Clustering Based Hybrid Precoding Design for Multi-User Massive MIMO Systems

11 Apr 2019  ·  Zhang Ling, Gui Lin, Ying Kai, Qin Qibo ·

Hybrid precoding has been recognized as a promising technology to combat the path loss of millimeter wave signals in massive multiple-input multiple-output (MIMO) systems. However, due to the joint optimization of the digital and analog precoding matrices as well as extra constraints for the analog part, the hybrid precoding design is still a tough issue in current research... In this paper, we adopt the thought of clustering in unsupervised learning and provide design schemes for fully-connected hybrid precoding (FHP) and adaptively-connected hybrid precoding (AHP) in multi-user massive MIMO systems. For FHP, we propose the hierarchical-agglomerative-clustering-based (HAC-based) scheme to explore the relevance among RF chains in optimal hybrid procoding design. The similar RF chains are merged into an individual RF chain when insufficient RF chains are equipped at the base station (BS). For AHP, we propose the modified-K-means-based (MKM-based) scheme to explore the relevance among antennas at the BS. The similar antennas are supported by the same RF chain to make full use of the flexible connection in AHP. Particularly, in proposed MKM-based AHP design, the clustering centers are updated by alternating-optimum-based (AO-based) scheme with a special initialization method, which is capable to individually provide feasible sub-connected hybrid precoding (SHP) design. Simulation results highlight the superior spectrum efficiency of proposed HAC-based FHP scheme, and the high power efficiency of proposed MKM-based AHP scheme. Moreover, all the proposed schemes are clarified to effectively handle the inter-user interference and outperform the existing work. read more

PDF Abstract
No code implementations yet. Submit your code now

Tasks


Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here