Practical Cognitive Radio Networks Research Articles

Resource Allocation in Multi-User Cognitive Radio Network With Stackelberg Game

Resource allocation with sensing-based interference price is considered for multi-users cognitive radio (CR) network, in which the primary base station (PBS) controls the secondary users' (SUs) transmission by pricing the SUs' interference power. SUs firstly initiates data transmission based on the sensing decision and then PBS sets the interference price according to each SU's interference power. Stackelberg game is formulated to jointly obtain the maximum revenue for PBS and optimize the resource allocation to maximize the transmission gain for SUs. Two practical CR network models are investigated: the sensing based spectrum sharing(SBSS) and the opportunistic spectrum access(OSA). For each scenario, the resource allocation strategy is investigated under the two pricing schemes, namely uniform interference pricing and non-uniform interference pricing. Especially, the stackelberg equilibriums for the proposed games is characterized, and the distributed sensing based interference price bargaining algorithm is proposed according to different channel state information (CSI) for the non-uniform interference pricing case. Numerical examples are carried out to demonstrate the effectiveness of the proposed game algorithm under different pricing scheme.

Cooperative Spectrum Sensing in Multichannel Cognitive Radio Networks With Energy Harvesting

This paper investigates cooperative spectrum sensing in multi-channel cognitive radio networks (CRNs) with energy harvesting. Our goal is two-fold: first, to determine the optimal sensing parameters for effective management of the limited energy budget in order to maximize the achievable throughput, and second, to exploit the benefits of a practical CRN towards improving the performance of the energy constrained CRN. Two different scenarios are considered. In the first, the secondary user (SU) is assigned a single radio frequency (RF) harvesting source, while in the second, the SU is assigned multiple RF harvesting sources and can opportunistically harvest from any of the sources. For these scenarios, the problem is formulated as a stochastic optimal control system with infinite and continuous state and action spaces. This is known to be computationally intractable and becomes even more complicated in a two-dimensional problem such as considered. In order to reduce the computational complexity, a myopic optimization approach is taken, and the problem is formulated into a mixed integer nonlinear problem (MINLP) to determine the channel assignment, the sensing duration, the distribution of the sensing duration associated with the assigned channels and the detection threshold under the constraint of energy causality and primary user (PU) protection. A near-optimal solution is obtained to the MINLP based on the alternating convex optimization technique. The simulation results obtained show that the considered work can improve the amount of energy harvested and, by extension, the active probability of the SUs by exploiting the multi-channel benefits of practical CRN for enhanced throughput.

Multi-MTTR Asynchronous-Asymmetric Channel-Hopping Sequences for Scalable Cognitive Radio Networks

In this paper, a family of channel-hopping (CH) sequences, so-called multi-maximum time-to-rendezvous (MTTR) asynchronous-asymmetric prime sequences (MAAPSs), for cognitive radio networks (CRNs) is designed and analyzed. They have known and enlarged cardinality (for the benefit of user identification), full degree of rendezvous, and pairwise shift invariant. For the first time, the MAAPSs can be partitioned into multiple subsets of CH sequences with adjustable MTTR. This unique property supports a trade-off between cardinality and MTTR in scalable CRNs. With a channel-contention-resolution mechanism in use, the rendezvous-success (RS) rate and variance of the MAAPSs in a practical CRN are analyzed. With short MTTR and smaller RS-rate variance, the MAAPSs can support quicker, more stable and uniform rendezvous processes than their counterparts. Finally, the analytical models of two combined metrics of TTR and RS rate, so-called maximum and average time-to-RS, are formulated and validated with computer simulation.

