An optimal link layer model for multi hop mimo n etworks
Download
1 / 22

An Optimal Link Layer Model for Multi-hop MIMO N etworks - PowerPoint PPT Presentation


  • 101 Views
  • Uploaded on
  • Presentation posted in: General

An Optimal Link Layer Model for Multi-hop MIMO N etworks. Yi Shi Virginia Tech, Dept. of ECE (with Jia Liu, Canming Jiang, Cunhao Gao , and Thomas Hou ). IEEE INFOCOM 2011 – Shanghai, China. MIMO. Multiple antennas at both transmitter and receiver Benefits

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'An Optimal Link Layer Model for Multi-hop MIMO N etworks ' - javan


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
An optimal link layer model for multi hop mimo n etworks

An Optimal Link Layer Model for Multi-hop MIMO Networks

Yi Shi

Virginia Tech, Dept. of ECE

(with Jia Liu, CanmingJiang, CunhaoGao, and

Thomas Hou)

IEEE INFOCOM 2011 – Shanghai, China


An optimal link layer model for multi hop mimo n etworks
MIMO

  • Multiple antennas at both transmitter and receiver

  • Benefits

    • Increase throughput, mitigate interference

    • Without additional bandwidth or transmit power

IEEE INFOCOM 20112


Current status
Current Status

  • Two modeling approaches

    • Matrix-based model

    • Degree of freedom (DoF)-based model

  • DoF-based model

    • Simple: Use DoF to identify a feasible rate region

    • Not optimal: Existing DoF-based models cannot achieve the maximum rate region

An optimal DoF-based model for multi-hop MIMO networks

  • Matrix-based model

    • Accurate: Characterize MIMO channel by a matrix

    • High complexity: Due to matrix manipulations

IEEE INFOCOM 20113


Zfbf scheme
ZFBF Scheme

  • DoF-based model is for the zero-force beam-forming (ZFBF) scheme

    • An effective MIMO technique

  • Two benefits associated with ZFBF

    • Spatial multiplexing(SM)

      • Enables multiple data streams on the same link

    • Interference cancellation(IC)

      • Enables more links to transmit at the same time

IEEE INFOCOM 20114


Spatial multiplexing an example
Spatial Multiplexing – An Example

  • Two data streams S1 and S2

  • Transmitter uses two transmit weight vectors and

  • Transmitted signal is

  • Signal arriving at receiver is

  • Receiver uses two receive weight vectors and

1

1

0

0

IEEE INFOCOM 20115


Interference cancellation an example
Interference Cancellation – An Example

  • Link causes interference at link

  • Interference for stream on link is

0

IEEE INFOCOM 20116


Matrix based model
Matrix-Based Model

  • For a time slot based scheduling, denote # of data streams on link in time slot t as

    • Assume each data stream has one unit rate

  • Link ’s average rate is

  • For SM, we need

  • For IC (if interferes with ), we need

IEEE INFOCOM 20117


Troubles with matrix based model
Troubles with Matrix-Based Model

Networking research using matrix-based model has very limited success

  • Need to verify the feasibility of each set of values for

  • The number of these sets is exponential with L

  • Verifying the feasibility of a particular set requires to solve a bilinear problem

    • A general solution to bilinear problems remains unknown

IEEE INFOCOM 20118


Understanding dof
Understanding DoF

  • DoF is associated with each transmit/receive vector

  • Initially, each vector has no constraint

    • Each element in a vector can be adjusted to optimize network performance

    • Feasible region of this vector includes all possible values

    • # of DoFsof this feasible region is equal to # of elements in a vector(or # of antennas at the node)

IEEE INFOCOM 20119


Understanding dof consumption an example
Understanding DoF Consumption- An Example -

  • Consider a transmit vector for a node with five antennas

  • Initially, there is no constraint: DoFs = 5

  • Consider two constraints and

    • The vector becomes

    • Remaining DoFs = 3

  • Consumed DoFs = 5-3 = 2

IEEE INFOCOM 201110


Understanding dof consumption a second example
Understanding DoF Consumption- A Second Example -

# of consumed DoFsdue to a set of constraints is equal to # of independent constraints

  • Consider three constraints

  • Since (7) is a linear combination of (5) and (6), we have only two independent constraints

    • The vector becomes

    • Remaining DoFs= 3

  • Consumed DoFs = 5-3 = 2

IEEE INFOCOM 201111


Dof consumption by sm
DoF Consumption by SM

  • All constraints in (8) and (9) are independent

  • The DoFconsumption for is

  • Similarly, the DoF consumption for is also

Transmit vector needs to satisfy

IEEE INFOCOM 201112


Dof consumption by ic
DoF Consumption by IC

  • Interference can be cancelled by either transmit or receive vector

    • Which vector?

    • To answer this question, we need an order among vectors

IEEE INFOCOM 201113


Ic dof consumption under an order
IC DoFConsumption Under An Order

  • For IC, vector must satisfy

    for

  • Consider one constraint

    • If is determined before , uses one DoF

    • If is determined after , the above constraint will be satisfied by in the future -- no DoFconsumption for

  • Similar results hold for

IEEE INFOCOM 201114


Vector level to node level a transformation
Vector-Level to Node-Level- A Transformation -

  • To achieve the maximum rate region, we prove that we only need an order among nodes

    • An order among vectors is unnecessary

  • We need an order between and

    • If is behind , # of DoFsconsumed at is and is 0

    • If is behind , # of DoFs consumed at is and is 0

IEEE INFOCOM 201115


Total dofs consumed by sm ic
Total DoFs Consumed by SM & IC

  • Is the total number of consumed DoFs a simple sum of those by SM and IC?

    • The answer is Yes!

    • Show that there is no dependency among SM and IC constraints

IEEE INFOCOM 201116


Dof based model
DoF-Based Model

Half-duplex constraint

Constraints for node activity

Ordering constraints

IEEE INFOCOM 201117


Dof based model cont d
DoF-Based Model (Cont’d)

DoFconsumption constraints

IEEE INFOCOM 201118


Matrix based model vs new dof based model
Matrix-Based Model vs. New DoF-Based model

Consider a three-link network

Two models achieve the same rate region

Complexity comparison

IEEE INFOCOM 201119


An application example
An Application Example

  • Objective: Maximize the sum of weighted session rates

  • A linear optimization problem

    • Similar complexity to that for single-antenna networks

IEEE INFOCOM 201120



Summary
Summary

  • The matrix-based MIMO model is too complex for network performance analysis

    • Results based on the matrix-based model are very limited

  • Developedan optimal DoF-based model

    • Retains the similar simplicity as single-antenna networks

    • Offers the same achievable rate region as that by the matrix-based model

  • Showed how to use our optimal DoF-based model for a multi-hop MIMO network problem

IEEE INFOCOM 201122


ad
  • Login