. Cognitive radio is suitable for wireless sensor networks because it reduces collisions and thereby achieves energy-efficient communication. To make cognitive radio practical, we propose a low-power multi-resolution spectrum sensing (MRSS) architecture that has flexibility in sensing frequency bands. The conventional MRSS scheme consumes much power and can be adapted only slightly to process scaling because it comprises analog circuits. In contrast, the proposed architecture carries out signal processing in a digital domain and can detect occupied frequency bands at multiple resolutions and with low power. Our digital MRSS module can be implemented in 180-nm and 65-nm CMOS processes using Verilog-HDL. We confirmed that the processes respectively dissipate 9.97 mW and 3.45 mW.

In this paper, we present a cognitive relay network with two primary transceivers that communicate via several distributed relay terminals. Spectrum sensing is deployed at the relays to sense the absence/presence of the primary transceivers based on energy detection. The primary network utilizes a two-step two-way amplify-and-forward (AF) scheme by using the cognitive radio (CR) terminals as its relay nodes when the primary network is not in operation, in contrast, the CRs communicate with their own base station (BS). In the first relaying step, the primary transceivers send their signal to the CRs/relays. Distributed beamforming is then performed in the second relaying step. Our aim is to set the beamforming weights so as to minimize the total power dissipated in the relay network while satisfying a target signal-to-noise ratio (SNR) at the primary transceivers and at the cognitive BS. This is achieved by solving an optimization problem that we formulate as a nonconvex quadratically constrained quadratic program (QCQP). This problem is solved efficiently by semidefinite relaxation (SDR) and Lagrangian duality. Simulation results are provided to demonstrate the superiority of our proposed technique, compared to classical beamforming techniques, in terms of power reduction.

This letter proposes a slot-based opportunistic spectrum access for cognitive radio networks. To reduce the slot-boundary impact, control frames are used to achieve channel reservation. The saturation throughput of our scheme is estimated by an analytical model. The accuracy of the model is validated by extensive simulation.

In this paper, we present a distributed and interactive admission and power control protocol for spectrum underlay environments. The protocol enables distributed primary users (PUs) to estimate and adjust the level of tolerable interference as their transmitting powers evolve to a given signal-to-interference-plus-noise ratio (SINR) target. The protocol also guides the powers of distributed secondary users (SUs) to achieve their own targets while restricting the transmitting powers from SUs so as not to interfere with the PUs. This restriction of interference from SUs to PUs is an essential part of cognitive radio networks (CRNs) and is facilitated by sending a warning tone from PUs to SUs in the proposed protocol. The SUs that have frequently received the warning tones turn off their transmitters and so autonomously drop from the system. This paper proves that, under the proposed interactive protocol, every PU finally achieves its target if it is originally feasible without SUs and the transmit powers of remaining SUs converge to a fixed point. The proposed method protects PUs perfectly in the sense that all the PUs reach their targets after power control. Numerical investigation shows how safely PUs are protected and how well SUs are admitted as a function of protocol parameters, the frequency of warning tones, the number of SUs to be admitted and the number of active PUs.

In this letter, we study the power control of a cognitive radio (CR) network, where the secondary user (SU) is allowed to share the spectrum with the primary user (PU) only if the signal to interference plus noise ratio (SINR) at the PU is higher than a predetermined level. Both PU fixed power control and PU adaptive power control are considered. Specifically, for the PU adaptive power control, the PU will cooperate with the SU by transmitting with adaptive power. The optimal power control schemes for the SU to maximize the SU throughput under the PU SINR constraint are derived. It is shown that the SU throughput achieved by the optimal power control with the PU adaptive power control is a significant improvement over the optimal power control with the PU fixed power control, especially under high power constraint and low SINR constraint.

A spectrum sensing scheme for cognitive radio that includes coarse and fine sensing stages based on cyclostationarity is proposed in this paper. The cyclostationary feature detection (CFD) based on a single cyclic frequency (SCF) is used in the coarse sensing stage and that based on multiple cyclic frequencies (MCF) is employed in the fine sensing stage. Whether the fine sensing stage is performed or not is decided by comparing the statistic constructed in the coarse sensing stage with two thresholds. Theoretical analyses and simulation results show that the proposed sensing scheme has superior sensing performance and needs shorter sensing time.

A hybrid overlay/underlay spectrum sharing method for cognitive radio networks based on user classification and convex optimization is proposed. Interference radii are configured for the primary receiver and each cognitive receiver. Cognitive users are divided into four groups and allocated different spectrum sharing patterns according to their distance from the primary transmitter and receiver. An optimal power allocation scheme that achieves the maximum sum rate of cognitive radio system on the premise of satisfying the interference constraint of primary receiver is acquired through the convex optimization method. Performance analysis and simulation results show that, compared with existing methods, our method leads to improved performance of achievable sum rate of cognitive users while guarantees the transmission of primary users.

Cognitive radio (CR) is a key solution for the problem of inefficient usage of spectral resources. Spectrum sensing in each CR aims at detecting whether a preassigned spectrum band is occupied by a primary user or not. Conventional techniques do not allow the CR to communicate with its own base station during the spectrum sensing process. So, only a part of the frame can be used for cognitive data transmission. In this paper, we introduce a new spectrum sensing framework that combines a blind source separation technique with conventional spectrum sensing techniques. In this way, the cognitive transmitter can continue to transmit during spectrum sensing, if it was in operation in the previous frame. Moreover, the accuracy is improved since the decision made by the spectrum unit in each frame depends on the decision made in the previous frame. We use Markov chain tools to model the behavior of our spectrum sensing proposal and to derive the parameters that characterize its performance. Numerical results are provided to confirm the superiority of the proposed technique compared to conventional spectrum sensing techniques.

