Research On MIMO Technology Based On AAS

Multiple-input multiple-output (MIMO) technology has become a hot topic in the field of wireless communications with its abundant degree of freedom. The current base station antenna structure can not make full use of the degree of freedom in the vertical dimension of space. The development of Active antenna system (AAS) provides the foundation to solve the problem. The introduction of small size AAS which can be controlled independently makes it possible for the Base Stations to control the distribution of signals in three-dimensional(3D) space and will support the development of two-dimensional(2D) antenna array and promote the development of Massive MIMO technology which will dramatically improve the system performance. Meanwhile the establishment of accurate and effective channel model is basic for MIMO system simulation and technology assessment. Most of the existing channel models are based on a2D plane, without considering the statistical properties of the vertical component which does not reflect the actual situation.Therefore, in order to simulate the actual MIMO channel more accurately, make full use of the vertical dimension of freedom of MIMO channel and take advantage of Massive MIMO technology, the paper mainly focus on the following three aspects:1. Modeling of the2D and3D channel. Most of the current channel models are based in a2D plane, which can not reflect the true state of the MIMO channel fully. This paper makes an effort to build the3D channel model geometric model based on the scatter which takes the vertical component into account. Simulation results show that the3D channel gets lower channel capacity than2D channel as the introduction of the vertical dimension strengthens the correlation.2. Modeling of the2D antenna array. The implementation of Massive MIMO requires an evolution of antenna array from1D to2D while the introduction of AAS provides a realistic basis. This paper presents the correlation modeling method for2D antenna array and give some simulation results using zero-forcing beamforming. Those results show that different antenna configurations and channel models lead to different performance and the system capacity can be improved by increase of the antenna element numbers and antenna spacing.3. Research on ZFBF algorithm for Massive MIMO scenarios. In order to utilize the MIMO spatial multiplexing gain, increase system capacity and energy efficiency, the system performance of single-user Massive MIMO scenarios is simulated. Furthermore, a low-complexity ZFBF algorithm for distributed Massive MIMO scenarios is introduced, which can reduce the computational complexity significantly and improve system performance effectively.