Signal Reception and Recovery
Use 5G Toolbox™ low-level functions to process and recover received signals. You can perform practical and perfect channel and timing estimation, synchronization, minimum mean-squared error (MMSE) equalization, cell identity search, MIB decoding, and SIB1 recovery.
Functions
nrPerfectChannelEstimate | Perfect channel estimation |
nrPerfectTimingEstimate | Perfect timing estimation |
nrChannelEstimate | Practical channel estimation |
nrTimingEstimate | Practical timing estimation |
nrEqualizeMMSE | Minimum mean-squared error (MMSE) equalization |
nrExtractResources | Extract resource elements from resource array |
Topics
- Extract PBCH Symbols and Channel Estimates for Decoding
This example shows how to extract physical broadcast channel (PBCH) symbols from a received grid and associated channel estimates.
Related Information
- Capture and Label NR and LTE Signals for AI Training (Wireless Testbench)
Featured Examples
NR Cell Search and MIB and SIB1 Recovery
Synchronize, demodulate, and decode live gNodeB signal.
5G NR Synchronization Signal Capture Using Software-Defined Radio
Use a software-defined radio to capture an SS burst. Analyze the capture and identify the strongest SSB.
NR PDSCH Throughput
Measure PDSCH throughput of the 5G NR downlink.
NR PUSCH Throughput
Measure PUSCH throughput of the 5G NR uplink.
NR SSB Beam Sweeping
Employ beam sweeping at gNB and UE and learn about beam management procedures during initial access.
NR Downlink Transmit-End Beam Refinement Using CSI-RS
Transmit multiple CSI-RS resources in different directions in a scattering environment and select the optimal transmit beam based on RSRP.
NR Phase Noise Modeling and Compensation
Analyze the impact of phase noise in a 5G OFDM system and use PT-RS in compensating the CPE.
Deep Learning Data Synthesis for 5G Channel Estimation
Generate deep learning training data for channel estimation using 5G Toolbox.
Neural Network for Beam Selection
Reduce the overhead of beam selection by using the receiver location rather than knowledge of the communication channels.
Train DQN Agent for Beam Selection
Train a deep Q-network (DQN) reinforcement learning agent to increase beam selection accuracy in a 5G NR communications system.
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