Title: Scaling and quantizing soft-decision metrics for decoding
Abstract: Soft metrics for multiple bursts transmitted at different times for a data packet are scaled, quantized, and rescaled prior to decoding. As each burst is received, input soft metrics for the burst are scaled with a scaling factor S(i), quantized based on a quantization scale factor Q(i), and stored in a buffer. The scaling factor and quantization scale factor are computed based on the statistics for the burst. After all bursts for the data packet have been received, the quantized soft metrics for each burst are rescaled based on the quantization scale factor Q(i) for that burst and a common scale factor to properly weight the soft metrics in the decoding process. The common scale factor is determined based on the quantization scale factors Q(i) for all bursts. The rescaled soft metrics for all bursts are requantized, deinterleaved, and decoded to obtain decoded data for the packet.
Patent Number: 6,986,096 Issued on 01/10/2006 to Chaudhuri, et al.
| Inventors:
|
Chaudhuri; Arunava (San Diego, CA);
Nadakuditi; Raj (Cambridge, MA)
|
| Assignee:
|
QUALCOMM, Incorporated (San Diego, CA)
|
| Appl. No.:
|
787003 |
| Filed:
|
February 24, 2004 |
| Current U.S. Class: |
714/780 |
| Current Intern'l Class: |
H03M 13/45 (20060101); H04L 1/00 (20060101) |
| Field of Search: |
714/780
|
References Cited [Referenced By]
U.S. Patent Documents
| 5271042 | Dec., 1993 | Borth et al.
| |
| Foreign Patent Documents |
| 1187344 | Mar., 2002 | EP.
| |
Other References
Reed, et al., "Iterative Multiuser Detection for CDMA with FEC: Near-Single-User
Performance", IEEE Transactions on Communications, vol. 46, No. 12, Dec. 1998,
pp. 1693-1699.
Y.K. Lee et al: "Normalization, windowing and quantization of soft-decision
Viterbi decoder inputs in CDMA"; Vehicular Technology Conference, 1999 IEEE
49th Houston, TX, May 16, 1999, pp. 221-225.
|
Primary Examiner: Baker; Stephen M.
Attorney, Agent or Firm: Wadsworth; Philip R., Brown; Charles D., Seo; Howard H.
Parent Case Text
RELATED APPLICATIONS
This application claims priority to U.S. Provisional Application No. 60/491,332
filed Jul. 29, 2003.
Claims
What is claimed is:
1. A method of processing a data transmission sent as a plurality of bursts in
a wireless communication system, comprising:
scaling input soft metrics for each of the plurality of bursts based on statistics
for the burst to obtain scaled soft metrics for the burst; and
rescaling the scaled soft metrics for each of the plurality of bursts based on
the statistics for the burst and the statistics for the plurality of bursts to
obtain rescaled soft metrics for decoding.
2. The method of claim 1, wherein the input soft metrics are log likelihood ratios (LLRs).
3. The method of claim 1, wherein the input soft metrics are multi-bit values
obtained for transmitted code bits.
4. The method of claim 1, wherein the rescaling includes
determining a common scale factor based on the statistics for the plurality of bursts,
determining a scale factor for each of the plurality of bursts based on the statistics
for the burst, and
rescaling the scaled soft metrics for each of the plurality of bursts based on
the scaling factor for the burst and the common scale factor.
5. The method of claim 1, further comprising:
storing rescaled soft metrics for a first transmission of the plurality of bursts;
deriving rescaled soft metrics for a second transmission of the plurality of
bursts; and
scaling and combining the rescaled soft metrics for the first transmission and
the rescaled soft metrics for the second transmission to obtain combined rescaled
soft metrics for decoding.
6. The method of claim 1, wherein the wireless communication system is a Global
System for Mobile Communications (GSM) system.
7. The method of claim 1, wherein the plurality of bursts are transmitted in
non-continuous time intervals.
8. The method of claim 1, wherein the statistics for each of the plurality of
bursts include a mean for the input soft metrics for the burst.
9. The method of claim 8, wherein the statistics for each of the plurality of
bursts further include a variance for the input soft metrics for the burst.
10. The method of claim 1, further comprising:
determining a scaling factor for each of the plurality of bursts based on a mean
of the input soft metrics for the burst, and wherein the input soft metrics for
each burst are scaled based on the scaling factor for the burst.
11. The method of claim 10, wherein the scaling factor for each of the plurality
of bursts is further determined based on a variance of the input soft metrics for
the burst.
12. The method of claim 1, further comprising:
quantizing the scaled soft metrics for each of the plurality of bursts based
on the statistics for the burst to obtain quantized soft metrics for the burst,
and wherein the rescaling is performed on the quantized soft metrics.
13. The method of claim 12, wherein the scaling and quantizing are performed
with one arithmetic operation on the input soft metrics using one scale factor.
14. The method of claim 12, wherein the rescaling includes
determining a common scale factor based on the statistics for the plurality of bursts,
determining a quantization scale factor for each of the plurality of bursts based
on the statistics for the burst, and
rescaling the quantized soft metrics for each of the plurality of bursts based
on the quantization scale factor for the burst and the common scale factor.
15. An apparatus in a wireless communication system, comprising:
a scaling unit operative to scale input soft metrics for each of a plurality
of bursts based on statistics for the burst to obtain scaled soft metrics for the
burst, wherein the plurality of bursts are for a data transmission received via
a wireless channel; and
a rescaling unit operative to rescale the scaled soft metrics for the plurality
of bursts based on the statistics for the burst and the statistics for the plurality
of bursts to obtain rescaled soft metrics for decoding.
16. The apparatus of claim 15, further comprising:
a quantizing unit operative to quantize the scaled soft metrics for each of the
plurality of bursts based on the statistics for the burst to obtain quantized soft
metrics for the burst, and wherein the resealing unit is further operative to rescale
the quantized soft metrics.
