Skip to content


New antenna technologies

Technologies that are able to dynamically adapt to the wireless channel – or even change the channel – are important for increasing rate and coverage in future wireless systems. Reconfigurable antennas can dynamically alter many of their primary characteristics, including the polarization, gain pattern, and frequency. Building MIMO arrays from reconfigurable antennas allows a tremendous amount of retuning to match the spectral and spatial considerations. For example, it would allow for optimizing the gain for the target bandwidth, changing the operating frequency without switching to a different array, and matching the polarization and direction of the signal better to the paths in the channel.

Machine learning

Machine learning has recently begun impacting modern wireless communications through their innate ability to recognize hidden patterns and correlations in raw data. Great engineering efforts have been devoted to optimizing individual components in the wireless physical (PHY) layer for the past decades. In contrast, learning provides an end-to-end optimization methodology, which can potentially prolong the ongoing wireless evolution. Though wireless communications is a maturing field, numerous machine learning applications in wireless systems have shown potential room for improvement. In these projects we seek to apply deep learning to a variety of communication problems, such as signal detection, relay selection, precoding, and beam alignment.

Reflective intelligent surfaces

With the ability to control the wireless environment, intelligent surfaces offer the potential for increased channel capacity, new solutions towards encoding information, and more efficient use of radio waves among many additional capabilities. Intelligent reflecting surfaces, a classification for intelligent surfaces that strictly reflect signals, are planar devices consisting of an array of reconfigurable, reflecting unit cells. This reconfigurable property allows for the manipulation of incoming wireless signals, leading to an adaptable and flexible propagation environment. With increased data traffic and the need to support a large number of users, IRSs and other classes of reconfigurable surfaces adapt the wireless environment to help meet these growing demands. Moreover, the low-power and low-cost nature of IRSs make them easily deployable into current wireless systems to help increase the spectral efficiency of the wireless link.

J. Carlson, M. R. Castellanos, and R. W. Heath, “Wideband reflected gain analysis for intelligent reflecting surface-aided communication,” in IEEE Globecom, Dec. 2022, pp. 55–60. doi: 10.1109/GCWkshps56602.2022.10008555.

Physical layer security

The PHY layer security is an important aspect in the design of a wireless systems. Central nodes are expected to transmit side-information such as position of mobile nodes to optimize the communication links. In such scenarios, adversaries can record and re-transmit the signals that contain outdated side-information to mislead the mobile nodes.  Motivated by these challenges, we focus on designing safe transmission strategies for communication by leveraging signal processing.

Joint communication and radar

Modern security and safety applications require the combination of high-resolution sensing and fast data transmission. Joint communication and radar (JCR) merges both of these functions into a single system, allowing for a smaller hardware and radio-frequency footprint. For communication and radar to function as intended, the transmit waveform must be intelligently selected with both objectives in mind. In this project, we analyze the tradeoffs in resource allocation for communication and radar and design systems that can achieve high performance in both functions.

J. A. Zhang, K. Wu, X. Huang, Y. J. Guo, D. Zhang, and R. W. Heath, “Integration of radar sensing into communications with asynchronous transceivers,” IEEE Communications Magazine, vol. 60, no. 11, pp. 106–112, Nov. 2022, doi: 10.1109/MCOM.003.2200096.
G. Han, J. Choi, and R. W. Heath, “Radar imaging based on IEEE 802.11ad waveform in V2I communications,” IEEE Transactions on Signal Processing, vol. 70, pp. 4981–4996, 2022, doi: 10.1109/TSP.2022.3213488.
J. A. Zhang et al., “An overview of signal processing techniques for joint communication and radar sensing,” IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 6, pp. 1295–1315, Nov. 2021, doi: 10.1109/JSTSP.2021.3113120.