The Ergodic Capacity of the Multiple Access Channel Under Distributed Scheduling - Order Optimality of Linear Receivers

Consider the problem of a multiple-input multiple-output multiple-access channel at the limit of large number of users. Clearly, in practical scenarios, only a small subset of the users can be scheduled to utilize the channel simultaneously. Thus, a problem of user selection arises. However, since solutions which collect channel state information from all users and decide on the best subset to transmit in each slot do not scale when the number of users is large, distributed algorithms for user selection are advantageous. In this paper, we analyze a distributed user selection algorithm, which selects a group of users to transmit without coordinating between users and without all users sending CSI to the base station. This threshold-based algorithm is analyzed for both zero-forcing and minimum mean square error receivers, and its expected sum rate in the limit of large number of users is investigated. It is shown that for large number of users, it achieves the same scaling laws as the optimal centralized scheme.

[1]  J. Galambos Review: M. R. Leadbetter, Georg Lindgren and Holger Rootzen, Extremes and related properties of random sequences and processes , 1985 .

[2]  Stephen V. Hanly,et al.  Min-max fair coordinated beamforming via large systems analysis , 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

[3]  Fumiyuki Adachi,et al.  User Selection Criteria for Multiuser Systems With Optimal and Suboptimal LR Based Detectors , 2010, IEEE Transactions on Signal Processing.

[4]  Babak Hassibi,et al.  A Comparison of Time-Sharing, DPC, and Beamforming for MIMO Broadcast Channels With Many Users , 2007, IEEE Transactions on Communications.

[5]  Gregory W. Wornell,et al.  Distributed space-time-coded protocols for exploiting cooperative diversity in wireless networks , 2003, IEEE Trans. Inf. Theory.

[6]  Babak Hassibi,et al.  Fundamental Limits in MIMO Broadcast Channels , 2007, IEEE Journal on Selected Areas in Communications.

[7]  Shlomo Shamai,et al.  On the capacity of some channels with channel state information , 1999, IEEE Trans. Inf. Theory.

[8]  Babak Hassibi,et al.  On the capacity of MIMO broadcast channels with partial side information , 2005, IEEE Transactions on Information Theory.

[9]  Chi-Fang Li,et al.  Coverage Enhancement for OFDM-based Spatial Multiplexing Systems by Scheduling , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[10]  B. Zwart,et al.  Gaussian expansions and bounds for the Poisson distribution applied to the Erlang B formula , 2008, Advances in Applied Probability.

[11]  Randall Berry,et al.  Distributed power allocation and scheduling for parallel channel wireless networks , 2005, Third International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt'05).

[12]  Eytan Modiano,et al.  Scheduling of multi-antenna broadcast systems with heterogeneous users , 2007, IEEE Journal on Selected Areas in Communications.

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

[14]  H. Vincent Poor,et al.  Opportunistic Scheduling and Beamforming for MIMO-SDMA Downlink Systems with Linear Combining , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.

[15]  E. Uysal-Biyikoglu,et al.  Low Complexity Multiuser Scheduling for Maximizing Throughput in the MIMO Broadcast Channel , 2004 .

[16]  Richard L. Smith Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone , 1989 .

[17]  Milton C. Chew Distributions in Statistics: Continuous Univariate Distributions-1 and 2 , 1971 .

[18]  Omer Gurewitz,et al.  Capacity of Distributed Opportunistic Scheduling in Nonhomogeneous Networks , 2014, IEEE Transactions on Information Theory.

[19]  Andrea J. Goldsmith,et al.  Finite-Rate Feedback MIMO Broadcast Channels with a Large Number of Users , 2006, 2006 IEEE International Symposium on Information Theory.

[20]  Junshan Zhang,et al.  Opportunistic multichannel Aloha: distributed multiaccess control scheme for OFDMA wireless networks , 2006, IEEE Transactions on Vehicular Technology.

[21]  Eytan Modiano,et al.  Efficient Scheduling of Multi-User Multi-Antenna Systems , 2006, 2006 4th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks.

[22]  Alireza Bayesteh,et al.  On the User Selection for MIMO Broadcast Channels , 2005, IEEE Transactions on Information Theory.

