Distributed MIMO has long been known theoretically to bring large throughput gains to wireless networks. Recent years have seen significant interest and progress in developing practical distributed MIMO systems. However, these systems only distribute the transmission function across the multiple nodes. The control fabric that synchronizes the nodes to a common reference phase still fundamentally requires a single leader that all nodes in the network are capable of hearing.

Recent years have seen a lot of work in moving distributed MIMO from theory to practice. While this prior work demonstrates the feasibility of synchronizing multiple transmitters in time, frequency, and phase, none of them deliver a full-fledged PHY capable of supporting distributed MIMO in real-time. Further, none of them can address dynamic environments or mobile clients. Addressing these challenges, requires new solutions for low-overhead and fast tracking of wireless channels, which are the key parameters of any distributed MIMO system. It also requires a software-hardware architecture that can deliver a distributed MIMO within a full-fledged 802.11 PHY, while still meeting the tight timing constraints of the 802.11 protocol. This architecture also needs to perform coordinated power control across distributed MIMO nodes, as opposed to simply letting each node perform power control as if it were operating alone. This paper describes the design and implementation of MegaMIMO 2.0, a system that achieves these goals and delivers the first real-time fully distributed 802.11 MIMO system.

We present a demonstration of a real-time distributed MIMO system, DMIMO. DMIMO synchronizes transmissions from 4 distributed MIMO transmitters in time, frequency and phase, and performs distributed multi-user beamforming to independent clients. DMIMO is built on top of a Zynq hardware platform integrated with an FMCOMMS2 RF front end. The platform implements a custom 802.11n compatible MIMO PHY layer which is augmented with a lightweight distributed synchronization engine. The demonstration shows the received constellation points, channels, and effective data throughput at each client. It also shows how these vary as a function of interference, the timeliness of channel feedback, and the transmission rates used by the different transmitters.

The emergence of data-intensive problems in areas like computational biology, astronomy, medical imaging, etc. has emphasized the need for fast and efficient very large Fourier Transforms. Recent work has shown that we can compute million-point transforms efficiently provided the data is sparse in the frequency domain. Processing input samples at rates approaching 1 GHz would allow real-time processing in several such applications. In this paper, we present a high-throughput FPGA implementation that performs a million-point sparse Fourier Transform on frequency-sparse input data, generating the largest 500 frequency component locations and values every 1.16 milliseconds. This design can process streamed input data at 0.86 Giga samples per second, and does not make any assumptions of the distribution of the frequency components beyond sparsity.

We present BigBand, a technology that can capture GHz of spectrum in realtime without sampling the signal at GS/s - i.e., without high speed ADCs. Further, it is simple and can be implemented on commodity low-power radios. Our approach builds on recent advances in the area of sparse Fourier transforms, which show that it is possible to reconstruct a sparse signal without sampling it at the Nyquist rate. To demonstrate our design, we implement it using 3 software radios, each sampling the spectrum at 50 MS/s, producing a device that captures 0.9 GHz - i.e., 6× larger digital bandwidth than the three software radios combined. Finally, an extension of BigBand can perform GHz spectrum sensing even in scenarios where the spectrum is not sparse.

Despite the rapid growth of next-generation cellular networks, researchers and end-users today have limited visibility into the performance and problems of these networks. As LTE deployments move towards femto and pico cells, even operators struggle to fully understand the propagation and interference patterns affecting their service, particularly indoors. This paper introduces LTEye, the first open platform to monitor and analyze LTE radio performance at a fine temporal and spatial granularity. LTEye accesses the LTE PHY layer without requiring private user information or provider support. It provides deep insights into the PHY-layer protocols deployed in these networks. LTEye's analytics enable researchers and policy makers to uncover serious deficiencies in these networks due to inefficient spectrum utilization and inter-cell interference. In addition, LTEye extends synthetic aperture radar (SAR), widely used for radar and backscatter signals, to operate over cellular signals. This enables businesses and end-users to localize mobile users and capture the distribution of LTE performance across spatial locations in their facility. As a result, they can diagnose problems and better plan deployment of repeaters or femto cells. We implement LTEye on USRP software radios, and present empirical insights and analytics from multiple AT&T and Verizon base stations in our locality.