Multiuser%20Resource%20Allocation%20in%20Multichannel%20Wireless%20Communication%20Systems - PowerPoint PPT Presentation

About This Presentation
Title:

Multiuser%20Resource%20Allocation%20in%20Multichannel%20Wireless%20Communication%20Systems

Description:

Multiuser Resource Allocation in Multichannel Wireless ... Smartly scheduled. Pre-determined. Users transmission order. Adaptive. Static. Resource Allocation ... – PowerPoint PPT presentation

Number of Views:253
Avg rating:3.0/5.0

less

Transcript and Presenter's Notes

Title: Multiuser%20Resource%20Allocation%20in%20Multichannel%20Wireless%20Communication%20Systems


1
Multiuser Resource Allocation in Multichannel
Wireless Communication Systems
  • Zukang Shen
  • Ph.D. Defense
  • Committee Members
  • Prof. Jeffrey G. Andrews (co-advisor)
  • Prof. Melba M. Crawford
  • Prof. Gustavo de Veciana
  • Prof. Brian L. Evans (co-advisor)
  • Prof. Robert W. Heath, Jr.
  • Prof. Edward J. Powers
  • Communications, Networks, and Systems Area
  • Dept. of Electrical and Computer Engineering
  • The University of Texas at Austin
  • Jan. 19, 2006 (updated slides)

2
Outline
Contribution Multichannel Objective New Constraints Low Complexity Algorithm
1 Multiuser orthogonal frequency division multiplexing Frequency domain Sum capacity Proportional user data rates Decouple subchannel and power allocation
2 Multiuser multi-antenna systems with block diagonalization Spatial domain Sum capacity Joint precoding and post-processing Receive antenna selection
3 User selection in multi-antenna systems with block diagonalization Spatial domain Sum capacity Systems with a large number of users Greedy capacity and channel norm based algorithms
3
Resource Allocation in Wireless Systems
  • High data rate transmission
  • Wireless local area networks (WLAN) 54 -- 108
    Mbps
  • Metropolitan area networks (WiMAX) 10 -- 100
    Mbps
  • Cellular systems (3GPP) 1 -- 4 Mbps
  • Limited resources shared by multiple users
  • Transmit power
  • Frequency bandwidth
  • Transmission time
  • Code resource
  • Spatial antennas
  • Resource allocation impacts
  • Power consumption
  • User throughput
  • System latency

4
Multiuser Diversity
  • Multiuser wireless communication systems
  • Independent fading channels
  • Multiuser diversity

Resource Allocation Resource Allocation
Static Adaptive
Users transmission order Pre-determined Smartly scheduled
Channel state information Not exploited Wellexploited
SystemPerformance Poor Good
5
Downlink Multiuser Multichannel Systems
  • Downlink systems
  • Centralized basestation transmits to multiple
    users simultaneously
  • Limited resources at basestation
  • Multiple channels created in
  • Frequency orthogonal frequency division
    multiplexing (OFDM)
  • Space multiple transmit and receive antennas
  • Adaptive resource allocation
  • Goal Optimize system throughput subject to
    constraints
  • Method Formulate resource allocation as
    optimization problem
  • Optimal solution typically computationally
    prohibitive to find
  • Low complexity resource scheduling algorithms
    desired
  • Assumption Perfect channel state information of
    all users known at basestation

6
Outline
  • Introduction
  • Contribution 1 Adaptive resource allocation in
    multiuser OFDM systems with proportional rate
    constraints
  • Optimization framework balancing throughput and
    fairness
  • Decoupling subchannel and power allocation
  • Allocating power optimally for a given subchannel
    allocation
  • Contribution 2 Sum capacity of downlink
    multiuser MIMO systems with block diagonalization
  • Contribution 3 Low complexity user selection
    algorithms in multiuser MIMO systems with block
    diagonalization
  • Conclusion

7
Multiuser OFDM (MU-OFDM)
  • Orthogonal frequency division multiplexing
  • Zero inter-symbol interference
  • Parallel frequency subchannels
  • Multiple access technology
  • Downlink multiuser OFDM
  • Users share subchannels and basestation transmit
    power
  • Users only decode their own data
  • Resource allocation methods
  • Static TDMA, FDMA
  • Dynamic multiuser diversity
  • Users feedback channelinformation to basestation
  • Basestation determinesresource allocation

