lteDLPerfectChannelEstimate

Downlink perfect channel estimation

Syntax

hest = lteDLPerfectChannelEstimate(enb,propchan)

hest = lteDLPerfectChannelEstimate(enb,propchan,timefreqoffset)

hest = lteDLPerfectChannelEstimate(enb,propchan,timefreqoffset,ntxants)

Description

hest = lteDLPerfectChannelEstimate(enb,propchan) performs perfect channel estimation for a system configuration given structures containing the cell-wide settings, and the propagation channel configuration. The perfect channel estimates are only produced for channel models created using lteFadingChannel or lteHSTChannel.

This function provides a perfect MIMO channel estimate after OFDM modulation. Perfect channel estimation is achieved by setting the channel with the desired configuration and sending a set of known symbols through it for each transmit antenna in turn.

example

hest = lteDLPerfectChannelEstimate(enb,propchan,timefreqoffset) specifies the timing and frequency offsets. This parameter allows hest to be the precise channel that results when the receiver is precisely synchronized.

example

hest = lteDLPerfectChannelEstimate(enb,propchan,timefreqoffset,ntxants) specifies the number of transmit antenna planes.

Note

This syntax is provided to allow modeling of greater than four transmit antenna planes. For this syntax, the enb.CellRefP field, is not required and, if included, is not used to define the number of antenna planes.

example

Examples

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Perform Perfect DL Channel Estimation

Open Live Script

Perform perfect channel estimation for a given propagation channel configuration in the downlink.

Initialize eNodeB and propagation channel configuration structures.

enb.NDLRB = 6;
enb.CyclicPrefix = 'Normal';
enb.CellRefP = 4;
enb.TotSubframes = 1;

chs.Seed = 1;
chs.DelayProfile = 'EPA';
chs.NRxAnts = 2;
chs.DopplerFreq = 5.0;
chs.MIMOCorrelation = 'Low';
chs.InitPhase = 'Random';
chs.InitTime = 0.0;
chs.ModelType = 'GMEDS';
chs.NTerms = 16;
chs.NormalizeTxAnts = 'On';
chs.NormalizePathGains = 'On';

Compute the downlink channel estimate and display the dimension of the output channel estimate.

H = lteDLPerfectChannelEstimate(enb,chs);
sizeH = size(H)

sizeH = 1×4

    72    14     2     4

Perfect DL Channel Estimation on a Time Offset Waveform

Open Live Script

Perform perfect channel estimation on a time offset waveform that has passed through a fading channel.

Configuration initialization

Initialize cell-wide configuration to R.12 (TxDiversity, 6 RB, CellRefP=4, normal cyclic prefix).
Initialize propagation channel configuration.

enb = lteRMCDL('R.1','FDD',1);
enb.TotSubframes = 1;

chan.Seed = 1;
chan.DelayProfile = 'EPA';
chan.NRxAnts = 1;
chan.DopplerFreq = 5.0;
chan.MIMOCorrelation = 'Low';
chan.InitPhase = 'Random';
chan.InitTime = 0.0;
chan.ModelType = 'GMEDS';
chan.NTerms = 16;
chan.NormalizeTxAnts = 'On';
chan.NormalizePathGains = 'On';

Waveform processing

Create waveform and add samples for channel delay.
Pass through a fading channel, generating time-domain receiver samples.

[txwave,txgrid,rmcCfg] = lteRMCDLTool(enb,[1;0;0;1]);
txwave = [txwave; zeros(25,enb.CellRefP)];
chan.SamplingRate = rmcCfg.SamplingRate;
rxwave = lteFadingChannel(chan,txwave);

Determine timing offset

Use lteDLFrameOffset to estimate time offset.
Account for the timing offset in the received waveform.

toffset = lteDLFrameOffset(enb,rxwave)

toffset = 
7

rxwave = rxwave(1+toffset:end,:);

Demodulation and perfect channel estimation

Demodulate rxwave to generate frequency-domain receiver data in rxgrid.
Equalize with perfect channel estimate using time offset.
Plot resource element grids to show impact of fading channel on the transmitted signal and recovery of the signal using the perfect channel estimate.

rxgrid = lteOFDMDemodulate(enb,rxwave);
hest = lteDLPerfectChannelEstimate(enb,chan,[toffset,0]);
sizeH = size(hest);
recoveredgrid = rxgrid./hest;

subplot(2,2,1)
mesh(abs(txgrid(:,:,1,1)))
title('Transmitted Grid');
subplot(2,2,2)
mesh(abs(rxgrid(:,:,1,1)))
title('Received Grid');
subplot(2,2,3)
mesh(abs(hest(:,:,1,1)))
title('Perfect Channel Estimate');
subplot(2,2,4)
mesh(abs(recoveredgrid(:,:,1,1)))
title('Recovered Grid');

Figure contains 4 axes objects. Axes object 1 with title Transmitted Grid contains an object of type surface. Axes object 2 with title Received Grid contains an object of type surface. Axes object 3 with title Perfect Channel Estimate contains an object of type surface. Axes object 4 with title Recovered Grid contains an object of type surface.

