Research On Preprocessing Algorithms For Massive MIMO Systems

In recent years,with the rise of the fifth generation mobile communication system,the massive multiple input multiple output(MIMO) system has drawn increasing concerns due to its much higher spectrum efficiency than conventional MIMO.However,the large mount of antennas of massive MIMO system makes the signal processing quite more complex.The complexity of corresponding detector and precoder is explosively increasing,leading to the huge circuit resource consumption and unacceptable throughput for communication standards.A new approach based on the joint algorithm of steepest descent and Jacobi iteration is proposed to iteratively realize linear detection for massive uplink MIMO systems and linear precoding for massive downlink MIMO systems.In the proposed scheme,steepest descent algorithm is employed to obtain an efficient searching direction for the following Jacobi iteration to speed up convergence.Moreover,promising initial estimation and hybrid iteration are utilized to further accelerate the convergence rate and reduce the complexity.Simulation results show that the proposed method outperforms Neumann Series,Richardson method and conjugate gradient based methods,while achieving the near-optimal performance of linear detectors with a small number of iterations.Meanwhile,the FPGA implementation results demonstrate that our proposed method can achieve high throughput owing to its high parallelism.To further reduce the overall complexity,we propose the improved conjugate gradient algorithm for the preprocessing of massive MIMO detection and precoding,with initial estimation,precondition and equivalent augmentation.The simulation results demonstrate that the proposed algorithm can achieve near optimal performance with a very small iteration number.Meanwhile,the avoiding of the Gram matrix computation makes the overall complexity significantly reduced.