Channels In Cognitive Radio Networks Research Articles

Efficient cooperative compressive spectrum sensing by identifying multi-candidate and exploiting deterministic matrix

Cooperative spectrum sensing exploits the spatial diversity to improve the detection of occupied channels in cognitive radio networks (CRNs). Cooperative compressive spectrum sensing (CCSS) utilizing the sparsity of channel occupancy further improves the efficiency by reducing the number of reports without degrading detection performance. In this paper, we firstly and mainly propose the referred multi-candidate orthogonal matrix matching pursuit (MOMMP) algorithms to efficiently and effectively detect occupied channels at fusion center (FC), where multi-candidate identification and orthogonal projection are utilized to respectively reduce the number of required iterations and improve the probability of exact identification. Secondly, two common but different approaches based on threshold and Gaussian distribution are introduced to realize the multi-candidate identification. Moreover, to improve the detection accuracy and energy efficiency, we propose the matrix construction based on shrinkage and gradient descent (MCSGD) algorithm to provide a deterministic filter coefficient matrix of low t-average coherence. Finally, several numerical simulations validate that our proposals provide satisfactory performance with higher probability of detection, lower probability of false alarm and less detection time.

CCRA: channel criticality based resource allocation in cognitive radio networks

SummaryIn cognitive radio networks, signal to interference plus noise ratio (SINR) is a quantity that is used to analyze the bounds of the capacity of a channel. This is the reason for SINR being one of the important parameters toward evaluating the performance of spectrum sharing in every network. To maximize the channel utilization in any network, the SINR of a channel should be considered to be within a threshold. This also leads to lesser power consumption, and the quality of service for the licensed users is maintained. Further, reduced SINR leads to an improvement in the quality of communication in the network. In this paper, a graph theoretic measure for the efficient utilization of channels in cognitive radio networks has been proposed and is named as channel criticality based resource allocation (CCRA). Using the SINR as the weight of the graph, a novel concept of channel criticality has also been introduced in this work. The proposed CCRA technique has also been compared with the existing interference aware channel assignment (IACA) technique in terms of the channel utilization. Through simulations, the CCRA has been observed to outperform the IACA scheme. The average channel utilization of the proposed CCRA was observed to have increased by 8%, when the secondary users were introduced in the network as compared with the IACA technique. Copyright © 2015 John Wiley & Sons, Ltd.

Combined channel assignment and network coded opportunistic routing in cognitive radio networks

In this paper, we investigate the joint opportunistic routing and channel assignment problem in multi-channel multi-radio (MCMR) cognitive radio networks (CRNs) for improving the aggregate throughput of the secondary users. We first present the nonlinear programming optimization model for this joint problem, taking into account the feature of CRNs-channel uncertainty. In order to reduce the computational complexity of the problem, we present a heuristic algorithm to select forwarding candidates and assign channels in CRNs, including candidate selection algorithm considering the queue state of a node and expected transmission count (ETX), and channel assignment algorithm taking into account the transmission time and the available time of a given channel. Our simulation results show that the proposed scheme Channel Assignment and Network Coded Opportunistic Routing, (CANCOR) performs better than the traditional routing and classical opportunistic routing in which channel assignment strategy is employed.

Distributed Stochastic Online Learning Policies for Opportunistic Spectrum Access

The fundamental problem of multiple secondary users contending for opportunistic spectrum access over multiple channels in cognitive radio networks has been formulated recently as a decentralized multi-armed bandit (D-MAB) problem. In a D-MAB problem there are M users and N arms (channels) that each offer i.i.d. stochastic rewards with unknown means so long as they are accessed without collision. The goal is to design distributed online learning policies that incur minimal regret. We consider two related problem formulations in this paper. First, we consider the setting where the users have a prioritized ranking, such that it is desired for the K-th-ranked user to learn to access the arm offering the K-th highest mean reward. For this problem, we present DLP, the first distributed policy that yields regret that is uniformly logarithmic over time without requiring any prior assumption about the mean rewards. Second, we consider the case when a fair access policy is required, i.e., it is desired for all users to experience the same mean reward. For this problem, we present DLF, a distributed policy that yields order-optimal regret scaling with respect to the number of users and arms, better than previously proposed policies in the literature. Both of our distributed policies make use of an innovative modification of the well-known UCB1 policy for the classic multi-armed bandit problem that allows a single user to learn how to play the arm that yields the K K-th largest mean reward.

