In millimeter wave （mmWave） massive multiple-input multiple-output （MIMO） systems, because of the high hardware cost and high power consumption, the traditional fully digital beamforming （DBF） cannot be implemen...In millimeter wave （mmWave） massive multiple-input multiple-output （MIMO） systems, because of the high hardware cost and high power consumption, the traditional fully digital beamforming （DBF） cannot be implemented easily. Meanwhile, analog beamforming which is implemented with phase shifters has high availability but suffers poor performance. Considering the advantages of above two, a potential solution is to design an appropriate hybrid analog and digital beamforming structure, where the available iterative optimization algorithm can get performance close to fully digital processing, but solving this sparse optimization problem faces with a high computational complexity. The key challenge of seeking out hybrid beamforming （HBF） matrices lies in leveraging the trade-off between the spectral efficiency performance and the computational complexity. In this paper, we propose an asymptotically unitary hybrid precoding （AUHP） algorithm based on antenna array response （AAR） properties to solve the HBF optimization problem. Firstly, we get the optimal orthogonal analog and digital beamforming matrices relying on the channel＇s path gain in absolute value by taking into account that the AAR matrices are asymptotically unitary. Then, an improved simultaneously orthogonal matching pursuit （SOMP） algorithm based on recursion is adopted to refine the hybrid combining. Numerical results demonstrate that our proposed AUHP algorithm enables a lower computational complexity with negligible spectral efficiency performance degradation.展开更多
Due to the high cost and power consumption of the radio frequency（RF） chains, it is difficult to implement the full digital beamforming in millimeter-wave（mm Wave） multiple-input multiple-output（MIMO） systems. F...Due to the high cost and power consumption of the radio frequency（RF） chains, it is difficult to implement the full digital beamforming in millimeter-wave（mm Wave） multiple-input multiple-output（MIMO） systems. Fortunately, the hybrid beamforming（HBF） is proposed to overcome these limitations by splitting the beamforming process between the analog and digital domains. In recent works, most HBF schemes improve the spectral efficiency based on greedy algorithms. However, the iterative process in greedy algorithms leads to high computational complexity. In this paper, a new method is proposed to achieve a reasonable compromise between complexity and performance. The novel algorithm utilizes the low-complexity Gram-Schmidt method to orthogonalize the candidate vectors. With the orthogonal candidate matrix, the slow greedy algorithm is avoided. Thus, the RF vectors are found simultaneously without any iteration. Additionally, the phase extraction is applied to satisfy the element-wise constant-magnitude constraint on the RF matrix. Simulation results demonstrate that the new HBF algorithm can make substantial improvements in complexity while maintaining good performance.展开更多
基金supported by the National Natural Science Foundation of China(61201134)State Key Science and Research Project(MJ-2014-S-37)the 111 Project(B08038)
文摘In millimeter wave （mmWave） massive multiple-input multiple-output （MIMO） systems, because of the high hardware cost and high power consumption, the traditional fully digital beamforming （DBF） cannot be implemented easily. Meanwhile, analog beamforming which is implemented with phase shifters has high availability but suffers poor performance. Considering the advantages of above two, a potential solution is to design an appropriate hybrid analog and digital beamforming structure, where the available iterative optimization algorithm can get performance close to fully digital processing, but solving this sparse optimization problem faces with a high computational complexity. The key challenge of seeking out hybrid beamforming （HBF） matrices lies in leveraging the trade-off between the spectral efficiency performance and the computational complexity. In this paper, we propose an asymptotically unitary hybrid precoding （AUHP） algorithm based on antenna array response （AAR） properties to solve the HBF optimization problem. Firstly, we get the optimal orthogonal analog and digital beamforming matrices relying on the channel＇s path gain in absolute value by taking into account that the AAR matrices are asymptotically unitary. Then, an improved simultaneously orthogonal matching pursuit （SOMP） algorithm based on recursion is adopted to refine the hybrid combining. Numerical results demonstrate that our proposed AUHP algorithm enables a lower computational complexity with negligible spectral efficiency performance degradation.
基金supported by the National Natural Science Foundation of China (61201134)the Hi-Tech Research and Development Program of China (2014AA01A704)the 111 Project (B08038)
文摘Due to the high cost and power consumption of the radio frequency（RF） chains, it is difficult to implement the full digital beamforming in millimeter-wave（mm Wave） multiple-input multiple-output（MIMO） systems. Fortunately, the hybrid beamforming（HBF） is proposed to overcome these limitations by splitting the beamforming process between the analog and digital domains. In recent works, most HBF schemes improve the spectral efficiency based on greedy algorithms. However, the iterative process in greedy algorithms leads to high computational complexity. In this paper, a new method is proposed to achieve a reasonable compromise between complexity and performance. The novel algorithm utilizes the low-complexity Gram-Schmidt method to orthogonalize the candidate vectors. With the orthogonal candidate matrix, the slow greedy algorithm is avoided. Thus, the RF vectors are found simultaneously without any iteration. Additionally, the phase extraction is applied to satisfy the element-wise constant-magnitude constraint on the RF matrix. Simulation results demonstrate that the new HBF algorithm can make substantial improvements in complexity while maintaining good performance.
