My research area is the physical-layer techniques for 5G and future wireless communication systems, with particular focus on precoding and hardware-informed beamforming design. Precoding usually refers to the transmit signal design that directs the desired data symbols to the intended users while limiting the inter-user interference, by exploiting the channel state information (CSI) and potentially the information of the data symbols, which has attracted significant interest in their development towards 5G. In the downlink transmission of a multi-user MIMO scenario, since users are usually separate and do not cooperate in the downlink transmission, in order to manage the potential multi-user interference, the transmitter needs to perform some signal processing techniques on the data symbols prior to transmission based on the CSI, and this is where the term ‘precoding’ comes from. Precoding approaches aims to design the precoding matrix to achieve certain targets, which include linear closed-form precoding schemes such as maximum-ratio transmission (MRT), zero-forcing (ZF) and regularized zero-forcing (RZF), non-linear precoding schemes such as dirty-paper coding (DPC), Tomlinson-Harashima precoding (THP) and vector perturbation (VP), and optimization-based precoding designs such as power minimization and SINR balancing. Precoding and beamforming designs find their applications in a variety of wireless scenarios, such as cognitive radio, physical-layer security, simultaneously wireless information and power transfer, reconfigurable intelligent surfaces, etc.

A general framework for MIMO precoding

One of my main research focus is interference exploitation via symbol-level precoding. Interference is traditionally viewed as a performance limiting factor in wireless MIMO communication systems, which is to be minimized or mitigated, as widely seen in traditional linear/non-linear precoding approaches. Nevertheless, a recent line of work has shown that by manipulating the interfering signals such that they add up constructively at the receiver side, known interference can be made beneficial and further improve the system performance in a variety of multi-user MIMO scenarios, achieved by symbol-level precoding. In the regime of symbol-level precoding, unlike tradtional block-level precoding schemes where the precoding matrix is only dependent on the knowledge of the wireless channel, symbol-level precoding exploits not only the channel state information (CSI) but also the knowledge of the data symbols that are also available at the transmitter. On a symbol-by-symbol basis, instantaneous interference can be characterized into constructive interference and destructive interference, where constructive interference is the interference that pushes the received signals away from all of their corresponding decision boundaries of the modulated-symbol constellation, which thus contributes to the useful signal power. Accordingly, the constructive region is characterized for a variety of PSK and QAM modulation, as depicted below where the green shaded area represents the constructive region. 

Constructive and destructive region for popular modulation types

Another research focus of mine is hardware-efficient massive MIMO precoding. Massive multiple-input multiple-output (MIMO) has widely been acknowledged as a key enabling technology for the fifth-generation (5G) and future wireless communication systems. In the downlink transmission of a massive MIMO system, it has been shown that low-complexity linear precoding approaches such as zero-forcing (ZF) and regularized ZF (RZF) can achieve near-optimal performance. Nevertheless, the near optimality is achieved assuming that fully-digital processing and high-resolution digital-to-analog converters (DACs) are employed at the base station (BS). Such fully-digital processing requires a dedicated radio frequency (RF) chain and a pair of high-resolution DACs for each antenna element, which results in a significant increase in the hardware complexity and cost when the number of transmit antennas scales up. Moreover, the resulting power consumption of the large number of hardware components will also be prohibitive for practical implementation. All of the above drawbacks make fully-digital processing highly undesirable for a massive MIMO BS. Accordingly, there have been several emerging techniques that aim to reduce the hardware complexity and the power consumption for a massive MIMO BS, among which the hybrid analog-digital (AD) precoding and the use of low-resolution DACs are the most popular ones, as shown below.

Examples for hardware-efficient massive MIMO architectures

(Left) Hybrid Analog-Digital Architecture; (Right) Low-Resolution DACs Architecture

The capacity of modern MIMO communication systems have shown to approach the Shannon limits. To meet the ever-increasing demand for data rates in 5.5G/6G and beyond, the concept of Electromagnetic Information Theory (EIT) has recently been proposed, which aims to merge the electro-magnetics and information theory that have been studied separately for years. Pattern reconfigurable MIMO based on reconfigurable antennas is able to affect the electro-magnetic fields via reconfiguring the radiation pattern, which bridges the gap between electro-magnetics and information theory. Compared with traditional antennas with fixed radiation characteristics, reconfigurable antennas can be configured to operate with different frequency bands, different polarizations or radiation patterns, and thus can provide additional performance gains by introducing additional degrees of freedom. Among different types of reconfigurable antennas, pattern reconfigurability can improve the degree of freedom (DoF) in the signal directions so that the ability of interference suppression and energy saving can be further enhanced, while the frequency reconfigurability can reduce the interference from other wireless signals operating in the same frequency band. The polarization reconfigurability can switch between left-handed circular polarization (LHCP) and right-handed circular polarization (RHCP) to reduce the polarization mismatch and employ the polarization coding. In the field of MR-MIMO, there are two main challenges preventing the practical application of MR-MIMO. On one hand, the mode selection mechanism brings unacceptable channel estimation overhead. On the other hand, the physical mechanism of how radiation pattern of MR-MIMO affects the channel has not been revealed, and it is not clear how to design the optimal radiation pattern for capacity maximization in MR-MIMO systems. Both of the above need further and in-depth investigations.

(Left) Legacy MIMO v.s. (Right) MR-MIMO