# Concurrent Channel Probing and Data Transmission in Full-duplex MIMO Systems

## TL;DR

This paper introduces a novel full-duplex MIMO system scheme that allows simultaneous channel probing and data transmission, significantly reducing feedback overhead and increasing throughput compared to traditional half-duplex methods.

## Contribution

It proposes a new concurrent channel probing and data transmission scheme using full-duplex radios, along with throughput optimal and greedy scheduling policies for improved performance.

## Key findings

- Achieves higher throughput than half-duplex schemes.
- Greedy policy attains at least 2/3 of optimal throughput.
- Demonstrates throughput gains via numerical simulations.

## Abstract

An essential step for achieving multiplexing gain in MIMO downlink systems is to collect accurate channel state information (CSI) from the users. Traditionally, CSIs have to be collected before any data can be transmitted. Such a sequential scheme incurs a large feedback overhead, which substantially limits the multiplexing gain especially in a network with a large number of users. In this paper, we propose a novel approach to mitigate the feedback overhead by leveraging the recently developed Full-duplex radios. Our approach is based on the key observation that using Full-duplex radios, when the base-station (BS) is collecting CSI of one user through the uplink channel, it can use the downlink channel to simultaneously transmit data to other (non-interfering) users for which CSIs are already known. By allowing concurrent channel probing and data transmission, our scheme can potentially achieve a higher throughput compared to traditional schemes using Half-duplex radios. The new flexibility introduced by our scheme, however, also leads to fundamental challenges in achieving throughout optimal scheduling. In this paper, we make an initial effort to this important problem by considering a simplified group interference model. We develop a throughput optimal scheduling policy with complexity $O((N/I)^I)$, where $N$ is the number of users and $I$ is the number of user groups. To further reduce the complexity, we propose a greedy policy with complexity $O(N\log N)$ that not only achieves at least 2/3 of the optimal throughput region, but also outperforms any feasible Half-duplex solutions. We derive the throughput gain offered by Full-duplex under different system parameters and show the advantage of our algorithms through numerical studies.