17. An apparatus in a wireless communication system, comprising:
means for scaling input soft metrics for each of a plurality of bursts based
on statistics for the burst to obtain scaled soft metrics for the burst, wherein
the plurality of bursts are for a data transmission received via a wireless channel; and
means for rescaling the scaled soft metrics for the plurality of bursts based
on the statistics for the burst and the statistics for the plurality of bursts
to obtain rescaled soft metrics for decoding.
18. The apparatus of claim 17, further comprising:
means for quantizing the scaled soft metrics for each of the plurality of bursts
based on the statistics for the burst to obtain quantized soft metrics for the
burst, and wherein the rescaling is performed on the quantized soft metrics.
19. A processor readable medium for storing instructions operable in a wireless
device to:
scale input soft metrics for each of a plurality of bursts based on statistics
for the burst to obtain scaled soft metrics for the burst, wherein the plurality
of bursts are for a data transmission received via a wireless channel; and
rescale the scaled soft metrics for the plurality of bursts based on the statistics
for the burst and the statistics for the plurality of bursts to obtain rescaled
soft metrics for decoding.
20. A method of processing a data transmission sent as a plurality of bursts
in a wireless communication system, comprising:
determining a scaling factor for each of the plurality of bursts based on statistics
for the burst;
scaling input soft metrics for each of the plurality of bursts with the scaling
factor for the burst to obtain scaled soft metrics for the burst;
quantizing the scaled soft metrics for each of the plurality of bursts based
on a quantization scale factor for the burst to obtain quantized soft metrics for
the burst;
determining a common scale factor based on quantization scale factors for the
plurality of bursts; and
rescaling the quantized soft metrics for each of the plurality of bursts based
on the quantization scale factor for the burst and the common scale factor to obtain
rescaled soft metrics for decoding.
21. The method of claim 20, wherein the scaling and quantizing for each of the
plurality of bursts are performed with one arithmetic operation on the input soft
metrics for the burst using one composite scale factor that includes the scaling
factor and the quantization scale factor for the burst.
22. The method of claim 20, wherein the common scale factor is equal to a maximum
quantization scale factor for the plurality of bursts if the number of bursts with
signal quality above a predetermined threshold is less than a threshold number.
23. The method of claim 20, wherein the common scale factor is set based on an
average of quantization scale factors for bursts with signal quality above a predetermined threshold.
24. The method of claim 20, further comprising:
requantizing the rescaled soft metrics for the plurality of bursts to obtain
requantized soft metrics; and
deinterleaving and decoding the requantized soft metrics for the plurality of bursts.
25. The method of claim 20, wherein the scaled soft metrics for each of the plurality
of bursts are quantized to M bits, where M is an integer greater than one.
26. The method of claim 25, wherein the quantization scale factor for each of
the plurality of bursts is selected to quantize the scaled soft metrics for the
burst to full range of the M bits.
27. The method of claim 20, wherein the scaling factor and quantization scale
factor for each of the plurality of bursts are determined based on a mean of the
input soft metrics for the burst.
28. The method of claim 27, wherein the scaling factor and quantization scale
factor for each of the plurality of bursts are further determined based on a variance
of the input soft metrics for the burst.
Description
BACKGROUND
1. Field
The present invention relates generally to communication, and more specifically
to techniques for scaling and quantizing soft-decision metrics for decoding in
a wireless communication system.
2. Background
In a wireless communication system, a transmitter encodes and interleaves traffic
data, typically one data packet at a time, to obtain interleaved data. The transmitter
may further partition each packet of interleaved data into multiple output blocks
for transmission at different times. The transmitter then modulates and transmits
these output blocks over a wireless channel at the designated times. If the transmission
for these output blocks is not continuous, then the transmission appears as "bursts",
one burst for each output block. The wireless channel distorts each transmitted
burst with a particular channel response and further degrades each transmitted
burst with noise and interference.
A receiver receives the transmitted bursts and processes each received burst
to
obtain soft-decision metrics (or simply, "soft metrics") for the burst. A soft
metric is a multi-bit value obtained by the receiver for a single-bit (or "hard")
value sent by the transmitter. In one conventional method, the receiver scales
the soft metrics for all received bursts for a given data packet with a single
scaling factor to obtain scaled soft metrics for these bursts. The scaling factor
is selected such that soft metrics with the proper amplitude are provided for subsequent
processing. Typically, the scaling factor is derived based on a signal-to-noise-and-interference
ratio (SNR) estimate for the bursts. The scaled soft metrics are then deinterleaved
and decoded to obtain decoded data for the packet.
Scaling all of the received bursts for a data packet with a single scaling
factor effectively gives these bursts equal weight in the decoding process. This
is acceptable if the wireless channel is relatively static and the bursts are received
with similar signal quality. However, if the bursts are transmitted at different
times, then these bursts may experience different channel conditions and achieve
different SNRs. In this case, scaling all bursts for a data packet with a single
scaling factor results in sub-optimal decoding performance.
In another conventional method, the scaling is performed based on an average
SNR
obtained for multiple prior bursts. For an additive white Gaussian noise (AWGN)
wireless channel with flat fading, the SNR may not change much from burst to burst,
and good performance may be achieved by scaling the current burst based on the
average SNR for the prior bursts. However, for a fading and interference wireless
channel, the channel conditions are not static, the statistics of the soft metrics
can change from burst to burst, and the SNR for the current burst may not correlate
well with the average SNR for the prior bursts. Furthermore, the average SNR is
not available for the first burst of a transmission or may be quite unreliable
after a long period of no transmission, such as for a discontinuous transmission
(DTX). Thus, decoding performance may be poor under uncertain conditions for this method.
