Mar 28 2024 5G-LENA and LENA performance optimizations

Precompute and reuse sincos until the channel model (conditions) change

The channel model contains analog beamforming and channel condition information that changes when eNB/gNB and UE move relative to each other and over time.

However, the delay of ray clusters and angle between the position of receiving/transmitting antennas could be cached until the channel conditions saved, skipping expensive sincos computations.

These computations were performed over and over inside the last five nested loops of ThreeGppSpectrumPropagationLossModel::GenSpectrumChannelMatrix(), that unnecessarily recalculated the sincos for every combination of port. Note: there are actually four loops inside GenSpectrumChannelMatrix, but this call is repeated for every receiver.

This was done in two separate merge requests already merged to ns-3: MR1427 and MR1894.

Reduce complex operations in the 3GPP propagation loss model

The same nested loops at the end of ThreeGppSpectrumPropagationLossModel::GenSpectrumChannelMatrix() that used to recalculate sincos frequently, remains one of the most expensive parts of the simulation.

This cost relates to complex multiplications performed inside the five nested loops, scaling the computational complexity with the number of receivers, the number of receiver ports, number of transmitting ports, number of sub-bands and number of ray clusters.

By precomputing the product of the Doppler term and the cached delay sincos outside of this primary callsite, which is independent of the number of ports, we eliminate half of the multiplications performed in these nested loops.

This was done in a merge request already merged to ns-3: MR1888

The three merge requests MR1427, 1888, 1894 combined result in a 1.93x speedup for lte-lena-comparison-user with the settings specified in MR1427. With all the other patches, we see a total speedup of 2.4x (278->115 seconds), as shown in the figures below.

Before optimization patches, as of ns-3.41 and nr-v3.0, total simulation time of 278 seconds

After optimization patches, which will be part of ns-3.42 and nr-v3.1, total simulation time of 115 seconds

Fast codebook search for MIMO CQI feedback

The NrPmFullSearch provides the exhaustive codebook search for the rank indicador (RI) and precoding matrix indicator (PMI) used for MIMO CQI feedback.

For our fast search, in MR137, we first separated the rank from the PMI search, then separated the wideband PMI (I1) from the sub-band PMI search (I2).

By cutting the search space at every step, we managed to get the CQI feedback 16x faster in large unrealistic scenarios with 32 ports on gNB and UE.

After applying all other optimization patches, cttc-nr-mimo-demo with the following parameters had an 11.8x total speedup (261->21 seconds).

cttc-nr-mimo-demo --numColumnsGnb=32 --numRowsGnb=2 --numHPortsGnb=8 --numVPortsGnb=2 --xPolGnb=1 --numColumnsUe=8 --numRowsUe=2 --numHPortsUe=8 --numVPortsUe=2 --xPolUe=1 --gnbUeDistance=300 --enableMimoFeedback=1 --fullSearchCb="ns3::NrCbTypeOneSp" --rankLimit=4

Before optimization patches, as of ns-3.41 and nr-v3.0, total simulation time of 261 seconds using --pmSearchMethod=ns3::NrPmFullSearch

After optimization patches, which will be part of ns-3.42 and nr-v3.1, total simulation time of 109 seconds using --pmSearchMethod=ns3::NrPmFullSearch

After optimization patches, which will be part of ns-3.42 and nr-v3.1, total simulation time of 21 seconds using --pmSearchMethod=ns3::NrPmFastSearch

Other optimizations

Unnecessary copies and memory allocations, incorrect container types, unnecessary processing of what should be constexpr, etc. We found a few of these during optimization work and fixed them.

These impact primarily smaller scenarios, where this expensive propagation and MIMO operations are not that frequent.

More information about the optimizations and their respective patches can be found in the table below. Speedups listed below are for cttc-nr-mimo-demo using its default settings.

We are excited about the next release, with more realistic models and reasonable runtimes.

The 5G-LENA team