Cooperative Spectrum Sensing With Heterogeneous Devices: Hard Combining Versus Soft Combining

Practical cognitive radio networks (CRNs) would have users with different capabilities and access levels to prior information about the primary users (PUs). For instance, some users may have the privilege to access information about the PU network, which can be utilized to increase the detection performance, whereas others may need to use simple detectors due to energy consumptions constraints, etc. This level of heterogeneity must be accommodated to enable coexistence of different secondary users’(SUs) networks. This paper presents performance analysis and comparison of hard and soft combining cooperative spectrum sensing schemes in heterogeneous CRNs. A centralized approach is considered for cooperative spectrum sensing with SUs that may use either energy detector, cyclostationary detector, pilot-based detector, or orthogonal frequency division multiplexing-based detector. For hard combining, each cooperative SU senses the spectrum using one of the available detectors and reports one-bit local decision to the fusion center which then applies $m$ -out-of- $K$ rule for the final decision. For soft combining, we derive an optimal soft combining scheme based on the Neyman–Pearson criterion. Then, we drive a suboptimal rule to reduce the complexity of the proposed scheme. For the sake of simplicity, we consider only energy and cyclostationary detectors for soft combining. In addition, we study the impact of the cyclic frequency offset in case of a cyclostationary detector. Finally, the paper is supported with extensive simulation results that demonstrate the performance of proposed schemes in terms of probability of detection and probability of false alarm.

Asynchronous-Symmetric Channel-Hopping Sequences Over Prime Field for Cognitive Radio Networks

In this letter, two families of asynchronous-symmetric channel-hopping (CH) sequences over prime field are constructed. With straightforward construction algorithms, they provide a flexible balance between adjustable cardinality, full degree-of-rendezvous, and short maximum-time-to-rendezvous (MTTR) for practical cognitive radio networks. These CH sequences have smaller rendezvous-success-rate variance than their counterparts, thus supporting a more stable and uniform rendezvous process. As secondary users cannot transmit in channels occupied by primary (licensed) users, the average MTTR of these CH sequences is analyzed under a random channel-replacement method.

Energy efficient design using statistical interference constraints for cognitive 5G networks under PU mobility

AbstractEnergy efficiency (EE) has now become one of the essential parameters in the design of 5G and other next‐generation communication networks mainly due to economic and environmental concerns. In this paper, we develop an EE maximisation algorithm for the orthogonal frequency‐division multiplexing (OFDM)–based cognitive radio (CR) systems where the PU activity is highly dynamic. The problem is formulated as a joint optimisation problem in 3 variables: sensing duration, transmission duration, and transmit power on the subcarrier, with statistical constraint on the primary user (PU) interference. The statistical interference constraint frees the CR transmitter from the additional burden of obtaining the instantaneous channel quality feedback from the PU receivers. Furthermore, PU mobility, which is the main consideration of this work, closely portrays a real and practical CR network. We analyse how the mobility of the PUs affect their Quality of Service, particularly in terms of the interference impinged on PUs by the SUs. Moreover, the mobility of the PUs has been modelled, and PU interference constraints are reformulated by considering collisions between the PUs and SUs during their overlap period. Moreover, closed‐form expressions for secondary throughput and EE have been derived. The original problem, which is mathematically intractable due to its nonconvexity, is reformulated in terms of 3 alternately iterating subproblems. Numerical results show that with the proposed optimal algorithm, CR users can achieve significantly higher EE compared with existing algorithms. The suboptimal algorithm guarantees reduced complexity with a performance gap close to zero with the original optimal problem.

Robust Power Control for OFDM-Based Cognitive Radio Networks with QoS Guarantee

Traditional power control schemes in Orthogonal Frequency Division Multiplexing based Cognitive Radio Networks (CRNs) are achieved under perfect channel state information (e.g., exact parameter information). Due to the effect of channel estimation errors and feedback delays, however, channel uncertainties are inevitable in practical CRNs. In this paper, considering bounded uncertainties of channel gains, a robust power control algorithm is proposed to minimize the total transmit power of Secondary Users (SUs) subject to interference power constraint of primary user and quality of service constraint of SU where the non-convex optimization problem is converted into a convex optimization problem that is solved by using robust optimization theory. Computer experiments demonstrate the effectiveness of the proposed algorithm by comparing with the non-robust algorithm in the aspect of suppressing the effect of parameter perturbation.

