Uplink Channel Estimation using SRS for Positioning

Channel estimation using Channel State Information Reference Signals (CSI-RS) in 5G networks is a fundamental technique employed to accurately estimate the characteristics of the wireless channel between the base station (gNodeB) and the user equipment (UE). Here’s some technical information on how CSI-RS is utilized for channel estimation in 5G:

1. Purpose: The primary goal of channel estimation using CSI-RS is to estimate the channel’s frequency response, which includes parameters such as amplitude and phase shift, across different subcarriers in the frequency domain.

2. CSI-RS Structure:

   • CSI-RS is a set of reference signals specifically designed for channel estimation purposes in 5G networks.

   • It is transmitted by the gNodeB periodically over a certain bandwidth and is known to the UE.

   • CSI-RS signals are transmitted with different frequency and time configurations to cover various channel conditions and transmission scenarios.

3. CSI-RS Configuration:

   • The gNodeB configures the CSI-RS parameters, including frequency position, bandwidth, density, and periodicity, based on network requirements and channel conditions.

   • CSI-RS can be configured with different beamforming schemes, such as beam sweeping or beamforming matrices, to adapt to varying channel conditions and UE locations.

4. Channel Estimation Process:

   • Upon receiving the CSI-RS signals, the UE utilizes them to estimate the channel’s frequency response.

   • The UE measures the received power and phase of the CSI-RS signals across different subcarriers and uses this information to estimate the channel response.

   • Complex algorithms, such as linear interpolation or advanced signal processing techniques, may be employed to interpolate or extrapolate channel estimates between CSI-RS locations for improved accuracy.

5. Channel Quality Reporting:

   • The UE may use the estimated channel response to generate Channel Quality Indicator (CQI) feedback, which provides information about the channel quality to the gNodeB.

   • The gNodeB utilizes the CQI feedback to adapt modulation and coding schemes, beamforming, and other transmission parameters to optimize communication performance based on channel conditions.

6. Dynamic Channel Adaptation: Channel estimation using CSI-RS enables dynamic adaptation of transmission parameters to changing channel conditions, such as fading, interference, and mobility, ensuring reliable and efficient communication in 5G networks.

In summary, channel estimation using CSI-RS in 5G networks plays a crucial role in accurately estimating the wireless channel’s characteristics and optimizing communication performance. By utilizing dedicated reference signals and sophisticated estimation techniques, CSI-RS enables dynamic adaptation of transmission parameters and ensures reliable communication in varying channel conditions.

class toolkit5G.ReceiverAlgorithms.ChannelEstimationSRS

This module estimates the uplink channel using SRS sequence.

Parameters:

None

Input:

• rxGrid (NumPy array (numBatches, numSequenceIDs, numCyclicShits, numSRSPorts, 14, \(12 \times\) numRBs) of np.complex64) – Defines the resource grid carrying the received unequilized pilots as per srsResourceMapper object.

• srsResourceMapper (object of class ResourceMapperSRS) – Defines the object for capturing the location of pilots in the rxGrid and generate the pilots to estimate the channel using rxGrid. * For each batch (numBatches), the pilot locations and pilot sequence must be same.

• interpolatorType (str) – Defines the interpolator type used to interpolate/extrapolate the channel at the data indices. It can take values from the set = {“NN”, “Linear”, “Cubic”, “Spline”, “Pchip”, “Akima”}. Default value is “NN”.

• order (int) – Defines the order of polynomial used for approximation/interpolation by Spline interpolator. Default value is 5. This parameter is relevant only when the interpolator type is “Spline”. It must be an odd number. For more information please read SciPy documentation.

• knots (int) – Defines the knots variable for Spline interpolator. Default value is None. For more information please read SciPy documentation.

Output:

NumPy array (numBatches, numSequenceIDs, numCyclicShits, numSRSPorts, 14, \(12 \times\) numRBs) of np.complex64 – Defines the channel estimates for the SRS Bandwidth

Note

The positioning support only single port SRS. Hence this implementation works only for rank = 1. The rxGrid and output will require modification when numSRSPorts is >1.

Raises:

• Exception – [Error-ChannelEstimationSRS]: ‘srsResourceMapper’ must be a ResourceMapperSRS type object!

• ValueError – [Error-ChannelEstimationSRS]: ‘rxGrid’ must be a NumPy array of complex numbers!

• ValueError – [Error-ChannelEstimationSRS]: ‘rxGrid’ must have 6 dimensions denoting ‘numBatches, numSequenceIDs, numCyclicShifts, numSRSPorts, 14, numRB*12’!

• Exception – [Error-ChannelEstimationSRS]: ‘number of Subcarriers’(rxGrid.shape[-1]) = “+str(numSC)+” is not a multiple of 12!

• Exception –

• ValueError – “[Error-ChannelEstimationSRS]: ‘interpolatorType’ must be a string”

• ValueError – “[Error-ChannelEstimationSRS]: ‘interpolatorType’ take a value from the set “”disabled”, “NN”,”Linear”,”Cubic”,”Spline”,”Akima”,”Pchip”” !”

• ValueError – “[Error-ChannelEstimationSRS]: ‘order’ must be an integer!”

• ValueError – “[Error-ChannelEstimationSRS]: ‘order’ should be an odd number!”

• ValueError – “[Error-ChannelEstimationSRS]: ‘knots’ must be an integer!”