Access In Cognitive Radio Networks Research Articles

Cluster-based adaptive multispectrum sensing and access in cognitive radio networks

Spectrum sensing and access have been widely investigated in cognitive radio network for the secondary users to efficiently utilize and share the spectrum licensed by the primary user. We propose a cluster-based adaptive multispectrum sensing and access strategy, in which the secondary users seeking to access the channel can select a set of channels to sense and access with adaptive sensing time. Specifically, the spectrum sensing and access problem is formulated into an optimization problem, which maximizes the utility of the secondary users and ensures sufficient protection of the primary users and the transmitting secondary users from unacceptable interference. Moreover, we explicitly calculate the expected number of channels that are detected to be idle, or being occupied by the primary users, or being occupied by the transmitting secondary users. Spectrum sharing with the primary and transmitting secondary users is accomplished by adapting the transmission power to keep the interference to an acceptable level. Simulation results demonstrate the effectiveness of our proposed sensing and access strategy as well as its advantage over conventional sensing and access methods in terms of improving the achieved throughput and keeping the sensing overhead low. Copyright © 2012 John Wiley & Sons, Ltd.

Hedonic Coalition Formation Game for Cooperative Spectrum Sensing and Channel Access in Cognitive Radio Networks

Cooperative spectrum sensing is an effective technique to improve the sensing performance and increase the spectrum efficiency in cognitive radio networks (CRNs). In this paper, we consider a CRN with multiple primary users (PUs) and multiple secondary users (SUs). We first propose a cooperative spectrum sensing and access (CSSA) scheme for all the SUs, where the SUs cooperatively sense the licensed channels of the PUs in the sensing subframe. If a channel is determined to be idle, the SUs which have sensed that channel will have a chance to transmit packets in the data transmission subframe. We then formulate this multi-channel spectrum sensing and channel access problem as a hedonic coalition formation game, where a coalition corresponds to the SUs that have chosen to sense and access a particular channel. The value function of each coalition and the utility function of each SU take into account both the sensing accuracy and the energy consumption. We propose an algorithm for decision node selection in a coalition. Moreover, we propose an algorithm based on the switch rule to allow the SUs to make decisions on whether to join or leave a coalition. We prove analytically that the set with all the SUs converges to a final network partition, which is both Nash-stable and individually stable. Besides, the proposed algorithms are adaptive to changes in network conditions. Simulation results show that our proposed CSSA scheme achieves a better performance than the closest PU (CPU) scheme and the noncooperative spectrum sensing and access (NSSA) scheme in terms of the average utility of the SUs.

Network utility maximization over partially observable Markovian channels

We study throughput utility maximization in a multi-user network with partially observable Markovian channels. Here, instantaneous channel states are unavailable and all controls are based on partial channel information provided by ACK/NACK feedback from past transmissions. Equivalently, we formulate a restless multi-armed bandit problem in which we seek to maximize concave functions of the time average reward vector. Such problems are generally intractable and in our problem the set of all achievable throughput vectors is unknown. We use an achievable region approach by optimizing the utility functions over a non-trivial reduced throughput region, constructed by randomizing well-designed round robin policies. Using a new ratio MaxWeight rule, we design admission control and channel scheduling policies that stabilize the network with throughput utility that is near-optimal within the reduced throughput region. The ratio MaxWeight rule generalizes existing MaxWeight-type policies for the optimization of frame-based control systems with policy-dependent frame sizes. Our results are applicable to limited channel probing in wireless networks, dynamic spectrum access in cognitive radio networks, and target tracking of unmanned aerial vehicles.

