Centralized Dynamic Spectrum Allocation in Cognitive Radio Networks Based on Fuzzy Logic and Q-Learning

A novel centralized approach for Dynamic Spectrum Allocation (DSA) in the Cognitive Radio (CR) network is presented in this paper. Instead of giving the solution in terms of formulas modeling network environment such as linear programming or convex optimization, the new approach obtains the capability of iteratively on-line learning environment performance by using Reinforcement Learning (RL) algorithm after observing the variability and uncertainty of the heterogeneous wireless networks. Appropriate decision-making access actions can then be obtained by employing Fuzzy Inference System (FIS) which ensures the strategy being able to explore the possible status and exploit the experiences sufficiently. The new approach considers multi-objective such as spectrum efficiency and fairness between CR Access Points (AP) effectively. By interacting with the environment and accumulating comprehensive advantages, it can achieve the largest long-term reward expected on the desired objectives and implement the best action. Moreover, the present algorithm is relatively simple and does not require complex calculations. Simulation results show that the proposed approach can get better performance with respect to fixed frequency planning scheme or general dynamic spectrum allocation policy.

The Novel Spectrum Allocation Algorithm in Cognitive Radio System

In order to reduce the disturbance on an authorizing user and lower the competition between cognitive users, assure the normal communication of a cognitive radio system, reliability theory is applied to describe if a channel can be used by a cognitive user or not and the probability that the channel is continually used for a period. Three aspects including space, time domain and frequency domain are united for the research on the distribution of frequency spectrum. The simulation result shows that, in the space domain, time domain, frequency domain algorithm, the transmitted data volume and the total throughput of the system are superior to those in greedy algorithm and time domain—frequency domain algorithm, the novel algorithm is helpful to reduce the disturbance caused by a cognitive user to an authorizing user and lower the competition between cognitive users, this simulation result shows that the proposed algorithm is effective.

Spectrum Allocation for Cognitive Radio Networks with Non-Deterministic Bandwidth of Spectrum Hole

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) through order statistics as well as its simplified form to describe the statistical properties of spectrum holes, with which a statistical spectrum allocation model based on stochastic multiple knapsack problem(MKP) is formulated for spectrum allocation with non-deterministic bandwidth of spectrum holes. To reduce the computational complexity, we transform this stochastic programming problem into a constant MKP through exploiting the properties of cumulative distribution function(CDF), which can be solved via MTHG algorithm by using auxiliary variables. Simulation results illustrate that the proposed statistical spectrum allocation algorithm can achieve better performance compared with the existing algorithms when the bandwidth of spectrum holes is time-varied.

Power Allocation for Sensing-Based Spectrum Sharing Cognitive Radio System with Primary Quantized Side Information

Spectrum access approach and power allocation scheme are important techniques in cognitive radio(CR) system,which not only affect communication performance of CR user(secondary user,SU) but also play decisive role for protection of primary user(PU).In this study,we propose a power allocation scheme for SU based on the status sensing of PU in a single-input single-output(SISO) CR network.Instead of the conventional binary primary transmit power strategy,namely the sensed PU has only present or absent status,we consider a more practical scenario when PU transmits with multiple levels of power and quantized side information known by SU in advance as a primary quantized codebook.The secondary power allocation scheme to maximize the average throughput under the rate loss constraint(RLC) of PU is parameterized by the sensing results for PU,the primary quantized codebook and the channel state information(CSI) of SU.Furthermore,Differential Evolution(DE) algorithm is used to solve this non-convex power allocation problem.Simulation results show the performance and effectiveness of our proposed scheme under more practical communication conditions.

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios is proposedin this paper. The key idea of the algorithm is to pursue the maximum total spectrum utilization of thesystem by adopting a parallel technique in every spectrum allocation, and to ensure the statistical fairnessrule by deploying a particular scheme during a series of allocations. The simulation results show that theproposed algorithm not only achieves a fairer and more efficient allocation of spectrum resources, but alsohas much shorter allocation duration than the color sensitive graph coloring (CSGC) algorithm.

