Precoder Design for User-Centric Network Massive MIMO with Matrix Manifold Optimization
<|reference_start|>Precoder Design for User-Centric Network Massive MIMO with Matrix Manifold Optimization: In this paper, we investigate the precoder design for user-centric network (UCN) massive multiple-input multiple-output (mMIMO) downlink with matrix manifold optimization. In UCN mMIMO systems, each user terminal (UT) is served by a subset of base stations (BSs) instead of all the BSs, facilitating the implementation of the system and lowering the dimension of the precoders to be designed. By proving that the precoder set satisfying the per-BS power constraints forms a Riemannian submanifold of a linear product manifold, we transform the constrained precoder design problem in Euclidean space to an unconstrained one on the Riemannian submanifold. Riemannian ingredients, including orthogonal projection, Riemannian gradient, retraction and vector transport, of the problem on the Riemannian submanifold are further derived, with which the Riemannian conjugate gradient (RCG) design method is proposed for solving the unconstrained problem. The proposed method avoids the inverses of large dimensional matrices, which is beneficial in practice. The complexity analyses show the high computational efficiency of RCG precoder design. Simulation results demonstrate the numerical superiority of the proposed precoder design and the high efficiency of the UCN mMIMO system.<|reference_end|>
@article{sun2024precoder,
    title={Precoder Design for User-Centric Network Massive MIMO with Matrix
  Manifold Optimization},
    author={Rui Sun, Li You, An-An Lu, Chen Sun, Xiqi Gao, Xiang-Gen Xia},
    journal={arXiv preprint arXiv:2404.07425},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07425},
    primaryClass={eess.SP cs.IT math.IT}
}