In this letter, we focus on the spectrum sharing cognitive radio system, wherein a single-input multi-output cognitive fading channel is considered. Subject to the joint average interference constraint and peak interference constraint at the primary receiver, the outage capacity of the cognitive channel involving joint beamforming and power control is analyzed. We derive the optimal beamforming and power control strategy and deduce the closed-form expression for the outage capacity under Rayleigh fading model, the functional regions of two kinds of interference constraints are discussed as well. Furthermore, considering zero-outage transmission, we investigate the delay-limited capacity and introduce a new concept called the zero-outage average interference wall. Extensive simulations corroborate our theoretical results.

In this paper, a preemptive priority queueing model is developed to derive the system dwelling time of secondary calls in a cognitive radio system in which a primary call's reoccupation of the channel is modeled as a preemptive event that forces a secondary call to attempt a spectrum handover. The suspension of secondary call service which may happen when the immediate spectrum handover fails, is included in our computation of the system dwelling time. The results are helpful in evaluating cognitive radio systems in terms of service delay and in determining system design parameters such as required buffer size and system capacity.

This paper provides conceptual and experimental analysis of a new approach in the study of kanji, our “Learner's Visualization (LV) Approach”. In a previous study we found that the LV Approach assists beginning learners in significantly updating their personal kanji deconstruction visualization. Additionally, in another study our findings provided evidence that beginning learners also receive a significant impact in the ability to acquire vocabulary. In this study, our research problem examines how beginning and intermediate students use visualization to cognitively deconstruct (divide) kanji in different ways, and how this affects their learning progress. We analyze the cognitive differences in how kanji learners explore and deconstruct novel kanji while using the LV Approach and how these differences affect their learning process while using the LV Approach. During the learning experience, our LILES System (Learner's Introspective Latent Envisionment System), based on the LV Approach, guides learners to choose from a set of possible “kanji deconstruction layouts” (layouts showing different ways in which a given kanji can be divided). The system then assists learners in updating their “kanji deconstruction level” (the average number of parts they visualize within kanji according to their current abilities). Statistical analysis based on achieved performance was conducted. The analysis of our results proves that there are cognitive differences: beginners deconstruct kanji into more parts (“blocks”) than intermediate learners do, and while both improve their kanji deconstruction scores, there is a more significant change in “kanji deconstruction level” in beginners. However, it was also found that intermediate learners benefit more in “kanji retention score” compared with beginners. Suggestions for further research are provided.

Cognitive radio in network core devices, such as basestations, is being considered as a spectrum management solution for future society's communication demands. Aside from new resource allocation algorithms, efficient inter- and intra-protocol processing should be considered. In this paper, we propose an opportunistic cross layer architecture called COmmon Layer Architecture (COLA) for information exchange between arbitrary layers in New Generation Networks with network-oriented cognitive radio. COLA provides a means for faster information exchange between OSI layers by introducing abstraction at each layer and designing primitives for communication between each abstraction layer. Emulation and simulation results showed improvements, compared to conventional networks, in disrupted service (42% reduction), average delay (47% reduced) and packet drop ratio (22% reduced) in the scenario analyzed.

In this letter, we propose a Partially Observable Markov Decision Process (POMDP) based Distributed Adaptive Opportunistic Spectrum Access (DA-OSA) Strategy for Cognitive Ad Hoc Networks (CAHNs). In each slot, the source and destination choose a set of channels to sense and then decide the transmission channels based on the sensing results. In order to maximize the throughput for each link, we use the theories of sequential decision and optimal stopping to determine the optimal sensing channel set. Moreover, we also establish the myopic policy and exploit the monotonicity of the reward function that we use, which can be used to reduce the complexity of the sequential decision.

As the demand for spectrum for future wireless communication services increases, cognitive radio technology has been developed for dynamic and opportunistic spectrum access, which enables the secondary users to use the underutilized licensed spectrum of the primary users. In particular, the recent studies on the MAC protocol for dynamic and opportunistic access have focused on sensing and using the vacant spectrum efficiently. Under the ad-hoc network environment, how the secondary users use the unused channels by the primary users affects the efficient utilization of channels and a cognitive radio system is required to follow the rapid and frequent changes in channel status. In this paper, we propose a self-scheduling multi-channel cognitive MAC (SMC-MAC) protocol, which allows multiple secondary users to transmit data though the sensed idle channels by two cooperative channel sensing algorithms, i.e., fixed channel sensing (FCS) and adaptive channel sensing (ACS), and by slotted contention mechanism to exchange channel request information for self-scheduling. The performance of the proposed SMC-MAC protocol is investigated via analysis and simulations. According to the results, the proposed SMC-MAC protocol is effective in allowing multiple secondary users to transmit data frames effectively on multi-channels and adaptively in response to the primary users' traffic dynamics.

This paper discusses the possibility of deploying a short-range cognitive radio (secondary communication system) within the service area of a primary system. Although the secondary system interferes with the primary system, there are certain locations in the service area of the primary system where the cognitive radio can reuse the frequency of the primary system without causing harmful interference to it and being disturbed by the primary system. These locations are referred to as having a spatial opportunity for communications in the secondary system, since it can reuse the frequency of the primary system. Simulation results indicate that the antenna gain, beamwidth, and propagation path loss greatly affect the spatial opportunity of frequency reuse for the secondary users. The results show that spatial spectrum reuse can be significantly increased when the primary system users are equipped with directional antennas. An important component in this study is the heterogeneous path loss model, i.e., the path loss model within the primary system is different from the model used to calculate the interference between the primary and the secondary systems. Our results show that the propagation models corresponding to the actual antenna heights in the primary/secondary system can largely impact the possibilities for spectrum reuse by the cognitive radios.