wlanVHTDataBitRecover

Recover bits from VHT-Data field

Since R2025a

Syntax

[dataBits,vhtsigbCRC] = wlanVHTDataBitRecover(rxDataSym,noiseEst,csi,cfg)

[dataBits,vhtsigbCRC] = wlanVHTDataBitRecover(___,userIdx)

[dataBits,vhtsigbCRC] = wlanVHTDataBitRecover(___,Name=Value)

Description

[dataBits,vhtsigbCRC] = wlanVHTDataBitRecover(rxDataSym,noiseEst,csi,cfg) recovers dataBits, a column vector of bits, from rxDataSym, the equalized OFDM symbols that make up the VHT-Data field of a very high-throughput (VHT) transmission. The function recovers the bits by using the noise variance estimate noiseVarEst, the channel state information csi, and VHT transmission parameters cfg.

[dataBits,vhtsigbCRC] = wlanVHTDataBitRecover(___,userIdx) specifies the user index userIdx in combination with the previous syntax.

[dataBits,vhtsigbCRC] = wlanVHTDataBitRecover(___,Name=Value) specifies algorithm options by using one or more name-value arguments in addition to any input argument combination from the previous syntaxes. For example, LDPCDecodingMethod="layered-bp" specifies the layered belief propagation low-density parity-check (LDPC) decoding algorithm.

Examples

Recover Bits from VHT-Data Field

Create a VHT configuration object with default parameters, specifying LDPC channel coding. Get the PSDU length of the configuration in bits.

cfg = wlanVHTConfig(ChannelCoding="LDPC");
psduBits = 8*cfg.PSDULength;

Generate a random sequence of PSDU bits.

txBits = randi([0 1],psduBits,1);

Use the PSDU bits to generate a time-domain waveform for the VHT-Data field of the configuration.

txData = wlanVHTData(txBits,cfg);

Generate a time-domain waveform for the very high-throughput long training field (VHT-LTF) of the configuration.

txVHTLTF = wlanVHTLTF(cfg);

Pass both time-domain waveforms through an AWGN channel with a signal-to-noise ratio of 20 dB, returning the noise variance.

snr = 20;
[rxVHTLTF,noiseEst] = awgn(txVHTLTF,snr);
rxData = awgn(txData,snr);

Demodulate the VHT-LTF and use the demodulated symbols to get a channel estimate.

demodVHTLTF = wlanVHTDemodulate(rxVHTLTF,"VHT-LTF",cfg);
chEst = wlanVHTLTFChannelEstimate(demodVHTLTF,cfg);

Demodulate the VHT-Data field and use the demodulated symbols to track and correct pilot errors.

demodData = wlanVHTDemodulate(rxData,"VHT-Data",cfg);
trackedSym = wlanVHTTrackPilotError(demodData,chEst,cfg,"VHT-Data");

Use the channel and noise estimates to equalize the pilot-error-tracked symbols. Return channel state information.

[eqSym,csi] = wlanVHTEqualize(trackedSym,chEst,noiseEst,cfg,"VHT-Data");

Get OFDM information for the configuration. Use the OFDM information to extract the data symbols and the associated channel state information.

info = wlanVHTOFDMInfo("VHT-Data",cfg);
rxDataSym = eqSym(info.DataIndices,:);
csi = csi(info.DataIndices,:);

Recover the PSDU bits by using belief propagation low-density parity-check (LDPC) decoding. Verify that the transmitted bits match the recovered bits.

[dataBits,vhtsibgCRC] = wlanVHTDataBitRecover(rxDataSym,noiseEst,csi,cfg, ...
    LDPCDecodingMethod="bp");
isequal(txBits,dataBits)

ans = logical
   1

Recover Data Bits from VHT Multi-User Transmission

Create a VHT configuration object with three users, three transmit antennas, and one space-time stream per user.

cfg = wlanVHTConfig(NumUsers=3,UserPositions=[0 1 2], ...
    NumTransmitAntennas=3,NumSpaceTimeStreams=[1 1 1],GroupID=1);

Create a three-element cell array of PSDU bits. Each element of the array contains the bits for one user.

psdu = cell(1,cfg.NumUsers);
for i = 1:cfg.NumUsers
    psdu{i} = randi([0 1],cfg.PSDULength(i)*8,1);
end

Generate time-domain waveforms for the VHT-LTF and VHT-Data field of the configuration.

txVHTLTF = wlanVHTLTF(cfg);
txData = wlanVHTData(psdu,cfg);

