Extreme MU-MIMO

The Argos project pushes MU-MIMO to its limits, scaling up the number of antennas in wireless systems to a point previously thought impossible. This enables us to achieve enormous capacity and power gains, and discover the true real-world limits of MU-MIMO.

Extreme MU-MIMO

The capacity of traditional single-user wireless systems is exclusively limited by the available spectrum and transmission power. Recent developments in information theory, however, have shown that such capacity limitations can be overcome by improving the spatial reuse efficiency through multi-user multiple-input multiple-output (MU-MIMO) technology, or its special form called multi-user beamforming (MUBF). With MUBF, a base station employs multiple antennas to simultaneously send independent data streams to multiple users, tremendously increasing the aggregated network capacity.
As MUBF theory dictates, the more antennas a base station has, the more users it can serve simultaneously. Encouragingly, this means that with enough base station antennas, the network capacity can be scalably grow to accomodate more users. This naturally motivates us to put as many antennas as possible on the base station to meet the increasing capacity demand from more and more users. However, to practically implement MUBF theory and realize its capacity benefit, one must address a set of challenges.
In the Argos project we develop new techniques that enable hundreds of antennas to be deployed on base stations, resulting in enormous network capacity gains. We use the WARP platform to build the first real-world prototype of a many-antenna MUBF base station and experimentally evaluate its performance. Our research demonstrates the feasibility of many-antenna base stations, and is designed to motivate industry adoption of this promising technology in the near future.

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Challenges

Building a many-antenna base station is non-trivial. Scaling up baseband processing, clock distribution, transmission synchronization, and channel estimation raises serious system challenges. Moreover, the cost, size, and power consumption of the base station limit the practicality of the many-antenna approach. Our work has already revealed many new research challenges on every level of the architecture.

Challenges

We must address serious practical challenges in order to realize many-antenna base stations. The first, and also the most important step, is to build such a base station with hundreds of antennas. In particular, the large number of antennas leads to the following system challenges:
(1) MUBF, especially zero-forcing MUBF (ZF-MUBF), requires non-trivial baseband processing. How do we design a base station architecture that properly partitions computation in order to easily and flexibly scale with the number of antennas?
(2) MUBF is extremely sensitive to fluctuations in wireless channels; therefore the channel state information (CSI) must be updated both quickly and accurately. How do we efficiently collect full CSI for a large number of base station antennas and users?
(3) MUBF requires accurate clock and transmission synchronization to ensure correct phase and symbol alignment for the signals sent from the multiple antennas. How do we synchronize the clock and transmission for a large number of antennas?
Argos is our proposed base station architecture that solves the above challenges. Briefly speaking, Argos adopts a fat-tree structure with daisy-chained lead nodes to improve scalability and reliability. We propose a local conjugate beamforming method, which gracefully distributes the processing to each antenna with close-to-optimal performance. We also devise a novel internal relative channel calibration procedure which enables full CSI to be collected very quickly. Finally, we employ a a central clock distribution board to accurately synchronize the all antennas. These techniques collectively lead to a practical base-station which can scale to hundreds or even thousands of base station antennas.

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Results

We have built three generations of many-antenna, or Massive MIMO, base stations. Our fundamental systems research reveals, solves, and optimizes many challenges unique to scaling up the number of base station antennas and multi-user spatial streams. By collecting and publishing realtime channel traces we reveal how Massive MIMO behaves and performs in the real world, helping guide system design. Our many-antenna architecture is able to improve spectral capacity and energy efficiency manyfold simultaneously.

Results

We have developed three generations of many-antenna MU-MIMO systems, and leveraged these platforms to do groundbreaking research. In particular, we were the first to demonstrate the real-world performance of Massive MIMO, and have since released comprehensive realtime channel traces in multiple environments to help guide system design. Traditional control channels are highly inefficient for many-antenna systems, thus we developed a novel control channel that scales to Massive MIMO while being highly efficient and providing complete spatial coverage. Please see our full list of publications for more details.

