Qing Yang
ABSTRACT
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.
- 3GPP TS 36.201--820: Evolved Universal Terrestrial Radio Access (E-UTRA); Long Term Evolution (LTE) physical layer; General description.Google Scholar
- C-RAN: The Road Towards Green RAN. http://labs.chinamobile.com/cran/wp-content/uploads/CRAN\_white\_paper\_v2\_5\_EN(1).pdf.Google Scholar
- HP ProLiant DL560 Gen8 . http://h10010.www1.hp.com/wwpc/us/en/sm/WF06b/15351--15351--3328412--241644--3328422--5268290--5288630--5288631.html?dnr=1.Google Scholar
- IEEE Standard for Local and Metropolitan Area Networks Part 11; Amendment: Enhancements for Very High Throughput for operation in bands below 6GHz. IEEE Std P802.11ac/Draft 4.0, 2012.Google Scholar
- E. Aryafar, N. Anand, T. Salonidis, and E. W. Knightly. (design and experimental evaluation of multi-user beamforming in wireless lans.Google Scholar
- D. P. Bertsekas and J. N. Tsitsiklis. Parallel and Distributed Computation: Numerical Methods. Athena Scientific, 2003. Google ScholarDigital Library
- S. Bhaumik, S. P. Chandrabose, M. K. Jataprolu, G. Kumar, A. Muralidhar, P. Polakos, V. Srinivasan, and T. Woo. CloudIQ: A framework for processing base stations in a data center. In Proceedings of MobiCom, pages 125--136, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- Cisco Inc. Cisco Visual Networking Index (VNI): Forecast and Methodology 2011--2016. Cisco, http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ ns705/ns827/white_paper\_c11--481360\_ns827\_Networking\_Solutions\_White\_Paper.html, 2012.Google Scholar
- B. Hochwald and S. Vishwanath. Space-Time Multiple Access: Linear Growth in the Sum Rate. In Proc. 40th Annual Allerton Conf. Communications, Control and Computing, 2002.Google Scholar
- J. Hoydis, S. ten Brink, and M. Debbah. Massive MIMO: How many antennas do we need? In Allerton Conference on Communication, Control, and Computing, pages 545 --550, sept. 2011.Google ScholarCross Ref
- H. Huh, G. Caire, H. Papadopoulos, and S. Ramprashad. Achieving "Massive MIMO" Spectral Efficiency with a Not-so-Large Number of Antennas. IEEE Transactions on Wireless Communications, 11(9):3226 --3239, september 2012.Google ScholarCross Ref
- J. Neel, P. Robert, and J. Reed. A Formal Methodology for Estimating the Feasible Processor Solution Space for A Software Radio. In Proceedings of the SDR Technical Conference and Product Exposition, 2005.Google Scholar
- C. Peel, B. Hochwald, and A. Swindlehurst. A vector-perturbation technique for near-capacity multiantenna multiuser communication - Part I: Channel inversion and regularization. IEEE Transactions on Communications, 53(1):195--202, 2005.Google ScholarCross Ref
- H. S. Rahul, S. Kumar, and D. Katabi. JMB: Scaling wireless capacity with user demands. In Proceedings of ACM SIGCOMM, pages 235--246, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- F. Rusek, D. Persson, B. K. Lau, E. Larsson, T. Marzetta, O. Edfors, and F. Tufvesson. Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays. Signal Processing Magazine, IEEE, 30(1):40 --60, jan. 2013.Google ScholarCross Ref
- C. Shepard, H. Yu, N. Anand, E. Li, T. Marzetta, R. Yang, and L. Zhong. Argos: Practical many-antenna base stations. In Proceedings of MobiCom, pages 53--64, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- K. Tan, H. Liu, J. Fang, W. Wang, J. Zhang, M. Chen, and G. Voelker. SAM: Enabling Practical Spatial Multiple Access in Wireless LAN. In Proceedings of MobiCom, 2009. Google ScholarDigital Library
- K. Tan, J. Zhang, J. Fang, H. Liu, Y. Ye, S. Wang, Y. Zhang, H. Wu, W. Wang, and G. M. Voelker. Sora: High performance software radio using general purpose multi-core processors. In NSDI 2009. Google ScholarDigital Library
- D. Tse and P. Vishwanath. Fundamentals of Wireless Communications. Plenum Press New York and London, 2005. Google ScholarDigital Library
- A. J. Viterbi and J. K. Omura. Principles of digital communication and coding. McGraw-Hill, 1979. Google ScholarDigital Library
- H. Wu, Z. Feng, C. Guo, and Y. Zhang. ICTCP: Incast Congestion Control for TCP in data center networks. In Proceedings of CoNEXT, pages 13:1--13:12, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
Index Terms
- BigStation: enabling scalable real-time signal processingin large mu-mimo systems
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