Mininet for VANET

I am trying to simulate and analyze ping-pong handovers in NetSim 5G. My understanding is that a handover is triggered if the SINR from the target gNB is 3 dB higher than the SINR from the serving gNB for a certain period.

- Is this handover mechanism implemented in NetSim?

- Can I log time-stamped SINR values? If yes, how can I obtain SINR values from both the serving and target gNBs?

- How can I verify the duration for which the SINR must be 3 dB higher to trigger the handover?

Any insights or guidance on this would be greatly appreciated.

I am trying to compare the performance of routing protocol like AODV, GPSR , DSDV , DYMO in VANET using OMNET++

Looking to simulate a 3-tier heterogeneous network. The base stations in each tier operate in a different frequency band, have a different transmit power and path loss exponent. Some have sector antennas while others have omni antennas. I need to change various handover parameters and see the effect. What may be the best tool for this?

Is this per the standard inter-gNB Xn procedure? Also, can the offset (handover margin) be adjusted? Is it based on event A3?

Briefly, each UE trains a local FL model with its collected data and then sends the trained FL model to an edge server at the gNB which aggregates the FL models and transmits it back to all the UEs. I plan to use MATLAB/Python for federated learning and NetSim for 5G simulation. (i) How can we establish a connection between NetSim and Python for this kind of application? (ii) Which KPIs should I monitor to assess the performance of federated learning?

I have implemented Q-learning, DQN, A2C, and PPO for 5G power control and scheduling using NetSim for 5G and Gymnasium framework for RL. While the algorithms perform well, each training run for a single scenario takes approximately two days. To generalize the algorithm across hundreds of scenarios would require months of training. What are some effective methods or optimizations to significantly reduce this training time?

The simulation should involve mobile users, dynamic environments with data, voice, command & control, position location, and ISR video transport over waveforms in UHF, L-band, and S-band frequencies. I am using NetSim, but guidance involving other tools is also welcome.

NetSim includes documentation and code for simulating various IoT attacks. I am looking to integrate machine learning techniques to detect these attacks. Can anyone provide suggestions on different ML techniques and how to approach this integration?

We are planning to run a lab course on comm. networks using NetSim. We need ideas on how to set this up effectively. Specifically, we are looking for a good set of experiments or exercises to include.

Our curriculum covers topics such as Wi-Fi, IoT & WSN, DDos Attacks, Routing, TCP, Vehicular networks and 4G/5G. We plan to use NetSim to explore these areas. How can we go about designing the coursework?