A Distributed Average Likelihood Ratio Detector for Detection of Signals in Frequency-Selective Nakagami Channels

In this paper, we study energy detection-based cooperative Spectrum Sensing (SS) over frequency selective channels in cognitive radio networks. We treat primary signal as an unknown deterministic signal with known power. We assume a generalized fading model for the sensing channel with multipath and Nakagami-m distribution and with a smaller Coherence Bandwidth (CB) than the bandwidth of the primary signal. We investigate an energy combining algorithm in frequency domain to take advantage of the diversity of multiple (relatively flat) sub-bands. We also show that although the channel gains of multiple paths may be uncorrelated, the frequency-domain gains will be correlated. Hence, we take the correlation among these sub-bands into account to improve detection performance. In this frequency domain average likelihood ratio detector (FD-ALRD), the likelihood ratio function of the normalized energies over multiple sub-bands is averaged over the channel distribution in frequency domain. Simulation results show that utilizing the frequency-domain diversity in the FD-ALRD improves the detection performance. Furthermore, exploiting the correlation in the frequency domain results in further improvement.

Sub-banding the Secondary Users’ Channel in Cognitive Radio Networks Considering Unreliable Spectrum Sensing

One of the most efficient methods to reduce the dropping and blocking probabilities of the secondary users (SUs) in cognitive radio networks is channel sub-banding strategy. This means that when all the channels are occupied by the primary and secondary users, then the SUs' channels can be divided into two sub-bands, and two SUs can use a sub-band, simultaneously. In this paper, we propose an opportunistic spectrum sharing system in cognitive radio networks in which, the channel sub-banding strategy is implemented. Furthermore, we describe the problem of channel sub-banding considering the spectrum sensing errors such as false alarm and miss-detection events for both initial and on-going SUs' calls. Due to unreliable spectrum sensing by the SUs and subsequently possible interference with the primary users, we assume that both primary and secondary users may lose the channel due to the collision. The proposed model is analyzed by a two-dimensional Markov chain model and for performance evaluation, metrics such as blocking and dropping probabilities and channel utilization are derived. Numerical and simulation results show the accuracy of the proposed model which can be used in the evaluation of future cognitive radio networks' performance.

MMSE Transmit Optimization for Multiuser Multiple-Input Single-Output Broadcasting Channels in Cognitive Radio Networks

In this paper, we address the problem of linear minimum mean-squared error (MMSE) transmitter design for the cognitive radio (CR) multi-user multiple-input single-output (MU-MISO) broadcasting channel (BC), where the cognitive users are subject to not only a sum power constraint, but also a interference power constraint. Evidently, this multi-constraint problem renders it difficult to solve. To overcome this difficulty, we firstly transform it into its equivalent formulation with a single constraint. Then by utilizing BC-MAC duality, the problem of BC transmitter design can be solved by focusing on a dual MAC problem, which is easier to deal with due to its convexity property. Finally we propose an efficient two-level iterative algorithm to search the optimal solution. Our simulation results are provided to corroborate the effectiveness of the proposed algorithm and show that this proposed CR MMSE-based scheme achieves a suboptimal sum-rate performance compared to the optimal DPC-based algorithm with less computational complexity.

Weighted sum-rate maximization for multi-user SIMO multiple access channels in cognitive radio networks

In this article, an efficient distributed and parallel algorithm is proposed to maximize the sum-rate and optimize the input distribution policy for the multi-user single input multiple output multiple access channel (MU-SIMO MAC) system with concurrent access within a cognitive radio (CR) network. The single input means that every user has a single antenna and multiple output means that base station(s) has multiple antennas. The main features are: (i) the power distribution for the users is updated by using variable scale factors which effectively and efficiently maximize the objective function at each iteration; (ii) distributed and parallel computation is employed to expedite convergence of the proposed distributed algorithm; and (iii) a novel water-filling with mixed constraints is investigated, and used as a fundamental block of the proposed algorithm. Due to sufficiently exploiting the structure of the proposed model, the proposed algorithm owns fast convergence. Numerical results verify that the proposed algorithm is effective and fast convergent. Using the proposed approach, for the simulated range, the required number of iterations for convergence is two and this number is not sensitive to the increase of the number of users. This feature is quite desirable for large scale systems with dense active users. In addition, it is also worth noting that the proposed algorithm is a monotonic feasible operator to the iteration. Thus, the stop criterion for computation could be easily set up.