Non-dominated sorting quantum particle swarm optimization and its application in cognitive radio spectrum allocation

In order to solve discrete multi-objective optimization problems, a non-dominated sorting quantum particle swarm optimization (NSQPSO) based on non-dominated sorting and quantum particle swarm optimization is proposed, and the performance of the NSQPSO is evaluated through five classical benchmark functions. The quantum particle swarm optimization (QPSO) applies the quantum computing theory to particle swarm optimization, and thus has the advantages of both quantum computing theory and particle swarm optimization, so it has a faster convergence rate and a more accurate convergence value. Therefore, QPSO is used as the evolutionary method of the proposed NSQPSO. Also NSQPSO is used to solve cognitive radio spectrum allocation problem. The methods to complete spectrum allocation in previous literature only consider one objective, i.e. network utilization or fairness, but the proposed NSQPSO method, can consider both network utilization and fairness simultaneously through obtaining Pareto front solutions. Cognitive radio systems can select one solution from the Pareto front solutions according to the weight of network reward and fairness. If one weight is unit and the other is zero, then it becomes single objective optimization, so the proposed NSQPSO method has a much wider application range. The experimental research results show that the NSQPS can obtain the same non-dominated solutions as exhaustive search but takes much less time in small dimensions; while in large dimensions, where the problem cannot be solved by exhaustive search, the NSQPSO can still solve the problem, which proves the effectiveness of NSQPSO.

Dynamic overlapped spectrum allocation based on potential game in cognitive radio networks

In order to make full use of wireless spectrum resources,the behavior of cognitive radio(CR)for dynamic spectrum allocation is analyzed based on the game theoretic framework.The traditional spectrum allocation schemes consider the spectrum allocation among independent frequency bands only,without taking into account mutually overlapped frequency bands.For this reason,an optimal allocation etiquette is defined to promote the cross characteristic of the frequency bands in a dynamic spectrum allocation model.New interference operator and interference temperature constraints are introduced in order to realize calculation of the interference,and the corresponding spectrum allocation scenario can be further formulated as a potential game.Based on the characteristic of dynamic selection using the game theory and the interference avoidance rule of interference temperature,the robustness of CR networks is increased and the scenario is more suitable for the dynamic changing of actual wireless communication and energy saving communication systems.Simulation results show that the signal to interference and noise ratio(SINR) level can be significantly improved through the optimal allocation of any available spectrum.The utilization rate of spectrum and throughput of overall CR networks are increased by fully utilizing the spectrum resources in the dynamic spectrum allocation model.

Cognitive Radio Spectrum Allocation Strategy Based on Improved Genetic Algorithm

With the rapid development of wireless communication industry, shortage situation of spectrum resource is increasingly significant. It has become an important topic to study cognitive radio spectrum allocation algorithm that is of higher spectrum utilization ratio, less system power consumption and better algorithm efficiency. Analyzes spectrum allocation models based on genetic algorithm, and then puts forward new improved genetic algorithm. The algorithm adopts niche crowding operation to avoid individual inbreeding. It adaptively adjusts crossover and mutation probability to keep them always in the appropriate state. It provides more equal individual competition opportunity by hierarchical measures, which can effectively avert premature convergence to local optimal solution. It obviously improves the district's total transfer rate on the premise that it has met the requirements of minimum user transfer rate and limitations of maximum total power and maximum bit error rate. Simulation results prove the effectiveness of the proposed algorithm.

Relay Selection and Power Allocation in Amplify-and-Forward Cognitive Radio Systems Based on Spectrum Sharing

In this paper, we consider a spectrum sharing scheme that is a joint optimization of relay selection and power allocation at the secondary transmitter, which aims to achieve the maximum possible throughput for the secondary user. This paper considers the scenario where the primary user is incapable of supporting its target signal-to-noise ratio (SNR). More especially, the secondary transmitter tries to assist the primary user with achieving its target SNR by cooperative amplify-and-forward (AF) relaying with two-phase. By exhaustive search for all candidate secondary transmitters, an optimal secondary transmitter can be selected, which not only can satisfy the primary user’s target SNR, but also maximize the secondary user’s throughput. The optimal secondary transmitter acts as a relay for the primary user by allocating a part of its power to amplify-and-forward the primary signal over the primary user’s licensed spectrum bands. At the same time, as a reward, the optimal secondary transmitter uses the remaining power to transmit its own signal over the remaining licensed spectrum bands. Thus, the secondary user obtains the spectrum access opportunities. Besides, there is no interference between the primary user and the secondary user. We study the joint optimization of relay selection and power allocation such that the secondary user’s throughput is maximized on the condition that it satisfies the primary user’s target SNR. From the simulation, it is shown that the joint optimization of relay selection and power allocation provides a significant throughput gain compared with random relay selection with optimal power allocation (OPA) and random relay selection with water-filling power allocation (WPA). Moreover, the simulation results also shown that our spectrum sharing scheme obtains the win-win solution for the primary system and the secondary system.