There is therefore a need in the art for techniques to more properly scale
soft metrics for bursts transmitted at different times and possibly having different
statistics and signal quality.
SUMMARY
Techniques for scaling, quantizing, and rescaling soft metrics for multiple
bursts transmitted at different times are provided herein. These techniques may
be used for various wireless communication systems and can provide good performance
for various types of wireless channel (e.g., an AWGN channel, a fading and interference
channel, and so on).
For the scaling and quantizing, as each burst is received, input soft metrics
for the burst are scaled with a scaling factor S(i) to obtain scaled soft metrics.
The scaling factor S(i) is determined based on the statistics for the burst. The
scaled soft metrics for the burst are quantized based on a quantization scale factor
Q(i) to obtain quantized soft metrics, which are stored in a buffer. The quantization
scale factor Q(i) is computed based on the statistics for the burst and is also
saved. The scaling adjusts the values of the input soft metrics for different bursts
observing different channel conditions, and quantization is such that the scaled
soft metrics can be stored in minimal memory. The same processing is performed
for each of N
B bursts transmitted for a data packet (e.g., for a message),
where N
B>1.
For the rescaling, after all N
B bursts for the same data packet (or
message) have been received, the quantized soft metrics for each burst are rescaled
based on the statistics for that burst as well as the statistics for all bursts
to properly weight the soft metrics in the decoding process. In one rescaling scheme,
a common scale factor is determined for all N
B bursts based on the quantization
scale factors Q(i) for these bursts. The quantized soft metrics for each burst
are then rescaled based on the quantization scale factor Q(i) for that burst and
the common scale factor. The rescaled soft metrics for all N
B bursts
are requantized, deinterleaved, and decoded to obtain decoded data for the packet.
Various aspects and embodiments of the invention are described in further
detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
The features and nature of the present invention will become more apparent from
the detailed description set forth below when taken in conjunction with the drawings
in which like reference characters identify correspondingly throughout and wherein:
FIG. 1 shows a base station and a terminal in a wireless system;
FIG. 2 shows a channel organization for the traffic channels in GSM;
FIG. 3 shows a transmit (TX) data processor at the base station and a receive
(RX) data processor at the terminal;
FIG. 4 illustrates the processing and transmission of a data packet in GSM;
FIG. 5 shows a process for scaling, quantizing, and rescaling N
B
bursts of soft metrics for the data packet;
FIG. 6 shows a process for scaling and quantizing one burst of soft metrics;
FIG. 7 shows a process for resealing the quantized soft metrics for the N
B
bursts of the data packet; and
FIG. 8 shows a process for deriving a common scale factor for rescaling.
DETAILED DESCRIPTION
The word "exemplary" is used herein to mean "serving as an example, instance,
or illustration." Any embodiment or design described herein as "exemplary" is not
necessarily to be construed as preferred or advantageous over other embodiments
or designs.
The techniques described herein for scaling, quantizing, and rescaling soft metrics
may be used for various wireless communication systems in which a data packet for
a message is divided it into multiple blocks and each transmitted block may face
different channel condition. For example, these techniques may be used for a Time
Division Multiple Access (TDMA) system, a Code Division Multiple Access (CDMA)
system, a Frequency Division Multiple Access (FDMA) system, an orthogonal frequency
division multiplexing (OFDM) based system, and so on. A TDMA system may implement
one or more TDMA standards such as Global System for Mobile Communications (GSM).
A CDMA system may implement one or more CDMA standards such as Wideband CDMA (W-CDMA),
IS-2000, IS-856, IS-95, and so on. These standards are well known in the art.
The scaling, quantizing, and rescaling techniques may be used for data transmission
on the downlink and uplink. The downlink (i.e., forward link) is the communication
link from a base station to a terminal, and the uplink (i.e., reverse link) is
the communication link from the terminal to the base station. These techniques
may be used for user-specific transmission sent on a traffic channel as well as
overhead transmission sent on a control channel. For clarity, these techniques
are specifically described below for user-specific transmission on the downlink
in a GSM system.
FIG. 1 shows a block diagram of a base station
110 and a terminal
150
in a wireless communication system
100. On the downlink, at base station
110, a TX data processor
120 receives traffic data for terminal
150
as well as other terminals. TX data processor
120 formats, codes, and interleaves
the data for each terminal based on the coding and interleaving schemes selected
for the terminal and provides interleaved data for the terminal. A modulator (MOD)
122 then modulates the interleaved data for all terminals and provides modulated
data. A transmitter unit (TMTR)
124 processes the modulated data to generate
a downlink signal, which is then transmitted via an antenna
126 and over
a wireless channel to the terminals.
At terminal
150, an antenna
152 receives the downlink signal transmitted
by base station
110 and provides a received signal to a receiver unit (RCVR)
154. Receiver unit
154 conditions and digitizes the received signal
and provides a stream of data samples. A demodulator (DEMOD)
156 processes
the data samples and provides demodulated data. An RX data processor
160
next deinterleaves and decodes the demodulated data to recover the traffic data
transmitted by base station
110 for terminal
150. The processing
by demodulator
156 and RX data processor
160 is complementary to
the processing by modulator
122 and TX data processor
120, respectively,
at base station
110.
Controllers
130 and
170 direct the operation at base station
110 and terminal
150, respectively. Memory units
132 and
172
store program codes and data used by controllers
130 and
170, respectively.
Buffer
174 stores data for RX data processor
160. For simplicity,
FIG. 1 only shows the processing units for downlink transmission and does not show
all of the processing units normally present at base station
110 and terminal
150.
GSM uses different types of channels to send different types of data. In particular,
user-specific data is sent on traffic channels (TCH), broadcast data is sent on
a broadcast control channel (BCCH), and control data and other data (e.g., paging
messages) are sent on a common control channel (CCCH).
