Spectrum Allocation Algorithm Based on Game Theory

This paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> focuses on spectrum allocation and medium access in future cognitive radio wireless networks, e.g. IEEE 802.22 networks. For a given network topology, the proposed spectrum allocation method first generates an interference graph (or conflict graph) based on the network topology, traffic flows and interference constraints. Then a graph coloring algorithm is applied to the interference graph and the chromatic number of the graph is obtained. This number is the minimum number of distinct spectrum slices required. Further, spectrum allocation is done based on the max-min fair share algorithm or proportional fair algorithm according to the spectrum demands for different spectrum blocks. For medium access among secondary users, we then describe and implement a MAC protocol for 802.22-type cognitive radio networks. Simulation results are presented to demonstrate its effectiveness.

Cognitive radio technology is proposed to improve spectrum utilization efficiency through exploiting the unused portions of spectrum in an opportunistic manner. In cognitive radio networks (CRNs), primary users (PUs) are allowed to lease out their unused spectrum to share with cognitive users (CUs). In a CRN consisting of multiple PUs and CUs, the spectrum pricing and resource allocation scheme have to be optimally designed, so that the efficient spectrum sharing among PUs and CUs can be supported and the QoS requirements of both PUs and CUs can be guaranteed. In this paper, we consider the joint spectrum pricing and resource allocation problem of a CRN with imperfect channel condition, i.e., channel state information of the PUs is not fully accessible to the CUs. To characterize the willingness of PUs to share subchannels and the intention of CU on accessing subchannels, a pricing scheme for both PUs and CUs is designed. Addressing the competition of CUs in channel accessing and power allocation, a non-cooperative Game model is formulated. Through solving the Nash equilibrium solution of the Game model, the optimal pricing and resource allocation strategy can be obtained. Numerical results demonstrate the efficiency of the proposed algorithm.

Empowered by the cognitive radio technology, and motivated by the sporadic channel utilization, both spatially and temporally, dynamic spectrum access networks (also referred to as cognitive radio networks and next generation wireless networks) have emerged as a solution to improve spectrum utilization and provide more flexibility to wireless communication. A cognitive radio network is composed of wireless users, referred to as secondary users, which are allowed to use a given licensed spectrum band as long as there are no primary, licensed, users occupying this band in their vicinity. This restricted spectrum access strategy leads to heterogeneity in channel availability among secondary users. Heterogeneity in channel availability forms a significant source of performance degradation for cognitive radio networks, and poses a great challenge on protocol design. In this dissertation, we propose spectrum allocation algorithms that take this heterogeneity property and its effect on the network performance into consideration. The spectrum allocation solutions proposed in this dissertation address three major objectives in cognitive radio mesh networks. The first objective is maximizing the network coverage, in terms of the total number of served clients, and at the same time simplifying the communication coordination function. To address this objective, we proposed a received based channel allocation strategy that alleviates the need for a common control channel, thus simplifying the coordination function, and at the same time maximizes the number of clients served with link reliability guarantees. We show the superiority of the proposed allocation strategy over other existing strategies. The second objective is improving the multicast throughput to compensate for the performance degradation caused by the heterogeneity in channel availability. We proposed a

Cognitive Radio provides an effective approach to improve the spectrum utilization. Spectrum allocation for cognitive radio networks is a multi-objective and interference-constraint optimization problem. This paper mainly discussed spectrum allocation performances based on genetic algorithm for cognitive networks and a Max-Overall-Performance algorithm is proposed, which best result of allocation is achieved with target of maximizing system’s overall performance. The simulation results show that the proposed MOP algorithm unifies system’s sum bandwidth reward and cognitive users’ access fairness.KeywordsCognitive radioSpectrum allocationgenetic algorithmMax-Sum-BandwidthMax-Access-FairMax-Overall-Performance

Cognitive radio network (CRN) is proposed to solve the problem of the scarce radio spectrum resources. In CRN, primary users (PUs) are allowed to lease out their unused spectrum sharing with cognitive users (CUs). In this paper, we propose a spectrum allocation algorithm based on CUs-demand and PUs-protection in CRN. Our objection is to make the allocated spectrum satisfy CUs demands as much as possible, avoiding the CUs interfering the PUs in the process of spectrum allocation. Simulation results indicate that this algorithm can improve the total spectrum reward, the satisfaction rate of CUs and the protection rate.