8
MU-OFDM Adaptive Resource Allocation
Objective Advantage Disadvantage
Max sum capacity Jang et al., 2003 Best sum capacity No data rate fairness among users
Max minimum users capacity Rhee et al., 2000 Equal user data rates Inflexible user data rates distribution
Max weightedsum capacity Cendrillon et al., 2004 Data rate fairness adjustable by varying weights No guarantee for required proportional user data rates
9
MU-OFDM with Proportional Rates
Contribution 1
  • Objective Sum capacity
  • Constraints
  • Total transmit power
  • No subchannel shared by multiple users
  • Proportional rate constraints
  • Advantages
  • In theory, fill gap of max sum capacity max-min
    capacity
  • In practice, allow different service privileges
    and different pricing

B Transmission bandwidth
K of users
N of subchannels
pk,n power in user ks subchannel n
hk,n channel gain of user ks subchannel n
N0 AWGN power density
Rk User ks capacity
System parameter for proportional rates
10
Subchannel Allocation
Contribution 1
  • Modified method of Rhee et al., 2000, but we
    keep the assumption of equal power distribution
    on subchannels
  • Initialization (Enforce zero initial
    conditions)Set , for
    . Let
  • For to (Allocate best
    subchannel for each user)
  • Find satisfying
    for all
  • Let ,
    and update
  • While (Then iteratively give
    lowest rate user first choice)
  • Find satisfying
    for all
  • For the found , find satisfying
    for all
  • For the found and , Let
    , and update

11
Power Allocation for a Single User
Contribution 1
  • Optimal power distribution for user
  • Order
  • Water-filling algorithm
  • How to find for

K of users
N of subchannels
pk,n power in user ks nth assigned subchannel
Hk,n Channel-to-noise ratio in user ks nth assigned subchannel
Nk of subchannels allocated to user k
Pk,tot Total power allocated to user k
12
Power Allocation among Many Users
Contribution 1
  • Use proportional rate and total power constraints
  • Solve nonlinear system of K equations
    /iteration
  • Two special cases
  • Linear case
    , closed-form solution
  • High channel-to-noise ratio and

where
13
Comparison with Optimal Solution
Contribution 1
14
Comparison with Max-Min Capacity
Contribution 1
15
Comparison with Max Sum Capacity
Contribution 1
16
Summary of Contribution 1
  • Adaptive resource allocation in multiuser OFDM
    systems
  • Maximize sum capacity
  • Enforce proportional user data rates
  • Low complexity near-optimal resource allocation
    algorithm
  • Subchannel allocation assuming equal power on all
    subchannels
  • Optimal power distribution for a single user
  • Optimal power distribution among many users with
    proportionality
  • Advantages
  • Evaluate tradeoff between sum capacity and user
    data rate fairness
  • Fill the gap of max sum capacity and max-min
    capacity
  • Achieve flexible data rate distribution among
    users
  • Allow different service privileges and pricing

17
Outline
  • Introduction
  • Contribution 1 Adaptive resource allocation in
    multiuser OFDM systems with proportional rate
    constraints
  • Contribution 2 Sum capacity of downlink
    multiuser MIMO systems with block diagonalization
  • Block diagonalization with receive antenna
    selection
  • Sum capacity of BD vs. DPC for given channels
  • Upper bound on the ratio of DPC and BD sum
    capacity in Rayleigh fading channels
  • Contribution 3 Low complexity user selection
    algorithms in multiuser MIMO systems with block
    diagonalization
  • Conclusion

18
Multi-Antenna Systems
  • Exploit spatial dimension with multiple antennas
  • Improve transmission reliability diversity
  • Combat channel fading Jakes, 1974
  • Combat co-channel interference Winters, 1984
  • Increase spectral efficiency multiplexing
  • Multiple parallel spatial channels created with
    multiple antennas at transmitter and receiver
    Winters, 1987 Foschini et al., 1998
  • Theoretical results on point-to-point multi-input
    multi-output (MIMO) channel capacity Telatar,
    1999
  • Tradeoff between diversity and multiplexing
  • Theoretical treatment Zheng et al., 2003
  • Switching between diversity and multiplexing
    Heath et al., 2005

19
MIMO Gaussian Broadcast Channels
  • Duality with multiple access channels Vishwanath
    et al., 2003
  • Dirty paper coding (DPC) Costa, 1983
  • Sum capacity achieved with DPC Vishwanath et
    al., 2003
  • Iterative water-filling algorithm Yu et al.,
    2004 Jindal et al., 2005
  • Capacity region Weingarten et al., 2004
  • Coding schemes approaching DPC sum
    capacityZamir et al., 2002 Airy et al., 2004
    Stojnic et al., 2004
  • Too complicated for cost-effective implementations

