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Dirty RF Impact on Interference Alignment

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Presentation on theme: "Dirty RF Impact on Interference Alignment"— Presentation transcript:

1 Dirty RF Impact on Interference Alignment
Per Zetterberg

2 Outline Goal Approach Interference-alignment and CoMP Implementation
Results Impairment-modeling (closening the gap theory-simulation) Conclusion

3 Goal New interesting and challenging techniques
Assumption: Results are general. Robust approaches Impairment- modeling FER EVM SINR Match! Detailed simulation Testbed Measurements (USRP) Channels

4 Approach Impairment model PC Basic simulation Test-bed Measurements
(USRP) Impairment- modeling Basic simulation Impairment model Transmitter Spectrum analyzer PC

5 Interference alignment
Cadambe/Jafar, ”Interference Alignment and Degrees of Freedom of the K-User Interference Channel”, IEEE Trans, Information Theory 2008. K-transmitters and K-receivers, K-links: K/2 simultaneous interference-free links. Requires coding over multiple channel realizations. Global channel knowledge required.

6 Interference-alignment incarnations
In frequency-domain: Something new –will be studied later. In antenna-domain: Co-ordinated beam-forming.

7 Co-ordinated Multi-Point CoMP

8 Implementation IA Feedback: Wired ethernet 𝒖 1 MS 1 BS 1 𝒗 1 𝒖 2 BS 2
𝒗 2 𝒖 3 MS 3 𝒗 3 BS 3

9 Implementation: CoMP Feedback: Wired ethernet 𝒖 1 MS 1 BS 1 𝒗 1 𝒖 2
𝒗 2 𝒖 3 MS 3 𝒗 3 BS 3

10 Beamformer SNIR 𝑘 = 𝒖 𝑘 ∗ 𝑯 𝑘,𝑘 𝒗 𝑘 𝒗 𝑘 ∗ 𝑯 𝑘,𝑘 ∗ 𝒖 𝑘 𝑛≠𝑘 𝒖 𝑘 ∗ 𝑯 𝑘,𝑛 𝒗 𝑛 𝒗 𝑛 ∗ 𝑯 𝑘,𝑛 ∗ 𝒖 𝑘 = Formulate virtual uplink SINR. Iterate “Approaching the Capacity of Wireless Networks through Distributed Interference Alignment", by Krishna Gomadam, Viveck R. Cadambe and Syed A. Jafar.

11 Frames Payload Payload 10 OFDM symbols 10 OFDM symbols
Demodulation reference signals CSI reference signals Payload 10 OFDM symbols Payload 10 OFDM symbols MS feed-back CSI to BS1. BS1 calculate beam-formers. BS1 sends weights to BS2, BS3. BS1-BS3 frequency locked. 38 subcarriers, 312.5kHz carrier-spacing QPSK, …., 256QAM 0.25, 0.5, 0.75 –rate LDPC codes

12 Preliminary Results 16QAM, 0.75 rate coded. 432 frames transmitted.
FER IA CoMP Uncoded 63% 9% Coded 19% 0%

13 How far from ideal ?

14 Power-Amplifier Non-linearity
OFDM signals: + 𝑠 𝑡 n 𝑡 y 𝑡 =𝐴𝑠 𝑡 +n(t) Modeled as noise: D Dardari, V. Tralli, A Vaccari “A theoretical characterization of nonlinear distortion effects in OFDM systems“, IEEE Trans. Comm., Oct 2000.

15 MIMO case OFDM signals: Correlation ? n 𝑡 𝑠 1 𝑡 +
𝑠 1 𝑡 n 𝑡 𝑦 1 𝑡 = 𝐴 1 𝑠 1 𝑡 + 𝑛 1 (t) 𝑦 2 𝑡 = 𝐴 2 𝑠 2 𝑡 + 𝑛 2 (t) 𝑠 2 𝑡 Correlation ?

16 Phase-noise Modeled as additive noise + CPE
BPF LNA LPF A/D CPE: Slowly varying between symbols y 𝑡 = exp 𝑗 𝜃 𝑡 𝑠 1 𝑡 + 𝑛 1 (t) R. Corvaja, E. Costa, and S. Pupolin, “M-QAM-OFDM system performance in the presence of a nonlinear amplifier and phase noise, IEEE Trans. Comm

17 CoMP Results Without impairment model With impairment model

18 IA Results Without impairment model With impairment model

19 Conclusion: What will be answered?
How much worse is IA practice than in theory? What practical impairments need to be modeled? (==> can lead to improved robust designs) Is IA still worthwhile with impairments compared to base-lines ? Other outputs Software environments that can be re-used (commodity hardware) Increased understanding of software and hardware issues and implementations in our research community and our PhDs in particular. Course-work for the above.

20 Structure of model RX TX TX RX Channel TX- impairment RX TX

21 Our Hardware GPS USRP N210 Sample-rate: 100MHz Streaming: 25MHz
PPS, 10MHz USRP N210 Sample-rate: 100MHz Streaming: 25MHz Gbit-Ethernet PC Linux We have 18 USRPs

22 The 4Multi Software FrameWork
(Multi-Antenna, Multi-User, Multi-Cell, Multi-Band) Send data in small bursts (relaxes computational load) Nodes synchronized by external trigering (PPS) The implementor (basically) only need to program three functions node::init, node::process and node::end_of_run. Simulate the system using “simulate” generic function. Everything that can be compiled with gcc can run (e.g IT++) Toolbox with coding&modulation. Store _all_ received signals for post-processing. Vision: “The coding should be as easy as performing ordinary (but detailed) desktop simulations”

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