Design and Implementation of an Underlay Control Channel for Cognitive Radios

Implementation of any cognitive radio network requires an effective control channel that can operate under various modes of activity from the primary users. This paper reports the design and implementation of a filter bank multicarrier spread spectrum (FBMC-SS) system for use as the control channel in cognitive radio networks. The proposed design is based on a filtered multitone (FMT) implementation. Carrier and timing acquisition and tracking methods as well as a blind channel estimation method are developed for the proposed control channel. We also report an implementation of the proposed FBMC-SS system on a hardware platform; a FlexRIO FPGA module from National Instruments.

Cluster-Based Control Channel Allocation in Opportunistic Cognitive Radio Networks

Cognitive radio networks (CRNs) involve extensive exchange of control messages, which are used to coordinate critical network functions such as distributed spectrum sensing, medium access, and routing, to name a few. Typically, control messages are broadcasted on a preassigned common control channel, which can be realized as a separate frequency band in multichannel systems, a given time slot in TDMA systems, or a frequency hopping sequence (or CDMA code) in spread spectrum systems. However, a static control channel allocation is contrary to the opportunistic access paradigm. In this paper, we address the problem of dynamically assigning the control channel in CRNs based on time- and space-varying spectrum opportunities. We propose a cluster-based architecture that allocates different channels for control at various clusters in the network. The clustering problem is formulated as a bipartite graph problem, for which we develop a class of algorithms that provide different tradeoffs between two conflicting factors: number of common channels in a cluster and the cluster size. Clusters are guaranteed to have a desirable number of common channels for control, which facilitates for graceful channel migration when primary radio (PR) activity is detected, without the need for frequent reclustering. We perform extensive simulations that verify the agility of our algorithms in adapting to spatial-temporal variations in spectrum availability.

Joint Admission Control and Channel Selection Based on Multi Response Learning Automata (MRLA) in Cognitive Radio Networks

We use multi response learning automata (MRLA) to control how secondary users should access the licensed primary channels in cognitive radio networks. We seek two aims in this paper: (1) estimating the availability probability of each primary channel and (2) admission control of secondary users to decrease the rate of collisions between them. We consider single and multiple secondary user scenarios. In the first scenario, the secondary user deploys learning automata to estimate the primary channel availability probability for efficient exploitation. In the second scenario, each secondary user deploys an algorithm based on MRLA to estimate primary traffic as well as the behavior of other secondary users in order to control the rate of collisions. Then, to have a better control on the rate of secondary collisions, when the number of secondary users is greater than the number of primary channels, we proposed an admission control scheme. In this scheme, some of secondary users are blocked in each time slot and do not have any interaction with the environment. The convergence of the proposed algorithms with and without admission schemes is analyzed. Simulation results are provided to show the improvement in the secondary users' total throughput and switching cost while maintaining the fairness between them.

Power Control and Allocation for MIMO Broadcast Channels in Cognitive Radio Networks

This paper considers multiple-input multiple-output broadcast channels in cognitive radio networks which consist of a cognitive base station (CBS), K secondary users (SUs) and N primary users (PUs). The multiantenna-based CBS concurrently operates with the PUs. The channel state information for SUs is assumed to be perfectly known at the CBS. To ensure the quality of service of PUs and maximize sum-rate capacity of SUs, two problems are considered: (1) What is the optimal power control of CBS? (2) What are the optimal power allocations of SUs? A two-level game is presented to jointly consider the benefits of power control of CBS and power allocations of SUs. Under this game model, the corresponding game algorithms are also proposed. Finally, numerical simulation results are provided to examine the performance of our proposed algorithms.

