Research: from academia to industry Jinliang Huang 2017-04-27 AAA

Jinliang Huang Academia Industry Industry What is research like M.Sc. 1999 B.S. Zhejiang University 2003 M.Sc. KTH 2005 Ph.D. 2009 Ericsson 2014 Huawei Academia Industry Industry What is research like

Content Introduction to Huawei Sweden Algorithm Group Research in industry Examples Link adaptation Channel estimation MMSE Equalization CoMP (Coordinated Multiple-Point) reception

The Algorithm Group Huawei Sweden Gunnar Peters Technical lead 5G RRM RRM architecture Machine Learning (Pablo Soldati, KTH) 4G RRM Coordinated scheduling Small packet optimization (Xiaojia Lu, CWC Oulo) 5G Baseband Receiver L1 algorithms Mac / Phy codesign (Jinliang Huang, KTH)

Research in industry Performance VS. Complexity Implementaion friendly Optimal algorithm/performance is always too complicated Implementaion friendly SW: low computational efficiency HW: low flexibility Priori information of parameter Statistics assumptions Stability Stable performance/complexity Academia Industry

Interview Q: What do you think is the biggest difference between academia and industry? ”Complexity is an important factor that always needs to be considered in industry.” Ahmet

Link adaptation in Uplink How to approach Shannon capacity? MCS selection Receiver design Have we achieved the Shannon capacity? No! Why is link adaptation difficult? Actual operating area

Link adaptation in Uplink Ideal link adaptation Doppler spread = 20Hz Noise limited Time of channel estimation Time of transmission Procedure: Channel est/pred Post equalizer SINR estimation MCS selection AWGN Constraints Doppler spread Frequency selective scheduling Resource for channel pred. SINR estimation error Quantization in MCS 10% Post eq. SINR

Link adaptation in Uplink Ideal link adaptation cont’d Interference limited, MMSE equalizer Constraints ... ... Unknown interference Known interference: complexity SINR estimation is sensitive to phase error Solution: outer loop link adaptaion

Receiver design What are we interested in today? Channel estimation Equalization Demapper +decoder

Channel Estimation Pilot allocation

Channel Estimation Frequency domain processing

Channel Estimation MMSE-based method[1]: Long term estimation [1] J.-J. van de Beek, O. Edfors, M. Sandell, S. Wilson, P. Borjesson, "On channel estimation in ofdm systems", vol. 2, pp. 815-819, jul. 1995.

Channel Estimation ZF + Transformed domain + windowing IFFT

Channel estimation Time domain processing Wiener filter Doppler spread data symbols Wiener filter Estimation error Doppler spread Limited data in frequency Limited data in time

MMSE equalization Multi-antenna combining Receiving BS Serving UE #1 Neighboring UE #3 Neighboring UE #2 Multi-antenna combining signal Interference Coordinated cells Non-coordinated cells Information exchange More channel estimation MIMO processing No information exchange Less channel estimation SIMO processing

Is MU-MIMO really good in UL? Total capacity UE #2 performance MLD is not possible Multi-user interference due to linear equalizer Receiving BS MU-MIMO SIMO Serving UE #1 Serving UE #2 Serving UE #3 Coverage issue Celll average performance

CoMP reception To improve the coverage MMSE equalizer interference noise To improve the coverage MMSE equalizer interference

CoMP reception In ideal situation The more, the better! Post SINR: In case The more, the better!

CoMP reception In real situation Constraint: Unbalanced SNR In real situation Constraint: joint detection is dependent on channel estimation

Key Takeaways Find out the physical constraints Be careful with the assumptions Know the tradeoff between performance and complexity

Any questions? Jinliang.huang@huawei.com Skalholtsgatan 9, 164 40 Kista, Sweden

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