I am an Assistant Professor in the Department of Computer Science at Princeton University and adjunct Reader of Wireless Systems and Networks at University College London.

Research Highlights

My research interests are in all aspects of wireless computer networks, from the basic architecture of the wireless physical layer to high-level security properties. The two main strands of work I have pursued involve bringing phased array signal processing indoors and improving the capacity of wireless networks in a world with many billions of wireless devices, most of which transmit in wireless spectrum that is unplanned by any central authority. Following are some snapshots of my group’s work:

Bringing Phased Array Signal Processing Indoors

Phased array signal processing has long been employed outdoors in radar, underwater in sonar, and underground in seismic monitoring. This line of research takes these concepts indoors in the context of Wi-Fi networks, where it must cope with strong multipath reflections, packetized data transmissions, and commodity hardware.


Lead postdoc: Longfei Shangguan

MobiTagBot, to appear at the 2016 ACM MobiSys Conference, is an RFID-based autonomous robot that can order books on a library shelf, or components in an industrial assembly line. The technology behind MobiTagBot leverages synthetic-aperture radar and a multipath detection algorithm to achieve and underlying 2–3 centimeter accuracy in RFID tag localization, allowing it to see when books are shelved in the correct order and when a book is misshelved.


Lead student: Jie Xiong

ArrayTrack, published at the 2013 USENIX NSDI Symposium, was the first indoor location service in the world to achieve a median location accuracy of within 20 cm and sub-second responsiveness, without infrastructure beyond typical Wi-Fi access points, enabing handheld route-finding and augmented reality applications not previously possible indoors.

ArrayTrack contributes novel multipath suppression algorithms that leverage the changes in multipath propagation inherent with small movements of the mobile handheld device. The NSDI paper describes design, implementation, and evaluation on a FPGA-based radio platform (Rice WARP), with an evaluation in a 50-node indoor wireless testbed situated in a real office working environment.


Lead student: Jon Gjengset

Two challenges in indoor phased-array systems must be overcome if they are to be of practical use on commodity hardware. First, phase differences between radios make readings unusable, and so must be corrected. Second, while the number of antennas on commodity access points is usually limited, most array processing increases in fidelity with more antennas. These issues work in synergistic opposition to array processing: without phase offset correction, no phase-difference array processing is possible, and with fewer antennas, automatic correction of these phase offsets becomes even more challenging.

Phaser is a system that solves these intertwined problems to make phased array signal processing that was previously only available on special-purpose hardware truly practical on already-deployed Wi-Fi access points. Published and presented (best presentation award to Jon Gjengset) at the 2014 ACM MobiCom Conference.

Making MIMO Sphere Decoding Practical

Lead postdoc: Konstantinos Nikitopoulos

Geosphere is a physical- and link-layer design for access point-based MIMO wireless networks that consistently improves network throughput. To send multiple streams of data in a MIMO system, conventional designs rely on a technique called zero-forcing, a way of "nulling" the interference between data streams by mathematically inverting the wireless channel matrix. In general, this is highly effective, significantly improving throughput. But in certain physical situations, the MIMO channel matrix can become "poorly conditioned," harming performance.

With these situations in mind, Geosphere uses sphere decoding, a more computationally demanding technique that can achieve higher throughput in such channels. To overcome the sphere decoder's computational complexity when sending dense wireless constellations at a high rate, we introduce search and pruning techniques that incorporate novel geometric reasoning about the wireless constellation. These techniques reduce computational complexity of 256-QAM systems by almost one order of magnitude, bringing computational demands in line with current 16- and 64-QAM systems already realized in ASIC.

Current Students and Postdocs

Alumni/Graduated Students


I am seeking graduate students and postdoctoral research associates with an interest in working on challenging open problems in wireless networks to join my lab. If you are interested, you must apply and be admitted to the graduate program at Princeton University. Because of the volume of mail I receive I regret that I'm not always able to reply to personal inquiries.