On the Sum Rate Capacity of MIMO Broadcast Channels in Cognitive Radio Networks with Interference Power Constraints

This paper investigates the sum rate capacity of MIMO broadcast channels (MIMO-BCs) in cognitive radio networks. A suboptimal user-selection algorithm is proposed to achieve a large sum rate capacity with reduced complexity. This algorithm consists of two steps. First, zero-forcing beamforming is utilized as a downlink precoding technique that precancels inter-user interference. Second, singular value decomposition is applied to the channel matrices of all the secondary users and only consider the singular vectors corresponding to the maximum singular values. The proposed user-selection algorithm chooses singular vectors which are nearly orthogonal to each other and nearly orthogonal to the vector of primary users. With this algorithm, the sum rate capacity of MIMO-BCs in CR networks with interference power constraints and transmit power constraints is derived. We formulate the sum rate capacity as a multi-constraint optimization problem and develop an algorithm to solve the problem in its equivalent form. Finally, numerical simulations are conducted to corroborate our theoretical results in flat Rayleigh fading environments. It is shown that the proposed algorithms are capable of achieving a large sum rate capacity with a very low complexity.

Adaptive Quiet Period Management Scheme over Time-variant Channels in Cognitive Radio Networks

Adaptive Quiet Period Management Scheme over Time-variant Channels in Cognitive Radio Networks

Hard and softened combination for cooperative spectrum sensing over imperfect channels in cognitive radio networks

Recently, cognitive radio (CR) access has received much attention to overcome spectrum scarcity problem. Spectrum sensing methods are often used for finding free channels to be used by CR. In this paper, the problem of cooperative spectrum sensing will be investigated in CR networks over realistic channels. This problem is not clarified until now by taking into account the error effect on the decision reporting. The analytical expressions of the hard and softened one bit and two bits hard combination scheme for cooperative spectrum sensing will be derived. These expressions are investigated to compare with simulation results. The analysis and simulation results show that the performance of cooperative spectrum sensing is limited by the probability of reporting errors. Moreover, it is shown that there is a significant performance loss when a final decision regarding to primary user's (PU) state made at the fusion depends on a set of local spectrum sensing information that are distorted by imperfect reporting channels during transmission. The probability of detection is degraded due to imperfect reporting channel by 16.5% and 12.2% with one bit hard combination and softened two bits hard combination, respectively. To reduce this performance loss, Amplify and Forward (AAF) relying mechanism will be proposed. The probability of detection is improved by 8% and 9.3% with one bit hard combination and softened two bits hard combination, respectively using AAF relaying mechanism.

A Selective-Relay Based Cooperative Spectrum Sensing Scheme without Dedicated Reporting Channels in Cognitive Radio Networks

Typically, each cooperative spectrum sensing process requires two phases: the primary user's signal detection phase, in which all cognitive users attempt to detect the presence of the primary user within a certain observation window (called signal detection overhead); and the initial detection result reporting phase, in which the cognitive users forward their detection results to a fusion center. To avoid interfering with the primary user in the reporting phase, previous research assumed that there is a common control channel (also known as dedicated reporting channel) between the cognitive users and fusion center, which, however, requires extra channel resources and introduces an additional complexity due to the dedicated channel resource management. In this paper, we propose a selective-relay based cooperative spectrum sensing scheme, which is able to control and reduce the interference from cognitive reporting users to primary user without the dedicated channel. We analyze the interference impact on the primary user and show that the interference induced by the reporting users is controllable and can be reduced to satisfy a given outage probability requirement of the primary transmissions. In addition, we investigate the receiver operating characteristics (ROC) of the traditional cooperative sensing scheme (with dedicated reporting channel) and the proposed scheme (without dedicated reporting channel) by jointly considering the signal detection and reporting phases. It is proven that, given a target detection probability, a unique optimal signal detection overhead exists to minimize an asymptotic overall false alarm probability in high SNR regions. We illustrate that, compared to the traditional scheme, the selective-relay based cooperative sensing scheme can save the dedicated channel resources without sacrificing ROC performance. Numerical results also show that, under a guaranteed overall detection probability, an overall false alarm probability can be minimized through an optimization of the signal detection overhead.