Pass both waveforms through an AWGN channel with a signal-to-noise ratio of 25 dB, returning the noise variance.

snr = 25;
[rxVHTLTF,noiseEst] = awgn(txVHTLTF,snr);
rxData = awgn(txData,snr);

Demodulate the VHT-LTF. Use the demodulated symbols to estimate the channel at the VHT-LTF.

demodVHTLTF = wlanVHTDemodulate(rxVHTLTF,"VHT-LTF",cfg);
chEst = wlanVHTLTFChannelEstimate(demodVHTLTF,cfg);

Demodulate the VHT-Data field.

field = "VHT-Data";
sym = wlanVHTDemodulate(rxData,field,cfg);

Use the demodulated symbols with the channel estimate to track and correct pilot errors.

trackedSym = wlanVHTTrackPilotError(sym,chEst,cfg,field);

Equalize the pilot-error-tracked symbols for the third user.

userIdx = 3;
[eqSym,csi] = wlanVHTEqualize(trackedSym,chEst,noiseEst,cfg,field,userIdx);

Get OFDM information for the configuration. Use the OFDM information to extract the data symbols and the associated channel state information.

info = wlanVHTOFDMInfo(field,cfg);
rxDataSym = eqSym(info.DataIndices,:);
csi = csi(info.DataIndices,:);

Recover the data bits for the third user. Verify that the transmitted bits match the recovered bits.

[dataBits,vhtsigbCRC] = wlanVHTDataBitRecover(rxDataSym,noiseEst,csi,cfg,userIdx);
isequal(psdu{userIdx},dataBits)

ans = logical
   1

Input Arguments

rxDataSym — Demodulated VHT-Data field

complex-valued array

Demodulated VHT-Data field, specified as a complex-valued array of size N_SD-by-N_SYM-by-N_SS.

• N_SD is the number of data subcarriers in the VHT-Data field.

• N_SYM is the number of OFDM symbols.

• N_SS is the number of spatial streams.

The contents and size of this input depend on the VHT format specified in the cfg input.

Data Types: double | single
Complex Number Support: Yes

noiseEst — Noise estimate

nonnegative real scalar

Noise estimate, specified as a nonnegative real scalar.

Data Types: double | single

csi — Channel state information

real-valued array

Channel state information, specified as a real-valued array of size N_SD-by-N_SS.

Data Types: double | single

cfg — PHY format configuration

wlanVHTConfig object

Physical layer (PHY) format configuration, specified as a wlanVHTConfig object.

userIdx — User index

positive integer

User index, specified as a positive integer.

Note

This input is required only when the STBC property of the cfg input is 0 (false), and the NumSpaceTimeStreams of the cfg input is a vector. If you specify this input in any other situation, the function ignores it.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: MaximumLDPCIterationCount=12,EarlyTermination=false specifies a maximum of 12 LDPC decoding iterations and disables early termination so that the decoder completes the 12 iterations.

LDPCDecodingMethod — LDPC decoding algorithm

"norm-min-sum" (default) | "bp" | "layered-bp" | "offset-min-sum"

LDPC decoding algorithm, specified as the name-value argument consisting of LDPCDecodingMethod and one of these values:

• "norm-min-sum" — Use the layered BP decoding algorithm with the normalized min-sum approximation. For more information, see Normalized Min-Sum Decoding.

• "bp" — Use the belief propagation (BP) decoding algorithm. For more information, see Belief Propagation Decoding.

• "layered-bp" — Use the layered BP decoding algorithm, suitable for quasi-cyclic parity check matrices (PCMs). For more information, see Layered Belief Propagation Decoding.

• "offset-min-sum" — Use the layered BP decoding algorithm with the offset min-sum approximation. For more information, see Offset Min-Sum Decoding.

Note

When you specify this input as "norm-min-sum" or "offset-min-sum", the function sets input log-likelihood ratio (LLR) values that are greater than 1e10 or less than -1e10 to 1e10 and -1e10, respectively. The function then uses these values when executing the LDPC decoding algorithm.

Dependencies

This argument applies when you specify the ChannelCoding property of the cfg input as "LDPC" for the user that corresponds to the userIdx input.

Data Types: char | string

MinSumScalingFactor — Scaling factor for normalized min-sum LDPC decoding

0.75 (default) | scalar in interval (0, 1]

Scaling factor for normalized min-sum LDPC decoding, specified as the name-value argument consisting of MinSumScalingFactor and a scalar in the interval (0, 1].