FIG. 2 shows an exemplary channel organization for the traffic channels in GSM.
The timeline for downlink transmission is divided into multiframes. For the traffic
channels, each multiframe includes 26 TDMA frames, which are labeled as TDMA frames
0 through
25. The traffic channels are sent in TDMA frames
0
through
11 and TDMA frames
13 through
24 of each multiframe.
A control channel (SACCH/T) is sent in TDMA frame
12 and is used to carry
inband signaling such as (1) measurement reports sent by terminals on the uplink
and (2) timing advances for the terminals sent by the base station on the downlink.
No data is sent in the idle frame, which is used by the terminals to make measurements
for neighbor base stations.
Each TDMA frame is further partitioned into 8 time slots, which are labeled
as time slots
0 through
7. Each active terminal/user is assigned
one time slot index for the duration of a call. User-specific data for each terminal
is sent in the time slot assigned to that terminal and in TDMA frames used for
the traffic channels (those labeled with "T" in FIG. 2). The transmission in each
time slot is referred to as a "burst" in GSM. The channel organization for the
traffic channels in GSM is described in detail in a document 3GPP TS 05.01, which
is publicly available.
FIG. 3 shows a block diagram of a TX data processor
120a at base
station
110 and an RX data processor
160a at terminal
150.
TX data processor
120a is an embodiment of TX data processor
120
in FIG. 1 and performs the transmitter processing for a traffic channel for a terminal
(e.g., terminal
150). RX data processor
160a is an embodiment
of RX data processor
160 in FIG. 1 and performs the receiver processing
for the traffic channel. In GSM, data to be sent on a traffic channel is coded
in groups of four data blocks, with each data block containing a specified number
of information bits. Each group of four data blocks may be viewed as one data packet
that contains all of the information bits for the four data blocks.
Within TX data processor
120a, a block encoder
310 performs
block encoding on a packet of information bits for terminal
150 and generates
parity bits for the packet. The parity bits are used for error detection by terminal
150. A convolutional encoder
312 performs convolutional encoding
on the output from block encoder
310 and provides a packet of code bits.
The coding scheme is determined by base station
110 based on the state (e.g.,
idle or traffic mode) of terminal
150 as well as the type of call (e.g.,
circuit switched call or packet switched call). For a packet switched call, the
coding scheme may also be determined by measurements sent by terminal
150.
An interleaver
314 reorders the code bits in the packet based on an interleaving
scheme and provides a packet of interleaved bits. A partitioning unit
316
partitions the interleaved data packet into N
B output blocks, where
N
B may be 4, 8, or 22 depending on the channel type (e.g., voice, circuit
switched, paging, or control channel). The partitioning and interleaving may be
performed in various manners. For example, the coded data may be interleaved first
and then partitioned into output blocks, as shown in FIG. 3. Alternatively, the
coded data packet may be partitioned first into blocks and then interleaved. The
partitioning and interleaving may also be performed in one operation.
Modulator
122 modulates each output block based on a modulation
scheme (e.g., GMSK or 8-PSK) selected for terminal
150 to obtain N
B
symbol blocks. These symbol blocks are subsequently transmitted in an assigned
time slot of N
B TDMA frames used for the traffic channel. For simplicity,
the processing by other units between modulator
122 and demodulator
156
is not shown in FIG. 3.
FIG. 4 illustrates the processing and transmission of a data packet in GSM.
Block encoder
310 processes a packet
408 of information bits and
provides a block
410 containing information bits, parity bits, and tail
bits. Convolutional encoder
312 then processes block
410 in accordance
with a (e.g., rate 1/2, 1/3, 1/4, or 1/5) convolutional code and provides a block
412 of coded data. Interleaver
314 interleaves the code bits in blocks
412 and provides a block
414 of interleaved bits. Partitioning unit
316 then partitions block
414 into N
B output blocks
416a
through
416n.
As shown in FIG. 4, first output block
416a is transmitted in time
slot x of TDMA frame n and is denoted as burst
0, second output block
416b
is transmitted in time slot x of TDMA frame n+1 and is denoted as burst
1,
and so on, and the last output block
416n is transmitted in time
slot x of TDMA frame n+N
B-1 and is denoted as burst N
B-1.
Here, x is the time slot assigned to terminal
150 and n is the TDMA frame
index. As shown in FIGS. 3 and 4, a data packet is encoded, interleaved, and transmitted
in N
B bursts. These bursts may experience different channel conditions
and achieve different SNRs.
Referring back to FIG. 3, at terminal
150, demodulator
156
obtains data samples from receiver unit
154. Each data sample is a complex
value with an inphase (I) component and a quadrature (Q) component. Each component
is digitized to L bits (e.g., L=16). Demodulator
156 processes the data
samples in accordance with an equalizer design and provides "input" soft metrics
z
in(i,k), where i denotes the i-th burst and k denotes the k-th soft
metric for the burst. Demodulator
156 may perform equalization based on
a maximum likelihood sequence estimator (MLSE) followed by soft-decision generation
(or simply, a "soft-output MLSE"), as described by Ono, S. et al. in "An MLSE Receiver
for the GSM Digital Cellular System," Proc. of the 44th IEEE Vehicular Technology
Conference, Jun. 8-10, 1994, pp. 230-233. Demodulator
156 may also perform
equalization based on a minimum mean square error (MMSE) equalizer or some other
type of equalizer. The equalization attempts to mitigate intersymbol interference
(ISI), which is a phenomenon whereby each symbol in a received signal acts as distortion
to subsequent symbols in the received signal. ISI is caused by frequency selective
fading, which is characterized by a frequency response that is not flat across
the system bandwidth. Demodulator
156 may alternatively or additionally
perform matched filtering.
