In this paper, distributed relay diversity systems are analyzed, modeled and evaluated in an Orthogonal Frequency Division Multiplexing (OFDM) based networks. The investigated distributed relay diversity schemes exten...In this paper, distributed relay diversity systems are analyzed, modeled and evaluated in an Orthogonal Frequency Division Multiplexing (OFDM) based networks. The investigated distributed relay diversity schemes extend the ideas of a single hop transmit antenna schemes such as Cyclic Delay Diversity (CDD), Space Time Transmit Diversity (STTD), transmit Coherent Combining (CC) and Selection Diversity (SD) to distributed diversity systems. In contrast to the classical single hop system, the antennas in the distributed systems belongs to distributed relays instead of being co-located at the transmitter. The distributed relay diversity methods considered in this paper: Relay CDD (RCDD), Relay Alamouti (i.e.STTD), Relay CC (RCC) and Relay SD (RSD) are compared to the traditional 1-hop system. Analytical expressions for the received Signal to Interference Noise Ratio (SINR) are derived and used in a dynamic multi-cell multi-user simulator. Results show considerable SINR gains for both Round Robin and Max-SINR schedulers. The SINR gains translate into substantial cell throughput gains, up to 200%, compared to 1-hop systems. Despite its low complexity, the RCDD scheme has similar performance to that of other more sophisticated 2-hop schemes such as Relay Alamouti and Relay Coherent Combining. Marginally better results are observed for the Relay Selection Diversity scheme.展开更多
The selection pressure of genetic algorithm reveals the degree of balance between the global exploration and local optimization.A novel algorithm called the hybrid multi-population cellular genetic algorithm(HCGA)is p...The selection pressure of genetic algorithm reveals the degree of balance between the global exploration and local optimization.A novel algorithm called the hybrid multi-population cellular genetic algorithm(HCGA)is proposed,which combines population segmentation with particle swarm optimization(PSO).The control parameters are the number of individuals in the population and the number of subpopulations.By varying these control parameters,changes in selection pressure can be investigated.Population division is found to reduce the selection pressure.In particular,low selection pressure emerges in small and highly divided populations.Besides,slight or mild selection pressure reduces the convergence speed,and thus a new mutation operator accelerates the system.HPCGA is tested in the optimization of four typical functions and the results are compared with those of the conventional cellular genetic algorithm.HPCGA is found to significantly improve global convergence rate,convergence speed and stability.Population diversity is also investigated by HPCGA.Appropriate numbers of subpopulations not only achieve a better tradeoff between global exploration and local exploitation,but also greatly improve the optimization performance of HPCGA.It is concluded that HPCGA can elucidate the scientific basis for selecting the efficient numbers of subpopulations.展开更多
文摘In this paper, distributed relay diversity systems are analyzed, modeled and evaluated in an Orthogonal Frequency Division Multiplexing (OFDM) based networks. The investigated distributed relay diversity schemes extend the ideas of a single hop transmit antenna schemes such as Cyclic Delay Diversity (CDD), Space Time Transmit Diversity (STTD), transmit Coherent Combining (CC) and Selection Diversity (SD) to distributed diversity systems. In contrast to the classical single hop system, the antennas in the distributed systems belongs to distributed relays instead of being co-located at the transmitter. The distributed relay diversity methods considered in this paper: Relay CDD (RCDD), Relay Alamouti (i.e.STTD), Relay CC (RCC) and Relay SD (RSD) are compared to the traditional 1-hop system. Analytical expressions for the received Signal to Interference Noise Ratio (SINR) are derived and used in a dynamic multi-cell multi-user simulator. Results show considerable SINR gains for both Round Robin and Max-SINR schedulers. The SINR gains translate into substantial cell throughput gains, up to 200%, compared to 1-hop systems. Despite its low complexity, the RCDD scheme has similar performance to that of other more sophisticated 2-hop schemes such as Relay Alamouti and Relay Coherent Combining. Marginally better results are observed for the Relay Selection Diversity scheme.
基金Supported by National Natural Science Foundation of China(61262019)the Aeronautical Science Foundation of China(2012ZA56001)+2 种基金the Natural Science Foundation of Jiangxi Province(20114BAB201046)the Science and Technology Research Project of Jiangxi Provincial Department of Education(GJJ12435)the Open-End Foundation of the Key Laboratory of Nondestructive Testing(Ministry of Education)
文摘The selection pressure of genetic algorithm reveals the degree of balance between the global exploration and local optimization.A novel algorithm called the hybrid multi-population cellular genetic algorithm(HCGA)is proposed,which combines population segmentation with particle swarm optimization(PSO).The control parameters are the number of individuals in the population and the number of subpopulations.By varying these control parameters,changes in selection pressure can be investigated.Population division is found to reduce the selection pressure.In particular,low selection pressure emerges in small and highly divided populations.Besides,slight or mild selection pressure reduces the convergence speed,and thus a new mutation operator accelerates the system.HPCGA is tested in the optimization of four typical functions and the results are compared with those of the conventional cellular genetic algorithm.HPCGA is found to significantly improve global convergence rate,convergence speed and stability.Population diversity is also investigated by HPCGA.Appropriate numbers of subpopulations not only achieve a better tradeoff between global exploration and local exploitation,but also greatly improve the optimization performance of HPCGA.It is concluded that HPCGA can elucidate the scientific basis for selecting the efficient numbers of subpopulations.
