Improved Quantum Genetic Algorithm for Competitive Spectrum Sharing in Cognitive Radios

When performing flutter analysis through the traditional methods, it is difficult to solve high‐order strong nonlinear equations. For overcoming this difficulty, this paper establishes a double‐parameter optimization model for searching the flutter critical wind speed and frequency. A new hybrid firefly algorithm called the quantum genetic firefly algorithm is presented to search the optimal solution to the optimization model. The proposed algorithm is the combination of the firefly algorithm and the quantum genetic algorithm. The results of the quantum genetic firefly algorithm are compared with the results shown by the firefly algorithm and quantum genetic algorithm. Numerical and experimental results of the proposed algorithm are competitive and in most cases are better than that of the firefly algorithm and quantum genetic algorithm.

Spectrum sharing in cognitive radio communication system using power constraints: A technical review

E-commerce has become the mainstream consumption mode for people, and with the continuous increase of online shopping business volume, controlling logistics costs has become an urgent problem to be solved. In order to solve the challenges in vehicle scheduling and achieve a scientific and reasonable vehicle scheduling scheme, this research is based on Hadoop Big data platform, introduces the concept of time axis, and builds a vehicle scheduling model based on Hadoop intelligent platform. Based on quantum genetic algorithm and combined with MapReduce model, a quantum improved genetic algorithm is constructed to solve vehicle scheduling optimization problems. The results show that the traditional quantum genetic algorithm converges after 20 iterations, achieving an optimal value of 575 and taking 360 seconds. The improved quantum genetic algorithm converged after 10 iterations and achieved an optimal value of 675, taking 200 seconds. Compared with quantum genetic algorithm, the improved quantum genetic algorithm reduces the time spent by 44.4%. Selecting customer data from a certain logistics company for testing, the improved algorithm shortens the delivery time and achieves the design of the optimal path scheduling plan. This study optimized transportation routes and resource scheduling, reduced transportation costs, and played an important role in optimizing vehicle scheduling in the logistics industry.

In the process of trajectory tracking using the linear quadratic regulator (LQR) for driverless wheeled tractors, a weighting matrix optimization method based on an improved quantum genetic algorithm (IQGA) is proposed to solve the problem of weight selection. Firstly, the kinematic model of the wheeled tractor is established according to the Ackermann steering model, and the established model is linearized and discretized. Then, the quantum gate rotation angle adaptive strategy is optimized to adjust the rotation angle required for individual evolution to ensure a timely jumping out of the local optimum. Secondly, the populations were perturbed by the chaotic perturbation strategy and Hadamard gate variation according to their dispersion degree in order to increase their diversity and search accuracy, respectively. Thirdly, the state weighting matrix Q and the control weighting matrix R in LQR were optimized using IQGA to obtain control increments for the trajectory tracking control of the driverless wheeled tractor with circular and double-shifted orbits. Finally, the tracking simulation of circular and double-shifted orbits based on the combination of Carsim and Matlab was carried out to compare the performance of LQR optimized by five algorithms, including traditional LQR, genetic algorithm (GA), particle swarm algorithm (PSO), quantum genetic algorithm (QGA), and IQGA. The simulation results show that the proposed IQGA speeds up the algorithm’s convergence, increases the population’s diversity, improves the global search ability, preserves the excellent information of the population, and has substantial advantages over other algorithms in terms of performance. When the tractor tracked the circular trajectory at 5 m/s, the root mean square error (RMSE) of four parameters, including speed, lateral displacement, longitudinal displacement, and heading angle, was reduced by about 30%, 1%, 55%, and 3%, respectively. When the tractor tracked the double-shifted trajectory at 5 m/s, the RMSE of the four parameters, such as speed, lateral displacement error, longitudinal displacement error, and heading angle, was reduced by about 32%, 25%, 37%, and 1%, respectively.

Two points need to be considered on power loading policy of cognitive radio (CR) systems. The first is to maximize the channel capacity over the given frequency band, which is detected usable for CR systems. The second is the out-of-band interference suppression of the CR systems. In the paper based on the mathematical expression of the interference power caused by MB-OFDM based CR systems, the relationship of the symbols on the sub-carriers can be obtained. To realize the relationship the sub-carrier weighting method can be adopted. With the effect of the weighting factors, the symbols relationship can be satisfied, and simultaneously the maximum channel capacity can be achieved on certain degree. The simulation results of the relationship among the three parameters, channel capacity, interference power and total transmission power, also shows that a better tradeoff can be achieved between the channel capacity and the interference power.

Cognitive radio (CR) systems make efficient use of the frequency spectrum by opportunistically exploiting unoccupied or under-utilized frequency bands. However, the frequency bands used by CR systems are expected to suffer from various forms of noise and interference with non-Gaussian distributions, such as narrowband and co-channel interference caused by the primary user and other CRs, respectively, and man-made impulsive noise. To mitigate the harmful effects of non-Gaussian noise and interference, we propose a robust L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm metric for CR systems that employ the popular combination of bit-interleaved coded modulation (BICM) and orthogonal frequency division multiplexing (OFDM). For the considered CR system we provide an approximate upper bound and an asymptotic closed-form approximation for the bit error rate (BER). Our results show that the proposed L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm metric can achieve large performance gains compared to the conventional L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm metric in non-Gaussian noise and interference.

