Strategies In Cognitive Radio Networks Research Articles

Transient analysis of energy-saving strategy for cognitive radio networks using G-queue with heterogeneity

Energy efficiency, high data speeds, and effective spectrum utilization stand as the primary factors governing 5G and beyond networks. A base station (BS) in a cognitive radio network (CRN) that conserves energy during sleep periods is a promising contender for achieving more effective spectrum allocation. An efficient energy-saving strategy is offered in this study to achieve greener communication in wireless cellular networks. This study aims to propose and analyze an energy-saving scheme for the BS, taking into account the heterogeneity and reliability of networks. The entire system is modeled as a three-dimensional Markov chain by establishing a discrete-time preemptive priority queueing model with single vacation, heterogeneous packets, and negative packets such as viruses. Then, the transient analysis of the Markov chain is performed using the recursive method. Utilizing the transient probability distribution, we present numerical results derived from reliability analysis and queueing analysis to substantiate the validity of the proposed model. Furthermore, results based on the energy-saving degree are showcased to affirm the effectiveness of the proposed scheme. Finally, a reward cost function is obtained depending on the successful transmission of secondary packets, and the Quasi-Newton method is used to determine the optimal reward cost.

A Comprehensive Survey on Effective Spectrum Sensing in 5G Wireless Networks through Cognitive Radio Networks

Spectrum sensing is a challenging issue in cognitive radio network. In particular, wideband spectrum sensing gains more attention due to emerging 5G wireless networks characterized by high data rates in the order of hundreds of Gbps. Conventional sensing techniques uses samples for its observations based on Nyquist rate. Due to hardware cost and sampling rate limitations those techniques can sense only one band at a time. In spite of this issue secondary users have to sense multiple frequency bands using frontend technologies which leads into increased cost, time and complexity. Considering the facts and issues, compressive sensing was introduced to minimize the computation time by improving the sensing process even for high dimensional resources. Holding the essential information and reduces the sample size which is related to high dimensional data acquisition is performed in compressive sensing. In the last decade various researchers paid more attention to improve the performance of compressive sensing in cognitive radio networks based on sensing matrix, sparse representation and recovery process. This survey paper provides an in-depth analysis of conventional models and its sensing strategies in cognitive radio networks along with its merits and demerits to obtain a detailed insight about compressive sensing.

Optimal Pricing Strategies in Cognitive Radio Networks With Heterogeneous Secondary Users and Retrials

In a cognitive radio (CR) system, excessive access services for secondary users (SUs) lead to a substantial increase in congestion and the retrial phenomenon, both of which degrade the performance of CR networks, especially in overload conditions. This paper investigates the price-based spectrum access control policy that characterizes the network operator’s provision to heterogeneous and delay-sensitive SUs through pricing strategies. Based on shared-use dynamic spectrum access (DSA), the SUs can occupy the dedicated spectrum without degrading the operations of primary users (PUs). The service to transmission of SUs can be interrupted by an arriving PU, while the interrupted SUs join a retrial pool called an orbit, later trying to use the spectrum to complete the service. In the retrial orbit, the interrupted SU competes fairly with other SUs in the orbit. Such a DSA mechanism is formulated as a retrial queue with service interruptions and general service times. Regarding the heterogeneity of delay-sensitive SUs, we consider two cases: the delay-sensitive parameter follows a discrete distribution and a continuous distribution, respectively. In equilibrium, we find that the revenue-optimal price is unique, while there may exist a continuum of equilibria for the socially optimal price. In addition, the socially optimal price is always not greater than the revenue-optimal price, and thus the socially optimal arrival rate is not less than the revenue-optimal one, which is contrary with the conclusion, i.e., the socially optimal and revenue-optimal arrival rates are consistent, drawn in the literature for homogeneous SUs. Finally, we present numerical examples to show the effect of various parameters on the operator’s pricing strategies and SUs’ behavior.

