massive MIMO networks using stochastic geometry Potential for better area spectral efficiency with massive

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  • © Robert W. Heath Jr. (2015)

    Analysis of massive MIMO networks using stochastic geometry

    Tianyang Bai and Robert W. Heath Jr.

    Wireless Networking and Communications Group

    Department of Electrical and Computer Engineering

    The University of Texas at Austin

    http://www.profheath.org

    Funded by the NSF under Grant No. NSF-CCF-1218338 and a gift from Huawei

  • © Robert W. Heath Jr. (2015)

    2

    Cellular communication

    Distributions of base stations in a major UK city*

    (1 mile by 0.5 mile area)

    * Data taken from sitefinder.ofcom.org.uk

    Base station

    Illustration of a cell in cellular networks

    User

    Uplink Downlink

    To network

    Irregular base station locations motivate the applications of stochastic geometry

  • © Robert W. Heath Jr. (2015)

    More spectrum Millimeter wave spectrum

    More base stations Network densification

    More spectrum efficiency Multiple antennas (MIMO)

    3

    5G cellular networks – achieving 1000x better

    This talk

    Other work

  • © Robert W. Heath Jr. (2015)

    7

    Massive MIMO concept

    * T. Marzetta, “Noncooperative cellular wireless with unlimited numbers of base station antennas,” IEEE Trans. Wireless Commun., Nov. 2011

    **X. Gao, O. Edfors, F. Rusek, and F. Tufvesson, “Massive MIMO in real propagation environments,” To appear in IEEE Trans. Wireless Commun., 2015

    Potential for better area spectral efficiency with massive MIMO

    > 64 antennas 1 to 8 antennas

    1 or 2 uses sharing same resources 10 to 30 users sharing same resources

    Conventional cell Massive MIMO cell

    MIMO (multiple-input multiple-output) a type of wireless

    system with multiple antennas at transmitter and receiver

  • © Robert W. Heath Jr. (2015)

     Massive MIMO: multi-user MIMO with lots of base station antennas*

     Allows more users per cell simultaneously served

     Analyses show large gains in sum cell rate using massive MIMO

     Real measurements w/ prototyping confirm theory**

    7

    Three-stage TDD mode (1): uplink training

    Users:

    Send pilots to the base stations

    * T. Marzetta, “Noncooperative cellular wireless with unlimited numbers of base station antennas,” IEEE Trans. Wireless Commun., Nov. 2011

    Base stations:

    Estimate channels based on

    training

    Uplink training

    Pilot contamination

    Channel estimation polluted by pilot contamination

    Assume perfect synchronization Assume full pilot reuse

  • © Robert W. Heath Jr. (2015)

     Massive MIMO: multi-user MIMO with lots of base station antennas*

     Allows more users per cell simultaneously served

     Analyses show large gains in sum cell rate using massive MIMO

     Real measurements w/ prototyping confirm theory**

    7

    Three-stage TDD mode (2): uplink data

    Users:

    Send data to base stations

    * T. Marzetta, “Noncooperative cellular wireless with unlimited numbers of base station antennas,” IEEE Trans. Wireless Commun., Nov. 2011

    Base stations:

    Matched filtering combining

    based on

    channel estimates

    Uplink data

    Simple matched filter receive combining based on channel estimate

  • © Robert W. Heath Jr. (2015)

     Massive MIMO: multi-user MIMO with lots of base station antennas*

     Allows more users per cell simultaneously served

     Analyses show large gains in sum cell rate using massive MIMO

     Real measurements w/ prototyping confirm theory**

    7

    Three-stage TDD mode (3): downlink data

    Users:

    Decode received signals

    * T. Marzetta, “Noncooperative cellular wireless with unlimited numbers of base station antennas,” IEEE Trans. Wireless Commun., Nov. 2011

    Base stations:

    Beamforming based on channel

    estimates

    Downlink data

    Simple matched filter transmit beamforming based on channel

    estimates

  • © Robert W. Heath Jr. (2015)

    Fading and noise become minor with large arrays [Ignore noise in analysis]

    TDD (time-division multiplexing) avoids downlink training overhead [Include pilot contamination]

    Simple signal processing becomes near-optimal, with large arrays [Assume simple beamforming]

    Large antenna arrays serve more users to increase cell throughput [Compare sum rate w/ small cells]