A soft metric is a multi-bit value that is indicative of a single-bit (or "hard")
value sent by a transmitter. For example, a soft metric may be derived by a receiver
for each code bit transmitted by the transmitter. A soft metric is commonly expressed
as a log likelihood ratio (LLR), which is the logarithm of the ratio of the probability
of a transmitted bit being a "1" over the probability of the transmitted bit being
a "0", where the probabilities are conditioned on the data samples obtained at
the receiver (i.e., the "observed" data samples). A soft metric may also be expressed
in other forms, and this is within the scope of the invention. For example, the
soft metric may simply be equal to a received symbol obtained by the receiver for
a transmitted symbol sent by the transmitter.
RX data processor
160a obtains the input soft metrics from demodulator
156. Within processor
160a, a scaler/quantizer
360
scales and quantizes the input soft metrics z
in(i,k) for each burst
and provides quantized soft metrics z
qn(i,k) to buffer
174. After
all N
B bursts for the data packet have been received, a rescaler
370
rescales the quantized soft metrics z
qn(i,k) for all N
B bursts
and provides rescaled soft metrics z
rs(i,k), which are requantized (not
shown in FIG. 3 for simplicity).
A deinterleaver
378 receives and deinterleaves the requantized soft metrics
z
rq(i,k) for all N
B bursts and provides deinterleaved soft
metrics to a Viterbi decoder
380. Viterbi decoder
380 performs convolutional
decoding on the deinterleaved soft metrics and provides decoded bits. A block decoder
382 performs error detection on the decoded bits and provides a recovered
data packet. Block decoder
382 also provides the status of the recovered
data packet, which is "good" if the packet is decoded correctly and "bad" or "erased"
if the packet is decoded in error.
The scaling/rescaling and deinterleaving may be performed in various manners.
For clarity, FIG. 3 shows the deinterleaving being performed on the rescaled soft
metrics after all N
B bursts have been received. In a more efficient
implementation, the deinterleaving is performed prior to the rescaling. For this
implementation, the quantized soft metrics z
qn(i,k) for each burst can
be stored in deinterleaved order in buffer
174. Alternatively, the quantized
soft metrics z
qn(i,k) can be retrieved from buffer
174 in deinterleaved
order and provided to rescaler
370. In any case, the deinterleaving can
be performed by storing or retrieving the quantized soft metrics to/from the proper
locations in buffer
174. Buffer
174 can thus (1) store N
B bursts
of quantized soft metrics for subsequent processing and (2) facilitate deinterleaving.
For GSM, the soft metrics for the Viterbi decoder are spread across N
B
bursts that are transmitted at different times and may achieve different SNRS.
Furthermore, the soft metrics are obtained after equalization at the receiver,
and the statistics of the soft metrics for each burst may not directly correspond
to the SNR for the burst. Ideally, the scaling should be performed on the soft
metrics for the bursts after all N
B bursts have been received. However,
the soft metrics provided by demodulator
156 may have many bits of resolution
(e.g., 16 bits), and buffer
174 may not have sufficient capacity to store
these soft metrics in its original form. In this case, the soft metrics for each
burst need to be scaled and quantized to a specified number of (M) bits as the
burst is received. The scaling and quantizing should be done in a manner such that
system performance is minimally degraded.
To achieve good performance, the soft metrics for each burst are scaled and quantized
based on the statistics obtained for the burst, as described below. This allows
the soft metrics for each burst to be stored using the full resolution of the M
bits. After all N
B bursts have been received, the soft metrics for each
burst are rescaled based on the statistics for that burst and the statistics for
all N
B bursts, as described below. This allows the soft metrics for
each burst to be given appropriate weight in the decoding process.
FIG. 5 shows a process
500 for scaling, quantizing, and rescaling N
B
bursts of soft metrics for a data packet. Block
510 is performed for each
burst as the burst is received. Block
520 is performed after all N
B
bursts have been received.
As a burst i is received, the input soft metrics z
in(i,k) for the
burst
are scaled with a scaling factor S(i) to obtain scaled soft metrics z
sc(i,k)
(block
512). The scaling factor S(i) is determined based on the statistics
for the burst. The scaled soft metrics z
sc(i,k) for burst i are quantized
based on a quantization scale factor Q(i) to obtain quantized soft metrics z
qn(i,k),
which are stored to buffer
174 (block
514). The quantization scale
factor Q(i) is also computed based on the statistics for the burst. Blocks
512
and
514 may be performed as described below.
A determination is then made whether or not all N
B bursts for the
data
packet have been received (block
516). If the answer is no, then the process
returns to block
512 to process the next burst. Otherwise, a common scale
factor Q
com is determined for all N
B bursts based on the
statistics for these bursts (block
522). The quantized soft metrics z
qn(i,k)
for each burst are then rescaled based on the quantization scale factor Q(i) for
the burst and the common scale factor Q
com for all bursts (block
524).
The rescaled soft metrics z
rs(i,k) for all N
B bursts are
then requantized, deinterleaved, and decoded to obtain decoded data for the packet
(block
526). Each of the blocks in FIG. 5 is described in further detail below.
FIG. 6 shows a process
510a for scaling and quantizing one burst
of soft metrics. Process
510a may be used for block
510 in
FIG. 5. Initially, pertinent statistics for the burst are obtained (block
612).
The soft metrics can take on both positive and negative values when expressed as
LLRs. The statistics may include the mean and variance of the absolute value of
the input soft metrics for the burst, which can be expressed as:
##EQU1##
where N
i is the number of soft metrics for burst i;
- μin(i) is the mean of the absolute value of the input
soft metrics for burst i; and
- σin2(i) is the variance of the absolute
value of the input soft metrics for burst i.
