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Comparison of Noncoherent Detectors for
SOQPSK and GMSK in Phase Noise Channels
Committee
Dr. Erik Perrins (Chair)
Dr. Glenn Prescott
Dr. Daniel Deavours
Masters Thesis Defense
Afzal Syed
August 17, 2007
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Publications Resulting from this work
A. Syed and E. Perrins, Comparison of Noncoherent
Detectors for SOQPSK and GMSK in Phase Noise Channels,
to appear in Proceedings of the International Telemetry
Conference (ITC), Las Vegas, NV, October 22-25, 2007.
Other
A. Syed, K. Demarest, D. Deavours, Effects of Antenna
Material on the Performance of UHF RFID Tags, In
Proceedings of IEEE International Conference on RFID
(IEEE-RFID), Grapevine, TX, March 26-28, 2007.
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Outline Motivation for this thesis/Research Objectives
Introduction Coherent Detection
Reduced Complexity Coherent Detectors
Noncoherent Detection Algorithm Serially Concatenated Systems
Simulation Results
Conclusions
Future work
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Motivation for this thesis SOQPSK and GMSK highly bandwidth efficient
CPMs.
Coherent receivers good performance in AWGN.
Noncoherent receivers favored phase noise channels
often encountered in practical scenarios.
No published results on how noncoherent detectors for
these schemes compare in phase noise channels for
uncoded and coded systems with iterative detection.
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Research Objectives Develop reduced complexity noncoherent detectors for
SOQPSK and GMSK.
Quantify performance of SOQPSK and GMSK in
channels with phase noise for uncoded and coded
systems which use these schemes as inner codes. Determine which is to be preferred for a given
requirement.
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Outline Motivation for this thesis/Research Objectives
Introduction CPM
SOQPSK
GMSK
Coherent Detection
Reduced Complexity Coherent Detectors
Noncoherent detection algorithm
Serially Concatenated Systems
Simulation Results
Conclusions
Future work
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Introduction : CPM CPM Characteristics
Constant envelope
Continuous phase
Memory
Advantages Simple transmitter
Power efficient
Bandwidth efficient
Flexible
Suitable for non-linear power amplifiers
I
Q
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Introduction : CPM Signal representation
CPM is completely defined by
h : modulation index
M: cardinality of the source alphabet
q(t) : phase pulse
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Introduction : CPM Applications
Aeronautical telemetry
Deep-space communication
Bluetooth
Wireless modems
Satellite communication
Battery-powered communication
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Introduction : SOQPSK Similar to OQPSK where I
and Q bits are transmitted
in offset fashion.
I
1
0
1
Q
1
0
1
Q
11
1000
01
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Introduction : SOQPSK
SOQPSK is a ternary CPM
with a precoder.
2 standards for SOQPSK SOQPSK-MIL full-response with
rectangular frequency pulse.
SOQPSK-TG partial-response with
L= 8.
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Introduction : GMSK GMSK is another widely used CPM.
Can achieve tradeoff between bandwidth efficiency,power efficiency, and detector complexity by
appropriately configuring theBT product.
GMSK is binary (M = 2) with h = .
We study 2 types of GMSK GMSK withBT = 0.3 (L = 3)
GMSK withBT = 0.25 (L= 4)
GMSK withBT = 0.3 is used in GSM.
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Introduction : GMSK GMSK has a Gaussian frequency pulse shape
Frequency and phase pulses for GMSK
withBT = 0.3
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Outline
Motivation for this thesis/Research Objectives
Introduction
Coherent Detection A closer look at the phase of the signal
Maximum-Likelihood (ML) Decoding
Reduced Complexity Coherent Detectors Noncoherent detection algorithm
Serially Concatenated Systems
Simulation Results Conclusions
Future work
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A closer look at the phase of the signal
Phase of the signal can be grouped into two terms
Symbols older thanL symbol times indicate the phase of the signalat the beginning of symbol interval (cumulative phase).
Phase change depends on the most recentL symbols (correlative
state). Thus the signal can be described with a finite state machine
444 3444 2143421
)(
100)(2)(2)(
t
n
Lni
i
Ln
i
i
n
i
i iTtqhhiTtqht
Ln
+=
==+==
( )44444 344444 21
LLL
branchespM
nnnLnLnnnnnLnn
L
,,,,,,,,, 12,1121 ++ ==
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Maximum-Likelihood (ML) decoding
Received signal corrupted by noise
ML detector matches the received signal with all possible
transmitted signals.
Implemented recursively via the Viterbi algorithm.
Organization of the trellis
Branch vector is the (L+1) tuple
Each branch has a starting state
And an ending state Number of phase states isp.