Opportunistic relay selection improves reliability–reliability tradeoff and security–reliability tradeoff in random cognitive radio networks

This study investigates the physical-layer security in random cognitive radio network (CRN) consisting of randomly distributed primary nodes, secondary nodes, relay nodes and eavesdroppers, where the secondary nodes share the spectrum of the primary nodes and the eavesdroppers attempt to intercept the secondary transmissions. The authors develop a framework to analysis the reliability-reliability tradeoff (RRT) and security-reliability tradeoff (SRT) in the random CRNs, where the security and reliability are quantified in terms of secrecy outage probability and connection outage probability. The RRT evaluates performance tradeoff between the primary and the secondary networks and the SRT evaluates the performance tradeoff inside the secondary network. Furthermore, they propose an opportunistic relay selection (ORS) scheme to enhance the secondary confidential transmission. It is demonstrated that the ORS scheme significantly improves the RRT and SRT as the density of relays increases, and outperforms the conventional direct transmission when the density of relays is larger than a certain value. This actually promotes the understandings of the performance tradeoffs and provides direct insights on system design for the practical CRNs.

Multiuser-diversity-based interference alignment in cognitive radio networks

As a promising interference management technique, interference alignment (IA) has been applied to cognitive radio (CR) networks. However, the received signal-to-interference-plus-noise ratio (SINR) may decrease dramatically under some channel conditions in IA-based CR networks, and this will reduce the quality of service (QoS) of primary users (PUs). In this paper, we study the problem of SINR decrease and propose a multiuser-diversity-based IA scheme to make it more practical to be applied to CR networks. Since the number of secondary users (SUs) is changing dynamically in practical CR networks, we present two schemes targeted at two different scenarios. In the first scenario with a large number of SUs, the IA network cannot accommodate all the PUs and SUs simultaneously with perfect elimination of interferences. The corresponding scheme is to select those SUs, which can maximally improve the QoS of PUs, to access to the spectrum by forming an IA network with the PUs. Thus the performance of PUs can be significantly improved. To further ensure the interest of SUs, the scheme is revised and a tradeoff is made between the PUs and SUs. In the second scenario with a smaller number of SUs, the IA network can accommodate all the users simultaneously without mutual interference. User selection and antenna selection strategies are adaptively employed to guarantee the performance of PUs. Furthermore, fairness among SUs is also investigated. Simulation results are presented to show the effectiveness of the proposed schemes for CR networks.

Evolution handoff strategy for real-time video transmission over practical cognitive radio networks

The transmission delay of real-time video packet mainly depends on the sensing time delay (short-term factor) and the entire frame transmission delay (long-term factor). Therefore, the optimization problem in the spectrum handoff process should be formulated as the combination of microscopic optimization and macroscopic optimization. In this paper, we focus on the issue of combining these two optimization models, and propose a novel Evolution Spectrum Handoff (ESH) strategy to minimize the expected transmission delay of real-time video packet. In the micro-optimized model, considering the tradeoff between Primary User's (PU's) allowable collision percentage of each channel and transmission delay of video packet, we propose a mixed integer non-linear programming scheme. The scheme is able to achieve the minimum sensing time which is termed as an optimal stopping time. In the macro-optimized model, using the optimal stopping time as reward function within the partially observable Markov decision process framework, the EHS strategy is designed to search an optimal target channel set and minimize the expected delay of packet in the long-term real-time video transmission. Meanwhile, the minimum expected transmission delay is obtained under practical cognitive radio networks' conditions, i.e., secondary user's mobility, PU's random access, imperfect sensing information, etc‥ Theoretical analysis and simulation results show that the ESH strategy can effectively reduce the transmission delay of video packet in spectrum handoff process.

On the Capacity of the Two-User Gaussian Causal Cognitive Interference Channel

This paper considers the two-user Gaussian causal cognitive interference channel (GCCIC), which consists of two source-destination pairs that share the same channel and where one full-duplex cognitive source can causally learn the message of the primary source through a noisy link. The GCCIC is an interference channel with unilateral source cooperation that better models practical cognitive radio networks than the commonly used model which assumes that one source has perfect noncausal knowledge of the other source's message. First, the sum-capacity of the symmetric GCCIC is determined to within a constant gap. Then, the insights gained from the study of the symmetric GCCIC are extended to more general cases. In particular, the whole capacity region of the Gaussian Z-channel, i.e., when there is no interference from the primary user, and of the Gaussian S-channel, i.e., when there is no interference from the secondary user, are both characterized to within 2 bits. The fully connected general, i.e., no-symmetric, GCCIC is also considered and its capacity region is characterized to within 2 bits when, roughly speaking, the interference is not weak at both receivers. The parameter regimes where the GCCIC is equivalent, in terms of generalized degrees-of-freedom, to the noncooperative interference channel (i.e., unilateral causal cooperation is not useful), to the non-causal cognitive interference channel (i.e., causal cooperation attains the ultimate limit of cognitive radio technology), and to bilateral source cooperation are identified. These comparisons shed light into the parameter regimes and network topologies that in practice might provide an unbounded throughput gain compared to currently available (non cognitive) technologies.

