ABSTRACT
Multi-User MIMO, the hallmark of IEEE 802.11ac and the upcoming 802.11ax, promises significant throughput gains by supporting multiple concurrent data streams to a group of users. However, identifying the best-throughput MU-MIMO groups in commodity 802.11ac networks poses three major challenges: a) Commodity 802.11ac users do not provide full CSI feedback, which has been widely used for MU-MIMO grouping. b) Heterogeneous channel bandwidth users limit grouping opportunities. c) Limited-resource on APs cannot support computationally and memory expensive operations, required by existing algorithms. Hence, state-of-the-art designs are either not portable in 802.11ac APs, or perform poorly, as shown by our testbed experiments. In this paper, we design and implement MUSE, a lightweight user grouping algorithm, which addresses the above challenges. Our experiments with commodity 802.11ac testbeds show MUSE can achieve high throughput gains over existing designs.
- M. X. Gong, B. Hart, and S. Mao, "Advanced Wireless LAN Technologies: IEEE 802.11ac and Beyond," in ACM GetMobile: Mobile Comp. and Comm., 2015. Google ScholarDigital Library
- "Ericsson 5G field trial gear achieves peak downlink throughput over 25 Gbps with MU-MIMO," in Ericsson press release, 2016.Google Scholar
- M. Esslaoui, F. Riera-Palou, and G. Femenias, "A fair MU-MIMO scheme for IEEE 802.11ac," in ISWCS, 2012, pp. 1049--1053.Google Scholar
- Z. Shen, R. Chen, J. G. Andrews, R. W. Heath, and B. L. Evans, "Low Complexity User Selection Algorithms For Multiuser MIMO Systems with Block Diagonalization," IEEE Transaction on Signal Processing, vol. 54, no. 9, 2006. Google ScholarDigital Library
- T. Yoo and N. J. A. Goldsmith, "Multi-Antenna Downlink Channels with Limited Feedback and User Selection," IEEE JSAC, vol. 25, no. 7, 2007. Google ScholarDigital Library
- T. Ji, C. Zhou, S. Zhou, and Y. Yao, "Low Complex User Selection Strategies for Multi-User MIMO Downlink Scenario," in Proc. of IEEE WCNC, 2007. Google ScholarDigital Library
- D. Gesbert, M. Kountouris, R. W. Heath, and C.-B. Chae, "Shifting the MIMO Paradigm," IEEE Signal Processing Magazine, vol. 24, no. 5, 2007.Google ScholarCross Ref
- X. Xie, X. Zhang, and K. Sundaresan, "Adaptive Feedback Compression for MIMO Networks," in Proc. of ACM MobiCom, 2013. Google ScholarDigital Library
- X. Xie and X. Zhang, "Scalable User Selection for MU-MIMO Networks," in Proc. of IEEE INFOCOM, 2014.Google Scholar
- T.-W. Kuo, K.-C. Lee, K. C.-J. Lin, and M.-J. Tsai, "Leader-Contention-Based User Matching for 802.11 Multiuser MIMO Networks," in IEEE Transactions on Wireless Communications, vol. 13, no. 8, 2014.Google ScholarCross Ref
- K. Nikitopoulos, J. Zhou, B. Congdon, and K. Jamieson, "Geosphere: Consistently Turning MIMO Capacity into Throughput," in ACM SIGCOMM, 2014. Google ScholarDigital Library
- "AR9331 Highly-Integrated and Cost Effective IEEE 802.11n 1×1 2.4 GHz SoC for AP and Router Platforms," in Atheros Data Sheet, 2010.Google Scholar
- A. Narendra, J. Lee, S.-J. Lee, and E. W. Knightly, "Mode and User Selection for Multi-User MIMO WLANs without CSI," in IEEE INFOCOM, 2015.Google Scholar
- IEEE Standards Association, "IEEE Standards 802.11ac-2013: Enhancements for Very High Throughput for Operation in Bands below 6 GHz," 2013.Google Scholar
- H. Lou, M. Ghosh, P. Xia, and R. Olesen, "A Comparison of Implicit and Explicit Channel Feedback Methods for MU-MIMO WLAN Systems," in IEEE PIMRC, 2013.Google Scholar
