Modeling and Testing an NR RF Receiver with LTE Interference Using 5G & ...

The example shows how to characterize the impact of radio frequency (RF) impairments in the RF reception of a new radio (NR) waveform when coexisting with a long-term evolution (LTE) interference. The baseband waveforms are generated using 5G Toolbox™ and LTE Toolbox™, and the RF receiver is modeled using RF Blockset™.

This example demonstrates how to model and test the reception of an NR waveform when coexisting with an LTE waveform.

The RF receiver consists of bandpass filters, amplifiers and an demodulator. To evaluate the impact of the LTE interference, the example modifies the gain of the LTE waveform and performs ACLR and EVM measurements. Click here to download the Simulink File:

https://drive.google.com/file/d/1mHcRLXXKVChdHNk-p2NRoQHOxWWtKf85/view?usp=sharing

Modeling and Testing an NR RF Transmitter Using Matlab 5G Toolbox

Modeling and Testing an NR RF Transmitter Using Matlab 5G Toolbox

This example shows how to characterize the impact of RF impairments such as IQ imbalance, phase noise, and PA nonlinearities in the performance of an NR RF transmitter. To evaluate the performance, the example considers these measurements:

• Error vector magnitude (EVM): vector difference at a given time between the ideal (transmitted) signal and the measured (received) signal.

• Adjacent channel leakage ratio (ACLR): measure of the amount of power leaking into adjacent channels and is defined as the ratio of the filtered mean power centered on the assigned channel frequency to the filtered mean power centered on an adjacent channel frequency.

• Occupied bandwidth: bandwidth that contains 99% of the total integrated power of the signal, centered on the assigned channel frequency.

• Channel power: filtered mean power centered on the assigned channel frequency.

• Complementary cumulative distribution function (CCDF): probability of a signal's instantaneous power to be a level specified above its average power.

The model works on a subframe by subframe basis. For each subframe, the workflow consists of these steps:

1. Generate the baseband waveform using 5G Toolbox functions.

2. Upconvert the generated waveform to the passband frequency and apply RF filtering and amplification using RF Blockset.

3. Downconvert the transmitted waveform to baseband frequency.

4. Calculate the ACLR/ACPR, occupied bandwidth, channel power, and CCDF using the Spectrum Analyzer block.

5. Demodulate the waveform at the receiver to measure EVM                                                                                                                                                This example demonstrates how to model and test an NR RF transmitter in Simulink. The RF transmitter consists of an IQ modulator, a bandpass filter and amplifiers. To evaluate the performance, the Simulink model considers ACLR and EVM measurements. The example highlights the effect of HPA nonlinearities on the performance of the RF Transmitter.                                                                                                                                                                                                                                                                                                                                We can explore the impact of altering other impairments as well. For example:

• Increase I/Q imbalance by using the I/Q gain mismatch (dB) and I/Q phase mismatch (Deg) parameters on the IQ Modulator tab of the RF Transmitter block.
• Increase the phase noise by using Phase noise offset (Hz) and Phase noise level (dBc/Hz) parameters on the IQ Modulator tab of the RF Transmitter block.

Additionally, you can check the occupied bandwidth, the channel power, and the CCDF measurements by using the Spectrum Analyzer block.

If you change the carrier frequency or the values in the Waveform Parameters block, you may need to update the parameters of the RF Transmitter components as these parameters have been selected to work for the default configuration of the example. For instance, a change in the carrier frequency requires revising the bandwidth of the filter.

 If you select a bandwidth wider than 20MHz, you may need to update the Impulse response duration and Phase noise frequency offset (Hz) parameters of the IQ Modulator block. The phase noise offset determines the lower limit of the impulse response duration. 

If the phase noise frequency offset resolution is too high for a given impulse response duration, a warning message appears, specifying the minimum duration suitable for the required resolution. 

Click Here to Download the Simulink File: https://drive.google.com/file/d/13oMe1pd9ovxNYAeVzEyGmnV466Cds77J/view?usp=sharing

 

Design & Simulation of Thermal Management System of Electric Vehicle us...

Electric Vehicle Thermal Management: This example models the thermal management system of a battery electric vehicle.This model has three scenarios set up. The drive cycle scenario simulates driving conditions in 30 degC weather with air conditioning on. The vehicle speed is based on the NEDC followed by 30 min of high speed to push the battery heat load. The cool down scenario simulates a stationary vehicle in 40 degC weather with air conditioning on. Finally, the cold weather scenario simulates driving conditions in -10 degC weather, which requires the battery heater and PTC heater to warm up the batteries and cabin, respectively.