Optimal Joining Strategies in Cognitive Radio Networks Under Primary User Emulation Attacks

Cognitive radio (CR) networks allow secondary users (SUs) to access and use the spectrum opportunistically. However, some misbehavior users mislead SUs to evacuate the spectrum by launching a primary user emulation attack (PUEA), which is a denial of service (DoS) attack. During the service of an SU, the arrival of the malicious misbehavior users (MMUs) will force the SU under service to leave the system. Another type of attack, different from the MMU, refers to the selfish misbehavior users (SMUs) who launch PUEA to interrupt the serving SU and then take over the spectrum for their own use. In this paper, we consider two types of misbehavior users, i.e., MMU and SMU. We first investigate the noncooperative behavior of SUs that maximize their own benefit and obtain a unique individual Nash equilibrium joining strategy. Next, we present a socially optimal joining strategy from the perspective of social planners. To eliminate the gap between individual equilibrium and socially optimal strategies, a fee imposed on SUs is proposed. We carry out a sensitivity analysis of joining strategies of SUs and social welfare regarding some main parameters of the system. Interestingly, we observe that the individual equilibrium and socially optimal joining probabilities along with the social welfare increase when the MMUs invade the system more frequently.

Cooperative Secondary Encryption for Primary Privacy Preserving in Cognitive Radio Networks

Aiming at protecting primary privacy messages and supporting secondary quality-of-service (QoS), we propose a secondary encrypted secure strategy for cognitive radio networks. In this scheme, a primary system directly transmits privacy messages or employs pre-transmitted secure secondary messages to encrypt the primary privacy information, and the secondary system acquires a fraction of the interference-free licensed spectrum. Following this idea, we consider two secure communication scenarios: the non-buffer scenario and the buffer-aided scenario. For the non-buffer scenario, the primary system first evaluates the channel quality of the direct transmission link. Then, the primary transmitter adaptively chooses to directly transmit the privacy messages or employ the encryption of the secure secondary messages according to the evaluation results. For this scenario, we investigate the primary ergodic secrecy performance and the secondary average performance. For the buffer-aided scenario, the secure secondary messages can be stored in the buffers at both the primary transmitter and receivers. According to the buffer states and channel quality, the primary system adaptively chooses to directly transmit the primary privacy information, permit the secondary secure transmission, or utilize the encryption of the stored secure secondary information. For this scenario, we also investigate the performances of both the primary and secondary systems, and derive the closed-form expression of the primary information delay. Numerical results are given to prove that the proposed scheme can provide privacy preserving for the primary information and acquire high secondary average transmission rate.

Distributed QoS Constraint based Resource Adaptation Strategy for Cognitive Radio Networks

This paper primarily investigates and addresses the optimal power adaptation strategy problem for multi-user cognitive radio network. The need to determine the optimal power transmission for the secondary user (SU) in a distributed network environment is challenging and important. This requires an efficient adaptive strategy with high convergence capability in order to meet up with the multi-users quality of service (QoS) requirements in a cognitive network and ultimately ensure more efficient resource utilization amongst users. In this paper, a distributed QoS contraint scheme which considered both the transmission power and outage probability has been proposed to maximize the network performance and to control the SUs transmission power. Firstly, the QoS constraint optimization problem for distributed secondary users is formulated and solved in order to adapt with the network dynamics of the cognitive networks. Subsequently, the solution has been used to dynamically adjust the radio power of the SUs to conform with the stringent network constraint and ensures coexistence amongst the users. More importantly, the simulation result shows that the scheme increases the overall network utility when compared to the scheme without adaptation.