Optimal Myopic Sensing and Dynamic Spectrum Access in Cognitive Radio Networks with Low-Complexity Implementations

Cognitive radio techniques allow secondary users (SU's) to opportunistically access underutilized primary channels that are licensed to primary users. We consider a group of SU's with limited spectrum sensing capabilities working cooperatively to find primary channel spectrum holes. The objective is to design the optimal sensing and access policies that maximize the total secondary throughput on primary channels accrued over time. Although the problem can be formulated as a Partially Observable Markov Decision Process (POMDP), the optimal solutions are intractable. Instead, we find the optimal sensing policy within the class of myopic policies. Compared to other existing approaches, our policy is more realistic because it explicitly assigns SU's to sense specific primary channels by taking into account spatial and temporal variations of primary channels. Contributions: (1) formulation of a centralized spectrum sensing/access architecture that allows exploitation of all available primary spectrum holes; and (2) proposing sub-optimal myopic sensing policies with low-complexity implementations and performance close to the myopic policy. We show that our proposed sensing/access policy is close to the optimal POMDP solution and outperforms other proposed strategies. We also propose a Hidden Markov Model based algorithm to estimate the parameters of primary channel Markov models with a linear complexity.

Nonconvex dynamic spectrum allocation for cognitive radio networks via particle swarm optimization and simulated annealing

Dynamic spectrum access is a promising technique designed to meet the challenge of rapidly growing demands for broadband access in cognitive radio networks. By utilizing the allocated spectrum, cognitive radio devices can provide high throughput and low latency communications. This paper introduces an efficient dynamic spectrum allocation algorithm in cognitive radio networks based on the network utility maximization framework. The objective function in this optimization problem is always nonconvex, which makes the problem difficult to solve. Prior works on network resource optimization always transformed the nonconvex optimization problem into a convex one under some strict assumptions, which do not meet the actual networks. We solve the nonconvex optimization problem directly using an improved particle swarm optimization (PSO) method. Simulated annealing (SA), combined with PSO to form the PSOSA algorithm, overcomes the inherent defects and disadvantages of these two individual components. Simulations show that the proposed solution achieves significant throughput compared with existing approaches, and it is efficient in solving the nonconvex optimization problem.

Reinforcement-Learning-Based Double Auction Design for Dynamic Spectrum Access in Cognitive Radio Networks

In cognitive radio networks, an important issue is to share the detected available spectrum among different secondary users to improve the network performance. Although some work has been done for dynamic spectrum access, the learning capability of cognitive radio networks is largely ignored in the previous work. In this paper, we propose a reinforcement-learning-based double auction algorithm aiming to improve the performance of dynamic spectrum access in cognitive radio networks. The dynamic spectrum access process is modeled as a double auction game. Based on the spectrum access history information, both primary users and secondary users can estimate the impact on their future rewards and then adapt their spectrum access or release strategies effectively to compete for channel opportunities. Simulation results show that the proposed reinforcement-learning-based double auction algorithm can significantly improve secondary users' performance in terms of packet loss, bidding efficiency and transmission rate or opportunity access.

Opportunistic Spectrum Access in Cognitive Radio Networks: Global Optimization Using Local Interaction Games

We investigate the problem of achieving global optimization for distributed channel selections in cognitive radio networks (CRNs), using game theoretic solutions. To cope with the lack of centralized control and local influences, we propose two special cases of local interaction game to study this problem. The first is local altruistic game, in which each user considers the payoffs of itself as well as its neighbors rather than considering itself only. The second is local congestion game, in which each user minimizes the number of competing neighbors. It is shown that with the proposed games, global optimization is achieved with local information. Specifically, the local altruistic game maximizes the network throughput and the local congestion game minimizes the network collision level. Also, the concurrent spatial adaptive play (C-SAP), which is an extension of the existing spatial adaptive play (SAP), is proposed to achieve the global optimum both autonomously as well as rapidly.

Coalitional Games in Partition Form for Joint Spectrum Sensing and Access in Cognitive Radio Networks

Unlicensed secondary users (SUs) in cognitive radio networks are subject to an inherent tradeoff between spectrum sensing and spectrum access. Although each SU has an incentive to sense the primary user (PU) channels for locating spectrum holes, this exploration of the spectrum can come at the expense of a shorter transmission time, and, hence, a possibly smaller capacity for data transmission. This paper investigates the impact of this tradeoff on the cooperative strategies of a network of SUs that seek to cooperate in order to improve their view of the spectrum (sensing), reduce the possibility of interference among each other, and improve their transmission capacity (access). The problem is modeled as a coalitional game in partition form and an algorithm for coalition formation is proposed. Using the proposed algorithm, the SUs can make individual distributed decisions to join or leave a coalition while maximizing their utilities which capture the average time spent for sensing as well as the capacity achieved while accessing the spectrum. It is shown that, by using the proposed algorithm, the SUs can self-organize into a network partition composed of disjoint coalitions, with the members of each coalition cooperating to jointly optimize their sensing and access performance. Simulation results show the performance improvement that the proposed algorithm yields with respect to the noncooperative case. The results also show how the algorithm allows the SUs to self-adapt to changes in the environment such as changes in the traffic of the PUs, or slow mobility.

