Serving 22 Users in Real-Time with a 128-Antenna Massive MIMO Testbed
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Presentation given by Paul Harris at IEEE SiPS in Dallas on 27th October 2016.
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Serving 22 Users in Real-Time with a 128-Antenna Massive MIMO Testbed
- 1. IEEE SiPS 27th October 2016, Dallas Serving 22 Users in Real-Time with a 128- Antenna Massive MIMO Testbed Paul Harris Siming Zhang, Wael Boukley Hasan, Steffen Malkowsky, Joao Vieira, Siming Zhang, Mark Beach, Liang Liu, Evangelos Mellios, Andrew Nix, Simon Armour, Angela Doufexi, Karl Nieman, Nikhil Kundargi Communication Systems and Networks Group University of Bristol, Bristol, UK http://www.bristol.ac.uk/engineering/research/csn/
- 2. IEEE SiPS 27th October 2016, Dallas Summary • System Overview • Measurement Setup • Experimental Results • Conclusions • Ongoing Work 2
- 3. IEEE SiPS 27th October 2016, Dallas The Massive MIMO Concept 3 Ultimate Spatial Resolution • Increased spectral efficiency and network capacity • Accurate spatial multiplexing Time Space Uplink Downlink Uplink Uplink Uplink Downlink Downlink Downlink Cellular View
- 4. IEEE SiPS 27th October 2016, Dallas NI Based ‘BIO’ Massive MIMO test-bed 4 • 128 Programmable Radio Heads (4 racks of 32 radios) • ‘TD-LTE’ like PHY (20 MHz BW) • 1.2 – 6.0GHz Carrier (3.51GHz used) • Centralised MMSE, ZF and MRC/MRT MIMO Processing • Supports up to 12 User Clients (Full FPGA Processing) • 24 user clients (decimated processing)
- 5. IEEE SiPS 27th October 2016, Dallas Functional Overview 5 Distributed FPGA Processing with PCIe links Compact Computer
- 6. IEEE SiPS 27th October 2016, Dallas Linear Decoding/Precoding 6 • MGS Full QR Decomposition • Partial parallel systolic array • One detection matrix per 12 subcarrier resource block
- 7. IEEE SiPS 27th October 2016, Dallas MIMO Processor • Wide Data Path 128 x 12 Linear Detector • Computes 128 x 12 by 128 x 1 matrix vector multiply in 160 ns • 24 Million times per second 7 𝒚𝑾 𝑴𝑴𝑺𝑬 12 x 128 128 x 1 × = = 𝒖 12 x 1 32 x 1 (4)12 x 32 (4) 𝑾 𝑴𝑴𝑺𝑬 𝟎 𝑾 𝑴𝑴𝑺𝑬 𝟏 𝑾 𝑴𝑴𝑺𝑬 𝟐 𝑾 𝑴𝑴𝑺𝑬 𝟑 𝒚 𝟎 𝒚 𝟏 𝒚 𝟐 𝒚 𝟑
- 8. IEEE SiPS 27th October 2016, Dallas Frame Schedule 8
- 9. IEEE SiPS 27th October 2016, Dallas Initial Indoor Deployment 9 • 5.4m Linear Array with half- wavelength spacing • Client Separation 2.5 – 6 λ • Equal and fixed UE Tx Gains • “LOS” Conditions
- 10. IEEE SiPS 27th October 2016, Dallas Initial Indoor Deployment 10
- 11. IEEE SiPS 27th October 2016, Dallas CDF Plots of SVS 11 Scenario 1-3 in ascending order of LOS distance. 200ms capture interval for 3 minutes. Averaged across frequency. Exploitation of azimuth spread Closest scenario is the worst for 32 elements
- 12. IEEE SiPS 27th October 2016, Dallas 𝑯𝑯 𝑯 for 12 users with scaled N 12 Scenario 2 (12.5m Straight Line). 200ms capture interval for 3 minutes. Averaged across frequency and time.
- 13. IEEE SiPS 27th October 2016, Dallas Real-Time Channel Information 13 Eigen Structure Individual Spatial Stream Rx Magnitude Power Delay profiles Frequency Domain profiles Fading over the array caused by stairwell
- 14. IEEE SiPS 27th October 2016, Dallas 12 Streams of 256-QAM 14
- 15. IEEE SiPS 27th October 2016, Dallas 2nd Phase Deployment (11th May 2016) 15 24.8m 3.51 GHz Patch Array 24 UEs 2.5λ spacing
- 16. IEEE SiPS 27th October 2016, Dallas 22 User Gram Matrix 16
- 17. IEEE SiPS 27th October 2016, Dallas 22 Streams of 256-QAM • With the same frame structure as before this equates to 145.6 bits/s/Hz (uncoded sum rate of 2.915 Gbps) 17 User Inactive User Inactive 131 bits/s/Hz 144 bits/s/Hz 145.7 bits/s/Hz
- 18. IEEE SiPS 27th October 2016, Dallas Conclusions • Average ratio of composite channel gain (eigenvalue) to inter-user correlation observed to be 10 dB or more for a ratio of up to 6:1 basestation antennas to users • Azimuth dominated array configurations could improve close range LOS performance 18
- 19. IEEE SiPS 27th October 2016, Dallas Ongoing Work 19
- 20. IEEE SiPS 27th October 2016, Dallas Acknowledgements and Thanks to… • Post Graduate Students: Wael Boukley Hasan, Siming Zhang, Henry Brice & Benny Chitambira • Academic Colleagues & post graduates at Lund University: Steffen Malkowsky, Joao Vieira, Liang Liu, Ove Edfurs & Fredrik Tufvesson • Academic Colleagues at Bristol: Mark Beach, Andrew Nix, Evangelos Mellios, Angela Doufexi and Simon Armour • NI Staff: Karl Nieman, Nikhil Kundargi, Ian Wong, Leif Johansson & James Kimery 20
- 21. IEEE SiPS 27th October 2016, Dallas Thank You Any questions? Communication Systems and Networks Group University of Bristol, Bristol, UK http://www.bristol.ac.uk/engineering/research/csn/