In this paper, we analysis wideband spectrum sensing with distance based weight combining for Cognitive Radio (CR) systems. CR systems is implemented the spectrum of the Primary User(PU) by using a energy detection method. Threshold is determined in accordance with the constant false alarm rate (CFAR) algorithm for energy detection. The signal of PU is BPSK signal and the wireless channel between a PU and CR systems is modeled as Gaussian channel. From the simulation results, the wideband sensing with distance based and Distance based weight Combing (DWC) methods shows higher spectrum sensing performance than single CR user spectrum sensing.

Abstract— One of the main problems underlying most optimization theories is local optimum. When time delays are presented, this issue becomes much more problematic. In such conditions, evolutionary optimization algorithms are proven to be helpful. In this paper, quantum genetic algorithm (QGA) has been used to tackle the stated problem in the framework of delay-dependent linear matrix inequality (LMI) robust H∞ control. QGA is employed to find suitable feedback gains and delay-dependent LMI solvers are concerned to resolve stability issues. In addition, to provide more balance between exploration and exploitation, to increase convergence rate as well as to prevent premature convergence, it is proposed that particle swarm optimization (PSO) is augmented with QGA. Simulation is dealt with LMI-based H∞ control scheme of the QGA and QGA-PSO optimization space from the design point of one-degree freedom single link scara robot. The whole controller satisfies the desired properties for uncertain-but-known constant bounded time delay. Furthermore, one of the drawbacks found in tests of most hybrid global-local strategies, i.e. premature convergence, has been cancelled by the proposed scheme of QGA and PSO.

This paper proposes a system called Wireless Link based on Global Communication Channel (WLGCC) to enhance the spectrum sharing between digital broadcasting (DB) services and the cognitive radio (CR) system in the licensed band of 470?790 ㎒. The WLGCC aims to enhance the spectrum sensing and geolocation database (GLD) spectrum sharing methods in the CR system. Spectrum sensing can be enhanced by receiving the status of the used frequencies from the WLGCC, thereby eliminating the need for a low detection threshold (i.e., avoiding the hidden node problem). In addition, the GLD can be enhanced by providing a reliable communication link between the database and the CR device in the form of an unused TV white space that is reserved as the proposed Global Communication Channel (GCC). This paper analyzes the coexistence of the new WLGCC system and the DB service in terms of avoiding additional interference. Specifically, we mathematically determine the WLGCC parameters, such as the in-band and out-of-band power levels, and operation coverage, and verify them using Monte Carlo simulation. The results show that WLGCC does not degrade the existing DB service and reliably transmits information of the vacant (or used) frequency bands to the CR.

This research paper is intended to derive, simulate and analyze the capacity of cognitive radio (CR) system in fading channels. Capacity being an information theoretic perspective for a wireless system plays a role in the amount of information bits which can be transmitted. As wireless channel is subjected to multipath effects in a CR system caused by fading, capacity analysis needs to be done. Simulation carried out in terms of capacity, mean square error for channel estimation and bit error rate (BER) for the proposed system model of cognitive radio can give valuable information about performance of CR system in terms of data bits handling capacity. The amount of capacity in flat fading channels and frequency selective fading channels are simulated. The obtained results in terms of capacity can be used as reference for further analysis to be explored relating to design of cognitive radio systems for developing applications for 5G systems.

Wire cleaner

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

Spectrum sensing is a fundamental problem in cognitive radio communication system. In this paper, the implementations of spectrum sensing based on the Covariance Absolute Value (CAV) on the test bed of cognitive radio system are presented. This demo cognitive radio system uses the spectrum holes which are determined by sensing for communication with the spectrum from 694MHz to 806MHz and can avoid impact immediately when detecting the primary users (no mater digital television or analog television) at working spectrum. The implementation further reduces the computational complexity and makes a great significance in practical applications. A series of tests show that the detection performance of Covariance Absolute Value is not liable to be affected by the noise uncertainty in practical application and meets the need of the system primly. Furthermore, the performances of detection are also verified with different kinds of source signals.

"Cognitive radio" is an emerging technique to improve the utilization of radio frequency spectrum in wireless networks. In this paper, we consider the problem of spectrum sharing among a primary user and multiple secondary users. We formulate this problem as an oligopoly market competition and use a Cournot game to obtain the spectrum allocation for secondary users. Nash equilibrium is considered as the solution of this game. We first present the formulation of a static Cournot game for the case when all secondary users can observe the adopted strategies and the payoff of each other. However, this assumption may not be realistic in some cognitive radio systems. Therefore, we formulate a dynamic Cournot game in which the strategy of one secondary user is selected solely based on the pricing information obtained from the primary user. The stability condition of the dynamic behavior for this spectrum sharing scheme is investigated.

This chapter explores the novel machine learning (ML) based approaches to cognitive radio (CR) systems developed here will lead to innovative human bond communication (HBC) applications that will serve the needs of a community. An example of a community could range from spectators in a sports arena to ad hoc vehicular networks to a community in a neighbourhood sharing common interests. This chapter explores the synergy between ML and cognitive radio systems. Based on this exploration, we will formulate novel algorithms to share spectrum through dynamic spectrum leasing methodologies and adaptive policy decision making processes that seek to maximize the utilization of available scarce spectrum. This exploration will also lead to a better understanding of the traditional CR techniques and to an integrated approach that will formulate cognitive cycles at multiple levels of a system and to cross-cognitive cycle approaches that will lead to further efficiencies in spectrum utilization. The research will also facilitate a tighter coupling between CR theories and ML theories and lead to new theoretical analyses and novel algorithms that could bring about greater synergy between wireless networks and computer science.