)();()( tntstr +=
nnnLnLnn ,,,, 12,1 += L
)12,1 ,,, += nnLnLnnS L
)nnLnLnnE ,, 1,21 ++=L
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Maximum-Likelihood (ML) decoding
For a CPM trellis
Trellis example, GMSK with
BT = 0.3. (h = , M = 2, L
= 3 and p =4).
16 states, 32 branches and 8
matched filters.
L
B pMN =
L
MF MN =
1=
L
S pMN
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Maximum-Likelihood (ML) decoding
Optimal coherent ML detector
Metric update for each state
is the sampled matched filter output. Serves as the benchmark detector for reduced
complexity and noncoherent detectors.
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Outline
Motivation for this thesis/Research Objectives
Introduction
Coherent Detection Reduced Complexity Coherent Detectors
Why reduced complexity detectors?
Reduced complexity approaches
Frequency Pulse Truncation Decision Feedback
Noncoherent detection algorithm
Serially Concatenated Systems
Simulation Results
Conclusions
Future work
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Why reduced complexity detectors?
Longer, smoother pulses higher bandwidth efficiency.
Decoding complexity increases exponentially withpulse lengthL.
The optimal detector for SOQPSK-TG 512 trellis
states (L = 8, p = 4, M = 2). Optimal detector for GMSK withBT = 0.25 32 trellis
states (L = 4, p = 4, M = 2).
Difficult to implement large trellis structures reduced
complexity approaches.
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Reduced complexity coherent detectors : Approach
Each trellis state is defined by
Removing/reducing coordinates from thisL-tuple is the key to
state complexity reduction.
Number of techniques discussed in literature
Frequency pulse truncation (PT) technique
Decision feedback
PT and decision feedback applied to GMSK for the first time in
this work.
4444 34444 21
L
statespM
nnLnLnn
L
S
1
12,1 ,,,
+=
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Frequency Pulse Truncation (PT)
Use a shorter phase pulse at the receiver:Lr
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PT performance
SOQPSK-TG
Pulse
truncated
fromL=8 to
Lr=1.
Reduction intrellis states
from 512 to 4.
Loss inperformance of
0.2 dB at5
10
=
bP
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Decision Feedback
Phase states chosen at run time.
Since phase state is defined by
knowing an estimate of the past symbols the phase state for eachtrellis state can be updated.
Using decision feedback to update phase for each trellis state reduces
the number of trellis states by a factorp.The state now is
444 3444 21L
statesM
nnLnn
L
S
1
12,1 ,,
+=
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Decision feedback applied to GMSK trellis
Actual trellis
16 states
Simplified trellis4 states
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Decision Feedback : Performance
Performance of GMSK using the simplified 4-state
trellis.
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Why Noncoherent?
Received signal model
Phase noise channels often encountered inpractice
Robust Easy to synchronize
Can recover input bits in the presence of
phase noise
)();()( )( tnetstr tj +=
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Noncoherent detection : Phase noise model
Motivation for noncoherent detector carrier phase is not known
and is varying.
Phase noise is given by
where are independent and identically distributed Gaussian
random variables with zero mean and variance
Phase noise is modeled as a first order Markov process with
Gaussian transition probability distribution.
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Outline
Motivation for this thesis/Research Objectives
Introduction
Coherent Detection
Reduced Complexity Coherent Detectors
Noncoherent Detection Algorithm
Serially Concatenated Systems Introduction
System Description
SISO Algorithm
Performance
Simulation Results
Conclusions
Future work
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Serially Concatenated Coded Systems : System Description
Outer code: rate-1/2 convolutional code
Inner code: SOQPSK and GMSK
Block lengthN=2048 andNi=5
SOQPSK
SISO 1 CC
SISO
)(tr
{ }1,0 na
CPM
MODULATORPRECODER
{ }1,0na
)(tr
AWGN
CHANNEL
CC
ENCODER
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Serially Concatenated Systems :SISO Algorithm
Outputs P(a,O) and P(c,O)
based on code constraints.
Forward and backward
recursions to update metrics associated with each trellis
state.
For a CPM SISO
SISO
module
P(c,I)
P(a,I)
P(c,O)
P(a
,O)
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Serially Concatenated Systems :SISO Algorithm
In case of noncoherent detection
where is the phase reference
associated with each state and is
updated only during the forwardrecursion.
The output probability distribution
for the bit/code word for symboltime kis computed as
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Performance of Coded SOQPSK Systems
High coding gain is achieved.
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Performance of Coded GMSK Systems
High coding gain is achieved.
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Performance of Coded Systems
Coding gains for serially concatenated SOQPSK and
GMSK
More bandwidth efficient schemes have higher codinggains.
