One of the most important methods used to cope with multipath fading effects,which cause the symbol to be received incorrectly in wireless communication systems,is the use of multiple transceiver antenna structures.By...One of the most important methods used to cope with multipath fading effects,which cause the symbol to be received incorrectly in wireless communication systems,is the use of multiple transceiver antenna structures.By combining the multi-input multi-output(MIMO)antenna structure with non-orthogonal multiple access(NOMA),which is a new multiplexing method,the fading effects of the channels are not only reduced but also high data rate transmission is ensured.However,when the maximum likelihood(ML)algorithm that has high performance on coherent detection,is used as a symbol detector in MIMO NOMA systems,the computational complexity of the system increases due to higher-order constellations and antenna sizes.As a result,the implementation of this algorithm will be impractical.In this study,the backtracking search algorithm(BSA)is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA systems.To emphasize the efficiency of the proposed algorithm,simulations have been made for the system with various antenna sizes.As can be seen from the obtained results,a considerable reduction in complexity has occurred using BSA compared to the ML algorithm,also the bit error performance of the system is increased compared to other algorithms.展开更多
The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powe...The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.展开更多
针对特征权重难以准确量化的问题,提出一种基于灰狼优化(grey wolf optimizer, GWO)算法和鸟群算法(bird swarm algorithm, BSA)的混合算法,用于特征权重的寻优。首先,将Chebyshev映射、反向学习与精英策略用于混合算法的初始种群生成;...针对特征权重难以准确量化的问题,提出一种基于灰狼优化(grey wolf optimizer, GWO)算法和鸟群算法(bird swarm algorithm, BSA)的混合算法,用于特征权重的寻优。首先,将Chebyshev映射、反向学习与精英策略用于混合算法的初始种群生成;其次,将改进后的GWO算法位置更新策略融入BSA的觅食行为中,得到一种新的局部搜索策略;然后,将BSA的警觉行为与飞行行为用作混合算法的全局搜索平衡策略,从而得到一种收敛的灰狼-鸟群算法(grey wolf and bird swarm algorithm, GWBSA),通过GWBSA的迭代寻优可获得各特征的权重值。利用标准测试函数和标准分类数据集进行了对比实验,与遗传算法、蚁狮算法等方法相比,GWBSA具有较快的收敛速度且不易陷入局部最优,可以提高模式分类问题的求解质量。展开更多
基金supported by the Scientific Research Projects Coordination Unit of Bandirma Onyedi Eylül University.Project Number BAP-19-MF-1004-005.
文摘One of the most important methods used to cope with multipath fading effects,which cause the symbol to be received incorrectly in wireless communication systems,is the use of multiple transceiver antenna structures.By combining the multi-input multi-output(MIMO)antenna structure with non-orthogonal multiple access(NOMA),which is a new multiplexing method,the fading effects of the channels are not only reduced but also high data rate transmission is ensured.However,when the maximum likelihood(ML)algorithm that has high performance on coherent detection,is used as a symbol detector in MIMO NOMA systems,the computational complexity of the system increases due to higher-order constellations and antenna sizes.As a result,the implementation of this algorithm will be impractical.In this study,the backtracking search algorithm(BSA)is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA systems.To emphasize the efficiency of the proposed algorithm,simulations have been made for the system with various antenna sizes.As can be seen from the obtained results,a considerable reduction in complexity has occurred using BSA compared to the ML algorithm,also the bit error performance of the system is increased compared to other algorithms.
基金supported by the National Natural Science Foundation of China(61271250)
文摘The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.
文摘针对特征权重难以准确量化的问题,提出一种基于灰狼优化(grey wolf optimizer, GWO)算法和鸟群算法(bird swarm algorithm, BSA)的混合算法,用于特征权重的寻优。首先,将Chebyshev映射、反向学习与精英策略用于混合算法的初始种群生成;其次,将改进后的GWO算法位置更新策略融入BSA的觅食行为中,得到一种新的局部搜索策略;然后,将BSA的警觉行为与飞行行为用作混合算法的全局搜索平衡策略,从而得到一种收敛的灰狼-鸟群算法(grey wolf and bird swarm algorithm, GWBSA),通过GWBSA的迭代寻优可获得各特征的权重值。利用标准测试函数和标准分类数据集进行了对比实验,与遗传算法、蚁狮算法等方法相比,GWBSA具有较快的收敛速度且不易陷入局部最优,可以提高模式分类问题的求解质量。