Dynamic spectrum allocations in multi-cells and intra-cell of cognitive network to enhance system performance

In this study, we propose new dynamic spectrum allocations in multi-cells and intra-cell of cognitive network to enhance system performance in terms of decreasing probability of interruption and spectrum handoff of communication services in a cognitive system. The inter-cells of the spectrum allocation mechanism is designed to share the risk of vacating spectrum caused by licensed incumbents re-occupying the spectrum and minimize probability of service interruption in the cognitive network. This mechanism also can guarantee fairness among multi-cells. The intra-cell of the proposed spectrum allocation is based on a service data hierarchical model and establishes a mapping mechanism between layered data and the spectrum. It can reduce probability of spectrum handoff. Finally, simulation results are given and show that the new mechanism can reduce service interruption ratio and the probability of spectrum handoff caused by licensed incumbents with re-occupying the spectrum.

A New Power Allocation Algorithm in Cognitive Radio Networks

Most resource allocation algorithms are based on interference power constraint in cognitive radio networks.Instead of using conventional primary user interference constraint,we give a new criterion called allowable signal to interference plus noise ratio(SINR) loss constraint in cognitive transmission to protect primary users.Considering power allocation problem for cognitive users over flat fading channels,in order to maximize throughput of cognitive users subject to the allowable SINR loss constraint and maximum transmit power for each cognitive user,we propose a new power allocation algorithm.The comparison of computer simulation between our proposed algorithm and the algorithm based on interference power constraint is provided to show that it gets more throughput and provides stability to cognitive radio networks.

A Robust Energy Efficiency Power AllocationAlgorithm in Cognitive Radio Networks

In order to solve the problem that traditional energy efficiency power allocation algorithms usually require the assumption of constant or perfect channel state information in cognitive radio networks(CRNs),which may lead to performance degradation in real systems with disturbances or uncertainties,we propose a robust energy efficiency power allocation algorithm for underlay cognitive radio(CR)systems with channel uncertainty in consideration of interference power threshold constraint and minimum target SINR requirement constraint.The ellipsoid sets are used to describe the channel uncertainty,and a constrained fractional programming for the allocation is transformed to a convex optimization problem by worst-case optimization approach.A simplified version of robust energy efficiency scheme by a substitutional constraint having lower complexity is presented.Simulation results show that our proposed scheme can provide higher energy efficiency compared with capacity maximization algorithm and guarantee the signal to interference plus noise ratio(SINR)requirement of each cognitive user under channel uncertainty.

A Dynamic Distributed Spectrum Allocation Mechanism Based on Game Model in Fog Radio Access Networks

With the explosive growth of highspeed wireless data demand and the number of mobile devices, fog radio access networks(F-RAN) with multi-layer network structure becomes a hot topic in recent research. Meanwhile, due to the rapid growth of mobile communication traffic, high cost and the scarcity of wireless resources, it is especially important to develop an efficient radio resource management mechanism. In this paper, we focus on the shortcomings of resource waste, and we consider the actual situation of base station dynamic coverage and user requirements. We propose a spectrum pricing and allocation scheme based on Stackelberg game model under F-RAN framework, realizing the allocation of resource on demand. This scheme studies the double game between the users and the operators, as well as between the traditional operators and the virtual operators, maximizing the profits of the operators. At the same time, spectrum reuse technology is adopted to improve the utilization of network resource. By analyzing the simulation results, it is verified that our proposed scheme can not only avoid resource waste, but also effectively improve the operator's revenue efficiency and overall network resource utilization.

Joint power and spectrum allocation in multi-hop cognitive radio networks

Various cognitive network technologies are developed rapidly. In the article, the power and spectrum allocation in multi-hop cognitive radio network （CRN） with linear topology is investigated. The overall goal is to minimize outage probability and promote spectrum utility, including total reward and fairness, while meeting the limits of total transmit power and interference threshold to primary user simultaneously. The problem is solved with convex optimization and artificial bee colony （ABC） algorithm jointly. Simulation shows that the proposed scheme not only minimizes outage probability, but also realizes a better use of spectrum.

