Adaptive gradient algorithm for hybrid precoding in mmWave MIMO system - Yu ZHANG; Zhi ZHANG; Xiaodai DONG (Tongxin xuebao)
To reduce the complexity of existing hybrid precoding algorithms based on alternating minimization (AltMin) and online learning via gradient descent with momentum in mmWave MIMO systems, aiming at the single-user scenario, the problem of designing the hybrid precoder was reconsidered and an equivalent single hidden layer neural network was proposed.Under the new architecture, the elements of the digital and analog precoder were equivalent to the connecting weights of a single hidden layer neural network, and their optimal solution could be obtained via the weights training method.Inspired by the back propagation (BP) algorithm in feed forward neural networks, an adaptive gradient (AG)-based BP algorithm for hybrid precoding was proposed.Furthermore, the proposed algorithm was extended to the multi-user scenario.The numerical results show that the proposed algorithm achieves approximately the same spectral efficiency as the fully-digital precoding in both the single-user and multi-user scenarios, while has lower complexity than the existing AltMin-based hybrid precoding algorithms and online learning hybrid precoding based on gradient descent with momentum.