2. R2 R3 R4 R5 AP The throughput does not grow in the same way as wireless demands Limited wireless spectrum & unlimited user demands AP R1 R6

3. Vision: APs grow in a distributed fashion AP1 R1 AP2 R2 AP3 R3 Separated Access points Multiple wireless devices The whole system works as if it were a single big MIMO system Same frequency band

4. Overview  Intuition & Challenges  Mathematical concepts  Implementation & experiments  Limitation  Conclusion

5. Intuition & ChallengesIntuition & Challenges AP2 R2 AP1 R1 AP R Traditional MIMO Distributed MIMO Different devices sitting on different boards, with different oscillators. The phase can not be tightly synchronized. To fall back equivalent classical MIMO, frequency offset is the main challenge.

6. Mathematical conceptsMathematical concepts  Some theoretical works  S. Aeron and V. Saligrama. Wireless ad hoc networks: Strategies and scaling laws for the fixed SNR regime. IEEE Transactions on Inf. Theor., 53(6), 2007.  O. Simeone, O. Somekh, H. Poor, and S. Shamai. Distributed MIMO in multi-cell wireless systems via finite-capacity links. ISCCSP, 2008.  A. Ozgur, O. Leveque, and D. Tse. Hierarchical cooperation achieves optimal capacity scaling in ad hoc networks. IEEE Trans. on Info. Theor., 2007.  K. Tan, H. Liu, J. Fang, W. Wang, J. Zhang, M. Chen, and G. M. Voelker. SAM: enabling practical spatial multiple access in wireless LAN. ACM MobiCom, 2009.

7. Mathematical conceptsMathematical concepts AP1 R1 AP2 R2 x1x1 x2x2 h 11 h 21 h 12 h 22 y1y1 y2y2 ω T1 ω T2 ω R1 ω R2

8. Mathematical conceptsMathematical concepts AP1 R1 AP2 R2 x1x1 x2x2 h 11 h 21 h 12 h 22 y1y1 y2y2 ω T1 ω T2 ω R1 ω R2

9. Mathematical conceptsMathematical concepts AP1 R1 AP2 R2 x2x2 h 11 h 21 h 12 h 22 y2y2 ω T1 ω T2 ω R1 ω R2 x1x1 y1y1

10. Physical meaning & ImplementationPhysical meaning & Implementation AP1 R1 AP2 R2 x2x2 h 11 h 21 h 12 h 22 y2y2 ω T1 ω T2 ω R1 ω R2 x1x1 y1y1 Transmitter 2 measures the frequency offset w. r. t. transmitter 1 Need channel measurement to perform beamforming Transmitters 1,2 then transmit signal

11. Physical meaning & ImplementationPhysical meaning & Implementation AP1 R1 AP2 R2 x2x2 h 11 h 21 h 12 h 22 y2y2 ω T1 ω T2 ω R1 ω R2 x1x1 y1y1 Receivers 1, 2 need to measure the frequency offset w. r. t. transmitter 1, then compensate the effect in decoding stage Notice that the matrix left now is diagonal, meaning there is no interference

12. Actual implementation and protocolActual implementation and protocol  H. Rahul, S. Kumar, D. Katabi. MegaMIMO: Scaling Wireless Capacity with User Demands. ACM SIGCOMM, 2012. AP1 R1 AP2 R2 x2x2 h 11 h 21 h 12 h 22 y2y2 ω T1 ω T2 ω R1 ω R2 x1x1 y1y1 AP1 Syn AP2 Syn R1 CFO R2 CFO h j1 est h j2 est h j1 est h j2 est … Multiple data transmission

13. Evaluation & LimitationEvaluation & Limitation  From theoretical work derivation, the throughput gain increase as N log SNR, where N is total number of antennas on independent APs.  Empirically, it is demonstrated that 10 mobile devices using 10 APs can obtain 8~9x throughput gain.  The limitation is mainly due to the synchronization error. One core measurement is interference noise ratio, where transmitters send null signals. The signal above noise flow is due to interference. It is shown that 10 APs have roughly 1.5dB interference noise.

14. Related workRelated work  S. Aeron and V. Saligrama. Wireless ad hoc networks: Strategies and scaling laws for the fixed SNR regime. IEEE Transactions on Inf. Theor., 53(6), 2007.  O. Simeone, O. Somekh, H. Poor, and S. Shamai. Distributed MIMO in multi-cell wireless systems via finite-capacity links. ISCCSP, 2008.  A. Ozgur, O. Leveque, and D. Tse. Hierarchical cooperation achieves optimal capacity scaling in ad hoc networks. IEEE Trans. on Info. Theor., 2007.  K. Tan, H. Liu, J. Fang, W. Wang, J. Zhang, M. Chen, and G. M. Voelker. SAM: enabling practical spatial multiple access in wireless LAN. ACM MobiCom, 2009.  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. NSDI, 2009.  H. Rahul, S. Kumar, D. Katabi. MegaMIMO: Scaling Wireless Capacity with User Demands. ACM SIGCOMM, 2012.