Comparing the transmitted grid to the recovered grid shows equalization of the received grid with the perfect channel estimate recovers the transmission.

Perform HST Model Perfect DL Channel Estimation

Open Live Script

Perform perfect channel estimation for a high speed train (HST) propagation channel configuration in the downlink. Include time and frequency offsets in the channel estimation computation.

Configuration initialization

Initialize configuration structures for eNodeB and HST propagation channel.

enb.NDLRB = 6;
enb.NCellID = 1;
enb.CyclicPrefix = 'Normal';
enb.CellRefP = 1;
enb.TotSubframes = 1;

hst.NRxAnts = 2;
hst.Ds = 100;
hst.Dmin = 500;
hst.Velocity = 200;
hst.DopplerFreq = 5.0;
hst.InitTime = 0.0;
hst.ModelType = 'GMEDS';
hst.NormalizeTxAnts = 'On';

Waveform processing

Create waveform and add samples for channel delay.
Pass through an HST channel, generating time-domain receiver samples.

[txwave,txgrid,rmcCfg] = lteRMCDLTool(enb,[1;0;0;1]);
txwave = [txwave; zeros(25,enb.CellRefP)];
hst.SamplingRate = rmcCfg.SamplingRate;
rxwave = lteHSTChannel(hst,txwave);

Determine timing and frequency offsets

Use lteDLFrameOffset to estimate time offset.
Account for the timing offset in the received waveform.
Use lteFrequencyOffset to estimate frequency offset.

toffset = lteDLFrameOffset(enb,rxwave)

toffset = 
7

rxwave = rxwave(1+toffset:end,:);
foffset = lteFrequencyOffset(enb,rxwave)

foffset = 
0.4953

Demodulation and perfect channel estimation

Demodulate rxwave to generate frequency-domain receiver data in rxgrid.
Equalize with perfect channel estimate using time and frequency offsets.

rxgrid = lteOFDMDemodulate(enb,rxwave);
hest = lteDLPerfectChannelEstimate(enb,hst,[toffset,foffset]);
sizeH = size(hest)

sizeH = 1×3

    72    14     2

recoveredgrid = rxgrid./hest;

Perform Perfect DL Channel Estimation for Eight Antenna Planes

Open Live Script

Perform perfect channel estimation for eight transmit antenna planes for a given propagation channel configuration in the downlink.

Initialize eNodeB and propagation channel configuration structures. Define a local variable for the number of transmit antenna planes.

enb.NDLRB = 6;
enb.CyclicPrefix = 'Normal';
enb.TotSubframes = 1;

chs.Seed = 1;
chs.DelayProfile = 'EPA';
chs.NRxAnts = 2;
chs.DopplerFreq = 5.0;
chs.MIMOCorrelation = 'Low';
chs.InitPhase = 'Random';
chs.InitTime = 0.0;
chs.ModelType = 'GMEDS';
chs.NTerms = 16;
chs.NormalizeTxAnts = 'On';
chs.NormalizePathGains = 'On';

txAntPlanes = 8;

Compute the downlink channel estimate and display the dimension of the output channel estimate.

chest = lteDLPerfectChannelEstimate(enb,chs,[0 0],txAntPlanes);
sizeH = size(chest)

sizeH = 1×4

    72    14     2     8

The dimensionality of chest indicates two receive and eight transmit antenna planes are included in the channel estimate.

Input Arguments

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`enb` — Cell-wide settings
scalar structure

Cell-wide settings, specified as a structure with the following fields.

Parameter Field	Required or Optional	Values	Description
`NDLRB`	Required	Scalar integer from 6 to 110	Number of downlink resource blocks ( $N_{RB}^{DL}$ )
`CyclicPrefix`	Optional	`'Normal'` (default), `'Extended'`	Cyclic prefix length
`CellRefP`	Required	1, 2, 4	Number of cell-specific reference signal (CRS) antenna ports
`TotSubframes`	Optional	Nonnegative scalar integer 1 (default)	Total number of subframes to generate

Data Types: struct

`propchan` — Propagation channel configuration
structure

Propagation channel configuration, specified as a structure that can contain these parameter fields. propchan must contain the fields required to parameterize the channel model for a fading channel (lteFadingChannel) or a high-speed train channel (lteHSTChannel).

