Deep Learning-Aided Projected Gradient Detector for Massive Overloaded MIMO Channels
This work addresses efficiency in wireless communication systems, but it is incremental as it builds on existing projected gradient methods with data-driven tuning.
The paper tackled the problem of signal detection in massive overloaded MIMO channels by proposing a deep learning-aided iterative algorithm, achieving comparable performance to known methods with lower computational cost.
The paper presents a deep learning-aided iterative detection algorithm for massive overloaded MIMO systems. Since the proposed algorithm is based on the projected gradient descent method with trainable parameters, it is named as trainable projected descent-detector (TPG-detector). The trainable internal parameters can be optimized with standard deep learning techniques such as back propagation and stochastic gradient descent algorithms. This approach referred to as data-driven tuning brings notable advantages of the proposed scheme such as fast convergence. The numerical experiments show that TPG-detector achieves comparable detection performance to those of the known algorithms for massive overloaded MIMO channels with lower computation cost.