Dependencies

This argument applies when you specify the LDPCDecodingMethod name-value argument as "norm-min-sum".

Data Types: double

MinSumOffset — Offset for min-sum LDPC decoding

0.5 (default) | nonnegative scalar

Offset for min-sum LDPC decoding, specified as the name-value argument consisting of MinSumOffset and a nonnegative scalar.

Dependencies

This argument applies when you specify the LDPCDecodingMethod name-value argument as offset-min-sum.

Data Types: double

MaximumLDPCIterationCount — Maximum number of LDPC decoding iterations

12 (default) | positive integer

Maximum number of LDPC decoding iterations, specified as the name-value argument consisting of MaximumLDPCIterationCount and a positive integer.

Dependencies

This argument applies when you set the ChannelCoding property of the cfg input to "LDPC" for the user that corresponds to the userIdx input.

Data Types: double | single

EarlyTermination — Enable early termination of LDPC decoding

false or 0 (default) | true or 1

Enable early termination of LDPC decoding, specified as the name-value argument consisting of EarlyTermination and 1 (true) or 0 (false).

• When you set this value to 0 (false), LDPC decoding completes the number of iterations specified in the MaximumLDPCIterationCount name-value argument regardless of parity check status.

• When you set this value to 1 (true), LDPC decoding terminates when all parity checks are satisfied.

Dependencies

This argument applies when you set the ChannelCoding property of the cfg input to "LDPC" for the user that corresponds to the userIdx input.

Output Arguments

dataBits — Bits recovered from VHT-Data field

binary-valued column vector

Bits recovered from the VHT-Data field, returned as a binary valued column vector of length 8 × L_PSDU. L_PSDU is equal to:

• The PSDULength property of the cfg input when there is a single user

• The vector entry of the PSDULength property that corresponds to the user you specify in userIdx when there are multiple users

Data Types: int8

vhtsigbCRC — VHT-SIG-B CRC results

binary column vector of length eight

VHT-SIG-B cyclic redundancy check (CRC) results, returned as a binary column vector of length eight.

Data Types: int8

More About

VHT-Data

The VHT-Data field carries one or more frames from the medium access control (MAC) layer. This field follows the VHT-SIG-B field in a VHT PPDU.

For a detailed description of the VHT-Data field, see section 21.3.10 of IEEE^® Std 802.11™-2020. The VHT Data field consists of four subfields.

• Service field — Contains a seven-bit scrambler initialization state, one bit reserved for future considerations, and eight bits for the VHT-SIG-B cyclic redundancy check (CRC) field

• PSDU — Variable-length field containing a PLCP service data unit

• PHY Pad — Variable number of bits passed to the transmitter to create a complete OFDM symbol

• Tail — Bits required to terminate a convolutional code (not required when the transmission uses LDPC channel coding)

Algorithms

This function supports these four LDPC decoding algorithms.

Belief Propagation Decoding

The function implements the BP algorithm based on the decoding algorithm presented in [2]. For transmitted LDPC-encoded codeword $$c=\left(c_0,c_1,\dots ,c_{n-1}\right)$$, the input to the LDPC decoder is the LLR given by

$$L\left(c_i\right)=\log \left(\frac{\mathrm{Pr}\left(c_i=0|\text{channel output for }{c}_i\right)}{\mathrm{Pr}\left(c_i=0|\text{channel output for }{c}_i\right)}\right)$$.

In each iteration, the function updates the key components of the algorithm based on these equations:

$$L\left(r_{ji}\right)=2\text{atanh}\left({\displaystyle \prod _{i^{\prime }\in V_j\backslash \left\{i\right\}}\tanh \left(\frac{1}{2}L\left(q_{i^{\prime }j}\right)\right)}\right)$$,

$$L\left(q_{ij}\right)=L\left(c_i\right)+{\displaystyle \sum _{j\text{'}\in C_i\backslash \left\{j\right\}}L\left(r_{j^{\prime }i}\right)}$$, initialized as $$L\left(q_{ij}\right)=L\left(c_i\right)$$ before the first iteration, and

$$L\left(Q_i\right)=L\left(c_i\right)+{\displaystyle \sum _{j^{\prime }\in C_i}L\left(r_{j^{\prime }i}\right)}$$.

At the end of each iteration, $$L\left(Q_i\right)$$ is an updated estimate of the LLR value for the transmitted bit, $$c_i$$. The value $$L\left(Q_i\right)$$ is the soft-decision output for $$c_i$$. If $$L\left(Q_i\right)$$ is negative, the hard-decision output for $$c_i$$ is 1. Otherwise, the output is 0.

