Multi-Objective DNN-based Precoder for MIMO Communications
This work addresses the need for efficient and robust precoding in MIMO communications, offering a domain-specific solution that is incremental by building on existing methods.
The paper tackles the problem of designing a unified precoder for MIMO networks with multiple objectives, such as data transmission and security, and achieves a reduction in computational complexity by over an order of magnitude while maintaining near-optimal performance at 99.45% of averaged optimal solutions.
This paper introduces a unified deep neural network (DNN)-based precoder for two-user multiple-input multiple-output (MIMO) networks with five objectives: data transmission, energy harvesting, simultaneous wireless information and power transfer, physical layer (PHY) security, and multicasting. First, a rotation-based precoding is developed to solve the above problems independently. Rotation-based precoding is new precoding and power allocation that beats existing solutions in PHY security and multicasting and is reliable in different antenna settings. Next, a DNN-based precoder is designed to unify the solution for all objectives. The proposed DNN concurrently learns the solutions given by conventional methods, i.e., analytical or rotation-based solutions. A binary vector is designed as an input feature to distinguish the objectives. Numerical results demonstrate that, compared to the conventional solutions, the proposed DNN-based precoder reduces on-the-fly computational complexity more than an order of magnitude while reaching near-optimal performance (99.45\% of the averaged optimal solutions). The new precoder is also more robust to the variations of the numbers of antennas at the receivers.