Note

Before execution of the channel itself, lteDLPerfectChannelEstimate sets SamplingRate internally to the sampling rate of the time domain waveform passed to lteFadingChannel or lteHSTChannel for channel filtering. Therefore, the propchan structure does not require the SamplingRate field. If one is included, it is not used.

propchan structure fields to be included for fading channel model case:

Parameter Field	Required or Optional	Values	Description
`NRxAnts`	Required	Positive scalar integer	Number of receive antennas
`MIMOCorrelation`	Required	`'Low'`, `'Medium'`, `'UplinkMedium'`, `'High'`, `'Custom'`	Correlation between UE and eNodeB antennas `'Low'` correlation is equivalent to no correlation between antennas. `'Medium'` correlation level is applicable to tests defined in TS 36.101 [1]. `'UplinkMedium'` correlation level is applicable to tests defined in TS 36.104 [2].
`NormalizeTxAnts`	Optional	`'On'` (default), `'Off'`	Transmit antenna number normalization. `'On'`, this function normalizes the model output by `1/sqrt(N_TX)`, where N_TX is the number of transmit antennas. Normalization by the number of transmit antennas ensures that the output power per receive antenna is unaffected by the number of transmit antennas. `'Off'`, normalization is not performed.
`DelayProfile`	Required	`'EPA'`, `'EVA'`, `'ETU'`, `'Custom'`, `'Off'`	Delay profile model. For more information, see Propagation Channel Models. Setting `DelayProfile` to `'Off'` switches off fading completely and implements a static MIMO channel model. In this case, the antenna geometry corresponds to `propchan.MIMOCorrelation`, `propchan.NRxAnts`, and the number of transmit antennas. The temporal part of the model for each link between transmit and receive antennas consists of a single path with zero delay and constant unit gain.
The following fields are applicable when `DelayProfile` is set to a value other than `'Off'`.
`DopplerFreq`	Required	Scalar	Maximum Doppler frequency, in Hz.
`InitTime`	Required	Scalar	Fading process time offset, in seconds.
`NTerms`	Optional	16 (default) scalar power of 2	Number of oscillators used in fading path modeling.
`ModelType`	Optional	`'GMEDS'` (default), `'Dent'`	Rayleigh fading model type. `'GMEDS'`, the Rayleigh fading is modeled using the Generalized Method of Exact Doppler Spread (GMEDS), as described in [4]. `'Dent'`, the Rayleigh fading is modeled using the modified Jakes fading model described in [3]. Note `ModelType` = `'Dent'` is not recommended. Use `ModelType` = `'GMEDS'` instead.
`NormalizePathGains`	Optional	`'On'` (default), `'Off'`	Model output normalization. `'On'`, the model output is normalized such that the average power is unity. `'Off'`, the average output power is the sum of the powers of the taps of the delay profile.
`InitPhase`	Optional	`'Random'` (default), scalar (in Radians), or N-by-L-by-N_TX-by-N_RX array	Phase initialization for the sinusoidal components of the model. `'Random'`, sets the phases randomly initialized according to `Seed`. A scalar, assumed to be in radians, is used to initialize the phases of all components. An N-by-L-by-N_TX-by-N_RX array is used to explicitly initialize the phase in radians of each component. In this case, N is the number of phase initialization values per path, L is the number of paths, N_TX is the number of transmit antennas, and N_RX is the number of receive antennas. (`NRxAnts`) Note When `ModelType` is set to `'GMEDS'`, N = 2 × `NTerms`. When `ModelType` is set to `'Dent'`, N = `NTerms`.
The following field is applicable when `DelayProfile` is set to a value other than `'Off'` and `InitPhase` is set to `'Random'`.
`Seed`	Required	Scalar	Random number generator seed. To use a random seed, set `Seed` to zero. Note MathWorks^® recommends using `Seed` values from 0 to 2³¹ – 1 – (K(K – 1)/2), where K = N_TX × N_RX, the product of the number of transmit and receive antennas. `Seed` values outside of this range are not guaranteed to give distinct results.
The following fields are applicable when `DelayProfile` is set to `'Custom'`.
`AveragePathGaindB`	Required	Vector	Average gains of the discrete paths, expressed in dB.
`PathDelays`	Required	Vector	Delays of the discrete paths, expressed in seconds. This vector must have the same size as `AveragePathGaindB`.
The following fields are applicable when `MIMOCorrelation` is set to `'Custom'`.
`TxCorrelationMatrix`	Required	Matrix	Correlation between each of the transmit antennas, specified as a N_TX-by-N_TX complex matrix.
`RxCorrelationMatrix`	Required	Matrix	Correlation between each of the receive antennas, specified as a complex matrix of size N_RX-by-N_RX.

propchan structure fields to be included for the high-speed train channel model case:

Parameter Field	Required or Optional	Values	Description
`NRxAnts`	Required	Positive scalar integer	Number of receive antennas
`Ds`	Required	Scalar	Train-to-eNodeB double initial distance, in meters. `Ds`/2 is initial distance between train and eNodeB, in meters
`Dmin`	Required	Scalar	eNodeB to railway track distance, in meters
`Velocity`	Required	Scalar	Train velocity, in kilometers per hour
`DopplerFreq`	Required	Scalar	Maximum Doppler frequency, in Hz.
`InitTime`	Required	Scalar	Doppler shift timing offset, in seconds
`NormalizeTxAnts`	Optional	`'On'` (default), `'Off'`	Transmit antenna number normalization. `'On'`, `lteHSTChannel` normalizes the model output by `1/sqrt(N_TX)`, where N_TX is the number of transmit antennas. Normalization by the number of transmit antennas ensures that the output power per receive antenna is unaffected by the number of transmit antennas. `'Off'`, normalization is not performed.