Spectrum Monitoring During Reception in Dynamic Spectrum Access Cognitive Radio Networks

If a frequency band has primary and secondary users, then the cognitive radios of the secondary users must monitor the band and be prepared to cease their transmissions if a primary user's radio begins to transmit. Traditional spectrum sensing requires the secondary radios to refrain from communicating while they check for the emergence of primary signals. We propose and evaluate methods by which the secondary radios can continue their communications while simultaneously monitoring the band to detect any transmissions that are initiated by the primary radios. Our methods for spectrum monitoring supplement traditional spectrum sensing and improve the communications efficiency of the secondary radios.

Medium Access Control for dynamic spectrum access in cognitive radio networks: analysis under uncertainty

ABSTRACTMedium Access Control is an important component in cognitive radio that allow secondary users to identify and access spectrum opportunity without interfering with primary users. In this paper, a queueing model to analyze the performances of the secondary users in a cognitive radio network is presented. The queueing model considers the transmissions of a secondary system where a Medium Access Control algorithm is used to enable the secondary users to sense and access the channels. Also, a simple scheduler is employed to assign transmission time slots to the secondary users. Because the value of the system parameters can be perturbed and cannot be determined precisely, the analysis is extended to take uncertainty into account. In this case, a robust optimization method to study the Markov chain with uncertainty is applied to obtain the stationary probabilities of the queueing model under uncertainty. The lower and upper bounds of performance measures are obtained and compared with the nominal value. Copyright © 2012 John Wiley & Sons, Ltd.

Optimal design for dynamic spectrum access in cognitive radio networks under Rayleigh fading

We consider a time slotted cognitive radio network under Rayleighfading where multiple secondary users (SUs) contend for spectrumusage over available primary users' channels. Weanalyze the performance of a channel access policy where each SU stochasticallydetermines whether to access a wireless channel or not based on agiven access probability. In the analysis, we focus on the queueing performance ofan arbitrary SU with the channel access policy.To improve the queueing performance of SUs, theaccess probability in our channel access policy is adapted to theknowledge on the wireless channel information, e.g., the number of available channels andthe nonfading probability of channels.It is then important to obtain the optimalaccess probabilities from the queueing performance perspective.  &nbsp In this paper we consider three scenarios. In the first scenario,all SUs have full information on wireless channel status andfading channel conditions. In the second scenario, all SUs havethe information on wireless channel status but do not know theirfading channel conditions, and in the last scenario all SUs do nothave any information on wireless channel status and conditions. Foreach scenario we analyze the queueing performance of anarbitrary SU and show how to obtain the optimal accessprobabilities with the help of the effective bandwidth theory.From our analysis we provide an insight on how to design an optimalchannel access policy in each scenario. We also show how the optimal channel accesspolicies in three scenarios are related with each other. Numericalresults are provided to validate our analysis. In addition, weinvestigate the performance behaviors of the optimal channel access policies.

Finding an Individual Optimal Threshold of Queue Length in Hybrid Overlay/Underlay Spectrum Access in Cognitive Radio Networks

Finding an Individual Optimal Threshold of Queue Length in Hybrid Overlay/Underlay Spectrum Access in Cognitive Radio Networks

A queueing-theoretical framework for QoS-enhanced spectrum management in cognitive radio networks

This article outlines the fundamental modeling issues of opportunistic spectrum access in cognitive radio networks. In particular, we identify the effects of connection-based channel usage on the QoS performance of spectrum management techniques. During the transmission period of a secondary user's connection, the phenomenon of multiple spectrum handoffs due to interruptions of primary users arises quite often. In addition to multiple interruptions, the connection-based channel usage behaviors are also affected by spectrum sensing time, switching between different channels, generally distributed service time, and channel contention between multiple secondary users. An analytical framework based on the preemptive resumption priority M/G/1 queueing theory is introduced to characterize the effects of the above factors simultaneously. The proposed generalized analytical framework can incorporate various system parameters into the design of very broad spectrum management techniques, including spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility. The applications of this analytical framework on spectrum decision as well as spectrum mobility are discussed, and some open issues using this framework are suggested in this article.