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Outline
Motivation for this thesis/Research Objectives
Introduction
Coherent Detection Reduced Complexity Coherent Detectors
Noncoherent Detection Algorithm
Serially Concatenated Systems Simulation Results
Performance of Noncoherent detectors
Performance of Noncoherent (Coded) systems
Conclusions
Future work
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Performance of Noncoherent SOQPSK detectors
Noncoherent
detection of
SOQPSK-TGwith no phase
noise.
Loss of 0.75
dB when
a = 0.875
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Performance of Noncoherent SOQPSK detectors
Noncoherent
detection of
SOQPSK-TG withphase noise of
sym.
Loss of 3.1 dBwhen a = 0.875
/2=
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Performance of Noncoherent SOQPSK detectors
Noncoherent
detection of
SOQPSK-TGwith phase noise
of sym.
Loss of 9.8 dBwhen a = 0.625
Lower value of
a betters tracksfaster phase
changes.
/5=
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Performance of Noncoherent GMSK detectors
Noncoherent
detection of
GMSK (BT = 0.3)with phase noise
sym.
Loss of 2.0 dBwhen a = 0.625
/5=
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Performance of Noncoherent detectors
Loss in dB for noncoherent systems with phase noise of
sym. at
GMSK (BT = 0.3) has the best performance. SOQPSK MIL and GMSK (BT = 0.25) are comparable.
/2=510=bP
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Performance of Noncoherent detectors
Loss in dB for noncoherent systems with phase noise of
sym. at
GMSK (BT = 0.3) has the best performance. SOQPSK TG performs significantly worse.
Lower values ofa enable faster carrier phase tracking.
/5=510=bP
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Performance of Noncoherent Coded Systems
Noncoherent detection of coded a) SOQPSKMIL and b)
SOQPSKTG with sym./5=
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Performance of Noncoherent Coded Systems
Noncoherent detection of coded a) GMSK (BT = 0.3) and b) GMSK
(BT = 0.25) with sym./5=
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Performance of Noncoherent Coded Systems
Loss in dB for noncoherent (coded) systems at
a chosen to be 0.875 for all cases asEb/N0 is low.
SOQPSK and GMSK have comparable performance when
/sym. GMSK is marginally better than SOQPSK for the severe phase
noise case i.e. /sym.
510=bP
=2
= 5
O li
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Outline
Motivation for this thesis/Research Objectives
Introduction
Coherent Detection
Reduced Complexity Coherent Detectors
Noncoherent Detection Algorithm Serially Concatenated Systems
Simulation Results
Conclusions Key contributions
Future work
C l i
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Conclusions
Noncoherent (uncoded) detectors for GMSK and
SOQPSK have comparable performance for low to
moderate phase noise, for severe phase noise GMSKperforms significantly better.
For coded systems noncoherent GMSK detectors havemarginally better performance than SOQPSK.
SOQPSK TG has the highest coding gain (it is also
the most bandwidth efficient).
C l i K t ib ti
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Conclusions : Key contributions
Developed reduced complexity coherent detectors for
GMSK for the first time.
Noncoherent detection algorithm which can be used for
uncoded and coded systems was applied to GMSK for
the first time. A comprehensive set of numerical performance results
for SOQPSK and GMSK noncoherent detectors in
phase noise channels were provided.
O tli
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Outline
Motivation for this thesis/Research Objectives
Introduction
Coherent Detection
Reduced Complexity Coherent Detectors
Noncoherent Detection Algorithm Serially Concatenated Systems
Simulation Results
Conclusions
Future work
F t W k
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Future Work
Noncoherent coded SOQPSK and GMSK performance
with other convolutional codes as outer codes.
Investigation of GMSK with lowerBTvalues (more
bandwidth efficient).
Other complexity reduction techniques such as thePAM decomposition for GMSK.
References
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References
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Transactions on Communication, Sept. 2000.
M. K. Howlader and X. Luo. Noncoherent iterative demodulation and decoding ofserially concatenated coded MSK. In Proc. IEEE Global Telecommunications
Confernce, Nov./Dec. 2004. L. Li and M. Simon. Performance of coded OQPSK and MIL-STD SOQPSK with
iterative decoding.IEEE Transactions on Communication, Nov. 2004.
P. Moqvist and T. Aulin. Serially concatenated continuous phase modulation withiterative decoding.IEEE Transactions on Communication, Nov. 2001.
A. Svensson, C.-E. Sundberg, and T. Aulin. A class of reduced-complexity Viterbi
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IEEE Transactions on Vehicular Technology, July 2003.
Questions/Thanks
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Questions/Thanks
The End
Thank you for listening!