Performance evaluation of broadcasting strategies in cognitive radio networks

Broadcasting is an important phenomenon, because it serves as simplest mode of communication in a network, via which each node disseminates information to their neighboring nodes simultaneously. Broadcasting is widely used in various kind of networks, such as wireless sensor networks, wireless networks, and ad-hoc networks. Similarly, in cognitive radio networks (CRNs), broadcasting is also used to perform many tasks including neighbor discovery, spectrum mobility, spectrum sharing, and dissemination of message throughout the network. The traditional approach that has been used as broadcasting in CRNs is simple flooding in which a message is disseminated in the network without any strategy check. Simple flooding can cause major setbacks in the network, such as excessive redundant rebroadcasts, and collision drops which collectively are termed as broadcast storm problem. To reduce the effects of broadcast storm problem in wireless networks, we propose and compare four broadcasting strategies for cognitive radio networks in this paper. These four strategies are: (1) probability based, (2) counter based, (3) distance based, and (4) area based. Extensive NS-2 based simulations are carried out on different threshold values for each broadcasting strategy. After experimental evaluation, it is demonstrated that counter based broadcasting surpasses other broadcasting strategies by achieving maximum delivery ratio of 60% and by decreasing redundant rebroadcasts and collision drops up to 44 and 37% respectively.

Optimal Channel Sensing Strategy for Cognitive Radio Networks With Heavy-Tailed Idle Times

In cognitive radio network (CRN), the secondary user (SU) opportunistically access the wireless channels whenever they are free from the licensed/primary user (PU). Even after occupying the channel, the SU has to sense the channel intermittently to detect reappearance of PU, so that it can stop its transmission and avoid interference to PU. Frequent channel sensing results in the degradation of SU’s throughput whereas sparse sensing increases the interference experienced by the PU. Thus, optimal sensing interval policy plays a vital role in CRN. In the literature, optimal channel sensing strategy has been analyzed for the case when the ON-OFF time distributions of PU are exponential. However, the analysis of recent spectrum measurement traces reveals that PU exhibits heavy-tailed idle times which can be approximated well with hyper-exponential distribution (HED). In this paper, we deduce the structure of optimal sensing interval policy for channels with HED OFF times through Markov decision process. We then use dynamic programming framework to derive suboptimal sensing interval policies. A new multishot sensing interval policy is proposed and it is compared with existing policies for its performance in terms of number of channel sensing and interference to PU.

An adaptive handoff strategy for cognitive radio networks

Spectrum handoff plays an important role in spectrum management as it is the process of seamlessly shifting the on-going transmission of a secondary user (SU) to a free channel without degrading the quality of service. In this paper, we develop an adaptive handoff algorithm that allows an SU to detect the arrival of a primary user (via sensing) and adapt to a reactive or a proactive handoff strategy accordingly. The adaptive handoff scheme first allows an SU to decide whether to stay and wait on current channel or to perform handoff. Then, in case of handoff, an SU intelligently shifts between proactive or reactive handoff modes based on primary use (PU) arrival rate. Further, a PU prioritized Markov approach is presented in order to model the interactions between PUs and SUs for smooth channel access. Numerical results show that the proposed handoff scheme minimizes the blocking probability, number of handoffs, handoff delay and data delivery time while maintaining channel utilization and system throughput at maximal level compared to simple reactive and proactive schemes.

Noncooperative and Cooperative Joining Strategies in Cognitive Radio Networks With Random Access

A cognitive radio (CR) system with retrial possibility and an admission cost for secondary users (SUs) to join the retrial group is investigated in this paper. If the SU finds the primary user (PU) band unavailable, it must decide with a probability estimate to either enter a retrial group or give up its service and leave the system. SUs in the retrial group independently retry after an exponentially distributed random time until they successfully access the spectrum. When the PU arrives, the SU's service on the band is interrupted. This interrupted SU is then assumed to occupy the PU band immediately when the PU completes its service. First, the noncooperative joining behavior of SUs that choose to maximize their benefit in a selfish distributed manner is investigated, and an inefficient Nash equilibrium is derived. Second, from the perspective of the social planner, the socially optimal joining strategy when SUs cooperate with each other is studied, and the corresponding Nash equilibrium is exactly derived. Finally, the result that an individually optimal strategy, in general, does not yield the socially optimal strategy is theoretically verified. Furthermore, to bridge the gap between the individually and socially optimal strategies, a novel strategy of imposing an admission fee on SUs to join the retrial group is proposed and investigated with the derivation of an optimal value for the admission fee. The numerical analysis indicates that the proposed admission fee as an equilibrium strategy and the socially optimal strategy of SUs improve efficiency in the utilization of the CR system.