A Cooperative Sensing Based Cognitive Relay Transmission Scheme Without a Dedicated Sensing Relay Channel in Cognitive Radio Networks

In this correspondence, we investigate a selective relay spectrum sensing and best relay data transmission (SRSS-BRDT) scheme for multiple-relay cognitive radio networks. Specifically, in the spectrum sensing phase, only selected cognitive relays are utilized to transmit/forward their initial detection results (without a dedicated sensing relay channel) to a cognitive source for fusion, where the dedicated sensing channel refers to the channel transmitting initial spectrum sensing results from cognitive relays to the cognitive source. In the data transmission phase, only the best relay is selected to assist the cognitive source for its data transmissions. By jointly considering the two phases, we derive a closed-form expression of the outage probability for the SRSS-BRDT scheme over Rayleigh fading channels. We show that the SRSS-BRDT scheme outperforms the traditional cognitive transmission scheme (with a limited dedicated sensing channel) in terms of the outage probability performance. In addition, numerical results illustrate that the outage probability of the SRSS-BRDT scheme can be minimized through an optimal allocation of the time durations between the spectrum sensing and data transmission phases.

Power and Time Allocation Between Multiple Channels in Cognitive Radio Networks

In this paper, we consider a wideband cognitive radio network (CRN) containing a single secondary user (SU) which can only serially sense multiple narrowband channels and thus maximize the utilization of perceived available channels. Before transmitting, during a period of time ?, SU allocates ? i to each channel and senses whether the channel is available or not. When transmitting, SU allocates different transmit power P i to each channel. We study the problem of designing the optimal spectrum sensing time allocation ? i , total sensing time ? and power allocation P i between the multiple channels so as to maximize the achievable rate of CRN subject to the constraint of the total transmit power and probability of detection. The problem is transformed into some subproblems with low complexity and solved by optimal means separately. From the time and power allocation parameters, we conclude that channels with low probability of transmission chance or low channel gain will be closed without sensing or transmission in order to get the maximum rate. We also analyze how the conclusions can be applied to the scenario of multiple SUs. Numerical investigation shows that the proposed scheme can significantly achieve spectrum access and the optimization process improves the rate obtained by CRN.

Robust cooperative spectrum sensing schemes for fading channels in cognitive radio networks

In cognitive radio systems, cooperative spectrum sensing can detect the presence of the primary user accurately. In practice, however, since the sensing observations are forwarded to a data collector through fading channels, the sensing performance is severely degraded. To deal with this problem, in this paper, we first propose a secondary user selection based cooperative spectrum sensing method to improve sensing performance by decreasing the reporting errors introduced by the fading channels. More specifically, only the secondary users with better signal-to-noise ratio (SNR) for reporting channels are allowed to send their local binary decisions (0 or 1) to the data collector while the others will send nothing. Additionally, we further propose a diversity based cooperative spectrum sensing method which applies existing space-time coding. By exploiting space diversity in CR network, the reporting errors introduced by the fading channel can be decreased and then the sensing performance is improved significantly. For either proposed cooperative spectrum sensing method, the sensing performance is derived and the analytical performance results are given. Our analysis and numerical results verify that proposed methods outperform the conventional cooperative spectrum sensing with OR-rule by taking into account the error effect on the reporting channels.

Location based spectrum sensing performance analysis over fading channels in cognitive radio networks

This paper analyzes the spectrum sensing performance over fading channel, in which a licensee and multiple unlicensed users coexist and operate in the licensed channel in a local area. The overall average probabilities of detection and false alarm by jointly taking the fading and the locations of all secondary users into account are derived, and a statistical model of cumulate interference is constructed. Based on the cumulate interference, a closed-form expression of outage probability at the primary user's receiver according to a specific distribution of the fading is obtained. Finally, the sensing parameters so as to minimize the total spectrum sensing error and maximize the average opportunistic throughput are obtained. It is noted that the overall average performance analysis and results here enable to benchmark the design of specific spectrum sensing algorithms.