The scaling factor S(i) for the burst is then determined based on its statistics
(block
614), as follows:
##EQU2##
The scaling factor S(i) in equation (3) is based on an assumption that the statistics
of the noise in the input soft metrics is approximately Gaussian.
Each input soft metric z
in(i,k) for the burst is scaled by the scaling
factor S(i) to obtain a corresponding scaled soft metric z
sc(i,k) (block
616), as follows:
zsc(
i,k)=
S(
i)·
zin(
i,k),
for
k=1 . . .
Ni. Eq (4)
For the quantization, pertinent statistics for the scaled soft metrics for the
burst are first obtained (block
622). For example, the mean and variance
of the absolute value of the scaled soft metrics for the burst can be computed
as:
##EQU3##
As shown in equations (5) and (6), the mean μ
sc(i) and variance
σ
sc2(i) for the scaled soft metrics are related to
and can be computed from the mean μ
in(i) and variance σ
in2(i)
for the input soft metrics. Thus, the statistics for the soft metrics for the burst
only need to be computed once.
The quantization scale factor Q(i) for the burst is then determined based on
its statistics (block
624), as follows:
Q(
i)=μ
sc(
i)+α·σ
sc2(
i), Eq (7)
where α is a coefficient that determines the amount of headroom for the
quantization. If μ
sc(i)=σ
sc2(i) because
of the scaling by μ
in(i)/σ
in2(i),
then the quantization scale factor Q(i) may be simplified as Q(i)=γ·μ
sc(i)
where γ=1+α. The coefficient γ can be selected, for example,
as a value within a range of 1.5 to 1.8.
Each scaled soft metric z
sc(i,k) for the burst is then quantized
based on the quantization scale factor Q(i) to obtain a corresponding quantized
soft metric z
qn(i,k) (block
626). The quantization of a scaled
soft metric to M bits (e.g., M=5) may be expressed as:
##EQU4##
For simplicity, the rounding to obtain an integer value for z
qn(i,k)
is not shown in equation (8). Equation (8) also shows the quantized soft metrics
being obtained with a division by Q(i). Division is more computationally intensive
than multiplication in hardware and software. Thus, the inverse of Q(i) can be
computed instead of Q(i). The quantized soft metrics can then be obtained by multiplication
with the inverse of Q(i).
The quantized soft metrics z
qn(i,k) as well as the quantization scale
factor Q(i) for the burst are stored in buffer
174 (block
628).
FIG. 6 shows a specific scheme for scaling and quantizing the input soft metrics
for a burst. The scaling factor S(i) and the quantization scale factor Q(i) may
be derived in other manners, and this is within the scope of the invention. For
example, scaling factor S(i) may be a function of the mean (but not the variance).
The scaling and quantizing may also be performed in other manners, and this is
within the scope of the invention.
The manner in which the scaling and quantizing can be performed efficiently may
be influenced by the design of demodulator
156. As an example, for the soft-output
MLSE (i.e., the MLSE with soft-decision generation), the mean-to-variance ratio
is equal to the inverse of the noise power for the burst, i.e., μ
in(i)/σ
in2(i)=1/σ
n2(i),
where σ
n2(i) is the burst noise power. The burst noise
power can be computed by (1) convolving the output of the MLSE with a channel impulse
response estimate for the wireless channel to obtain estimated soft metrics z
est(i,k),
(2) subtracting the input soft metrics from the estimated soft metrics to obtain
the noise, i.e., n(i,k)=z
est(i,k)-z
in(i,k), and (3) computing
the power of the noise n(i,k) to obtain σ
n2(i). If
the burst noise power is already computed by demodulator
156 for other uses
and is available, then the scaling factor S(i) can be computed based on the burst
noise power as S(i)=1/σ
n2(i), instead of based on the
mean μ
in(i) and variance σ
in2(i) of
the input soft metrics. If the mean μ
in(i) and variance σ
n2(i)
are not computed (e.g., because the scaling factor S(i) is computed based on the
burst noise power), then the mean μ
sc(i) and variance σ
sc2(i)
can be computed for the scaled soft metrics, as shown in equations (5) and (6).
The mean μ
sc(i) and variance σ
sc2(i)
are then used to determine the quantization scale factor Q(i) for the burst, as
shown in equation (7).
For clarity, the scaling and quantizing of the soft metrics are shown as separate
blocks in FIG. 6. The scaling and quantizing may also be performed in one step.
In this case, the quantization scale factor for each burst may be computed as
##EQU5##
The quantization may be performed directly on the input soft metrics z
in(i,k)
with Q′(i) to obtain the M-bit quantized soft metrics z
qn(i,k),
as shown in equation (8) with z
in(i,k) substituting for z
sc(i,k)
and Q′(i) substituting for Q(i). The quantization scale factor may also
be computed as Q′(i)=μ
in(i)+α·σ
in2(i)
or in some other manner, and this is within the scope of the invention.
After all of the N
B bursts have been received, the quantized soft
metrics for these bursts are rescaled to obtain rescaled soft metrics for subsequent
processing. The soft metrics for each burst are initially scaled and quantized
(1) based only on the statistics for that burst and without regard to the statistics
for the other bursts and (2) in a manner to occupy the full range of the M bits.
The rescaling is performed to properly weight the soft metrics for the N
B
bursts in the decoding process.
In an embodiment, the rescaling is performed based on a common scale factor Q
com,
which is one of the factors that determine the weight that will be given to the
soft metrics for each burst. The common scale factor Q
com is obtained
based on (as a function of) the statistics for all N
B bursts and may
be derived in various manners. In general, more weight should be given to soft
metrics for bursts with high signal quality and less or no weight should be given
to soft metrics for bursts with low signal quality.