This paper presents an overview of Cognitive Radio Spectrum Allocation Based on IoT and Genetic Algorithm (CRSAA). CRSAA was created to solve the problem of spectrum allocation in Cognitive Radio Networks (CRN). The main focus is on how to allocate cognitive radios based on IoT services. CRSAA introduces a novel Genetic Algorithm (GA) and an Internet of Things (IoT) system. GA is the core algorithm that deals with the task of how to allocate spectrum channels. A theoretical model shows that CRSAA can improve the total throughput and reduce the power consumption of CRN by 50% when compared to traditional Spectrum Sensing techniques. The traditional particle swarm optimization algorithm is improved, and the initial population is constructed by chaotic logic mapping so that the population is uniformly distributed in the solution space. Due to the introduction of the idea of particle mutation, the new algorithm is easier to fall into the local extremum than the original algorithm. Experimental results show that the algorithm can quickly converge to the spectrum allocation of cognitive radio, thus obtaining better overall benefits.

Cognitive radio has developed as a novelapproach that enables efficient use of unexploited or underutilized parts of frequency bands. Wireless cognitive radio networks aim to speculativelyfeat the empty or underutilized parts of the spectrum by listening to the frequency bands of their secondary users with various sensing techniques. For this purpose, the process of switching from a frequency channel used by secondary users for various reasons to another free channel is defined as spectrum allocation. At the beginning of the cases where the spectrum allocation takes place, the frequency channels allocated to secondary users are put back into the service of primary users. Severaltechniques are utilized for the spectrum allocation process decided by the access points. Artificial intelligence techniques are used to make the decision to realize the spectrum allocation transaction. In this paper, we present a fuzzy logic based spectrum allocation technique and thus ensure better management. The simulation results shows the performance in terms of received signal strength and signal to noise power.

In this paper we have studied the subcarrier and optimal power allocation strategy for OFDM-based cognitive radio (CR) networks. Firstly, in order to protect the primary user communication from the interference of the cognitive user transmissions in fading wireless channels, we design an opportunistic power control scheme to maximize the cognitive user capacity without degrading primary user's QoS. The mathematical optimization problem is formulated as maximizing the capacity of the secondary users under the interference constraint at the primary receiver and the Lagrange method is applied to obtain the optimal solution. Secondly, in order to limit the outage probability within primary user's tolerable range we analyze the outage probability of the primary user with respect to the interference power of the secondary user for imperfect CSI. Finally, in order to get the better tradeoff between fairness and system capacity in cognitive radio networks, we proposed an optimal algorithm of jointing subcarrier and power allocation scheme among multiple secondary users in OFDM-based cognitive radio networks. Simulation results demonstrate that our scheme can improve the capacity performance and efficiently guarantee the fairness of secondary users.

The spectrum allocation for cognitive radio networks (CRNs) has received considerable studies under the assumption that the bandwidth of spectrum holes is static. However, in practice, the bandwidth of spectrum holes is time-varied due to primary user/secondary user (PU/SU) activity and mobility, which result in non-determinacy. This paper studies the spectrum allocation for CRNs with non-deterministic bandwidth of spectrum holes. We present a novel probability density function (PDF) model through order statistic to describe the non-deterministic bandwidth of spectrum holes and provide a bound to approximate it. After that, a statistical spectrum allocation model based on stochastic multiple knapsack problem (MKP) is established for spectrum allocation with non-deterministic bandwidth of spectrum holes. To reduce the computational complexity, we transform this stochastic programming probleminto a constant MKP though exploiting the properties of cumulative distribution function (CDF), which can be solved via MTHG algorithm by using auxiliary variable. Simulation results illustrate that the proposed statistical spectrum allocation algorithm can achieve better performances compared to the existing algorithms when the bandwidth of spectrum holes istime-varied.

In cognitive radio networks, the idle spectrum bands that cognitive users sensed are usually discontinuous. Only one idle spectrum band may not be able to fulfill cognitive users' bandwidth requirements. In order to let cognitive users access allocated spectrum bands successfully and further improve the efficiency of spectrum utilization, we propose spectrum aggregation-based graph coloring algorithm (SAGCA), a spectrum allocation algorithm in cognitive radio networks. SAGCA considers bandwidth requirements of cognitive users and limitation of spectrum range that equipment can utilize due to hardware constraint. Numerical results show that the proposed algorithm can achieve greater performance in total bandwidth and percentage of cognitive users that networks can support compared to the original algorithm.

