Yongqiang Hei

An Improved Particle Swarm Optimization Algorithm for Pattern Synthesis of Phased Arrays

In this paper an improved particle swarm optimization algorithm (IPSO) for electromagnetic applications is proposed. In order to overcome the drawbacks of standard PSO, some improved mechanisms for velocity updating, the exceeding boundary control, global best perturbation and the simplified quadratic interpolation (SQI) operator are adopted. To show the effectiveness of the proposed algorithm, a selected set of numerical examples, concerned with linear as well as planar array, is presented. Simulation results show that the refined pinpointing search ability and the global search ability of the proposed algorithm are significantly improved when compared to the particle swarm optimization (PSO) and Genetic Algorithm (GA).

IMPROVED GA AND PSO CULLED HYBRID ALGORITHM FOR ANTENNA ARRAY PATTERN SYNTHESIS

In this paper, a new evolutionary learning algorithm based on a hybrid of improved real-code genetic algorithm (IGA) and particle swarm optimization (PSO) called HIGAPSO is proposed. In order to overcome the drawbacks of standard genetic algorithm and particle swarm optimization, some improved mechanisms based on non-linear ranking selection, competition and selection among several crossover offspring and adaptive change of mutation scaling are adopted in the genetic algorithm, and dynamical parameters are adopted in PSO. The new population is produced through three approaches to improve the global optimization performance, which are elitist strategy, PSO strategy and improved genetic algorithm (IGA) strategy. The effectiveness of the proposed algorithm has been compared with GAs and PSO, synthesizing a circular array, a linear array and a base station array. Results show that the proposed algorithm is able to adapt itself to different electromagnetic optimization problems more effectively.

Energy efficient techniques with sensing time optimization in cognitive radio networks

The cooperative spectrum sensing techniques in cognitive radio networks increase the energy consumption while improving the throughput of the system. The energy efficient techniques with sensing time optimization are considered in this paper. The optimization model is established, in which the sensing time is to be optimized to maximize the energy efficiency. Then, the golden section search algorithm is used to get the optimal solution based on the proof that energy efficiency is a unimodal function in terms of sensing time. The initial value of the golden section search algorithm is given, and the optimal value is obtained by iteration. Simulation results show that the energy efficiency can be improved obviously by optimizing sensing time.

A survey on impulse-radio UWB localization

Impulse radio ultra-wideband (IR-UWB) technique has good performance in the application of high-precision localization since it possesses unique properties such as large instantaneous bandwidth and high time resolution. Making IR-UWB localization technology a growing hot topic in recent research field, therefore, it is necessary for us to give an overview of it in this paper. The TOA estimation, error analysis, NLOS identification and NLOS localization are studied in details based on the ranging methods. Simultaneously the UWB localization applications and practical problems are pointed out. At last, we outline the challenges for further research of IR-UWB localization.

PATTERN SYNTHESIS OF CONFORMAL ARRAYS BY A MODIFIED PARTICLE SWARM OPTIMIZATION

A method of designing a cylindrical conformal array with shaped-beam and reconflgurable dual-beam using a modifled particle swarm optimization algorithm is proposed in this paper. The proposed algorithm is easy to implement and e-cient to be used in synthesizing conformal arrays with digital attenuators and digital phase shifters. Moreover, the proposed synthesis has taken the actual active element patterns into account, which can reduce the error between computation and realization. Good agreement can be obtained between the desired patterns and the synthesized patterns.

