Cooperative Channel Estimation for Coordinated Transmission with Limited Backhaul

TL;DR

This paper introduces a decentralized algorithm that optimally combines local feedback and limited backhaul exchange to improve global channel estimation accuracy for coordinated transmission systems.

Contribution

It presents a novel MMSE-optimal decentralized method adaptable to various initial conditions and noise levels, enhancing CSI exchange under backhaul constraints.

Findings

Outperforms conventional CSI exchange methods in simulations.

Effective with limited backhaul capacity.

Applicable to multi-transmitter CoMP systems.

Abstract

Obtaining accurate global channel state information (CSI) at multiple transmitter devices is critical to the performance of many coordinated transmission schemes. Practical CSI local feedback often leads to noisy and partial CSI estimates at each transmitter. With rate-limited bi-directional backhaul, transmitters have the opportunity to exchange few CSI-related bits to establish global channel state information at transmitter (CSIT). This work investigates possible strategies towards this goal. We propose a novel decentralized algorithm that produces minimum mean square error (MMSE)-optimal global channel estimates at each device from combining local feedback and information exchanged through backhauls. The method adapts to arbitrary initial information topologies and feedback noise statistics and can do that with a combination of closed-form and convex approaches. Simulations for coordinated multi-point (CoMP) transmission systems with two or three transmitters exhibit the advantage of the proposed algorithm over conventional CSI exchange mechanisms when the coordination backhauls are limited.