For clarity, an exemplary scheme for deriving the common scale factor Q
com
is described below. For this scheme, bursts with low signal quality below a predetermined
threshold (e.g., SNR<0 dB) are considered as "bad" bursts and given no weight
in the decoding process. These bad bursts also do not affect the rescaling for
the remaining "good" bursts. If there are some good bursts with marginal signal
quality and some good bursts with high signal quality (i.e., if the signal quality
varies by a large amount among the good bursts), then the rescaling is performed
such that the full range of the high quality bursts is retained. This would give
the high quality bursts more weight in the decoding process. If the signal quality
of the good bursts varies by a smaller amount, then the resealing is performed
based on an average of the quantization scale factors for these bursts. This would
give good bursts with lower signal quality equal weight in the decoding process.
FIG. 7 shows a process
520a for rescaling the quantized soft metrics
for the N
B bursts of one packet. Process
520a may be used
for block
520 in FIG. 5.
Initially, the good bursts among the N
B bursts are identified
and counted (block
712). A burst may be deemed as good if its quantization
scale factor Q(i) is equal to or greater than a predetermined threshold Q
good,th,
i.e., burst i is deemed as good if Q(i)≧Q
good,th. The number
of good bursts is denoted as N
G. The maximum, average, and minimum of
the quantization scale factors Q(i) for the N
G good bursts are determined
and denoted as Q
max, Q
avg, and Q
min, respectively
(block
714). The common scale factor Q
com is then derived based
on the parameters N
B, N
G, Q
max, Q
avg,
and Q
min (block
716).
FIG. 8 shows a process
716a for deriving the common scale factor
Q
com for resealing. Process
716a may be used for block
716 in FIG. 7. A determination is first made whether or not the number of
good bursts is less than a threshold number of good bursts (N
th) (block
812). The threshold number N
th may be set equal to half the number
of bursts (e.g., N
th=N
B/2) or to some other value. If the
answer is 'yes' for block
812, then the common scale factor is set equal
to the maximum quantization scale factor (i.e., Q
com=Q
max)
(block
816). If the number of good bursts is small, where "small" for this
parameter is quantified by N
th, then these good bursts are given greater
weight in the decoding process by setting Q
com=Q
max.
If at least N
th good bursts were received (i.e., the answer is "no"
for block
812), then a determination is made whether or not the ratio of
the maximum quantization scale factor to the minimum quantization scale factor
(Q
max/Q
min) for the good bursts is less than a threshold
value Q
var,th (block
822). The ratio Q
max/Q
min
is indicative of the variability in the quality or SNRs of the good bursts.
If the variability is sufficiently small, where "small" for this parameter is quantified
by Q
var,th, then the common scale factor is set equal to the average
quantization scale factor for all of the good bursts (i.e., Q
com=Q
avg)
(block
826). This would give good bursts with lower signal quality equal
weight in the decoding process. The Q
var,th threshold may be set to
six (e.g., Q
var,th=6) or some other value.
If the variability in quality is not sufficiently small (i.e., the answer is
"no"
for block
822), then a determination is made whether or not the average
quantization scale factor is greater than a threshold value Q
avg,th
(block
824). The average quantization scale factor is indicative of the
average quality of the good bursts. If the average quality is sufficiently high,
where "high" for this parameter is quantified by Q
avg,th, then the common
scale factor is set equal to the average quantization scale factor for the N
G
good bursts (i.e., Q
com=Q
avg) (block
826). Otherwise,
the common scale factor is set equal to the maximum quantization scale factor (i.e.,
Q
com=Q
max) (block
816). The Q
avg,th threshold
may be set to three (e.g., Q
avg,th=3) or some other value.
FIG. 8 shows a specific embodiment for deriving the common scale factor Q
com
for rescaling. Q
com may also be derived in other manners and based on
other functions of the statistics of the N
B bursts, and this is within
the scope of the invention. In general, the derivation of Q
com may (1)
take into account the parameters described above (N
B, N
G,
Q
max, Q
avg, Q
min, Q
var,th and Q
avg,th)
and/or other parameters, (2) consider the criteria described above (e.g., relative
number of good bursts, variability in the quality of the good bursts, and absolute
value of the average quality) and/or other criteria, and (3) use other functions
of the parameters and criteria.
Referring back to FIG. 7, a rescaling factor R(i) is computed for each
burst based on the quantization scale factor Q(i) for that burst and the common
scale factor Q
com (block
722), as follows:
##EQU6##
The rescaling factor R(i) for a bad burst is set to zero to give that burst no
weight in the decoding process. In equation (9), a burst is considered "bad" if
its quantization scale factor Q(i) is less than the Q
good,th threshold value.
Each quantized soft metric z
qn(i,k) for each burst is retrieved from
buffer
174 and rescaled by the rescaling factor R(i) for that burst to obtain
a corresponding rescaled soft metric z
rs(i,k) (block
724), as follows:
zrs(
i,k)=
R(
i)·
zqn(
i,k),
for
k=1
. . . Ni. Eq (10)
Each rescaled soft metric z
rs(i,k) is then requantized to the required
number of bits (block
726). Typically, the rescaled soft metrics are requantized
to the same number of bits with which the soft metrics were stored in buffer
174,
which is M bits. In this case, the requantization can be achieved by saturating
the rescaled soft metrics to M bits. If Q
com=Q
max, then all
of the rescaled soft metrics will be M bits or less and saturating is not needed.
If Q
com=Q
avg, then the rescaled soft metrics for bursts with
Q(i)>Q
avg may need to be saturated to obtain M-bit rescaled soft metrics.
The requantized soft metrics z
rq(i,k) for all N
B bursts
are deinterleaved and decoded to obtain the decoded data for the packet (block
728). Since the rescaling factors for bad bursts are set to zero for the
embodiment described above, only the requantized soft metrics for the N
G good
bursts are used for the decoding process.
