NeTS: Large: Collaborative Research: Practical Foundations for Networking with Many-Antenna Base Stations
This project is a collaborative endeavor funded by the NSF NeTS program
. It involves Rice University and Ohio State University. It leverages the Argovs many-antenna MU-MIMO base-station testbed
built and hosted at Rice University.
Principal Investigator and Co-Principal Investators
- Lin Zhong (PI)
- Edward W Knightly (Co-PI)
- Ashutosh Sabharwal (Co-PI)
- Ness Shroff (Co-PI) (Ohio State)
Graduate Students and Postdocs
- Clay Shepard
- Abeer Javed
- Jian Ding
- Evan Everett (graduated)
- Joe Chen
- Andrew Kwong
- Xing Zhang
- Muhammed Haider
- Zhenzhi Qian (Ohio State)
- Fei Wu (Ohio State)
- Yin Sun (Postdoc, Ohio State)
- Leo Meister
- Andrew Brooks
- Michael Tsehaie
The goal of the proposed project is to provide the much needed practical foundations for networking with many-antenna base stations. In the project, we will not only explore novel approaches toward scaling up the number of base-station antennas to 10s and even 100s; but also rethink the entire network architecture exploiting the emergent properties as the number of base-station antennas grows large. Our approach is a combination of theory-driven simplification, measurement, and testbed-based implementation. In particular, the project will leverage ArgosNet, a multi-cell reconfigurable wireless network testbed of many-antenna base-stations recently funded by an NSF CRI grant with up to 400 antennas per base station. With many-antenna base stations deployed on rooftops and indoors on Rice campus and fully mobile terminals, the testbed provides unprecedented opportunities to study many-antenna MIMO systems in real world.
Toward providing the practical foundations for many-antenna MIMO networks, the project targets at three interrelated areas of innovations. (i) Scalable Control and Coordination. The project will develop scalable designs of control and coordination functions for MU-MIMO networks. In particular, it will design a suite of protocols for network-wide CSI collection that significantly reduce its overhead.
(ii) Scalable Resource Allocation. The project will develop novel resource allocation solutions for many-antenna MIMO networks in order to support both high data rates and low-latency requirements. It will contribute a novel scalable scheduling framework that use slow-time-scale information or statistical channel information and design scheduling policies based on MIMO rateless codes.
(iii) Empirical Foundations from Measurements. The project will perform previously impossible real-time measurements of MU-MIMO channels in order to understand channel correlation, variation and reciprocity and their relationships with spectrum band, mobility, and hardware impairment. In particular, the project will derive novel models, reciprocity calibration methods, and novel channel state representations that will power research in network designs.
In YEAR ONE
Large-Scale MIMO Channel Measurement and Analysis
To better understand these MU-MIMO channels in the real-world, we built a realtime wideband many-antenna MU- MIMO channel measurement system that supports high time- frequency resolution across the UHF, 2.4 GHz, and 5 GHz bands. We built this system on the ArgosV2 platform and leveraged the Faros control channel design to provide time-frequency synchronization and Channel State Information (CSI) collection. To support UHF, we ported Argos and Faros to the WURC platform.
We performed an extensive measurement campaign that includes fully mobile traces across the UHF, 2.4 GHz, and 5 GHz bands in diverse environments. At 2.4 and 5 GHz, we collected traces with up to 104 base station antennas serving 8 users in both indoor and outdoor environments, with varying mobility. At UHF, we collected traces with up to 8 base station antennas serving 6 users in both indoor and outdoor environments, with varying mobility. These traces typically have frame lengths, i.e. time resolution, varying from 2 ms to 40 ms.
These traces allowed us to investigate large-scale MIMO channels. We are in the process to make them openly available via argos.rice.edu.