T-preemptive priority queue and its application to the analysis of an opportunistic spectrum access in cognitive radio networks

We propose a new priority discipline called the T-preemptive priority discipline. Under this discipline, during the service of a customer, at every T time units the server periodically reviews the queue states of each class with different queue-review processing times. If the server finds any customers with higher priorities than the customer being serviced during the queue-review process, then the service of the customer being serviced is preempted and the service for customers with higher priorities is started immediately. We derive the waiting-time distributions of each class in the M/G/1 priority queue with multiple classes of customers under the proposed T-preemptive priority discipline. We also present lower and upper bounds on the offered loads and the mean waiting time of each class, which hold regardless of the arrival processes and service-time distributions of lower-class customers. To demonstrate the utility of the T-preemptive priority queueing model, we take as an example an opportunistic spectrum access in cognitive radio networks, where one primary (licensed) user and multiple (unlicensed) users with distinct priorities can share a communication channel. We analyze the queueing delays of the primary and secondary users in the proposed opportunistic spectrum access model, and present numerical results of the queueing analysis.

Non-cooperative spectrum access in cognitive radio networks: A game theoretical model

Cognitive radio networks provide the capability to share the wireless channel with licensed (primary) users in an opportunistic manner. Primary users have a license to operate in a certain spectrum band; their access can only be controlled by the primary operator and is not affected by any other unlicensed (secondary) user. On the other hand, secondary users (SUs) have no spectrum license, and they attempt to exploit the spectral gaps left free by primary users. This work studies the spectrum access problem in cognitive radio networks from a game theoretical perspective. The problem is modeled as a non-cooperative spectrum access game where secondary users access simultaneously multiple spectrum bands left available by primary users, optimizing their objective function which takes into account the congestion level observed on the available spectrum bands. As a key innovative feature with respect to existing works, we model accurately the interference between SUs, capturing the effect of spatial reuse. Furthermore, we consider both non-elastic and elastic user traffic, to model real-time as well as data transfer applications. Finally, we consider an alternative formulation of the spectrum access problem, where players use replicator dynamics to adjust their strategies, and we derive convergence conditions to Nash equilibrium points. We demonstrate the existence of the Nash equilibrium, and derive equilibrium flow settings. Finally, we provide numerical results of the proposed spectrum access game in several cognitive radio scenarios, and study the impact of the interference between SUs on the game efficiency. Our results indicate that the congestion cost functions we propose in this paper lead to small gaps between Nash equilibria and optimal solutions in all the considered network scenarios, thus representing a starting point for designing pricing mechanisms so as to obtain a socially optimal use of the network.

Fair subcarrier and power allocation for multiuser orthogonal frequency-division multiple access cognitive radio networks using a Colonel Blotto game

The problem of subcarrier allocation (SA) and power allocation (PA) for both the downlink and uplink of cognitive radio networks (CRNs) is studied. Two joint SA and PA schemes based on Blotto games are presented for orthogonal frequency-division multiple access (OFDMA)-based CRNs. In this work, the authors consider a more practical scenario by taking into account the correlation between adjacent subcarriers. In the proposed games, secondary users (SUs) simultaneously compete for subcarriers using a limited budget. In order to win as many good subcarriers as possible, the SUs are required to wisely allocate their budget subject to the transmit power, budget and interference temperature constraints. Two PA and budget allocation strategies are derived to enable fair sharing of spectrum among the SUs. It is shown that by manipulating the total budget available for each SU, competitive fairness can be enforced. In addition, the conditions to ensure the existence and uniqueness of Nash equilibrium (NE) in the proposed methods are established and algorithms which ensure convergence to NE are proposed. Simulation results show that the proposed methods can converge rapidly and allocate resources fairly and efficiently in correlated fading OFDMA channels.

