Skip to main content
Log in

GCC: Group-Based CSI Feedback Compression for MU-MIMO Networks

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Multi-user Multiple Input Multiple Output networks (MU-MIMO) adopts beamforming to enable Access Point (AP) to transmit packets concurrently to multiple users, which brings formidable overhead. The overhead of collecting Channel State Information (CSI) feedback matrix may even overwhelm real data transmission when the scale of network is large, which incurs unsatisfactory performance and huge waste of resources. In this paper, we address this urgent problem with GCC, a Group-based CSI feedback Compression scheme for MU-MIMO networks, which enables users to feedback their CSI in terms of group determined by their location. Instead of traditional per-packet scheme, GCC limit the quantity of CSI feedback in each transmission round regardless of the size of network by allowing the location-related users to share a CSI matrix. We use a novel metric to do the tradeoff between throughput and capacity loss of the system. We realize GCC in different scenarios and compare it with exist works, evaluation result reveals that GCC can achieve much higher throughput and is robust to various situations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Chen Y, Guizani M, Zhang Y, Wang L, Crespi N, Lee GM (2017) When traffic flow prediction meets wireless big data analytics. arXiv:1709.08024

  2. Gast MS (2013) 802.11 ac: a survival guide: Wi-Fi at gigabit and beyond. O’Reilly Media Inc., Sebastopol

    Google Scholar 

  3. Xie X, Zhang X, Sundaresan K (2013) Adaptive feedback compression for mimo networks. In: Proceedings of the 19th annual international conference on mobile computing & networking. ACM, pp 477–488

  4. Park J, Kim M, Kim H, Jung Y, Kim J (2016) Low-complexity mimo detection algorithm with adaptive interference mitigation in dl mu-mimo systems with quantization error. J Commun Netw 18(2):210–217

    Google Scholar 

  5. Lu B, Zeng Z, Wang L, Peck B, Qiao D, Segal M (2016) Confining wi-fi coverage: a crowdsourced method using physical layer information. In: 2016 13th annual IEEE international conference on sensing, communication, and networking (SECON). IEEE, pp 1–9

  6. Chen Y, Crespi N, Siano P (2017) erouting: an eco-friendly navigation algorithm for traffic information industry. IEEE Trans Ind Inf 13(2):562–571

    Article  Google Scholar 

  7. Halperin D, Hu W, Sheth A, Wetherall D (2011) Tool release: gathering 802.11 n traces with channel state information. ACM SIGCOMM Comput Commun Rev 41(1):53–53

    Article  Google Scholar 

  8. Zhang Y, Ji C, Liu Y, Malik W Q, O’Brien DC, Edwards DJ (2008) A low complexity user scheduling algorithm for uplink multiuser mimo systems. IEEE Trans Wirel Commun 7(7):2486–2491

    Article  Google Scholar 

  9. Zhou A, Wei T, Zhang X, Liu M, Li Z (2015) Signpost: scalable mu-mimo signaling with zero csi feedback. In: Proceedings of the 16th ACM international symposium on mobile ad hoc networking and computing. ACM, pp 327–336

  10. Anand N, Lee J, Lee S-J, Knightly EW (2015) Mode and user selection for multi-user mimo wlans without csi. In: 2015 IEEE conference on computer communications (INFOCOM). IEEE, pp 451–459

  11. Huang S, Yin H, Wu J, Leung VCM (2013) User selection for multiuser mimo downlink with zero-forcing beamforming. IEEE Trans Veh Technol 62(7):3084–3097

    Article  Google Scholar 

  12. Shen W-L, Lin K C-J, Chen M-S, Tan K (2015) Sieve: Scalable user grouping for large mu-mimo systems. In: 2015 IEEE conference on computer communications (INFOCOM). IEEE, pp 1975–1983 2015

  13. Shen W-L, Lin K C-J, Gollakota S, Chen Ming-Syan (2014) Rate adaptation for 802.11 multiuser mimo networks. IEEE Trans Mob Comput 13(1):35–47

    Article  Google Scholar 

  14. Xie X, Zhang X (2014) Scalable user selection for mu-mimo networks. In: INFOCOM, 2014 Proceedings IEEE. IEEE, pp 808–816

  15. Garikipati KC, Shin KG (2014) Measurement-based transmission schemes for network mimo. In: Proceedings of the 15th ACM international symposium on mobile ad hoc networking and computing. ACM, pp 387–396

  16. Bejarano O, Pierre R, Hoefel F, Knightly EW (2016) Resilient multi-user beamforming wlans: mobility, interference, and imperfect csi. In: IEEE INFOCOM 2016-The 35th annual IEEE international conference on computer communications. IEEE, pp 1–9

  17. Zhao F, Wang C, Chen H, Bie R (2014) Game-theoretic joint power allocation and beamforming for cognitive mimo systems with finite feedback. Mob Netw Appl 19(4):512–521