Index sets $$C_i\backslash \left\{j\right\}$$ and $$V_j\backslash \left\{i\right\}$$ are based on the PCM such that the sets $$C_i$$ and $$V_j$$ correspond to all nonzero elements in column i and row j of the PCM, respectively.

This figure shows how to compute these index sets for PCM $$H$$ for the case i = 5 and j = 3:

To avoid infinite numbers in the algorithm equations, atanh(1) and atanh(–1) are set to 19.07 and –19.07, respectively. Due to finite precision, MATLAB^® returns 1 for tanh(19.07) and –1 for tanh(–19.07).

When you specify the EarlyTermination name-value pair argument as 0 (false), the decoding terminates after the number of iterations specified by the MaximumLDPCIterationCount name-value argument. When you specify EarlyTermination as 1 (true), the decoding terminates when all parity checks are satisfied ($$H{c}^T=0$$) or after the number of iterations specified by the MaximumLDPCIterationCount name-value argument.

Layered Belief Propagation Decoding

The function implements the layered BP algorithm based on the decoding algorithm presented in Section II.A of [3]. The decoding loop iterates over subsets of rows (layers) of the PCM.

For each row, m, in a layer and each bit index, j, the implementation updates the key components of the algorithm based on these equations:

(1) $$L\left(q_{mj}\right)=L\left(q_j\right)-R_{mj}$$

(2) $$\Psi (x)=\log \left(\left|\tanh \left(x/2\right)\right|\right)$$

(3) $$A_{mj}={\displaystyle \sum _{n\in N(m)\backslash \left\{j\right\}}\Psi }\left(L\left(q_{mn}\right)\right)$$

(4) $$s_{mj}={\displaystyle \prod _{n\in N(m)\backslash \left\{j\right\}}\mathrm{sgn}}\left(L\left(q_{mn}\right)\right)$$

(5) $$R_{mj}=-s_{mj}\Psi \left(A_{mj}\right)$$

(6) $$L\left(q_j\right)=L\left(q_{mj}\right)+R_{mj}$$

For each layer, the decoding equation (6) works on the combined input obtained from the current LLR inputs, $$L\left(q_{mj}\right)$$, and the previous layer updates, $$R_{mj}$$.

Because the layered BP algorithm updates only a subset of the nodes in a layer, this algorithm is faster than the BP algorithm. To achieve the same error rate as attained with BP decoding, use half the number of decoding iterations when using the layered BP algorithm.

Normalized Min-Sum Decoding

The function implements the normalized min-sum decoding algorithm by following the layered BP algorithm with equation (3) replaced by

$$A_{mj}={\min }_{n\in N(m)\backslash \left\{j\right\}}\left(\alpha \left|L\left(q_{mn}\right)\right|\right)$$,

where α is the scaling factor specified by the MinSumScalingFactor name-value argument. This equation is an adaptation of equation (4) presented in [4].

Offset Min-Sum Decoding

The function implements the offset min-sum decoding algorithm by following the layered BP algorithm with equation (3) replaced by

$$A_{mj}=\max \left({\min }_{n\in N(m)\backslash \left\{j\right\}}\left(\left|L\left(q_{mn}\right)\right|-\beta \right),0\right)$$,

where β is the offset specified by the MinSumOffset name-value argument. This equation is an adaptation of equation (5) presented in [4].

References

[1] IEEE Std 802.11-2020 (Revision of IEEE Std 802.11-2016). “Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications.” IEEE Standard for Information Technology — Telecommunications and Information Exchange between Systems — Local and Metropolitan Area Networks — Specific Requirements.

[2] Gallager, Robert G. Low-Density Parity-Check Codes. Cambridge, MA: MIT Press, 1963.

[3] Hocevar, D.E. "A Reduced Complexity Decoder Architecture via Layered Decoding of LDPC Codes." In IEEE Workshop on Signal Processing Systems, 2004. SIPS 2004., 107-12. Austin, Texas, USA: IEEE, 2004. https://doi.org/10.1109/SIPS.2004.1363033.

[4] Jinghu Chen, R.M. Tanner, C. Jones, and Yan Li. "Improved Min-Sum Decoding Algorithms for Irregular LDPC Codes." In Proceedings. International Symposium on Information Theory, 2005. ISIT 2005., 449-53, 2005. https://doi.org/10.1109/ISIT.2005.1523374.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Version History

Introduced in R2025a