Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Principles of Digital Communication and Coding
Principles of Digital Communication and Coding
Fundamentals of wireless communication
Fundamentals of wireless communication
Sora: high performance software radio using general purpose multi-core processors
NSDI'09 Proceedings of the 6th USENIX symposium on Networked systems design and implementation
SAM: enabling practical spatial multiple access in wireless LAN
Proceedings of the 15th annual international conference on Mobile computing and networking
ICTCP: Incast Congestion Control for TCP in data center networks
Proceedings of the 6th International COnference
JMB: scaling wireless capacity with user demands
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
Argos: practical many-antenna base stations
Proceedings of the 18th annual international conference on Mobile computing and networking
CloudIQ: a framework for processing base stations in a data center
Proceedings of the 18th annual international conference on Mobile computing and networking
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Multi-user multiple-input multiple-output (MU-MIMO) is the latest communication technology that promises to linearly increase the wireless capacity by deploying more antennas on access points (APs). However, the large number of MIMO antennas will generate a huge amount of digital signal samples in real time. This imposes a grand challenge on the AP design by multiplying the computation and the I/O requirements to process the digital samples. This paper presents BigStation, a scalable architecture that enables realtime signal processing in large-scale MIMO systems which may have tens or hundreds of antennas. Our strategy to scale is to extensively parallelize the MU-MIMO processing on many simple and low-cost commodity computing devices. Our design can incrementally support more antennas by proportionally adding more computing devices. To reduce the overall processing latency, which is a critical constraint for wireless communication, we parallelize the MU-MIMO processing with a distributed pipeline based on its computation and communication patterns. At each stage of the pipeline, we further use data partitioning and computation partitioning to increase the processing speed. As a proof of concept, we have built a BigStation prototype based on commodity PC servers and standard Ethernet switches. Our prototype employs 15 PC servers and can support real-time processing of 12 software radio antennas. Our results show that the BigStation architecture is able to scale to tens to hundreds of antennas. With 12 antennas, our BigStation prototype can increase wireless capacity by 6.8x with a low mean processing delay of 860μs. While this latency is not yet low enough for the 802.11 MAC, it already satisfies the real-time requirements of many existing wireless standards, e.g., LTE and WCDMA.