The output scope shows the vehicle speed, heat dissipation, cabin temperature, component temperatures, and control commands for the drive cycle scenario. At the beginning, the coolant loop is in serial mode. After about 1100 s, it switches to parallel mode and the chiller is used to keep the batteries below 35 degC.

Click here to download the Simulink File:
https://drive.google.com/file/d/19U-hGWgid6MBJbVJCWRtjuCS00BHmLi_/view?usp=sharing

This example models the thermal management system of a battery electric vehicle. The system consists of two coolant loops, a refrigeration loop, and a cabin HVAC loop. The thermal load are the batteries, powertrain, and cabin.

The two coolant loops can be joined together in serial mode or kept separate in parallel mode using the 4-way valve. In cold weather, the coolant loops are in serial mode so that heat from the motor warms the batteries. If necessary, a heater can provide additional heat. In warm weather, the coolant loops remain in serial mode and both the batteries and the powertrain are cooled by the radiator. In hot weather, the coolant loop switches to parallel mode and separates. One loop cools the powertrain using the radiator. The other cools the batteries using the chiller in the refrigeration loop.

The refrigeration loop consists of a compressor, a condenser, a liquid receiver, two expansion valves, a chiller, and an evaporator. The chiller is used to cool the coolant in hot weather when the radiator alone is insufficient. The evaporator is used to cool the vehicle cabin when air conditioning is turned on. The compressor is controlled such that the condenser can dissipate the heat absorbed by either or both the chiller and the evaporator.

The HVAC loop consists of a blower, an evaporator, a PTC heater, and the vehicle cabin. The PTC heater provides heating in cold weather; the evaporator provides air conditioning in hot weather. The blower is controlled to maintain the specified cabin temperature setpoint.

This model has three scenarios set up. The drive cycle scenario simulates driving conditions in 30 degC weather with air conditioning on. The vehicle speed is based on the NEDC followed by 30 min of high speed to push the battery heat load. The cool down scenario simulates a stationary vehicle in 40 degC weather with air conditioning on. Finally, the cold weather scenario simulates driving conditions in -10 degC weather, which requires the battery heater and PTC heater to warm up the batteries and cabin, respectively.

Model

Scenario Subsystem

This subsystem sets up the environment conditions and inputs to the system for the selected scenario. The battery current demand and powertrain heat load are a function of the vehicle speed based on tabulated data.

Controls Subsystem

This subsystem consists of all of the controllers for the pumps, compressor, fan, blower, and valves in the thermal management system.

Parallel-Serial Mode Valve Subsystem

The 4-way valve in this subsystem controls whether the coolant loop operates in parallel or serial mode. When ports A and D are connected and ports C and B are connected, it is in parallel mode. The two coolant loops are separated with their own coolant tanks and pumps.

When ports A and B are connected and ports C and D are connected, it is in serial mode. The two coolant loops are merged and the two pumps are synchronized to provide the same flow rate.

Motor Pump Subsystem

This pump drives the coolant loop that cools the charger, motor, and inverter.

Charger Subsystem

This subsystem models a coolant jacket around the charger, which is represented by a heat flow rate source and a thermal mass.

Motor Subsystem

This subsystem models a coolant jacket around the motor, which is represented by a heat flow rate source and a thermal mass.

Inverter Subsystem

This subsystem models a coolant jacket around the inverter, which is represented by a heat flow rate source and a thermal mass.

Radiator Subsystem

The radiator is a rectangular tube-and-fin type heat exchanger that dissipates coolant heat to the air. The air flow is driven by the vehicle speed and the fan located behind the condenser.

Radiator Bypass Valve Subsystem

In cold weather, the radiator is bypassed so that heat from the powertrain can be used to warm up the batteries. This is controlled by the the 3-way valve that either sends coolant to the radiator or bypasses the radiator.

Battery Pump Subsystem

This pump drives the coolant loop that cools the batteries and the DC-DC converter.

Chiller Subsystem

The chiller is assumed to be a shell-and-tube type heat exchanger that lets the refrigerant absorb heat from the coolant.

Chiller Bypass Valve Subsystem

The chiller operates in an on-off manner depending on the battery temperature. This is controlled by the the 3-way valve that either sends coolant to the chiller or bypasses the chiller.

Heater Subsystem

The battery heater is modeled as a heat flow rate source and a thermal mass. It is turned on in cold weather to bring the battery temperature above 5 degC.

DCDC Subsystem

This subsystem models a coolant jacket around the DC-DC converter, which is represented by a heat flow rate source and a thermal mass.