20
Block Diagonalization (BD)
  • Linear precoding technique
  • Zero inter-user interference Spencer et al.,
    2004
  • in the null space of
  • Advantages Simple transceiver design
  • Effective point-to-point MIMO channel
  • Disadvantages Suboptimal for sum capacity
  • Channel energy wasted for orthogonalizing user
    channels
  • Transmit signal covariance matrices not optimal

21
BD with Receive Antenna Selection
Contribution 2
  • Why joint processing?
  • Confine to be selection matrix, e.g.
  • Lower system overhead for conveying
  • BD with receive antenna selection
  • Exhaustive search for optimal selection matrices

22
BD vs. DPC Given Channels
Contribution 2
  • Theorem The ratio of DPC sum capacity over BD is
    bounded by
  • Ratio of DPC sum capacity over TDMA bounded by
    Jindal et al., 2005
  • TDMA only serves one user at a time
  • BD supports multiple users
  • Valid for any SNR, , , and
  • Lemma If user channels are orthogonal, then
  • Lemma If and user channelsare in
    same vector space

23
BD vs. DPC Rayleigh Fading Channels
Contribution 2
  • Lower bound on BD ergodic sum capacity
  • Fix a subset of users to serve
  • Each users effective channel still Rayleigh
  • Equal power allocated for every MIMO eigenmode
  • Upper bound on DPC ergodic sum capacity
  • Allow user cooperation (effectively
    point-to-point channel)
  • Cooperative channel
  • Space-time water-filling for effective
    cooperative MIMO channel
  • Upper bound on ratio of DPC and BD ergodic sum
    capacity
  • Easy evaluation with numerical integrations
  • Bound is tight for
  • Medium to high SNR, or

24
Simulation Results
Contribution 2
25
Simulation Results
Contribution 2
26
Summary of Contribution 2
  • Sum capacity in downlink multiuser MIMO systems
    with block diagonalization
  • Formulated joint transmitter precoding and
    receiver post-processing (shown in dissertation)
  • Combined block diagonalization with receive
    antenna selection
  • Block diagonalization vs. dirty paper coding
  • Sum capacity for given channels
  • Ergodic sum capacity in Rayleigh fading channel
  • Block diagonalization achieves a significant part
    of the optimal sum capacity

27
Outline
  • Introduction
  • Contribution 1 Adaptive resource allocation in
    multiuser OFDM systems with proportional rate
    constraints
  • Contribution 2 Sum capacity of downlink
    multiuser MIMO systems with block diagonalization
  • Contribution 3 Low complexity user selection
    algorithms in multiuser MIMO systems with block
    diagonalization
  • Capacity based user selection
  • Channel Frobenius norm based user selection
  • Conclusion

28
Need of User Selection for BD
  • Zero inter-user interference requires in
    null space of
  • Dimension of
  • Maximum number of simultaneous users
  • Assuming active users utilize all receive
    antennas
  • Select subset of users to maximize total
    throughput
  • Exhaustive search
  • Optimal for total throughput
  • Computationally prohibitive
  • Related work
  • Semi-orthogonal user set construction Yoo et
    al., 2005
  • Antenna selection Gharavi-Alkhansari et al.,
    2004

29
Greedy User Selection Algorithms
Contribution 3
30
Computational Complexity
Contribution 3
(m x n) complex matrix operation Flop counts
Frobenius norm
Gram-Schmidt orthogonalization
Water-filling algorithm
Singular value decomposition
  • Proposed algorithms have complexity

Average CPU run time (Pentium M 1.6G Hz PC)
31
Monte Carlo Results
Contribution 3
32
Summary of Contributions
  • Adaptive resource allocation in multiuser OFDM
  • Balanced throughput and proportional user data
    rates
  • Derived optimal power allocation given subchannel
    allocation
  • Sum capacity of downlink multiuser MIMO systems
  • Combined block diagonalization with receive
    antenna selection
  • Analyzed sum capacity of BD vs. DPC for given
    channels
  • Derived upper bound on ratio of DPC and BD sum
    capacity in Rayleigh fading channels
  • Low complexity user selection algorithms in
    multiuser MIMO systems with block diagonalization
  • Proposed two algorithms with linear complexity in
    no. of total users
  • Achieved near-optimal sum capacity
Write a Comment
User Comments (0)
About PowerShow.com