Entropy-based active learning for wireless scheduling with incomplete channel feedback

Most of the opportunistic scheduling algorithms in literature assume that full wireless channel state information (CSI) is available for the scheduler. However, in practice obtaining full CSI may introduce a significant overhead. In this paper, we present a learning-based scheduling algorithm which operates with partial CSI under general wireless channel conditions. The proposed algorithm predicts the instantaneous channel rates by employing a Bayesian approach and using Gaussian process regression. It quantifies the uncertainty in the predictions by adopting an entropy measure from information theory and integrates the uncertainty to the decision-making process. It is analytically proven that the proposed algorithm achieves an ź fraction of the full rate region that can be achieved only when full CSI is available. Numerical analysis conducted for a CDMA based cellular network operating with high data rate (HDR) protocol, demonstrate that the full rate region can be achieved our proposed algorithm by probing less than 50% of all user channels.

[1]  Mohamed-Slim Alouini,et al.  A Threshold-Based Channel State Feedback Algorithm for Modern Cellular Systems , 2007, IEEE Transactions on Wireless Communications.

[2]  Aditya Gopalan,et al.  Low-delay wireless scheduling with partial channel-state information , 2012, 2012 Proceedings IEEE INFOCOM.

[3]  Randall Berry,et al.  Opportunistic splitting algorithms for wireless networks , 2004, IEEE INFOCOM 2004.

[4]  H. Hallen,et al.  Long Range Prediction of Fading Signals : Enabling Adaptive Transmission for Mobile Radio Channels , 2000 .

[5]  M. Andrews,et al.  Scheduling Over Nonstationary Wireless Channels With Finite Rate Sets , 2006, IEEE/ACM Transactions on Networking.

[6]  Juan José Murillo-Fuentes,et al.  Gaussian Processes for Nonlinear Signal Processing , 2013, ArXiv.

[7]  Andreas Krause,et al.  Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies , 2008, J. Mach. Learn. Res..

[8]  Atilla Eryilmaz,et al.  Scheduling with rate adaptation under incomplete knowledge of channel/estimator statistics , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[9]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[10]  Juan José Murillo-Fuentes,et al.  Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances , 2013, IEEE Signal Processing Magazine.

[11]  Michael L. Littman,et al.  Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach , 1993, NIPS.

[12]  Qing Zhao,et al.  Distributed Learning in Multi-Armed Bandit With Multiple Players , 2009, IEEE Transactions on Signal Processing.

[13]  R. Srikant,et al.  Stable scheduling policies for fading wireless channels , 2005, IEEE/ACM Transactions on Networking.

[14]  Michael J. Neely,et al.  Max weight learning algorithms with application to scheduling in unknown environments , 2009, 2009 Information Theory and Applications Workshop.

[15]  Rajeev Agrawal,et al.  Joint scheduling and resource allocation in uplink OFDM systems for broadband wireless access networks , 2009, IEEE Journal on Selected Areas in Communications.

[16]  Leandros Tassiulas,et al.  Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks , 1992 .

[17]  Jeffrey G. Andrews,et al.  Rethinking information theory for mobile ad hoc networks , 2007, IEEE Communications Magazine.

[18]  Robert W. Heath,et al.  Opportunistic feedback for downlink multiuser diversity , 2005, IEEE Communications Letters.

[19]  Holger Boche,et al.  Joint Opportunistic Scheduling and Selective Channel Feedback , 2013, IEEE Transactions on Wireless Communications.

[20]  Alexandra Duel-Hallen,et al.  Fading Channel Prediction for Mobile Radio Adaptive Transmission Systems , 2007, Proceedings of the IEEE.

[21]  Mehdi Bennis,et al.  A Q-learning based approach to interference avoidance in self-organized femtocell networks , 2010, 2010 IEEE Globecom Workshops.

[22]  Mihaela van der Schaar,et al.  Learning to Compete for Resources in Wireless Stochastic Games , 2009, IEEE Transactions on Vehicular Technology.

[23]  Mark J. Schervish,et al.  Nonstationary Covariance Functions for Gaussian Process Regression , 2003, NIPS.

[24]  Fady Alajaji,et al.  A Model for Correlated Rician Fading Channels Based on a Finite Queue , 2008, IEEE Transactions on Vehicular Technology.

[25]  Dinan Gunawardena,et al.  Exploiting Channel Diversity in White Spaces , 2011 .

[26]  Hans D. Hallen,et al.  Long-range prediction of fading signals , 2000, IEEE Signal Process. Mag..

[27]  Tansu Alpcan,et al.  A framework for optimization under limited information , 2011, Journal of Global Optimization.

[28]  Claude E. Shannon,et al.  Communication theory of secrecy systems , 1949, Bell Syst. Tech. J..

[29]  Robert W. Heath,et al.  An overview of limited feedback in wireless communication systems , 2008, IEEE Journal on Selected Areas in Communications.

[30]  Bhaskar Krishnamachari,et al.  Dynamic Multichannel Access With Imperfect Channel State Detection , 2010, IEEE Transactions on Signal Processing.

[31]  Matha Deghel,et al.  Queueing stability and CSI probing of a TDD wireless network with interference alignment , 2015, 2015 IEEE International Symposium on Information Theory (ISIT).

[32]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[33]  Anton Schwaighofer,et al.  GPPS: A Gaussian Process Positioning System for Cellular Networks , 2003, NIPS.

[34]  Aditya Gopalan,et al.  On Wireless Scheduling With Partial Channel-State Information , 2012, IEEE Transactions on Information Theory.

[35]  Matthew S. Grob,et al.  CDMA/HDR: a bandwidth-efficient high-speed wireless data service for nomadic users , 2000, IEEE Commun. Mag..

[36]  Atilla Eryilmaz,et al.  Distributed Channel Probing for Efficient Transmission Scheduling in Wireless Networks , 2015, IEEE Transactions on Mobile Computing.

[37]  W. C. Jakes,et al.  Microwave Mobile Communications , 1974 .

[38]  Özgür Erçetin,et al.  Smart scheduling and feedback allocation over non-stationary wireless channels , 2012, 2012 IEEE International Conference on Communications (ICC).

[39]  Bhaskar Krishnamachari,et al.  Combinatorial Network Optimization With Unknown Variables: Multi-Armed Bandits With Linear Rewards and Individual Observations , 2010, IEEE/ACM Transactions on Networking.

[40]  Michael P. Fitz,et al.  Distance spectrum analysis of space-time trellis-coded Modulations in quasi-static Rayleigh-fading channels , 2003, IEEE Trans. Inf. Theory.

[41]  Ming Ouyang,et al.  On optimal feedback allocation in multichannel wireless downlinks , 2010, MobiHoc '10.

[42]  Prasanna Chaporkar,et al.  Scheduling with limited information in wireless systems , 2009, MobiHoc '09.

[43]  Dongbing Gu,et al.  Spatial Gaussian Process Regression With Mobile Sensor Networks , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[44]  Holger Boche,et al.  Throughput Optimal Scheduling with Dynamic Channel Feedback , 2012, ArXiv.

[45]  Thomas F. La Porta,et al.  Max Weight Learning Algorithms for Scheduling in Unknown Environments , 2012, IEEE Transactions on Automatic Control.