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Clayton Shepard, Rahman Doost-Mohammady, Ryan E. Guerra, and Lin Zhong. "ArgosNet: A Multi-Cell Many-Antenna MU-MIMO Platform," in Proc. IEEE Asilomar Conf. on Signals, Systems, and Computers, November 2017. (data, slides*)
Clayton Shepard, Rahman Doost-Mohammady, Ryan E. Guerra, and Lin Zhong. "ArgosV3: An Efficient Many-Antenna Platform," Extended Abstract for demonstration in Proc. ACM Int. Conf. Mobile Computing and Networking (MobiCom), October 2017. (HotWireless '17 slides)
Clayton Shepard. "Argos: Practical Many-Antenna MU-MIMO Systems," Ph.D. Thesis, Rice University, September, 2017.
Clayton Shepard, Jian Ding, Ryan Guerra, and Lin Zhong, "Understanding Real Many-Antenna MU-MIMO Channels," in Proc. IEEE Asilomar Conf. on Signals, Systems, and Computers, November 2016. (data, slides*)
Ryan Guerra, Narendra Anand, Clayton Shepard, and Edward Knightly, "Opportunistic Channel Estimation for Implicit 802.11af MU-MIMO," in Proc. Int. Conf. in Networking Science & Practice (ITC), September 2016. (data, slides*)
Evan Everett, Clayton Shepard, Lin Zhong, and Ashutosh Sabharwal, "SoftNull: Many-Antenna Full-Duplex Wireless via Digital Beamforming," IEEE Transactions on Wireless Communications, September, 2016. (data)
Evan Everett. "Full-duplex Wireless with Large Antenna Arrays," Ph.D. Thesis, Rice University, September, 2017.
Clayton Shepard, Abeer Javed, and Lin Zhong, "Control Channel Design for Many-Antenna MU-MIMO," in Proc. ACM Int. Conf. Mobile Computing and Networking (MobiCom), September 2015. (clip, video, slides*)
Patrick Murphy, Clayton Shepard, Lin Zhong, Chris Dick, Ashutosh Sabharwal. "FPGAs Help Characterize Massive-MIMO Channels," Xcell Journal, October, 2014.
David Talbot. "A 96-Antenna System Tests the Next Generation of Wireless," MIT Technology Review, January, 2014.
Clayton Shepard, Lin Zhong, Narendra Anand, Hang Yu, Oscar Bejarano, Edward Knightly, Li Erran Li. "Argos: Practical Massive-MIMO," IEEE 802 Plenary, November, 2013. (slides)
Clayton Shepard, Hang Yu, and Lin Zhong. "ArgosV2: A Flexible Many-Antenna Research Platform," Extended Abstract for demonstration in Proc. ACM Int. Conf. Mobile Computing and Networking (MobiCom), October 2013.
Clayton Shepard*, Narendra Anand*, and Lin Zhong. "Practical Performance of MU-MIMO Precoding in Many-Antenna Base Stations," in Proc. ACM workshop on Cellular networks: operations, challenges, and future design (CellNet), June 2013. (*equal contribution) (slides*)
Lin Zhong. "Networking with massive MU-MIMO," Invited talk, LTE Summit, April, 2013. (slides*)
Clayton Shepard, Hang Yu, Narendra Anand, Erran Li, Thomas Marzetta, Richard Yang, Lin Zhong. "Argos: Practical Many-Antenna Base Stations," in Proc. ACM Int. Conf. Mobile Computing and Networking (MobiCom), August 2012. (slides*)
Jade Boyd. "How to feed data-hungry mobile devices? Use more antennas," Rice University press release, August 2012. Video.
Clayton Shepard. "Argos: Practical Base Stations with Large-scale Multi-user Beamforming," M.S. Thesis, Rice University, April 2012. (slides*)