Trainable Projected Gradient Detector for Massive Overloaded MIMO Channels: Data-driven Tuning Approach
This addresses the challenge of efficient signal detection in wireless communication systems with more transmit than receive antennas, offering a scalable solution with lower computation, though it appears incremental as it builds on existing gradient descent and deep learning techniques.
This paper tackles the problem of signal detection in massive overloaded MIMO systems by proposing a trainable projected gradient-detector (TPG-detector) with data-driven tuning, achieving comparable performance to state-of-the-art methods like the IW-SOAV detector while reducing computational cost.
This paper presents a deep learning-aided iterative detection algorithm for massive overloaded multiple-input multiple-output (MIMO) systems where the number of transmit antennas $n$ is larger than that of receive antennas $m$. Since the proposed algorithm is based on the projected gradient descent method with trainable parameters, it is named the trainable projected gradient-detector (TPG-detector). The trainable internal parameters, such as the step-size parameter, can be optimized with standard deep learning techniques, i.e., the back propagation and stochastic gradient descent algorithms. This approach is referred to as data-driven tuning, and ensures fast convergence during parameter estimation in the proposed scheme. The TPG-detector mainly consists of matrix-vector product operations whose computational cost is proportional to $m n$ for each iteration. In addition, the number of trainable parameters in the TPG-detector is independent of the number of antennas. These features of the TPG-detector result in a fast and stable training process and reasonable scalability for large systems. Numerical simulations show that the proposed detector achieves a comparable detection performance to those of existing algorithms for massive overloaded MIMO channels, e.g., the state-of-the-art IW-SOAV detector, with a lower computation cost.