The Optimal Spectrum Sensing Time for Maximizing Throughput of 802.11-Based MAC Protocol for Cognitive Radio Networks Under Unsaturated Traffic Conditions

Cognitive radio has attracted considerable attention as an enabling technology for addressing the problem of radio frequency shortages. In cognitive radio networks (CRNs), secondary users (SUs) are allowed to opportunistically utilize the licensed spectrum bands of primary users (PUs) when these bands are temporarily unused. Thus, SUs should monitor the licensed spectrum bands to detect any PU signal. According to the sensing outcomes, SUs should vacate the spectrum bands or may use them. Generally, the spectrum sensing accuracy depends on the sensing time which influences the overall throughput of SUs. That is, there is a fundamental tradeoff between the spectrum sensing time and the achievable throughput of SUs. To determine the optimal sensing time and improve the throughput of SUs, considerable efforts have been expended under the saturated traffic and ideal channel assumptions. However, these assumptions are hardly valid in practical CRNs. In this paper, we provide the framework of an 802.11-based medium access control for CRNs, and we analyze this framework to find the optimal spectrum sensing time under the saturated and unsaturated traffic condition. Through simulation, the proposed analytic model is verified and the fundamental problem of the sensing-throughput tradeoff for CRNs is investigated.

Radio environment map as enabler for practical cognitive radio networks

Latest regulations on TV white space communications and emerging trends of spectrum access through geolocation databases relax the regulatory constraints on Cognitive Radios (CRs). Geolocation databases are designed to store information related to incumbents, and CRs are envisioned to consult this database before spectrum access. Spectrum occupancy and related environment information can be constructed using these geolocation databases. In that regard, Radio Environment Map (REM) is a promising tool that provides a practical means for the realization of cognitive radio networks (CRNs). It constructs a comprehensive map of the CRN by utilizing multi-domain information from geolocation databases, characteristics of spectrum use, geographical terrain models, propagation environment, and regulations. REMs contribute to cognition engines by building long-term knowledge via processing spectrum measurements collected from sensors to estimate the state of locations where there is no measurement data. In addition, REM utilizes feedback from the CRs and can apply various learning tools. The vision is to design CRNs such that CRs, though being simple devices without advanced cognitive functionalities, can become cognitive via REMs and operate in an efficient manner. In this paper, an overview of the REM concept is presented in various dimensions ranging from its architecture and stored information to REM construction techniques as well as REM quality metrics.

Collaborative Sensing Management for Cognitive Radio Networks with Reporting Overhead

Many cognitive radio (CR) networks adopt the collaborative sensing strategy that combines the sensing results of multiple CR stations, for getting the better primary user (PU) detection performance. In the practical CR networks with collaborative sensing, since a station reports the sensing result to a fusion center through a message, the reporting overhead is not negligible. In this paper, we design a sensing management scheme for CR networks, while taking this reporting overhead into account. First, we formulate an optimization problem that aims at finding the optimal number of sensing stations which collaborate to maximize the PU detection probability for given sensing/reporting time. Then, we propose a heuristic scheme to efficiently select the collaborative sensing stations, without a priori information for the locations of stations. Furthermore, we determine the optimal sensing interval for minimizing the PU detection delay while maintaining the CR channel utilization to a target value, by using a Markov analysis. The simulation results show that the proposed sensing management scheme significantly improves the PU detection performance.

