In this paper, we investigate the downlink (DL) precoder design for massive multiple-input multiple-output (MIMO) multiple low earth orbit (LEO) satellite communication (SATCOM). We first establish the DL beam based channel model for massive MIMO multiple LEO SATCOM systems by using sampled array response vectors. We propose a closed-form statistical channel state information (sCSI) based space domain DL precoder design for multi-satellite systems by maximizing the average signal-to-leakage-plus-noise ratio (ASLNR). Then, with the proposed beam based channel model, we transform the design of space domain DL precoder into that of lower-dimensional beam domain vector and the resulting space domain precoder is beam structured. Moreover, by leveraging the properties of the beam matrix, we propose a low-complexity design and implementation for the beam structured DL precoder. Simulation results demonstrate that the proposed beam structured DL precoder can achieve near performance to the space domain approach with significantly reduced computational complexity.

We investigate the linear precoding for sum-rate maximization in network massive multiple-input multiple-output (MIMO) transmission, where the cooperative transmission by all base stations (BSs) enhances the capacity, reliability, and robustness. To address the growing complexity of traditional iterative algorithms in large-scale systems, we leverage the weighted minimum mean square error (WMMSE) solution and show that the precoding vectors can be fully reconstructed from a set of low-dimensional parameters. By exploiting the structure and relationship of these parameters, we reformulate the original problem in a reduced-dimensional space while preserving equivalence to the original solution. Deep learning techniques are employed to solve this reformulated problem, where equivalent scaling of the variables facilitates pre-processing for training and further reduces the dimension of the learning input. A neural network is trained on the refined low-dimensional objectives with a tailored loss, allowing the precoding vectors to be directly calculated from its output. As demonstrated by numerical results, the proposed deep learning-based precoder performs well with considerably reduced online processing complexity.

This paper investigates the distributed precoding for network massive multi-input multi-output (MIMO) communications without data sharing between cells. In order to restrict the information exchange, which imposes significant requirements on signaling overhead, we first reformulate the original weighted sum-rate maximization problem into a cell-specific form. With this reformulated problem, we take a virtual weighted sumrate, whose expression only depends on precoders in a single cell and some initial values, as the objective function of an approximated problem. A stationary point of this non-concave virtual weighted sum-rate maximization problem is then achieved iteratively through the minorize-maximize (MM) algorithm. After exchanging a virtual covariance matrix generated locally, each base station (BS) can solely optimize its precoding matrix in parallel without any exchange during the optimization procedure. Numerical results show that the proposed method performs well in the sense of achievable sum-rate.

In this paper, we consider the power allocation problem for downlink multiceli massive multiple-input multiple-output (MIMO) communications over Rician fading channels. Each link between a user equipment (UE) and a base station (BS) forms a jointly correlated Rician channel, on which some properties of the channel covariance matrices in the massive MIMO scenario are presented. Based on these properties, assuming perfect channel state information (CSI) at UEs and statistical CSI, i.e. the channel coupling matrix (CCM) and the line-of-sight (LOS) component, at BSs, we design a near-optimal power allocation algorithm in terms of maximizing the deterministic equivalent of the ergodic sum-rate in the beam domain. The closed-form objective function of the power allocation problem turns out to be a difference of concave functions, thus we search for the solutions iteratively. Numerical results show that the proposed method performs well in terms of achievable sum-rate.