The techniques described herein may advantageously be used for incremental redundancy
(IR) transmission whereby portions of a data packet are retransmitted due to errors
at the receiver. As an example, each of the N
B bursts may include an
error detection value (e.g., a CRC value, a checksum, and so on) that allows the
receiver to determine whether or not the burst was correctly received by the receiver.
The receiver can signal to the transmitter which bursts have been incorrectly received,
and the transmitter can retransmit these bursts. The receiver can process the transmitted
and retransmitted bursts in various manners to decode the packet.
In one scheme, the receiver substitutes the error bursts with the retransmitted
bursts and discards the error bursts. Each retransmitted burst can be scaled and
quantized based on its statistics, in the same manner as for a transmitted burst.
All of the N
B bursts (where zero, one, or multiple bursts can be retransmitted
bursts) are then rescaled based on their statistics, requantized, deinterleaved,
and decoded, as described above.
In another scheme, the receiver combines the transmitted version and retransmitted
versions (if any) for each of the N
B bursts to obtain a composite burst.
The combining of all versions of a given burst i can be expressed as:
##EQU7##
where N
ver,i is the number of versions available for burst i;
- zin,j(i,k) is the input soft metrics for the j-th version/transmission
of burst i;
- Sj(i) is the scaling factor for the j-th version of burst
i; and
- z′sc(i,k) is the scaled soft metrics for composite
burst i.
The scaling factor Sj(i) for each version of burst i is determined
based on the statistics for that version, as follows:
##EQU8##
where μin,j(i) is the mean of the absolute value of the input
soft metrics for the j-th version of burst i; and
- σin,j2(i) is the variance of the absolute
value of the input soft metrics for the j-th version of burst i.
All of the NB bursts (where zero, one, or multiple bursts can be composite
bursts) are then rescaled based on their statistics, requantized, deinterleaved,
and decoded, as described above.
In yet another scheme, the receiver combines a current retransmitted version
and
a prior combined version (if any) for each of the N
B bursts to obtain
a new combined version for the burst. For the first transmission, the receiver
performs scaling, quantization, and rescaling for the N
B bursts in accordance
with a common scale factor, Q
com,1, derived for the first transmission,
as described above. The rescaled soft metrics z
in,1(i,k) for the first
transmission are then decoded. If there is a decoding failure, then the rescaled
soft metrics z
in,1(i,k) and the common scale factor Q
com,1 are
stored. For the first retransmission, the receiver performs scaling, quantization,
and rescaling for the bursts in the retransmission in accordance with a common
scale factor, Q
com,2, derived for the retransmission, in the same manner
as for the first transmission. The receiver then scales the rescaled soft metrics
z
in,1(i,k) for the first transmission and the rescaled soft metrics
z
in,2(i,k) for the first retransmission based on a function of the common
scale factors Q
com,1and Q
com,2. As an example, the rescaled
soft metrics z
in,1(i,k) may be scaled by Q
com,1/(Q
com,1+Q
com,2)
to obtain z′
in,1(i,k), and the rescaled soft metrics z
in,2(i,k)
may be scaled by Q
com,2/(Q
com,1+Q
com,2) to obtain
z′
in,2(i,k). Other functions of Q
com,1 and Q
com,2
may also be used. The receiver then combines z′
in,1(i,k)
and z′
in,2(i,k) to obtain combined rescaled soft metrics z"
in,2(i,k),
which are then decoded. If there is a decoding failure, then the combined rescaled
soft metrics z"
in,2(i,k) and a combined common scale factor, Q"
com,2=Q
com,1+Q
com,2,
are stored. For the next retransmission, the receiver processes the bursts in the
new retransmission and combines these bursts with the stored bursts in similar
manner as for the first retransmission.
The scaling, quantizing, and rescaling techniques described herein provide good
performance for a transmission sent as bursts that observe different channel conditions
and achieve different signal quality. The scaling and quantizing of each burst
(as it is received) based on its statistics allow the soft metrics to be stored
(1) with a limited number of (M) bits, thereby reducing storage requirement, and
(2) with the full resolution available for these M bits. The rescaling of the quantized
soft metrics for all bursts based on their statistics allows these soft metrics
to be given appropriate weight in the decoding process, thereby improving performance.
The techniques described herein can provide better performance than the conventional
method that scales all bursts with a single scaling factor and the conventional
method that scales each burst based on the average SNR of multiple prior bursts.
These techniques can also provide improved performance for messages received following
a discontinuous transmission (DTX), such as paging messages that are commonly sent
in cellular systems.
The scaling, quantizing, and rescaling techniques described herein may be implemented
by various means. For example, these techniques may be implemented in hardware,
software, or a combination thereof. For a hardware implementation, the processing
units used to perform scaling, quantizing, and rescaling may be implemented within
one or more application specific integrated circuits (ASICs), digital signal processors
(DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs),
field programmable gate arrays (FPGAs), processors, controllers, micro-controllers,
microprocessors, other electronic units designed to perform the functions described
herein, or a combination thereof.
For a software implementation, the scaling, quantizing, and rescaling techniques
may be implemented with modules (e.g., procedures, functions, and so on) that perform
the functions described herein. The software codes may be stored in a memory unit
(e.g., memory unit
172 in FIG. 1) and executed by a processor (e.g., controller
170). The memory unit may be implemented within the processor or external
to the processor, in which case it can be communicatively coupled to the processor
via various means as is known in the art.
The previous description of the disclosed embodiments is provided to enable any
person skilled in the art to make or use the present invention. Various modifications
to these embodiments will be readily apparent to those skilled in the art, and
the generic principles defined herein may be applied to other embodiments without
departing from the spirit or scope of the invention. Thus, the present invention
is not intended to be limited to the embodiments shown herein but is to be accorded
the widest scope consistent with the principles and novel features disclosed herein.
*