Hybrid-Strategy Attacks in Collaborative Spectrum Sensing of Cognitive Radio Networks

AbstractByzantine attacks in collaborative spectrum sensing (CSS) systems of cognitive radio networks (CRNs) is a critical threat and has attracted considerable attention. However, in existing works, attacking strategies are generally modeled in a simple way, i.e., Single-Strategy, and the attackers can be detected easily. In practical CRNs, Byzantine attackers could be more sophisticated, and they could adjust their malicious actions according to the actions of normal nodes. In this paper, we propose a new type of attacks, i.e., Hybrid-Strategy attacks (HSAs), which are more flexible and destructive than Single-Strategy attacks. We analyze the properties of proposed HSAs and the limitations of existing attacker-detection methods when they encounter HSAs. Furthermore, we prove that the HSAs can avoid being detected by existing distance based detection algorithms when they evolve into stealthy attacks.

Reinforcement Learning for Repeated Power Control Game in Cognitive Radio Networks

Cognitive radio (CR) users are expected to be uncoordinated users that opportunistically seek the spectrum resource from primary users (PUs) in a competitive way. In most existing works, however, CR users are required to share the interference channel information and power strategies to conduct the game with pricing mechanisms that incur the frequent exchange of information. The requirement of significant communication overheads among CR users impedes fully distributed solutions for the deployment of CR networks, which is a challenging problem in the research communities. In this paper, a robust distributed power control algorithm is designed with low implementation complexity for CR networks through reinforcement learning, which does not require the interference channel and power strategy information among CR users (and from CR users to PUs). To the best of our knowledge, this research provides the solution for the first time for the incomplete-information power control game in CR networks. During the repeated game, CR users can control their power strategies by observing the interference from the feedback signals of PUs and transmission rates obtained in the previous step. This procedure allows achieving high spectrum efficiency while conforming to the interference constraint of PUs. This constrained repeated stochastic game with learning automaton is proved to be asymptotically equivalence to the traditional game with complete information. The properties of existence, diagonal concavity and uniqueness for the game are studied. A Bush-Mosteller reinforcement learning procedure is designed for the power control algorithm, and the properties of convergence and learning rate of the algorithm are analyzed. The performance of the learning-based power control algorithm is thoroughly investigated with simulation results, which demonstrates the effectiveness of the proposed algorithm in solving variety of practical CR network problems for real-world applications.

MAP: Multiauctioneer Progressive Auction for Dynamic Spectrum Access

Cognitive radio (CR) is a promising paradigm to achieve efficient utilization of the limited spectrum resource by allowing the unlicensed users to access the licensed spectrum, and dynamic spectrum access (DSA) is one of the fundamental functions of CR networks. Market-driven spectrum auction has been recognized as an effective way to achieve DSA. In spectrum auction, the primary spectrum owners (POs) act as auctioneers who are willing to sell idle spectrum bands for additional revenue, and the secondary users (SUs) act as bidders who are willing to buy spectrum bands from POs for their services. However, conventional spectrum auction designs are restricted within the scenario of single auctioneer. In this paper, we study the spectrum auction with multiple auctioneers and multiple bidders, which is more realistic for practical CR networks. We propose MAP, a Multiauctioneer Progressive auction mechanism, in which each auctioneer systematically raises the trading price and each bidder subsequently chooses one auctioneer for bidding. The equilibrium is defined as the state that no auctioneer and bidder would like to change his decision. We show analytically that MAP converges to the equilibrium with maximum spectrum utilization of the whole system. We further analyze the incentive for POs and SUs joining the auction and accepting the auction result. Simulation results show that MAP well converges to the equilibrium, and the spectrum utilization is arbitrary closed to the global optimal solution according to the length of step.

Sensing error aware delay-optimal channel allocation scheme for cognitive radio networks

Spectrum sensing is not always perfect in practical cognitive radio networks. In this paper, two kinds of sensing errors are considered into the channel allocation scheme. Our work focuses on the cases that the channel availability varies fast during a channel allocation period, in which case the channel dynamics needs to be considered. The sensing errors are modeled to derive the metric of mean delay for each user-channel combination using the vacation queueing model. Further, the optimal resource allocation is determined based on the mean delay metric by bipartite graph matching. The simulation results indicate that the proposed mean delay metric can represent the transmission performance successfully, and the proposed resource allocation scheme is robust to sensing errors.