Convolutional neural network aided movable antenna array design for channel estimation

To provide reliable and high-quality services in the sixth-generation (6G) systems, movable antennas (MAs) have attracted much attention since they can use the spatial degree of freedom adequately. Compared to the traditional fixed position arrays, MAs give much better performance in multi-user and multi-antenna scenarios, which implement efficient beamforming and interference suppression in various communication cases. However, the MA array design strategy and the associated channel estimation problems require high-complexity iterative computation algorithms, making it difficult to be exploited in practical applications. In this work, a novel channel estimation method with the MA arrays is proposed based on the convolutional neural network (CNN), which considers the complexity of the algorithm and time consumption while accomplishing the optimal channel estimation. By comparing it with different benchmarks, especially for the orthogonal matching tracking, the CNN-based channel estimation method implements a better trade-off between the mean square error and the computational complexity and the designed examples are provided to verify the effectiveness of the proposed approaches.