Cognitive Radio is a promised solution to conquer the inefficiency utilization of spectrum resources, which is induced by the traditional fixed spectrum allocation scheme. In cognitive wireless networks, deficient matching between users' demands and spectrum resource may cause communication deterioration while superfluous matching may cause spectrum inefficient utilization. In this context, by considering multiple spectrums' attributive characters and the cognitive users' service type, a service-oriented adaptive spectrum allocation algorithm based on Graph-Coloring is proposed to improve the match degree between service demands and spectrum resource, so that more efficient spectrum utilization could be achieved. The simulation results demonstrate the proposed algorithm can better satisfy the user's demand and achieve better utility reward and fairness than a set of existing algorithms.

Cognitive radio (CR) technology is designed to improve reliability in communication between users through efficient and dynamic spectrum exploitation. CRs address the problems in spectrum allocation and channel access and improve the rate of radio resource utilization. The flexibility of the CR networks (CRN) and communication medium exposes it to a variety of threats; primary user emulation attack (PUEA) is a malicious and denial-of-service kind of adversary that defaces CRN performance. This manuscript proposes an adaptive learning-based attack detection in CRN for detecting and mitigating PUEA by analyzing the received power of the transmitter. The learning process endorses some beneficial features by distinguishing low spectrum legitimate PU from an adversary. The learning process adopts cyclostationary feature analysis for distinguishing adversaries and low power PU in CR communications. The process of learning is further enhanced by estimating distance variance and communication time-based analysis for improving the rate of signal classification and SU communication rate. The experimental analysis proves the stability of the proposed detection method by improving the SU throughput, with lesser signal classification time and misdetection probability.

ABSTRACTThe invention of cognitive radio (CR) concept aims to overcome the spectral scarcity issues of emerging radio systems by exploiting under‐utilization of licensed spectrum. Determining how to allocate unused frequency bands among CR is one of the most important problems in CR networks. Because different CRs may have different quality‐of‐service requirements, they may have different objectives. Invoice communication, high‐speed transmission is the most important factor; hence,voice radiosalways try to maximize their transmission rate. However, indata communication, the most important factor is the bit error rate. Thedata radiosalways try to maximize their signal‐to‐interference‐plus‐noise ratio (SINR). In this paper, two non‐cooperative games named interference minimization game and capacity maximization game, which reflect the target ofdata radiosandvoice radios, respectively, are proposed. From the simulations, after these games are applied, the average SINRs of all players at each channel are improved. The average SINR of players in each channel after applying the capacity maximization game is smaller than that after applying the interference minimization game. However, in comparison with that after applying the interference minimization game, the average capacity of players after applying capacity maximization approach is larger. Copyright © 2012 John Wiley &amp; Sons, Ltd.

A hybrid overlay/underlay cognitive radio network (CRN) is demonstrated to provide high spectrum efficiency and channel capacity. Decision‐making in joining CRNs is one of the major issues of secondary users (SUs). Each SU has the option of joining the spectrum or balking according to the trade‐off between the service profit and the delay cost. In this study, the decision‐making problem in accessing the hybrid overlay/underlay CRN is addressed using queuing models and game theory. An almost unobservable queuing server is used to model hybrid overlay/underlay CRN. The interaction between the cognitive users is treated as a non‐cooperative game. A closed‐form expression for the average number of cognitive users as well as associated expected waiting time in both overlay and underlay CRNs is derived. The strategic behaviour for spectrum access of cognitive users in hybrid overlay/underlay CRN is investigated and the existence of symmetric Nash equilibrium is proved as well. Finally, we provide extensive validation results using a wide range of realistic simulation scenarios. The proposed model gives detailed guidance to the decision makers to deploy the suitable strategy according to the available channel situation.

Coalition Game Theory (CGT) enhances cooperation in a game. To state, examine and discover a key for spectrum sensing and allocation in Cognitive Radio Networks (CRN), collaboration takes a leading role. This paper states game theory's role in resolving the problems present in the spectrum sensing and allocation. The cooperative and non-cooperative game theory is discussed. The major emphasis of this paper is on how the CGT is imperative and cooperates to resolve the problems present in spectrum sensing in CRN. It specifies the merits and demerits of CGT over Bargain Game theory to fix the problems present in CRN. Finally, it is proved that the utility function of Secondary Users enhanced using CGT compared to Bargain Game Theory during spectrum sensing.