Battery Subsystem

The batteries are modeled as four separate packs surrounded by a coolant jacket. The battery packs generate voltage and heat based on the current demand. The coolant is assumed to flow in narrow channels around the battery packs.

Pack 1 Subsystem

Each battery pack is modeled as a stack of lithium-ion cells coupled with a thermal model. Heat is generated based on the power losses in the cells.

Compressor Subsystem

The compressor drives the flow in the refrigerant loop. It is controlled to maintain a pressure of 0.3 MPa in the chiller and the evaporator, which corresponds to a saturation temperature of around 1 degC.

Condenser Subsystem

The condenser is a rectangular tube-and-fin type heat exchanger that dissipates refrigerant heat to the air. The air flow is driven by the vehicle speed and the fan. The liquid receiver provides storage for the refrigerant and permits only subcooled liquid to flow into the expansion valves.

Chiller Expansion Valve Subsystem

This expansion valve meters refrigerant flow to the chiller to maintain a nominal superheat.

Evaporator Expansion Valve Subsystem

This expansion valve meters refrigerant flow to the evaporator to maintain a nominal superheat.

Evaporator Subsystem

The evaporator is a rectangular tube-and-fin type heat exchanger that lets the refrigerant absorb heat from the air. It also dehumidifies the air when the air is humid.

Blower Subsystem

The blower drives the air flow in the HVAC loop. It is controlled to maintain the cabin temperature setpoint. The source of air can come from the environment or from recirculated cabin air.

Recirculation Flap Subsystem

The recirculation flap is modeled as two restrictions operating in the opposite manner to let either environment air or cabin air to the blower.

PTC Subsystem

The PTC heater is modeled as a heat flow rate source and a thermal mass. It is turned on in cold weather to provide heating to the vehicle cabin.

Cabin Subsystem

The vehicle cabin is modeled as a large volume of moist air. Each occupant in the vehicle is a source of heat, moisture, and CO2.

Cabin Heat Transfer Subsystem

This subsystem models the thermal resistances between the cabin interior and the external environment.

Simulation Results from Scopes

The following scope shows the vehicle speed, heat dissipation, cabin temperature, component temperatures, and control commands for the drive cycle scenario. At the beginning, the coolant loop is in serial mode. After about 1100 s, it switches to parallel mode and the chiller is used to keep the batteries below 35 degC.

🔴 LIVE | # DAY 3 | APPLICATIONS TO SIGNALS & SYSTEMS | FREE COURSE !!

Course Objectives: # 3

APPLICATIONS TO SIGNALS & SYSTEMS

Hands on Approach

•  Analysis of Second order Transfer Function behavior
•  Analysis of Time domain Response of a second order function.
•  Laplace Transform & Inverse Laplace Transform
•  Fourier Transform & Inverse Fourier Transform
•   Z Transform & Inverse Z Transform
•  introduction to Matlab Signal Analyzer App

QSHB_HBPS_Simulation & Algorithm analysis of Two Hybrid Beamforming meth...

Hybrid MIMO Beamforming with QSHB and HBPS Algorithms

This example presents a Simulink® model of a multiple input multiple output (MIMO) wireless communication system. The wireless system uses hybrid beamforming technique to improve system throughput.

Introduction

5G and other modern wireless communication systems extensively use MIMO beamforming technology for signal to noise ratio (SNR) enhancement and spatial multiplexing to improve the data throughput in scatterer rich environments. In a scatterer-rich environment, there may not exist line-of-sight (LOS) paths between the transmit and receive antennas. To gain the high throughput, MIMO beamforming implements precoding on the transmitter side and combining on the receiver side to increase SNR and separate spatial channels. A full digital beamforming structure requires each antenna to have a dedicated RF-to-baseband chain, which makes the overall hardware expensive and power consumption high. As a solution, hybrid MIMO beamforming is proposed [1], in which fewer RF-to-baseband chains are employed and partial of precoding and combining are implemented in the RF portion. With deliberate selection of the weights for precoding and combining, hybrid beamforming can achieve comparable performance as that of full beamforming.

In this example, we introduce a Simulink model with hybrid MIMO beamforming. This model shows two hybrid beamforming algorithms: Quantized Sparse Hybrid Beamforming (QSHB) [2] and Hybrid Beamforming with Peak Search (HBPS).

The following figure shows the structure of a hybrid beamforming system.

In the figure,  is the number of signal streams;  is the number of transmit antennas;  is the number of transmit RF chains;  is the number of receive antennas; and  is the number of receive RF chains. In this example, two signal streams, 64 transmit antennas, 4 transmit RF chains, 16 receive antennas, and 4 receive RF chains.