[23]  L. Haan,et al.  Extreme value theory : an introduction , 2006 .

[24]  Shlomo Shamai,et al.  The Capacity Region of the Gaussian Multiple-Input Multiple-Output Broadcast Channel , 2006, IEEE Transactions on Information Theory.

[25]  G. Caire MIMO downlink joint processing and scheduling : a survey of classical and recent results , 2006 .

[26]  M. R. Leadbetter,et al.  Extremes and Related Properties of Random Sequences and Processes: Springer Series in Statistics , 1983 .

[27]  Randall Berry,et al.  Exploiting multiuser diversity for medium access control in wireless networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[28]  Feng Qi,et al.  Analysis and Design of Channel Estimation in Multicell Multiuser MIMO OFDM Systems , 2015, IEEE Transactions on Vehicular Technology.

[29]  Jeffrey G. Andrews,et al.  The capacity gain from intercell scheduling in multi-antenna systems , 2008, IEEE Transactions on Wireless Communications.

[30]  R. Michael Buehrer,et al.  The impact of multiuser diversity on space-time block coding , 2003, IEEE Communications Letters.

[31]  Changho Suh,et al.  Preamble design for channel estimation in MIMO-OFDM systems , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[32]  Randall Berry,et al.  Distributed approaches for exploiting multiuser diversity in wireless networks , 2006, IEEE Transactions on Information Theory.

[33]  Jeffrey G. Andrews,et al.  Low Complexity User Selection Algorithms for Multiuser MIMO Systems with Block Diagonalization , 2006, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[34]  PAUL EMBRECHTS,et al.  Modelling of extremal events in insurance and finance , 1994, Math. Methods Oper. Res..

[35]  Omer Gurewitz,et al.  Opportunistic Scheduling in Heterogeneous Networks: Distributed Algorithms and System Capacity , 2012, ArXiv.

[36]  Shlomo Shamai,et al.  Information theoretic considerations for cellular mobile radio , 1994 .

[37]  Antonia Maria Tulino,et al.  Random Matrix Theory and Wireless Communications , 2004, Found. Trends Commun. Inf. Theory.

[38]  Thomas L. Marzetta,et al.  Multiple-antenna channel hardening and its implications for rate feedback and scheduling , 2004, IEEE Transactions on Information Theory.

[39]  Robert W. Heath,et al.  Spatially greedy scheduling in multi-user MIMO wireless systems , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[40]  Edward W. Knightly,et al.  802.11ec: Collision Avoidance Without Control Messages , 2012, IEEE/ACM Transactions on Networking.

[41]  Omer Gurewitz,et al.  Capacity of distributed opportunistic scheduling in heterogeneous networks , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[42]  Joonho Lee,et al.  A scheduling algorithm combined with zero-forcing beamforming for a multiuser MIMO wireless system , 2005, VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005..

[43]  R. Durrett Probability: Theory and Examples , 1993 .

[44]  Umberto Spagnolini,et al.  Channel aware scheduling for broadcast MIMO systems with orthogonal linear precoding and fairness constraints , 2005, IEEE International Conference on Communications, 2005. ICC 2005. 2005.

[45]  H. Bolcskei,et al.  Multiuser Space-Time/Frequency Code Design , 2006, 2006 IEEE International Symposium on Information Theory.

[46]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[47]  Mohamed-Slim Alouini,et al.  How much feedback is multi-user diversity really worth? , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[48]  Eric P. Smith,et al.  An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.

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

[50]  Vincent K. N. Lau,et al.  On the design of MIMO block-fading channels with feedback-link capacity constraint , 2004, IEEE Transactions on Communications.

[51]  Raymond Knopp,et al.  Information capacity and power control in single-cell multiuser communications , 1995, Proceedings IEEE International Conference on Communications ICC '95.

[52]  Hyuncheol Park,et al.  Performance Analysis of MIMO System with Linear MMSE Receiver , 2008, IEEE Transactions on Wireless Communications.

[53]  W. R. Buckland,et al.  Distributions in Statistics: Continuous Multivariate Distributions , 1973 .

[54]  John M. Cioffi,et al.  On the distribution of SINR for the MMSE MIMO receiver and performance analysis , 2006, IEEE Transactions on Information Theory.