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CNIT-POLIMI: technical expertise and people in Dep. 1

CNIT-POLIMI: technical expertise and people in Dep. 1. Researchers: Umberto Spagnolini (spagnoli@elet.polimi.it) Arnaldo Spalvieri (spalvier@elet.polimi.it) Monica Nicoli (nicoli@elet.polimi.it) Maurizio Magarini (magarini@elet.polimi.it) PhD students: Osvaldo Simeone (simeone@elet.polimi)

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CNIT-POLIMI: technical expertise and people in Dep. 1

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  1. CNIT-POLIMI: technical expertise and people in Dep. 1 Researchers: Umberto Spagnolini (spagnoli@elet.polimi.it) Arnaldo Spalvieri (spalvier@elet.polimi.it) Monica Nicoli (nicoli@elet.polimi.it) Maurizio Magarini (magarini@elet.polimi.it) PhD students: Osvaldo Simeone (simeone@elet.polimi) Roberto Bosisio (bosisio@elet.polimi.it) Stefano Savazzi (savazzi@elet.polimi.it) Matteo Albanese (albanese @elet.polimi.it) Technical expertise: channel estimation and equalization; coding techniques and soft-decision decoding algorithms; soft-iterative receiver structures; advanced signal processing for MIMO/OFDM wireless systems; FPGA implementation of coding and equalization algorithms

  2. Subspace methods for channel estimation and tracking • Technical background: • Subspace methods for channel estimation, tracking and equalization of time-varying space-time structured channels in multiple-antenna OFDM receivers. • Ongoing cooperations with other universities: • 1. Signals and Systems - University of Uppsala (within Dep. 1 and Project C):adaptive transmission in MIMO-OFDM systems using subspace-based channel tracking (6-month exchange of 1 PhD student). • 2. CWC - University of Oulu (not within NEWCOM): soft-iterative SIMO/MIMO subspace-based receivers with soft-based channel estimation and synchronization (6-month exchange of 1 Master student). • Offers for cooperations: CNIT-Polimi is open to cooperations/exchange of researchers • Contact persons: R. Bosisio, M. Nicoli, S. Savazzi, O. Simeone, U. Spagnolini • [1] M. Nicoli and U. Spagnolini, "Subspace-methods for space-time processing," in Smart Antennas State of the Art, Chapter 1: Receiver Processing, EURASIP-Book series, Hindawi Publ. Corp., to be published, 2005. • [2] M. Cicerone , O. Simeone, N. Geng , U. Spagnolini, “Modal analysis/filtering to estimate time-varying MIMO-OFDM channels,” ITG Workshop on Smart Antennas (WSA), March 18-19, Munich, 2004 (journal paper submitted). • [3] R. Bosisio, M. Nicoli and U. Spagnolini, "Kalman filter of channel modes in time-varying wireless systems," Proc. IEEE ICASSP 2005 (journal paper submitted).

  3. Reduced complexity detection schemes for MIMO-OFDM systems Technical background: Extension of the mapping by set partitioning principle to develop a low complexity detection scheme for flat-fading MIMO systems with spatial multiplexing [4] Offers for cooperation: Design of efficient reduced complexity detection schemes for MIMO-OFDM systems. The starting point is represented by suboptimal architectures that are proposed for flat-fading MIMO systems. Contact persons:Arnaldo Spalvieri, Maurizio Magarini [4] M. Magarini, A. Spalvieri, “A suboptimal detection scheme for MIMO systems with non-binary constellations,” IEEE PIMRC 2004.

  4. Subspace methods for channel estimation and tracking CNIT-POLIMI

  5. Multipath channel model NR NT = [ ] * E β β φ ( n ) - d, d, n d l l Fading amplitudes Power profile Antenna arrays gains Delay profile Space-Time channel matrix: Clarke model

  6. Channel modal analysis Rearrangement of the parameters to separate slow/fast varying components: Angles/delays Fast-fading Subspace channel model: Modal amplitudes ( ) = b = b = H h T U ΣV Ud l l l l with Space-time modes Adaptive channel estimation based on the subspace model (MODAL ESTIMATION) • Slow tracking of the space-time channel modes by subspace-tracking algorithms • Fast tracking of the modal fading amplitudes by Kalman Filter tracking

  7. Simulation results MSE vs. SNR ( ) 50 LEAST SQUARES 40 MODAL ESTIMATION 30 20 10 MSE [dB] LOWER BOUND 0 -10 -20 -30 -40 -30 -20 -10 0 10 20 SNR [dB]

  8. Efficient modal-amplitude tracking Simplified Kalman method: it decouples channel tracking into a set of independent tracking algorithms, one for each modal amplitude. -10 MSE vs. SNR for different algorithms ( ) LMS -20 MSE [dB] WIENER LMS -30 KALMAN KALMAN SIMPLIFIED -40 0 5 10 15 20 25 30 35 40 SNR [dB]

  9. A suboptimal detection scheme for MIMO systems with non-binary constellations CNIT-POLIMI

  10. Maximum Likelihood Detector (MLD) In a flat-fading MIMO transmission systems, the ML detector performs the estimation of the transmitted signal vector according to By denoting as q the size of the scalar QAM constellation transmitted from each antenna and N the number of transmitting antennas, an exhaustive search over a total of qN is required The complexity can be prohibitively extensive when N and q are high

  11. Proposed detector Real and the imaginary part of each QAM symbol belongs to the integer set ℤ The binary partition ℤ/2ℤ is considered in each dimension of the QAM constellation for each transmitted substream A list of 22N candidate subsets is generated by considering the 22N combinations of LSB’s for the N entries of the transmitted vector ã Let q=22k be the size of the scalar QAM constellation The V-BLAST can be applied to perform the detection in each of these 22N subsets each containing N22(k-1)points At each stage the detector examines the decision statistic for the symbol sent from antenna n and compares it with the candidate symbols that are drawn from the current subset associated to substream n At the end of the procedure a list of 22N candidate vectors is generated

  12. Proposed detector A final decision is taken by applying the MLD to this reduced set where Ar is the reduced set containing the 22N candidate vectors The complexity of the MLD on the reduced set of candidate vectors is independent of the size of the constellation in use

  13. Simulation results

  14. Conclusions Main contribution Extension of the mapping by set partitioning principle, used in RSSD, to the development of suboptimal receivers in spatial multiplexing systems Performance of the resulting suboptimal receiver is close to that of the Maximum Likelihood (ML) receiver for low-to-intermediate SNR Offers for cooperation: Design of efficient reduced complexity detection schemes for MIMO-OFDM systems. The starting point is represented by suboptimal architectures that are proposed for flat-fading MIMO systems. Contact persons: Arnaldo Spalvieri (spalvier@elet.polimi.it) Maurizio Magarini (magarini@elet.polimi.it)

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