Distributed two-stage multi-cell precoding

Padmanabhan, Ayswarya; Tölli, Antti; Atzeni, Italo (2019-08-29)
 
Avaa tiedosto
Lataukset: 

URL:
https://doi.org/10.1109/SPAWC.2019.8815440

Padmanabhan, Ayswarya
Tölli, Antti
Atzeni, Italo
Institute of Electrical and Electronics Engineers
29.08.2019

A. Padmanabhan, A. Tölli and I. Atzeni, "Distributed Two-Stage Multi-Cell Precoding," 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cannes, France, 2019, pp. 1-5. doi: 10.1109/SPAWC.2019.8815440

https://rightsstatements.org/vocab/InC/1.0/
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1109/SPAWC.2019.8815440
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe202001081453
Tiivistelmä

Abstract

This paper proposes a distributed downlink precoding design for multi-cell massive multiple-input multiple-output systems. Two-stage precoding is adopted assuming that the user equipments (UEs) in each base station (BS) are grouped according to matching channel statistics. In this regard, the channel dimension is first reduced by means of statistical, group-specific processing. Subsequently, the UE-specific inner beamformers (IBFs) are optimized based on the resulting (lower-dimensional) effective channels, with sensibly reduced computational complexity. We begin by formulating a centralized IBF design that derives from iteratively solving the Karush-Kuhn-Tucker conditions of the weighted sum rate maximization problem. Then, we propose a distributed algorithm where inter-cell interference (ICI) terms and dual variables are periodically exchanged among neighboring BSs via backhaul signaling, whereas the inter-group interference (IGI) within each BS is handled locally. Furthermore, the ICI updates between the BSs are allowed to take place less frequently than the local IGI updates. Numerical results show that enabling backhaul signaling every 5—10 iterations of the algorithm yields a remarkably small performance loss with respect to the case with full information exchange between the BSs.

Kokoelmat