Tsinghua National Laboratory for Information Science and Technology, Spectrum-Efficient Superimposed Pilot Design Based on Structured Compressive Sensing for Downlink Large-Scale MIMO Systems Linglong Dai, Zhen Gao, Zhaocheng Wang, and Zhixing Yang Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China 2014-08-23

Contents 1 Technical Background 2 Proposed Solution 3 Simulation Results 4 Conclusions

5G and its key technologies Key requirement of 5G: ~1000 folds of increase in data traffic Three main directions

Technical background of 5G - Large-scale MIMO What is large-scale MIMO and why ? Use hundreds of antennas at the BS to simultaneously serve a set of users Increase the spectral and power efficiency by orders of magnitude Conventional MIMO M:2~8, K:1~4 (LTE-A) Massive MIMO M: ~100~1000, K: 16~64 T. L. Marzetta, “Non-cooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas,” IEEE Transactions on Wireless Communications, vol. 9, no. 11, pp. 3590-3599, Nov. 2010. (2013 IEEE Marconi Prize)

Technical background of 5G - Large-scale MIMO Feb 2012, Rice university & Bell labs, Argos, 64 antennas, 15 users, 85 bit/s/Hz, 1/64 power consumption Sep 2013, Rice university & Bell labs, ArgosV2, 96 antennas, 32 users July 2013, Linköping & Lund University, 128 antennas, 36 users

Challenges for large-scale MIMO Prospective Considered as the promising key technology for 5G [Vargas’13] Challenges [Rusek’13] Channel measurement and modeling Design of large antenna arrays Pilot pattern design under pilot contamination Channel feedback Low-complexity precoding/signal detection

Orthogonal pilots in conventional MIMO systems What will happen if Tx=128 Orthogonal pilots (Tx=2) for LTE/LTE-A New theory Different channels are distinguished by orthogonal pilots 7

Compressive sensing (CS) Sparse signals with high dimension can be perfectly reconstructed from measurements with low dimension by solving an optimization problem

Application of compressive sensing A new fundamental theory against the conventional Shannon-Nyquist theory Successful application in many fields Single pixel camera(Rice University) MRI (MIT) Hyper spectral imager (Yale University) DNA array sensor (UIUC) CS for wireless networks Sparse channel estimation positioning Multiple access Cognitive radios 9

Application of compressive sensing Example of CS application CS Image separation Image is sparse in a certain domain

Contents 1 Technical Background 2 Proposed Solution 3 Simulation Results 4 Conclusions

Spatial-temporal correlation of MIMO channels Common Sparse Support Common sparsity holds for different CIRs for adjacent transmit antennas in several adjacent time slots

Non-orthogonal pilots at the transmitter Conventional orthogonal pilots： Proposed superimposed pilots (non-orthogonal)：

Simultaneous channel reconstruction at the receiver Received pilots Channel is sparse !

Simultaneous channel reconstruction at the receiver Structured CS exploits spatial-temporal correlation of MIMO channels to improve the channel estimation accuracy and reduce the pilot overhead Adjacent antennas Adjacent time slots

Proposed structured SP (SSP) algorithm

Contents 1 Technical Background 2 Proposed Solution 3 Simulation Results 4 Conclusions

Simulation Results Simulation setup Transmit antennas: N=64 OFDM size: 4096 CP length: 256 System bandwidth: 10MHz Channel model: ITU-VB Pilot number: 800 Overall pilot overhead: 19.53% 平均每发射天线需12.5导频！平均仅占0.31%！

Simulation Results Pilot number per antenna: 12.5! Only 0.31%!

Contents 1 Technical Background 2 Proposed Solution 3 Simulation Results 4 Conclusions

Conclusions This paper focuses on the downlink training and channel estimation for large-scale MIMO systems. In contrast to standardized orthogonal pilots with the prohibitive overhead increasing with the number of transmit antennas, the proposed superimposed pilot design based on structured CS can efficiently solve the pilot overhead problem. At the receiver, the proposed SSP algorithm can exploit the spatial and temporal correlations of large-scale MIMO channels for simultaneous recovery of multiple channels. Moreover, the proposed superimposed pilot design and the corresponding channel estimator can be applied in the uplink too, and conventional small-scale MIMO can also adopt the proposed scheme to reduce the pilot overhead and improve the channel estimation performance.