Clustering-based Spectrum Sharing Strategy for Cognitive Radio Networks

In this paper, we propose a clustering-based resource allocation (RA) scheme for the multiuser orthogonal frequency division multiplexing (OFDM)-based cognitive radio network, where we aim to maximize the sum capacity of the secondary users (SUs) subject to practical constraints in wireless environment. Our general RA optimization task leads to a challenging mixed integer programming problem that is computationally intractable. We first introduce a simple and efficient clustering method to divide all the SUs into multiple groups based on their mutual interference degrees, where the SUs in different groups can share the same OFDM subchannels to improve spectrum utilization efficiency, while the SUs with heavy mutual interference cluster together in the same group and employ different subchannels to alleviate their mutual interference. Then we develop efficient radio RA algorithms to maximize the sum rate of the SUs in each cluster. A user-oriented subchannel assignment method is presented to remove the awkward integer constraints of the formulated RA problem, followed by a fast power distribution algorithm that can work out optimal solutions with an approximate linear complexity. Simulation results indicate that our proposed RA scheme can improve the throughput of the SUs significantly as compared with other methods. Moreover, our proposed RA algorithms converge stably and quickly.

Broadcasting strategies for cognitive radio networks: Taxonomy, issues, and open challenges

Broadcasting is the simplest form of communication in which nodes disseminate the same information simultaneously to all of their neighbors. Broadcasting has been widely used in many types of networks including wireless networks, wireless sensor networks, and mobile ad-hoc networks. Likewise these networks, broadcasting is also used in cognitive radio networks (CRNs) to accomplish various tasks such as spectrum sensing, spectrum sharing, spectrum management, and spectrum mobility. This article investigates and provides a comprehensive overview of various broadcasting strategies that have been proposed so far for cognitive radio networks. Moreover, it provides a detailed study of broadcast storm problem in CRNs. Finally, it discusses issues, challenges and future research directions for broadcasting strategies in CRNs.

Systematic construction of common channel hopping rendezvous strategies in cognitive radio networks

Abstract Cognitive radio networks (CRNs) have emerged as a promising paradigm that can solve the shortage of spectrum resources, providing the ability to adapt and opportunistically exploit the spectrum holes. The rendezvous process allows cognitive users to find a common channel and establish a communication link. In this paper, we focus on the design of fast blind rendezvous algorithms to guarantee that every node should be able to rendezvous in all common available channels. We follow a systematic approach by first proposing a role-based algorithm that ensures maximum rendezvous diversity and then extending it to a common strategy through the use of multiples radios. Both proposals guarantee rendezvous under symmetric and asymmetric models. Simulation results show that our proposals outperform other recently developed rendezvous protocols with similar approaches in terms of expected time to rendezvous and maximum time to rendezvous.

Dynamic Spectrum Access for Cognitive Radio Networks With Prioritized Traffics

We develop a dynamic spectrum access (DSA) strategy for cognitive radio networks where prioritized traffic is considered. Assume that there are three classes of traffic, one traffic class of the primary user and two traffic classes of the secondary users, namely, Class 1 and Class 2. The traffic of the primary user has the highest priority, i.e., the primary users can access the spectrum at any time with the largest bandwidth demand. Furthermore, Class 1 has higher access and handoff priority as well as larger bandwidth demand as compared to Class 2. To evaluate the performance of the proposed DSA, we model the state transitions for DSA as a multi-dimensional Markov chain with three-state variables, which present the number of packets in the system of the primary users, the secondary Class 1, and secondary Class 2. In particular, the blocking and dropping probabilities of the two secondary traffic classes are assessed.

