The ever-increasing demands for mobile and wireless data have placed a huge strain on wireless networks, and have led the Federal Communications Commission to release more than 14 GigaHerz of bandwidth for both licensed and unlicensed use in the millimeter wave frequency bands. Millimeter Wave (mmWave) bands are expected to deliver multi-Gigabit-per-second (Gbps) wireless data rates that will be central to next-generation (5G) cellular networks as well as wireless local networks. Networks using these bands will also enable new applications such as virtual and augmented reality, vehicle-to-vehicle communications, and a surge of multimedia and Internet-of-Things traffic. Millimeter wave technology, however, is fundamentally different from existing wireless technologies such as WiFi and cellular networks due to its physical properties, including directionality, wide bandwidth, and sparsity. As a result, today's mmWave systems face new challenges, in terms of mobility, medium access, and control overhead, which prevent them from scaling and supporting mobile networks. The research proposed in this project will address these challenges to unleash the potential of mmWave technologies. The project will design and build practical, scalable, mobile millimeter-wave networks for next generation communication systems. The project will also integrate mmWave communication into novel applications like virtual reality (VR) and self-driving cars. The proposed research will be disseminated through close collaboration with industry and publication in top research venues. It will also be integrated into education through the design of new undergraduate and graduate wireless classes and involvement in community outreach programs.
The project will enable practical, agile and mobile mmWave networks and integrate them into higher layer applications. It will address the above challenges by introducing novel cross-layer protocols that exploit the underlying sparsity of mmWave networks to deliver new protocols that can perform beam alignment, tracking, interference management and scheduling in real-time and with low overhead that allows them to scale to large networks. The proposed research will be executed in three key thrusts: (1) It will develop link establishment protocols that can discover the best alignment between access points and clients and track the client's direction. (2) It will develop new medium access control protocols that will scale mmWave to multi-link networks. (3) It will integrate mmWave networks into higher layer applications like VR and self-driving cars by developing a joint communication and sensing system. The proposal will design, build and empirically test the proposed systems in wireless testbeds.
■ Fast mmWave Beam Alignment | ■ Many-to-Many mmWave Beam Alignments | ||
There is much interest in integrating millimeter wave radios (mmWave)
into wireless LANs and 5G cellular networks to benefit from their
multi-GHz of available spectrum. Yet, unlike existing technologies,
e.g., WiFi, mmWave radios re- quire highly directional antennas. Since
the antennas have pencil-beams, the transmitter and receiver need to
align their beams before they can communicate. Existing systems scan
the space to find the best alignment. Such a process has been shown
to introduce up to seconds of delay, and is unsuitable for wireless
networks where an access point has to quickly switch between users and
accommodate mobile clients. This project presents Agile-Link, a new
protocol that can find the best mmWave beam alignment without
scanning the space. Given all possible directions for setting the an-
tenna beam, Agile-Link provably finds the optimal direction in
logarithmic number of measurements. Further, Agile-Link works within
the existing 802.11ad standard for mmWave LAN, and can support both
clients and access points. We have implemented Agile-Link in a mmWave
radio and evaluated it empirically. Our results show that it reduces
beam align- ment delay by orders of magnitude. In particular, for
highly directional mmWave devices operating under 802.11ad, the delay
drops from over a second to 2.5 ms. |
Millimeter Wave (mmWave) networks can deliver multi-Gbps
wireless links that use extremely narrow directional beams.
This provides us with a new opportunity to exploit spatial reuse in order to
scale network throughput. Exploiting such spatial reuse, however, requires
aligning the beams of all nodes in a network. Aligning the beams is a difficult
pro- cess which is complicated by indoor multipath, which can create
interference, as well as by the inefficiency of carrier sense at detecting
interference in directional links. This project presents BounceNet, the first
many-to-many millimeter wave beam alignment protocol that can exploit dense
spatial reuse to allow many links to operate in parallel in a confined space
and scale the wireless throughput with the number of clients. Results from
three millimeter wave testbeds show that BounceNet can scale the throughput
with the number of clients to deliver a total network data rate of more than 39
Gbps for 10 clients, which is up to 6.6× higher than current 802.11 mmWave
standards. | ||
■ Online mmWave Phased Array Calibration | ■ Through Fog High Resolution Imaging Using Millimeter Wave Radar | ||
This project proposes a new over-the-air (OTA) cali- bration method for millimeter wave phased arrays. Our method leverages the channel estimation process which is a fundamental part of any wireless communication system. By performing the channel estimation while changing the phase of an antenna element, the phase response of the element can be estimated. The relative phase of the phased array can also be obtained by collecting all the estimated phase responses with a shared reference state. Hence, the phase mismatches of the phased array can be resolved. Unlike prior work, our calibration method embraces all the array components such as power-divider, phase shifter, amplifier and antenna and thus, spans the full chain. By overriding channel estimation, our proposed technique does not require any additional circuits for calibration. Furthermore, the calibration can be performed online without the need to pause the communication. We tested our method on an eight element phased array at 24GHz which we designed and fabricated in PCB for verification. The measured beam pattens prove the viability of our proposed method. |
This project demonstrates high-resolution imaging using millimeter wave (mmWave) radars that can function even in dense fog. We leverage the fact that mmWave signals have favorable propagation characteristics in low visibility conditions, unlike optical sensors like cameras and LiDARs which cannot penetrate through dense fog. Millimeter wave radars, however, suffer from very low resolution, specularity, and noise artifacts. We introduce HawkEye, a system that leverages a cGAN architecture to recover high-frequency shapes from raw low-resolution mmWave heatmaps. We propose a novel design that addresses challenges specific to the structure and nature of the radar signals involved. We also develop a data synthesizer to aid with large-scale dataset generation for training. We implement our system on a custom-built mmWave radar platform and demonstrate performance improvement over both standard mmWave radars and other competitive baselines. | ||
■ Practical Null Steering in Millimeter Wave Networks | ■ Wireless Network-on-Chip using Deep Reinforcement Learning | ||
Millimeter wave (mmWave) is playing a central role in pushing the performance and scalability of wireless networks by offering huge bandwidth and extremely high data rates. Millimeter wave radios use phased array technology to modify the antenna beam pattern and focus their power towards the transmitter or receiver. In this paper, we explore the practicality of modifying the beam pattern to suppress interference by creating nulls, i.e. directions in the beam pattern where almost no power is received. Creating nulls in practice, however, is challenging due to the fact that practical mmWave phased arrays offer very limited control in setting the parameters of the beam pattern and suffer from hardware imperfections which prevent us from nulling interference. |
Wireless Network-on-Chip (NoC) has emerged as a promising solution to scale chip multi-core processors to hundreds and thousands of cores. The broadcast nature of a wireless network allows it to significantly reduce the latency and overhead of many-to-many multicast and broadcast communication on NoC processors. Unfortunately, the traffic patterns on wireless NoCs tend to be very dynamic and can change drastically across different cores, different time intervals and different applications. New medium access protocols that can learn and adapt to the highly dynamic traffic in wireless NoCs are needed to ensure low latency and efficient network utilization. |