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Signal Reception and Recovery

Frame synchronization, channel estimation, MMSE equalization, cell identity search, MIB decoding, and SIB1 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 (Since R2019b)
`nrTimingEstimate`	Practical timing estimation (Since R2019b)
`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

NR Cell Search and MIB and SIB1 Recovery

Synchronize, demodulate, and decode live gNodeB signal.

NR PDSCH Throughput

NR PDSCH Throughput

Measure PDSCH throughput of the 5G NR downlink.

NR PUSCH Throughput

NR PUSCH Throughput

Measure PUSCH throughput of the 5G NR uplink.

NR SSB Beam Sweeping

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

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.

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NR Phase Noise Modeling and Compensation

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.

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Deep Learning Data Synthesis for 5G Channel Estimation

Deep Learning Data Synthesis for 5G Channel Estimation

Generate deep learning training data for channel estimation using 5G Toolbox.

Neural Network for Beam Selection

Neural Network for Beam Selection

Reduce the overhead of beam selection by using the receiver location rather than knowledge of the communication channels.

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Train DQN Agent for Beam Selection

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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