The scattering channel is denoted by . The hybrid beamforming weights are represented by the analog precoder , digital precoder , analog combiner , and digital combiner . For a more detailed introduction to hybrid beamforming, please refer to the MATLAB Introduction to Hybrid Beamforming example.

Exploring the Model

The Simulink model consists of four main components: MIMO Transmitter, MIMO Channel, MIMO Receiver, and Weights Calculation.

The MIMO transmitter generates the signal stream and then applies the precoding. The modulated signal is propagated through a scattering channel defined in the MIMO channel and then decoded and demodulated at the receiver side.

MIMO Scattering Channel

The MIMO scattering channel is represented by a channel matrix. In addition, this example uses an enabled subsystem to periodically change this matrix to simulate the fact that a MIMO channel may vary over time.

Hybrid Beamforming Weights Computation

In a hybrid beamforming system, both the precoding and the corresponding combining process are done partly at baseband and partly in the RF band. In general, the beamforming achieved in the RF band only involves phase shifts. Therefore, a critical part in such a system is to determine how to distribute the weights between the baseband and the RF band based on the channel. This is done in the Weight Calculation block where the precoding weights, Fbb and FrfAng, and combining weights, Wbb and WrfAng, are computed based on the channel matrix, H. In this example, we assume the channel matrix is known and provide both QSHB and HBPS algorithms.

Quantized Sparse Hybrid Beamforming (QSHB)

Literature [2, 3] shows that given the channel matrix, H, of a MIMO scattering channel, the hybrid beamforming weights can be computed via an iterative algorithms [2]. Using an orthogonal matching pursuit algorithm, the resulting analog precoding/combining weights are just steering vectors corresponding to the dominant modes of the channel matrix. For the detailed description of the algorithm, please refer to the Introduction to Hybrid Beamforming example.

Quantized Sparse Hybrid Beamforming with Peak Search (HBPS)

HBPS is a simplified version of QSHB. Instead of searching for the dominant mode of channel matrix iteratively, HBPS projects all the digital weights into a grid of directions and identifies the  and  peaks to form the corresponding analog beamforming weights. This works well especially for large arrays, like arrays used in massive MIMO systems, since for large arrays, the directions are more likely to be orthogonal.

Because the channel matrix can change over time, the weights computation also needs to be performed periodically to accommodate the channel variation.

Results and Displays

QSHB

Following figures shows the recovered 16 QAM symbol streams at the receiver using QSHB algorithms. The resulting constellation shows that compared to the source constellation, the recovered symbols properly located in both streams. This means that using the hybrid beamforming technique, we can improve the system capacity by sending the two streams simultaneously. In addition, the constellation diagram shows that the variance of the first recovered stream is better than the second recovered stream as the points are less dispersed in the constellation of the first stream. This is because the first stream uses the most dominant mode of the MIMO channel so it has the best SNR.

HBPS

The result of HBPS is shown in the following figures. The constellation diagram shows that it achieves similar performance compared to QSHB. This means that the HBPS is a good choice for the simulated 64x16 MIMO system.

Summary

This example provides the Simulink model of two hybrid beamforming methods, QSHB and HBPS. The MIMO scattering channel is used to provide a realistic channel model for massive MIMO systems. The Simulink model is partitioned according to the functions in the signal flow, which gives guidance for hardware implementation. For a given H, the number of symbols can vary to simulate the variable coherent channel length. With this Simulink model, various system parameters and new hybrid beamforming algorithms can be studied. The system structure facilitates the hardware implementation.

Reference

[1] Andreas F. Molisch, et al. "Hybrid Beamforming for Massive MIMO: A Survey", IEEE Communications Magazine, Vol. 55, No. 9, September 2017, pp. 134-141

[2] Oma El Ayach, et al. "Spatially Sparse Precoding in Millimeter wave MIMO Systems, IEEE Transactions on Wireless Communications", Vol. 13, No. 3, March 2014

[3]. Emil Bjornson, Jakob Hoydis, Luca Sanguinetti, "Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency", Foundations and Trends in Signal Processing: Vol. 11, No. 3-4, 2017

VESIT_ ATAL _FDP on "Modeling and Simulation of an Electric Vehicles us...

FDP Contents:

• Introduction to Electric Vehicles & Its Types
• Modeling & Simulation of Electric Vehicles
• Simulink Design Optimization using Matlab Response Optimization App
• Matlab Inbuilt Hybrid & Electric Vehicle Reference Application Projects

Click here to get the PPT of this session:

https://drive.google.com/file/d/1CEM-...