Data Types: struct

`timefreqoffset` — Timing and frequency offset
[0, 0] (default) | two element row vector, [`toffset`, `foffset`] | nonnegative scalar, `toffset` | optional

Timing and frequency offset, specified as a nonnegative scalar providing toffset or two element row vector providing [toffset, foffset].

`toffset` — Timing offset
0 (default) | nonnegative scalar | optional

Timing offset in samples from the start of the output of the channel to the OFDM demodulation starting point, specified as a nonnegative scalar. The timing offset accounts for delay introduced during propagation, which is useful to obtain the perfect estimate of the channel seen by a synchronized receiver. Use lteDLFrameOffset to derive toffset.

`foffset` — Frequency offset
0 (default) | scalar | optional

Frequency offset in Hertz of the time-domain waveform, specified as a scalar. Use lteFrequencyOffset to derive foffset.

Example: [3 100] indicates a time offset of three samples and a frequency offset of 100 Hz.

Data Types: double

`ntxants` — Number of transmit antenna planes
1 (default) | nonnegative integer | optional

Number of transmit antenna planes, specified as a nonnegative integer.

Output Arguments

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`hest` — Perfect channel estimate
4-D array

Perfect channel estimate, returned as an N_SC-by-N_SYM-by-N_RX-by-N_TX array.

N_SC is the number of subcarriers.
N_SYM is the number of OFDM symbols.
N_RX is the number of receive antennas as specified by propchan.NRxAnts.
N_TX is the number of transmit antenna planes, specified either by the input ntxants or by enb.CellRefP. If ntxants is provided as an input, the enb.CellRefP field is not required and, if included, is not used.

Data Types: double
Complex Number Support: Yes

References

[1] 3GPP TS 36.101. “Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) Radio Transmission and Reception.” 3rd Generation Partnership Project; Technical Specification Group Radio Access Network. URL: https://www.3gpp.org.

[2] 3GPP TS 36.104. “Evolved Universal Terrestrial Radio Access (E-UTRA); Base Station (BS) Radio Transmission and Reception.” 3rd Generation Partnership Project; Technical Specification Group Radio Access Network. URL: https://www.3gpp.org.

[3] Dent, P., G. E. Bottomley, and T. Croft. “Jakes Fading Model Revisited.” Electronics Letters. Vol. 29, 1993, Number 13, pp. 1162–1163.

[4] Pätzold, Matthias, Cheng-Xiang Wang, and Bjørn Olav Hogstad. “Two New Sum-of-Sinusoids-Based Methods for the Efficient Generation of Multiple Uncorrelated Rayleigh Fading Waveforms.” IEEE Transactions on Wireless Communications. Vol. 8, 2009, Number 6, pp. 3122–3131.

Version History

Introduced in R2013b

lteDLPerfectChannelEstimate

Syntax

Description

Examples

Perform Perfect DL Channel Estimation

Perfect DL Channel Estimation on a Time Offset Waveform

Perform HST Model Perfect DL Channel Estimation

Perform Perfect DL Channel Estimation for Eight Antenna Planes

Input Arguments

enb — Cell-wide settings scalar structure

propchan — Propagation channel configuration structure

timefreqoffset — Timing and frequency offset [0, 0] (default) | two element row vector, [toffset, foffset] | nonnegative scalar, toffset | optional

toffset — Timing offset 0 (default) | nonnegative scalar | optional

foffset — Frequency offset 0 (default) | scalar | optional

ntxants — Number of transmit antenna planes 1 (default) | nonnegative integer | optional

Output Arguments

hest — Perfect channel estimate 4-D array

References

Version History

See Also

`enb` — Cell-wide settings
scalar structure

`propchan` — Propagation channel configuration
structure

`timefreqoffset` — Timing and frequency offset
[0, 0] (default) | two element row vector, [`toffset`, `foffset`] | nonnegative scalar, `toffset` | optional

`toffset` — Timing offset
0 (default) | nonnegative scalar | optional

`foffset` — Frequency offset
0 (default) | scalar | optional

`ntxants` — Number of transmit antenna planes
1 (default) | nonnegative integer | optional

`hest` — Perfect channel estimate
4-D array