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Sphere Decoding Algorithm for MIMO Detection. Arslan Zulfiqar. Motivation. Future mobile applications include Mobile TV High Speed Internet Future wireless systems need to provide High Data Rate High Quality of Service (QoS) Key challenges Hostile propagation environment

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Presentation Transcript
motivation
Motivation
  • Future mobile applications include
    • Mobile TV
    • High Speed Internet
  • Future wireless systems need to provide
    • High Data Rate
    • High Quality of Service (QoS)
  • Key challenges
    • Hostile propagation environment
    • Bandwidth is a limited resource
  • How do we meet these challenges?
    • Multiple-Input multiple-output (MIMO) systems
motivation contd
Motivation contd.
  • How can MIMO help?
    • Spatial Multiplexing
    • Diversity
  • MIMO has been proposed in modern wireless standards
    • IEEE 802.11n
    • IEEE 802.16e (WiMax)
    • 3GPP LTE
  • Tradeoff: Increased complexity of the decoder!
problem formulation
Problem Formulation
  • Simple model of a communication system:

Channel

Estimate

Channel

RX vector

TX vector

MIMO

Decoding

M decoded

symbols

N receive

antennas

M complex

symbols to be transmitted

M transmit

antennas

How do we do this?

problem formulation contd
Problem Formulation contd.
  • First, convert the problem involving complex quantities to one that involves real quantities dimensions scale by 2.
  • Optimal ML solution=

Dimensions

Model

ml solution
ML solution
  • How do we compute ?
    • Brute force search
      • Search over all
    • Smart search: Sphere decoding algorithm
      • This algorithm finds a subset of that lie in a sphere around
experiment
Experiment
  • 64-QAM constellation
  • QAM alphabet set = ={-7,-5,-3,-1,1,3,5,7}
  • 4x6 MIMO system
  • SNR considered:
    • 15dB,18dB,20dB
  • Inputs to MIMO decoder:
    • received vector
    • channel matrix
experiment brute force search
Experiment: Brute Force Search
  • ML equation:
  • Total number of possibilities for

~16 minutes!

experiment sphere decoding algorithm
Experiment: Sphere Decoding Algorithm
  • ML equation:
  • Proposed by Fincke and Pohst
  • Pick a radius such that,

d

optimizations
Optimizations
  • Parallel tree traversal
  • Look ahead transformation
  • Schnorr-Euchner modification