Novel scheduling strategy for downlink multiuser MIMO system: Particle swarm optimization

In this paper the scheduling problem in downlink multiuser MIMO system is described as an optimization problem and particle swarm optimization (PSO) algorithm is introduced to address such problem. Two PSO scheduling methods with different objective functions applicable to different requirements on capacity and complexity are investigated. One is the capacity based PSO(C-PSO) scheduling method aiming at achieving the near optimal capacity; and the other is the lower bound of eigenvalue based PSO (LBE-PSO) scheduling method with the purpose of reducing computational complexity and at the same time achieving as large as possible capacity gain. Furthermore, convergence analysis of PSO from both the particle and the velocity aspects is also presented to derive the convergent condition, which is validated by several examples of different parameter values. Simulation results reveal that the C-PSO can obtain nearly the same capacity as the exhaustive search method with lower complexity, while the LBE-PSO provides a viable approach by striking a better tradeoff between capacity and computational complexity.

Optimization of Multiuser MIMO Cooperative Spectrum Sensing in Cognitive Radio Networks

This paper investigates the multiuser multiple-input–multiple-output (MIMO) linear cooperative spectrum sensing optimization problem, in which the primary user (PU) and the cognitive radio (CR) are equipped with multiple antennas. By optimizing the different weights assigned on the received signals of CRs, the cooperative spectrum sensing optimization aims at maximizing the probability of detection given a targeted probability of false alarm. Statistical characteristics of parameters in MIMO cooperative spectrum sensing systems have been determined for the PU with a single antenna and the CR with multiple antennas, the PU with multiple antennas and the CR with a single antenna, and both the PU and the CR with multiple antennas. Because of the non-convex characteristic of the optimization problem, an alternative approach based on a genetic algorithm (GA) instead of convex approaches is proposed to find the optimal weight vectors without solution domain restrictions and convexity constraints. Furthermore, several classical GA crossover operators have been provided to investigate their effect on sensing performance. The simulation results show that, the reliability of spectrum sensing in cooperative spectrum sensing system can be significantly improved with multiple antennas. Furthermore, the GA method is a promising approach in addressing the cooperative spectrum sensing problem.

Investigation on the evolutionary algorithms with their applications in MIMO detecting systems

SUMMARY In this paper, with the purpose of integrating the advantages of both the genetic algorithm and the particle swarm optimization, a new genetic particle swarm optimization (GPSO) algorithm is proposed. Furthermore, these three evolutionary algorithms are successfully applied to address the MIMO detection problem. Simulation results reveal that the GPSO-based detection algorithm takes much less population size and iteration number when compared with the particle swarm optimization-based detection method and the genetic algorithm-based detection method. Besides, when compared with the optimal maximum likelihood detection method, the GPSO-based detection algorithm can strike a much better balance between the BER performance and the computational complexity. Copyright

Low-complexity signal detection based on relaxation iteration method in massive MIMO systems

Minimum mean square error (MMSE) detection algorithm can achieve nearly optimal performance when the number of antennas at the base station (BS) is large enough compared to the number of users. But the traditional MMSE involves complicated matrix inversion. In this paper, we propose a modified MMSE algorithm which exploits the channel characteristics occurring in massive multiple-input multiple-output (MIMO) channels and the relaxation iteration (RI) method to avoid the matrix inversion. A proper initial solution is given to accelerate the convergence speed. In addition, we point out that the channel estimation scheme used in channel hardening-exploiting message passing (CHEMP) receiver is very appropriate for our proposed detection algorithm. Simulation results verify that the proposed algorithm can achieve very close performance of the traditional MMSE algorithm with a small number of iterations.

Optimization of Non-convex Cooperative Spectrum Sensing with Modified Artificial Bee Colony Algorithm

This paper investigates multiband cooperative spectrum sensing problem in cognitive radio system aiming at maximizing total opportunistic throughput when the interference to primary users is given. It can be formulated as a combinatorial optimization problem with two different kinds of parameters, weight coefficients and decision thresholds. Due to the non-convex nature of the formulated problem, we first propose an artificial bee colony algorithm (ABC) to solve it. To enhance the searching ability of the algorithm in terms of finding the optimal solution, we further introduce some modifications into the ABC and thus, the modified ABC (MABC) is devised. The simulation results indicate that MABC exhibits a promising performance in dealing with such problems when compared with other intelligence algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO).