Message-Passing Based Channel Estimation for Reconfigurable Intelligent Surface Assisted MIMO Hang Liu * , Xiaojun Yuan † , and Ying-Jun Angela Zhang * * Department of Information Engineering, The Chinese University of Hong Kong † Center for Intelligent Networking and Communications, University of Electronic Science and Technology of China H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 1 / 19
Presenter Hang Liu Department of Information Engineering The Chinese University of Hong Kong Email: lh117@ie.cuhk.edu.hk H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 2 / 19
Overview Reconfigurable Intelligent Surface (RIS) 1 RIS-Assisted MIMO Channel Estimation 2 Bayesian Inference and Result 3 H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 3 / 19
What is Reconfigurable Intelligent Surface (RIS) One of the key technologies to realize smart radio environments: • An artificial surface formed by a sub-wavelength array of sub-wavelength metallic or dielectric scattering particles; H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 4 / 19
What is Reconfigurable Intelligent Surface (RIS) Main features of RISs: • (Real-time) configurability: Can modify the direction of the reflected waves; • Low-power-consuming, nearly-passive, cheap; • Easily placed in/on the wall/ceilings. H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 5 / 19
RIS-Assisted MIMO • M -antenna Base station; • K (single-antenna) users; • An RIS to assisted the communication in between; • The RIS can be seen as an L -antenna uniform rectangular array; • Each RIS element induces an independent phase shift on the incident EM wave: ψ ( t ) � [ ̟ 1 ( t ) e jψ 1 ( t ) , ̟ 2 ( t ) e jψ 2 ( t ) , · · · , ̟ L ( t ) e jψ L ( t ) ] T ; ◦ ̟ l ( t ) ∈ { 0 , 1 } : ON/OFF; ◦ ψ l ( t ) ∈ [0 , 2 π ) : phase shift; RIS H RB h UR K , h UB K , h h h UR ,1 UR ,2 User K ,2 h UB UB ,1 BS User 2 User 1 H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 6 / 19
RIS-Assisted MIMO 1 By optimizing the phase shift vector ψ , received power scales as O ( L 2 ) for a large L . • Significant improvement compared to massive MIMO ( O ( M ) ); • To optimize ψ , channel state information is critical! 1Q. Wu and R. Zhang, ”Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming”. H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 7 / 19
RIS-Assisted MIMO Channels • User-to-BS direct channels: h UB,k ; • RIS-to-BS channel: H RB ; • User-to-RIS channels: h UR,k . RIS H RB h UR K , h , UB K h h ,1 ,2 h UR UR User K h UB ,2 ,1 BS UB User 2 User 1 H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 8 / 19
RIS-Assisted MIMO Channel Estimation • Dedicate T time slots for uplink RIS channel training ⇒ Training signal matrix X ∈ C K × T ; • Choose a constant RIS phase shift ⇒ ψ ( t ) = [1 , 1 , · · · , 1] • By turning off the RIS, h UB,k can be estimated by using conventional channel estimation methods for multiuser MIMO systems. H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 9 / 19
RIS-Assisted MIMO Channel Estimation Received Signal (after canceling the direct channels): Y = H RB H UR X + N ���� AWGNMatrix • Passive RIS has very limited signal processing capability; • Cascaded Channel Estimation : The BS estimates H RB and H UR from a noisy cascade of them. H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 10 / 19
RIS-to-BS Channel Model • Block Fading Channel Model: Channel coefficients remain invariant within the coherence time; • Channel coherence time is determined by the mobility of the mobile ends (e.g., the users); • BS and RIS rarely move after deployment ⇒ quasi-static end-to-end MIMO channel; H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 11 / 19
RIS-to-BS Channel Model • (Most of) the channel components (from static scattering clusters) evolves much more slowly ⇒ slow-varying channel components ; • The remaining ones (from non-static clusters) are fast-varying channel components ; � � ¯ 1 � κ • Rician fading model: H RB = + H RB H RB κ +1 κ +1 � �� � � �� � slow − varying fast − varying User-to-RIS Channels BS User-to-BS Channels Slow-Varying Components in RIS-to-BS Channel Fast-Varying Components in RIS-to-BS Channel Moving Scatterer Static Scatterers User K User 1 RIS H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 12 / 19
User-to-RIS Channel 2 • Far-field, Non-LOS channels have sparse coefficients in the angular domain specified by the angular response due to - Limited number of scattering clusters; - Limited angular spread; Figure: Illustration of limited Figure: Sparse Coefficient under the scatterers. DFT angular response. 2Figures from H. Xie et al., ”Channel estimation for TDD/FDD massive MIMO systems with channel covariance computing,” and J. Zhang et al. ”Blind signal detection in massive MIMO: Exploiting the channel sparsity”. H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 13 / 19
Problem Formulation Given two (over-complete) angular array response A B and A R • H UR = A R G with sparse G • � H RB = A B SA H R with sparse S ⇒ Y = H RB H UR X + N , = ( H 0 + A B SR ) GX + N . H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 14 / 19
Problem Formulation Y = ( H 0 + A B SR ) GX + N . • H 0 , A B , R , X , and Y are known; • S and G are two sparse matrices to be estimated; Linear regression Y = ZX + N ; Joint task of Sparse matrix factorization Z = WG ; Matrix calibration W = H 0 + A B SR ; H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 15 / 19
Bayesian Inference • Model the channel estimation problem by the Bayesian inference framework; • The MMSE estimator is given by the mean of the marginal posteriors; • Approximate the MMSE estimator by performing message passing over the associated factor graph; ws w zwg g p g ( ) ml mk ml lk lk ( ) s p s z qz q p y ( | q ) m l ' ' m l ' ' mk mt mt mt mt t l ' m l ' m k • Introduce Gaussian approximations to reduce complexity; • Refer to https://arxiv.org/abs/1912.09025 for more details. H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 16 / 19
Simulation Result 3 The Proposed Algorithm Concatenate LR The Proposed Algorithm Concatenate LR Oracle Bound Sequential Channel Estimation Oracle Bound Sequential Channel Estimation 0 0 0 0 Ave. NMSE of h UR , k (dB) Ave. NMSE of h UR , k (dB) NMSE of H RB (dB) NMSE of H RB (dB) -10 -10 -10 -10 -20 -20 -30 -30 -20 -20 70 80 90 100 110 70 80 90 100 110 1 1.5 2 2.5 1 1.5 2 2.5 1 / τ N (dB) 1 / τ N (dB) η η Figure: Normalize MSEs versus the Figure: Normalize MSEs versus the inverse of the noise power. array response sampling resolution. 3The baseline algorithm (the green curves) is from Q.-U.-A. Nadeem et al., ”Intelligent reflecting surface assisted multi-user MISO communication”. H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 17 / 19
Conclusions • RIS-assisted MIMO systems and channel models; • Cascaded channel estimation formulation; • Bayesian inference algorithm and Gaussian approximations; H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 18 / 19
Thank you H. Liu, X. Yuan, Y. J. Zhang CE for RIS-Assisted MIMO June 8th, 2020 19 / 19
Recommend
More recommend