Game theory-based resource management strategy for cognitive radio networks

This paper investigates the application of game theory tools in the context of cognitive radio networks (CRN). Specifically, we propose a resource management strategy with the objective to maximize a defined utility function subject to minimize the mutual interference caused by secondary users (SUs) with protection for primary users (PUs). In fact, we formulate a utility function to reflect the needs of PUs by verifying the outage probability constraint, and the per-user capacity by satisfying the signal-to-noise and interference ratio (SNIR) constraint, as well as to limit interference to PUs. Furthermore, the existence of the Nash equilibrium of the proposed game is established, as well as its uniqueness under some sufficient conditions. Theoretical and simulation results based on a realistic network setting, and a comparison with a previously published resource management method are provided in this paper. The reported results demonstrate the efficiency of the proposed technique in terms of CRN deployment while maintaining quality-of-service (QoS) for the primary system.

A frequency-domain entropy-based detector for robust spectrum sensing in cognitive radio networks

Sensitivity to noise uncertainty is a fundamental limitation of current spectrum sensing strategies in cognitive radio networks (CRN). Because of noise uncertainty, the performance of traditional detectors such as matched filters, energy detectors, and even cyclostationary detectors deteriorates rapidly at low Signal-to-Noise Ratios (SNR). To counteract noise uncertainty, a new entropy-based spectrum sensing scheme is introduced in this letter. The entropy of the sensed signal is estimated in the frequency domain with a probability space partitioned into fixed dimensions. It is proven that the proposed scheme is robust against noise uncertainty. Simulation results confirm the robustness of the proposed scheme and show 6dB and 5dB performance improvement compared with energy detectors and cyclostationary detectors, respectively. In addition, the sample size is significantly reduced compared to an energy detector.

Optimal multi-channel cooperative sensing in cognitive radio networks

In this paper, optimal multi-channel cooperative sensing strategies in cognitive radio networks are investigated. A cognitive radio network with multiple potential channels is considered. Secondary users cooperatively sense the channels and send the sensing results to a coordinator, in which energy detection with a soft decision rule is employed to estimate whether there are primary activities in the channels. An optimization problem is formulated, which maximizes the throughput of secondary users while keeping detection probability for each channel above a pre-defined threshold. In particular, two sensing modes are investigated: slotted-time sensing mode and continuous-time sensing mode. With a slotted-time sensing mode, the sensing time of each secondary user consists of a number of mini-slots, each of which can be used to sense one channel. The initial optimization problem is shown to be a nonconvex mixed-integer problem. A polynomial-complexity algorithm is proposed to solve the problem optimally. With a continuous-time sensing mode, the sensing time of each secondary user for a channel can be any arbitrary continuous value. The initial nonconvex problem is converted into a convex bilevel problem, which can be successfully solved by existing methods. Numerical results are presented to demonstrate the effectiveness of our proposed algorithms.

Mobility Management Strategies in Heterogeneous Cognitive Radio Networks

Mobility management strategies in cognitive radio networks are paramount for seamless operation across different frequency bands and diverse radio access technologies. In order to satisfy mobility management requirements in cognitive radio networks, cross layer optimization and cooperation within a cognitive radio protocol stack is necessary. This paper examines spectrum mobility requirements and solutions suitable for deployment in cognitive radio networks. The requirements for mobility management tasks in cognitive radio networks are described together with mobility management protocol architecture intended to fulfill these requirements.

Mutual interference considered power allocation in OFDM-based cognitive networks: The single SU case

Efficient and conflict-free power allocation strategies in cognitive radio networks are being extensively studied in current research in order to meet the requirements of future wireless applications. In this paper, specifying the single secondary user (SU) with multiple primary user (PU) case, a novel power allocation scheme is developed for orthogonal frequency division multiplexing (OFDM) – based cognitive radio networks designed to maximize the capacity of the SU while limiting the interference to the PUs. First, the mutual interference between the SU and the PUs is comprehensively formulated as the restrictions on the SU’s transmission power. Secondly, the power allocation problem is analyzed and formulated as a constrained optimization problem which is concluded to a peak-power constrained water-filling formulation. Finally, a novel low-complexity water-filling algorithm is proposed to solve the optimization problem and the optimal power allocation result is achieved. In a simplified scenario, numerical results are exhibited to confirm the efficiency of the proposed water-filling algorithm, and the influence of the mutual interference on the power allocation and the system capacity is further illustrated.