    Advantages of massive MIMO & implications

    8

    Out-of-cell interference reduced due to asymptotic orthogonality of channels [Show SIR convergence]

  • © Robert W. Heath Jr. (2015)

    Modeling cellular system performance

    using stochastic geometry

  • © Robert W. Heath Jr. (2015)

    Stochastic geometry in cellular systems

    10

    Desired signal

    Serving BS

    Typical user Interference link

    Stochastic geometry allows for simple characterizations of SINR distributions

    Desired signal power

    Interference from PPP interferers

    Modeling base stations locations as Poisson point process

    T. X Brown, ``Practical Cellular Performance Bounds via Shotgun Cellular System,'' IEEE JSAC, Nov. 2000.

    M. Haenggi, J. G. Andrews, F. Baccelli, O. Dousse, and M. Franceschetti, “ Stochastic geometry and random graph for the analysis and design of wireless networks”,

    IEEEJSAC 09

    J. G. Andrews, F. Baccelli, and R. K. Ganti, “ A tractable approach to coverage and rate in cellular networks”, IEEE TCOM 2011.

    H. S. Dhillon, R. K. Ganti, F. Baccelli, and J. G. Andrews, “ Modeling and analysis of K-tier downlink heterogeneous cellular networks”, IEEE JSAC, 2012

    Thermal noise

    (often ignored)

    & many more…

  • © Robert W. Heath Jr. (2015)

    11

    Who* cares about antennas anyway?

    Diversity

    Changes fading distribution

    Multiplexing

    Multivariate performance measures

    Interference cancelation

    Changes received interference

    Beamforming

    Changes caused interference

    * why should non-engineers care at all about antennas

  • © Robert W. Heath Jr. (2015)

    Challenges of analyzing massive MIMO

    13

    Does not directly extend to massive MIMO

    X

    Single user per cell Multiple user per cell

    Single base station antenna Massive base station antennas

    Rayleigh fading Correlated fading MIMO channel

    No channel estimation Pilot contamination

    Mainly focus on downlink Analyze both uplink and downlink

  • © Robert W. Heath Jr. (2015)

    Related work on massive MIMO w/ SG

     Asymptotic analysis using stochastic geometry [1]

     Derived distribution for asymptotic SIR with infinite BS antennas

     Considered IID fading channel, not include correlations

     Assumed BSs distributed as PPP marked with fixed-circles as cells

    Nearby cells in the model may heavily overlap (not allowed in reality)

     Concluded same SIR distributions in UL/DL (not matched to simulations)

     Scaling law between user and BS antennas [2]

     BS antennas linearly scale with users to maintain mean interference

     The distribution of SIR is a more relevant performance metric

    14

    Need advanced system model for massive MIMO analysis

    [1] P. Madhusudhanan, X. Li, Y. Liu, and T. Brown, “Stochastic geometric modeling and interference analysis for massive MIMO systems,” Proc.of WiOpt, 2013

    [2] N. Liang, W. Zhang, and C. Shen, “An uplink interference analysis for massive MIMO systems with MRC and ZF receivers,” Proc. of WCNC, 2015.

  • © Robert W. Heath Jr. (2015)

    Massive MIMO system model

  • © Robert W. Heath Jr. (2015)

    16

    Proposed system model

    Each BS has M antennas serving K users

    Base stations distributed as a PPP

    : n-th base station : k-th scheduled user in n-th cell

    Scheduled user

    Unscheduled user

    Need to characterize scheduled users’ distributions

    Users uniformly distributed w/ high density

    (each BS has at least K associated users)

  • © Robert W. Heath Jr. (2015)

    Scheduled users’ distribution

     Locations of scheduled users are

    correlated and do not form a PPP [1,2]

     Correlations prevent the exact analysis

    of UL SIR distributions

    17 [1] H. El Sawy and E. Hossain, “On stochastic geometry modeling of cellular uplink transmission with truncated channel inversion power control” IEEE TCOM, 2014

    [2] S. Singh, X. Zhang, and J. Andrews, “ Joint rate and SINR coverage analysis for decoupled uplink-downlink biased cell association in HetNet,” Arxiv, 2014

    1st scheduled user

    2nd scheduled user

    Base station

    Locations of scheduled users are

    correlated and do not form a PPP

    Non-PPP users’ distributions make exact analysis difficult

    Presence of a “red” user in one cell

    prevents those of the other red

  • © Robert