Angular Domain Representation of Large-Scale MIMO Channel
A key challenge in large-scale MIMO is the large overhead in CSI acquisition. We designed two novel types of angle-of-arrival (AoA) based beamforming schemes that harness the reciprocity of dominant AoA. Both schemes require CSI acquisition overhead that only scales with the number of served mobiles, not the number of base-station antennas. We analyze the performance of the proposed schemes both analytically and numerically. We show that both our proposed schemes lead to sum throughput that scales with the number of base-station antennas, and hybrid beamforming performs close to ideal zero-forcing beamforming.
Mobility-aware MAC Protocol Design
Performance of many-antenna basestations is highly sensitive to client mobility. For example, large-scale MIMO link can lead to high directivity. As a result, they must address new link training and adaptation challenges due to both client and environmental mobility. We designed, implemented and evaluated MOCA, a protocol for Mobility resilience and Overhead Constrained Adaptation for high directivity links. The team introduced Beam Sounding as a mechanism invoked before each data transmission to estimate the link quality for MIMO precoding designs, and identify and adapt to link impairments. We devised proactive techniques to restore broken directional links with low overhead and design a mechanism to jointly adapt beamwidth and data rate, targeting throughput maximization that incorporates data rate, overhead for CSI collection, and mobility resilience.
Scalable MU-MIMO Uplink for Many-Antenna Base Stations
Mobile devices have fewer antennas than APs due to size and energy constraints. This antenna asymmetry restricts uplink capacity to the client antenna array size rather than the AP’s. To overcome antenna asymmetry, multiple clients can be grouped into a simultaneous multi-user transmission to achieve a full rank transmission that matches the number of antennas at the AP. In our work, we design, implement, and experimentally evaluate, the first distributed and scalable system to achieve full-rank uplink multi-user capacity without control signaling for channel estimation, channel reporting, or user selection.
Limited Channel State Information on Massive MIMO Network Performance
In recent years, there have been significant efforts on the research and development of Massive MIMO (M-MIMO) technologies at the physical layer. So far, however, the understanding of how M-MIMO could affect the performance of network control and optimization algorithms remains rather limited. We analyze the performance of the queue-length-based joint congestion control and scheduling framework (QCS) over M-MIMO cellular networks with limited channel state information (CSI).
- A. Flores, S. Quadri, and E. Knightly (2016). A Scalable Multi-User Uplink for Wi-Fi. Proceedings of NSDI
- A. Kwong and A. Sabharwal (2015). Overcoming Conjugate Beamforming Limitations with Side-Channel Cooperative Decoders. IEEE Asilomar Conference on Signals, Systems and Computers (ASILOMAR).
- Clayton Shepard, Abeer Javed, and Lin Zhong (2015). Control channel design for many-antenna MU-MIMO. Proc. ACM Int. Conf. Mobile Computing and Networking (MobiCom).
- Clayton Shepard, Abeer Javed, Ryan Guerra, Jian Ding, and Lin Zhong (2016). Many-Antenna MU-MIMO Channel Measurements. IEEE ASILOMAR Conference.
- J. Liu, A. Eryilmaz, N. B. Shroff, and E. Bentley (2016). Understanding the Impact of Limited Channel State Information on Massive MIMO Network Performances. Proc. ACM MobiHoc
- Xing Zhang, John Tadrous, Evan Everett, Feng Xue, and Ashutosh Sabharwal (2015). Angle-of-arrival based beamforming for FDD massive MIMO. IEEE Asilomar Conference on Signals, Systems and Computers (ASILOMAR).
- F. Wu, Y. Yang, O. Zhang, K. Srinivasan, and N. B. Shroff (2016). Anonymous-Query based Rate Control for Wireless Multicast: Approaching Optimality with Constant Feedback. ACM MobiHoc.
- Y. Sun, E. Uysal-Biyikoglu, R. Yates, C. E. Koksal, and N. B. Shroff (2016). Update or Wait: How to Keep Your Data Fresh. IEEE Infocom.
- Z. Qian, B. Ji, K. Srinivasan, and N. B. Shroff (2016). Achieving Delay Rate-function Optimality in OFDM Downlink with Time-correlated Channels. IEEE Infocom.