- P. Wang and L. Ping, "On Maximum Eigenmode Beamforming and Multi-User Gain," in IEEE Transactions On Information Theory, vol. 57, no. 7, 2011. Google ScholarDigital Library
- Xiaomi Technology Co. Ltd., "Xiaomi Mi 4i," 2015.Google Scholar
- Atheros, in Minstrel Rate Adaptation, 2009.Google Scholar
- I. Pefkianakis, Y. Hu, S. H. Wong, H. Yang, and S. Lu, "MIMO Rate Adaptation in 802.11n Wireless Networks," in ACM MobiCom, 2010. Google ScholarDigital Library
- S. H. Wong, H. Yang, S. Lu, and V. Bharghavan, "Robust Rate Adaptation for 802.11 Wireless Networks," in ACM MobiCom, 2006. Google ScholarDigital Library
- D. Tse and P. Viswanath, Fundamentals of Wireless Communication. Cambridge University Press, 2005. Google ScholarDigital Library
- J. A. Gubner, "Probability and Random Processes for Electrical and Computer Engineers." Cambridge University Press, 2006. Google ScholarDigital Library
- G. Judd, X. Wang, and P. Steenkiste, "Efficient Channel-aware Rate Adaptation in Dynamic Environments," in ACM MobiSys, 2010. Google ScholarDigital Library
- D. Halperin, W. Hu, A. Sheth, and D. Wetherall, "Predictable 802.11 Packet Delivery from Wireless Channel Measurements," in ACM SIGCOMM, 2010. Google ScholarDigital Library
- W.-L. Shen, K. C.-J. Lin, M.-S. Chen, and T. Kun, "SIEVE: Scalable User Grouping for Large MU-MIMO Systems," in IEEE INFOCOM, 2015.Google Scholar
- S. Sen, B. Radunovic, J. Lee, and K.-H. Kim, "CSpy: Finding the Best Quality Channel without Probing," in ACM MobiCom'13, 2013. Google ScholarDigital Library
- A. Goldsmith, S. A. Jafar, N. Jindal, and S. Vishwanath, "Capacity Limits of MIMO Channels," IEEE JSAC, vol. 21, no. 5, 2003. Google ScholarDigital Library
- O. Bejarano, E. Magistretti, O. Gurewitz, and E. W. Knightly, "MUTE: Sounding inhibition for MU-MIMO WLANs," in IEEE SECON, 2014.Google Scholar
- C. Shepard, H. Yu, N. Anand, E. Li, T. Marzetta, R. Yang, and L. Zhong, "Argos: Practical Many-Antenna Base Stations," in Proc. of ACM MobiCom, 2012. Google ScholarDigital Library
- A. Zhou, T. Wei, X. Zhang, M. Liu, and Z. Li, "Signpost: Scalable MU-MIMO Signaling with Zero CSI Feedback," in Proc. of ACM MobiHoc, 2015. Google ScholarDigital Library
- W.-L. Shen, Y.-C. Tung, K.-C. Lee, K. C.-J. Lin, S. Gollakota, D. Katabi, and M.-S. Chen, "Rate Adaptation for 802.11 Multiuser MIMO Networks," in Proc. of ACM Mobicom, 2012. Google ScholarDigital Library
- H. Rahul, S. Kumar, and D. Katabi, "MegaMIMO: Scaling Wireless Capacity with User Demands," in ACM SIGCOMM, Helsinki, Finland, August 2012.Google Scholar
- H. V. Balan, R. Rogalin, A. Michaloliakos, K. Psounis, and G. Caire, "AirSync: Enabling Distributed Multiuser MIMO With Full Spatial Multiplexing," IEEE/ACM Transactions on Networking, vol. 21, no. 6, 2013. Google ScholarDigital Library
- X. Zhang, K. Sundaresan, M. A. A. Khojastepour, S. Rangarajan, and K. G. Shin, "NEMOx: Scalable Network MIMO for Wireless Networks," in Proc. of ACM MobiCom, 2013. Google ScholarDigital Library
- J. Xiong, K. Sundaresan, K. Jamieson, M. Khojastepour, and S. Rangarajan, "MIDAS: Empowering 802.11ac Networks with Multiple-Input Distributed Antenna Systems," in ACM CoNEXT, 2014. Google ScholarDigital Library
Index Terms
- Practical MU-MIMO user selection on 802.11ac commodity networks
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