Statistical CSIT Aided User Scheduling for Broadcast MU-MISO System

Recent studies show that the statistical channel state information (SCSI) helps to largely increase the capacity of communication systems when the instantaneous perfect CSI is unavailable. In this paper, we consider multiuser multiple-input-single-output broadcast channels where the transmitter has the knowledge of SCSI. The major issue of concern in our paper is to improve the average group-rate of the whole system by scheduling users over different time slots. With SCSI at the transmitter side, we are able to precode signals and, hence, compute the theoretical achievable group-rate of arbitrary user groups. Based on the group-rates, we propose tier-2 Munkres user scheduling algorithm (T2-MUSA) that leads to higher average group-rate than existing algorithms with generally better fairness. The optimality of the proposed algorithm in energy-fair user scheduling space is proved and we derive a lower bound of a special case to verify the validity of our simulations. In addition, many conventional user scheduling algorithms maintain queue stability by solving a weighted sum-rate (WSR) problem, using queue lengths to represent weight coefficients. Inspired by T2-MUSA, we propose a QoS-based MUSA (QB-MUSA) aimed at stabilizing queue lengths and maximizing throughput. In results, we show that QB-MUSA exhibits higher throughput than the conventional WSR-based algorithm.

[1]  Wolfgang Utschick,et al.  Stochastic transceiver design in multi-antenna channels with statistical channel state information , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Giuseppe Caire,et al.  Joint Beamforming and Scheduling for a Multi-Antenna Downlink with Imperfect Transmitter Channel Knowledge , 2007, IEEE Journal on Selected Areas in Communications.

[3]  Andrea J. Goldsmith,et al.  Dirty-paper coding versus TDMA for MIMO Broadcast channels , 2005, IEEE Transactions on Information Theory.

[4]  Preben E. Mogensen,et al.  A stochastic MIMO radio channel model with experimental validation , 2002, IEEE J. Sel. Areas Commun..

[5]  Cheng Wang,et al.  Adaptive downlink multi-user MIMO wireless systems for correlated channels with imperfect CSI , 2006, IEEE Transactions on Wireless Communications.

[6]  ZVI GALIL,et al.  Efficient algorithms for finding maximum matching in graphs , 1986, CSUR.

[7]  Mahesh K. Varanasi,et al.  Optimal Spatial Correlations for the Noncoherent MIMO Rayleigh Fading Channel , 2007, IEEE Transactions on Wireless Communications.

[8]  J. Munkres ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .

[9]  M. J. Gans,et al.  On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas , 1998, Wirel. Pers. Commun..

[10]  Michael Joham,et al.  QoS constrained power minimization in the MISO broadcast channel with imperfect CSI , 2015, Signal Process..

[11]  Akbar M. Sayeed,et al.  Semiunitary Precoding for Spatially Correlated MIMO Channels , 2011, IEEE Transactions on Information Theory.

[12]  Yuan Zhang,et al.  User assignment for MU-MIMO downlink system based on Munkres algorithm , 2013, 2013 International Conference on Wireless Communications and Signal Processing.

[13]  Yong-Hwan Lee,et al.  Effect of transmit correlation on the sum-rate capacity of two-user broadcast channels , 2009, IEEE Transactions on Communications.

[14]  Emre Telatar,et al.  Capacity of Multi-antenna Gaussian Channels , 1999, Eur. Trans. Telecommun..

[15]  David Gesbert,et al.  Precoding Methods for the MISO Broadcast Channel with Delayed CSIT , 2012, IEEE Transactions on Wireless Communications.

[16]  John M. Cioffi,et al.  Transmit power optimization for Gaussian vector broadcast channels , 2005, IEEE International Conference on Communications, 2005. ICC 2005. 2005.

[17]  Andrea J. Goldsmith,et al.  On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming , 2006, IEEE Journal on Selected Areas in Communications.

[18]  Bruno Clerckx,et al.  AMMSE optimization for multiuser MISO systems with imperfect CSIT and perfect CSIR , 2014, 2014 IEEE Global Communications Conference.

[19]  Bruno Clerckx,et al.  Transmit Beamforming for MISO Broadcast Channels With Statistical and Delayed CSIT , 2015, IEEE Transactions on Communications.

[20]  Bruno Clerckx,et al.  MU-MIMO with Channel Statistics-Based Codebooks in Spatially Correlated Channels , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[21]  Michail Matthaiou,et al.  Precoder Design for Multiuser MISO Systems Exploiting Statistical and Outdated CSIT , 2013, IEEE Transactions on Communications.

[22]  Holger Boche,et al.  Solution of the multiuser downlink beamforming problem with individual SINR constraints , 2004, IEEE Transactions on Vehicular Technology.

[23]  Raj Jain,et al.  A Quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems , 1998, ArXiv.

[24]  Stephen V. Hanly,et al.  Statistical Beamforming on the Grassmann Manifold for the Two-User Broadcast Channel , 2011, IEEE Transactions on Information Theory.

[25]  Chen Sun,et al.  User Scheduling Algorithms for Downlink MU-MIMO System Based on the SCSI , 2013, IEICE Trans. Commun..

[26]  Shi Jin,et al.  Statistical eigenmode SDMA transmission for a two-user downlink , 2012, 2012 IEEE International Conference on Communications (ICC).

[27]  Ernst Bonek,et al.  A stochastic MIMO channel model with joint correlation of both link ends , 2006, IEEE Transactions on Wireless Communications.

[28]  François Bourgeois,et al.  An extension of the Munkres algorithm for the assignment problem to rectangular matrices , 1971, CACM.

[29]  Matthew R. McKay,et al.  Transmit Designs for the MIMO Broadcast Channel With Statistical CSI , 2014, IEEE Transactions on Signal Processing.

[30]  Nihar Jindal MIMO broadcast channels with finite rate feedback , 2005, GLOBECOM.

[31]  Milton Abramowitz,et al.  Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables , 1964 .

[32]  Mohammad Ali Maddah-Ali,et al.  Completely Stale Transmitter Channel State Information is Still Very Useful , 2010, IEEE Transactions on Information Theory.