    Article  Google Scholar 

  18. Al-Ali MH, Ho DKC (2017) Precoding for mimo channels in cognitive radio networks with csi uncertainties and for mimo compound capacity. IEEE Trans Signal Process 65(15):3976–3989

    Article  MathSciNet  Google Scholar 

  19. Huang K, Heath RW Jr, Andrews JG (2009) Limited feedback beamforming over temporally-correlated channels. IEEE Trans Signal Process 57(5):1959–1975

    Article  MathSciNet  Google Scholar 

  20. Mo J, Heath RW (2015) Limited feedback in multiple-antenna systems with one-bit quantization. In: 2015 49th Asilomar conference on signals, systems and computers. IEEE, pp 1432–1436

  21. Yan L, Fang X, Wang C-X (2016) Position-based limited feedback scheme for railway mu-mimo systems. IEEE Trans Veh Technol 65(10):8361–8370

    Article  Google Scholar 

  22. Sboui L, Rezki Z, Alouini M-S (2017) Achievable rates of cognitive radio networks using multilayer coding with limited csi. IEEE Trans Veh Technol 66(1):395–405

    Google Scholar 

  23. Chen Y, Zhang Y, Maharjan S (2017) Deep learning for secure mobile edge computing. arXiv:1709.08025

  24. Chintalapudi K, Iyer AP, Padmanabhan VN (2010) Indoor localization without the pain. In: Proceedings of the sixteenth annual international conference on mobile computing and networking. ACM, pp 173–184

  25. Kumarasiri R, Alshamaileh K, Tran NH, Devabhaktuni V (2016) An improved hybrid rss/tdoa wireless sensors localization technique utilizing wi-fi networks. Mob Netw Appl 21(2):286–295

    Article  Google Scholar 

  26. Yang S, Dessai P, Verma M, Gerla M (2013) Freeloc: calibration-free crowdsourced indoor localization. In: INFOCOM, 2013 Proceedings IEEE. IEEE, pp 2481–2489

  27. Yang Z, Zhou Z, Liu Y (2013) From rssi to csi: indoor localization via channel response. ACM Comput Surv(CSUR) 46(2):25

    MATH  Google Scholar 

  28. Jiang Z-P, Xi W, Li X, Tang S, Zhao J-Z, Han J-S, Zhao K, Wang Z, Xiao B (2014) Communicating is crowdsourcing: Wi-fi indoor localization with csi-based speed estimation. J Comput Sci Technol 29(4):589–604

    Article  Google Scholar 

  29. Wang X, Gao L, Mao S, Pandey S (2017) Csi-based fingerprinting for indoor localization: a deep learning approach. IEEE Trans Veh Technol 66(1):763–776

    Google Scholar 

  30. Wang Y, Wu K, Ni LM (2017) Wifall: device-free fall detection by wireless networks. IEEE Trans Mob Comput 16(2):581–594

    Article  Google Scholar 

  31. Wu C, Yang Z, Zhou Z, Liu X, Liu Y, Cao J (2015) Non-invasive detection of moving and stationary human with wifi. IEEE J Sel Areas Commun 33(11):2329–2342

    Article  Google Scholar 

  32. Vasisht D, Kumar S, Katabi D (2016) Decimeter-level localization with a single wifi access point. In: NSDI, pp 165–178

  33. Kotaru M, Joshi K, Bharadia D, Katti S (2015) Spotfi: decimeter level localization using wifi. In: ACM SIGCOMM computer communication review, vol 45. ACM, pp 269–282

  34. Inserra D, Tonello AM (2013) A frequency-domain los angle-of-arrival estimation approach in multipath channels. IEEE Trans Veh Technol 62(6):2812–2818

    Article  Google Scholar 

  35. He J, Swamy MNS, Omair Ahmad M (2012) Joint space-time parameter estimation for underwater communication channels with velocity vector sensor arrays. IEEE Trans Wirel Commun 11(11):3869–3877

    Article  Google Scholar 

  36. Abraham SP, Merlin S, Sampath H, Wentink MM, Knowles Jones V IV Channel state information (csi) feedback protocol for multiuser multiple input, multiple output (mu-mimo), August 22 2017. US Patent 9,742,590

Download references

Acknowledgements

This work is supported by “National Natural Science Foundation of China” with No. 61733002 and “the Fundamental Research Funds for the Central Universities” with No. DUT17LAB16, No. DUT2017TB02. This work is also supported by Tianjin Key Laboratory of Advanced Networking (TANK), School of Computer Science and Technology, Tianjin University, Tianjin China, 300350.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lei Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fang, J., Wang, L., Qin, Z. et al. GCC: Group-Based CSI Feedback Compression for MU-MIMO Networks. Mobile Netw Appl 23, 407–418 (2018). https://doi.org/10.1007/s11036-018-1015-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-018-1015-1

Keywords

Navigation