Membrane-inspired quantum shuffled frog leaping algorithm for spectrum allocation

To solve discrete optimization difficulty of the spectrum allocation problem,a membrane-inspired quantum shuffled frog leaping（MQSFL） algorithm is proposed.The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm,which is an effective discrete optimization algorithm.Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems.By hybridizing the quantum frog colony optimization and membrane computing,the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure.The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time.Simulation results for three utility functions of a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.

Relay Selection and Power Allocation in Amplify-and-Forward Cognitive Radio Networks:A Special Spectrum Sharing Scheme

In this paper, we present a special spectrum sharing scheme that is a joint optimization of relay selection and power allocation at the secondary transmitter, where the primary user is incapable of supporting its target signal-to-noise ratio(SNR). Specifically, the selected secondary transmitter assists the primary user with achieving its target SNR via two-phase cooperative amplify-and-forward relaying. By searching for the candidate secondary transmitters which have already satisfied the primary user's target SNR, we can select the optimal secondary transmitter. This optimal secondary transmitter not only satisfies the primary user's target SNR,but also maximizes the throughput of the secondary user. We study this joint optimization problem such that the secondary user's throughput is maximized under the constraint that satisfies the primary user's target SNR.Numerical results show that our scheme can maximize the throughput of the secondary user, and can obtain the win-win solution for the primary and secondary systems.

Cognitive Spectrum Handover Technology

Based on spectrum sensing, dynamic spectrum allocation and reconfiguration technologies in Cognitive Radio (CR), this paper fully considers the matching issues between short-term spectrum characteristic and service characteristic. In the communication process, the network-side selects switch terminals to initiate handover command to solve problems due to limited capacity of the system, such as overload and new session decline, improving the success rate of switching on the premise of ensuring user’s QoS, reducing blocking probability with more session admission, eliminating overload, increasing system throughput, and improving the whole system performance.

GRAPH-THEORETIC ALGORITHM FOR SPECTRUM ALLOCATION IN IEEE 802.22 WIRELESS MESH NETWORK

The IEEE 802.22 standard based on wireless Cognitive Radio (CR) is an optimal solution to resolve the inefficient spectrum utility problem. In this paper, we focus on the spectrum allocation in IEEE 802.22 mesh networks and propose a new graph-theory algorithm. The algorithm aims at two objectives: one is the sum of the allocated channel bandwidth is maximum, and the other is the number of users can be active simultaneity is maximum. In this proposed algorithm, the topology of network was modeled as a general graph and could be transformed into a weighted complete bipartite-graph by three processes. The simulations show that the presented algorithm can improve the performance of spectrum allocation.

Game-theoretic spectrum sharing for cognitive radio based on the expectation of primary users

Cognitive radio is a new intelligent wireless communication technique for remedying the shortage of spectrum resource in recent years. Secondary users have to pay when they share available spectrum with primary users while price is an important factor in the spectrum allocation. Based on the game theory, an improved pricing function is proposed by considering the expectation of primary users. In this article, expectation represents the positivity of sharing spectrum with primary users. By introducing the positivity, price not only becomes different for different secondary users, but also can be adjusted according to the positivity. It is proved that the Nash Equilibrium of the new utility function exists. The simulation results show that spectrum sharing can not only be determined by the channel quality of secondary users, but also can be adapted according to the expectation of primary users. Besides, the proposed algorithm improves the fairness of sharing.

Optimization of QoS Parameters in Cognitive Radio Using Combination of Two Crossover Methods in Genetic Algorithm

Radio Cognitive (RC) is the new concept introduced to improve spectrum utilization in wireless communication and present important research field to resolve the spectrum scarcity problem. The powerful ability of CR to change and adapt its transmit parameters according to environmental sensed parameters, makes CR as the leading technology to manage spectrum allocation and respond to QoS provisioning. In this paper, we assume that the radio environment has been sensed and that the SU specifies QoS requirements of the wireless application. We use genetic algorithm (GA) and propose crossover method called Combined Single-Heuristic Crossover. The weighted sum multi-objective approach is used to combine performance objectives functions discussed in this paper and BER approximate formula is considered.