A Two-Level MAC Protocol Strategy for Opportunistic Spectrum Access in Cognitive Radio Networks

In this paper, we consider medium access control (MAC) protocol design for random-access cognitive radio (CR) networks. A two-level opportunistic spectrum access strategy is proposed to optimize the system performance of the secondary network and to adequately protect the operation of the primary network. At the first level, secondary users (SUs) maintain a sufficient detection probability to avoid interference with primary users (PUs), and the spectrum sensing time is optimized to control the total traffic rate of the secondary network allowed for random access when the channel is detected to be available. At the second level, two MAC protocols called the slotted cognitive radio ALOHA (CR-ALOHA) and cognitive-radio-based carrier-sensing multiple access (CR-CSMA) are developed to deal with the packet scheduling of the secondary network. We employ normalized throughput and average packet delay as the network metrics and derive closed-form expressions to evaluate the performance of the secondary network for our proposed protocols. Moreover, we use the interference and agility factors as the performance parameters to measure the protection effects on the primary network. For various frame lengths and numbers of SUs, the optimal performance of throughput and delay can be achieved at the same spectrum sensing time, and there also exists a tradeoff between the achievable performance of the secondary network and the effects of protection on the primary network. Simulation results show that the CR-CSMA protocol outperforms the slotted CR-ALOHA protocol and that the PUs' activities have an influence on the performance of SUs for both the slotted CR-ALOHA and CR-CSMA.

Joint Design of Spectrum Sensing and Channel Access in Cognitive Radio Networks

Spectrum sensing is an essential functionality of cognitive radio networks. However, the effect of errors in the spectrum sensing process on the performance of the multiple access layer of both primary and secondary networks has not gained much attention. This paper aims at bridging the gap between the study of spectrum sensing and the multiple access of cognitive radio networks. To achieve this goal we pose and answer the question how the spectrum sensing errors affects the performance of cognitive radio networks from a multiple access protocol design point of view. The negative effects of the spectrum sensing errors on the throughput of both primary and secondary networks are characterized through queuing theory analysis of both networks. To alleviate these negative effects a novel joint design of the spectrum sensing and channel access mechanisms is proposed. This design is based on the observation that, in a binary hypothesis testing problem, the value of the test statistics could be used as a confidence measure for the test outcome. Therefore, this value will be used to define different channel access probabilities for secondary users. Results reveal a significant performance improvement in the maximum stable throughput of both primary and secondary networks by virtue of the proposed technique.

A Recommendation System in Cognitive Radio Networks With Random Data Traffic

A recommendation system is proposed to enhance the efficiency of spectrum access in cognitive radio networks, in which secondary users broadcast the indices of channels that they have successfully accessed. The probabilities of different actions, i.e., taking a recommendation or probing an unrecommended channel, could be either fixed or adjustable. For the constant probability case with and without retransmission, the system is modeled as a Markov random process, and the corresponding state transition probabilities are obtained. For the adjustable probability case, the anytime multiarmed bandit technique is used to adopt the strategies to the uncertain environments, and a performance lower bound is obtained. Numerical simulation results demonstrate that the proposed recommendation system can effectively orient the channel selections and significantly improve the performance of cognitive radio networks.

Opportunistic Multiple Access for Cognitive Radio Networks

In this paper, opportunistic multiple access to the under-utilized channel resources is investigated. Exploiting source burstiness, secondary cognitive nodes utilizes primary nodes' periods of silence to access the channel and transmit their packets. Cognitive relays could also make use of these silence periods to offer spatial diversity without incurring bandwidth efficiency losses. First, we consider the cognitive cooperation protocol and propose two different relay assignment schemes. Comparison between the proposed schemes is carried out through a maximum stable throughput analysis of the network. Then, secondary nodes access to the remaining idle channel resources is investigated. Queueing theoretical analysis and numerical results reveal that despite the fact that relays occupy part of the idle resources to provide cooperation, secondary nodes surprisingly achieve higher throughput in the presence of relays. The rationale is that relays help primary nodes empty their queues at faster rates, therefore, secondary nodes observe increased access opportunities to the channel.