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Iterative detection and decoding to approach MIMO capacity

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Title: Iterative detection and decoding to approach MIMO capacity


1
Iterative detection and decodingto approach MIMO
capacity
  • Jun Won Choi

2
Introduction
  • MIMO capacity (CSI only at receiver)
  • Fast fading scenario Ergodic I.I.D. Rayleigh
    fading Channel
  • Under fast fading assumption, transmission of
    independent data stream with same power is
    sufficient to achieve capacity (V-BLAST).
  • Capacity achieving Gaussian codes are used at
    each antenna as outer code.
  • Slow fading scenario
  • Coding across transmit antennas is needed ?
    space-time coding, advanced layering

Teletar, 1999
3
Introduction
  • Optimal transmitter structure

Signal processing operation (V-BLAST, D-BLAST,
space-time coding)
AWGN coder (outer code)
H
AWGN coder (outer code)
noise
Inner code
Coding across transmit antennas is needed in slow
fading
4
Introduction
  • Optimal receiver structure
  • Maximum a posteriori (MAP) decoder
  • Model a received signal as Markov process whose
    trellis is formed to include AWGN code,
    space-time coding, and MIMO channel.
  • Map decoding rule is optimal.
  • Computationally infeasible !
  • Iterative detection and decoding (IDD)
  • Divide decoding job into MIMO detection (inner
    code) and AWGN channel decoding (outer code).
  • Approximately approach to optimal performance via
    information exchange between two constitutional
    blocks.

5
Transmitter design example 1 (IDD)
  • Turbo-Blast (Haykin 2002)
  • Random layered space time coding

Diagonal Layering
AWGN coder
Interleaver
M-ary modul
AWGN coder
Interleaver
M-ary modul
Space-time interleaver
6
Transmitter design example 2
  • Space-time bit interleaved coded modulation
    (Tonello, 2000)

M-ary mapper
AWGN coder
Interleaver
S/P
M-ary mapper
7
Principle of IDD
  • Iterative (MIMO) detection and (channel) decoding

MIMO detector
Deinterleaver
SISO demapper
Information exchange
SISO channel decoder
Interleaver
Soft information is expressed as L-value
Priori LLR
Extrinsic LLR
8
IDD
  • SISO Channel decoder
  • BCJR algorithm based on trellis-based search
  • Low-complexity APP decoder - LOG-MAX algorithm,
    Soft output viterbi algorithm (SOVA)
  • MIMO detector
  • Complexity and performance trade-off
  • MAP versus Sub-optimal detector with linear
    structure

9
Definition (Space-time bit interleaved coded
modulation)
10
Map detector
  • Map detector
  • A posteriori L-value of the bit

Extrinsic information (output)
11
Map decoder
  • Map detection rule
  • Log-Max approximation
  • Complexity
  • Complexity of MAP decoder is exponential in
    modulation size, antenna size.

There are combinations for
each hypothesis.
12
List sphere decoding
  • Idea (Hochwald, 2003)
  • Find the combinations of symbol vector that are
    highly likely to be transmitted. It is called
    candidate list.
  • Define the candidate list, L as
  • Then, extrinsic L-value can be find over such
    candidate list, i.e.,

13
List sphere decoding
  • List sphere decoding
  • Efficient tree pruning problem

Form skewed lattice
Number of points
Sphere constraint
Lattice
14
Define the cost metric
Sphere constraint is violated.
Prune sub-tree.
15
List sphere decoding
  • Procedure
  • 1. Find the points inside sphere by tree search.
  • 2. Select closest points. (when number
    of points found is larger than predefined list
    size)
  • 3. Increase radius and restart the search. (when
    number of points found is less than list size)
  • 4. If candidate list has no common entry with
    or , the extrinsic L-value is
    set to inf or inf depending on the sign of
    entries.
  • How to choose B?,
  • For true x

16
Turbo-Blast detector
  • Turbo-Blast detector
  • Sub-optimal detector with linear structure
  • Derive based on linear MMSE criterion

Assume that are
available.
Interference cancellation step
Interference nulling step
Let
17
Turbo-Blast detector
  • Interference cancellation step
  • Interference nulling step

18
Turbo-Blast detector
  • Gaussian approximation

Interference noise term
There are only combinations
for each hypothesis.
19
Conclusions
  • Capacity achieving MIMO architecture
  • Transmitter architecture
  • V-BLAST AWGN code for fast fading
  • Coding across transmit antenna for slow fading ?
    space time coding, D-BLAST, Treaded space time
    coding
  • Receiver architecture
  • Global MAP decoding
  • Iterative detection and decoding
  • Map decoding
  • List sphere decoder
  • Linear MMSE detector
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