Scalable Synchronization and Reciprocity Calibration for Distributed Multiuser MIMO

TL;DR

This paper introduces scalable over-the-air synchronization and calibration protocols for distributed multiuser MIMO systems, enabling accurate joint precoding with inexpensive hardware in large networks.

Contribution

It proposes novel synchronization and reciprocity calibration methods that scale with network size and use standard RF front-ends, improving distributed MU-MIMO performance.

Findings

Protocols achieve sufficient accuracy for effective MU-MIMO downlink

Performance evaluated using Cramer-Rao bound for timing and frequency estimation

Applicable to networks with inexpensive, non-reciprocal hardware

Abstract

Large-scale distributed Multiuser MIMO (MU-MIMO) is a promising wireless network architecture that combines the advantages of "massive MIMO" and "small cells." It consists of several Access Points (APs) connected to a central server via a wired backhaul network and acting as a large distributed antenna system. We focus on the downlink, which is both more demanding in terms of traffic and more challenging in terms of implementation than the uplink. In order to enable multiuser joint precoding of the downlink signals, channel state information at the transmitter side is required. We consider Time Division Duplex (TDD), where the {\em downlink} channels can be learned from the user uplink pilot signals, thanks to channel reciprocity. Furthermore, coherent multiuser joint precoding is possible only if the APs maintain a sufficiently accurate relative timing and phase synchronization. AP synchronization and TDD reciprocity calibration are two key problems to be solved in order to enable distributed MU-MIMO downlink. In this paper, we propose novel over-the-air synchronization and calibration protocols that scale well with the network size. The proposed schemes can be applied to networks formed by a large number of APs, each of which is driven by an inexpensive 802.11-grade clock and has a standard RF front-end, not explicitly designed to be reciprocal. Our protocols can incorporate, as a building block, any suitable timing and frequency estimator. Here we revisit the problem of joint ML timing and frequency estimation and use the corresponding Cramer-Rao bound to evaluate the performance of the synchronization protocol. Overall, the proposed synchronization and calibration schemes are shown to achieve sufficient accuracy for satisfactory distributed MU-MIMO performance.