Zoo API: Telecommunications

The xinfer::zoo::telecom module provides hyper-optimized, low-latency solutions specifically designed for the telecommunications industry.

Modern networks, particularly 5G and beyond, are incredibly complex, dynamic systems. Managing them efficiently and reliably requires AI that can make decisions in real-time. This is a domain where the performance of C++ and the optimization of xInfer are not just beneficial—they are a fundamental requirement.

The classes in this module are designed to be integrated directly into the control plane of network infrastructure, providing a level of autonomous optimization that is impossible with slower, non-specialized frameworks.

NetworkControlPolicy

Executes a trained Reinforcement Learning (RL) policy to dynamically control network parameters.

This is a powerful tool for automating complex network management tasks like resource allocation, beamforming, and traffic shaping.

Header: #include <xinfer/zoo/telecom/network_control_policy.h>

Use Case: Real-Time Radio Access Network (RAN) Optimization

Imagine an RL agent trained to manage a 5G cell tower. At every timestep, it analyzes the current state of the network (e.g., user load, signal interference, data demand) and chooses the optimal configuration for hundreds of parameters. This requires a policy that can run in milliseconds.

#include <xinfer/zoo/telecom/network_control_policy.h>
#include <xinfer/core/tensor.h>
#include <iostream>
#include <vector>
 
// This function would be part of the network's control loop, running continuously
void manage_network_slice(xinfer::zoo::telecom::NetworkControlPolicy& policy) {
    // 1. Get the current network state from telemetry data.
    //    This data is compiled into a single state vector.
    std::vector<float> current_state_vector;
    // ... collect real-time data from network monitoring tools ...
    
    // 2. Create a GPU tensor from the state vector.
    auto input_shape = {1, current_state_vector.size()}; // Batch size 1
    xinfer::core::Tensor state_tensor(input_shape, xinfer::core::DataType::kFLOAT);
    state_tensor.copy_from_host(current_state_vector.data());
 
    // 3. Execute the policy to get the optimal actions.
    //    This is a single, low-latency call to the TensorRT engine.
    xinfer::core::Tensor action_tensor = policy.predict(state_tensor);
 
    // 4. Decode the action tensor and apply the new configuration.
    std::vector<float> new_config_params(action_tensor.num_elements());
    action_tensor.copy_to_host(new_config_params.data());
    
    // ... send new_config_params to the network hardware controllers ...
    std::cout << "New network configuration applied.\n";
}
 
int main() {
    // 1. Configure and initialize the policy engine.
    //    The engine is pre-built from a model trained in a simulator.
    xinfer::zoo::telecom::NetworkControlPolicyConfig config;
    config.engine_path = "assets/ran_optimization_policy.engine";
 
    xinfer::zoo::telecom::NetworkControlPolicy policy(config);
    
    // 2. Run the control loop.
    // while (true) {
    //     manage_network_slice(policy);
    // }
 
    return 0;
}

Config Struct: NetworkControlPolicyConfig Input: xinfer::core::Tensor representing the current state of the network. Output: xinfer::core::Tensor representing the new configuration parameters (the "actions"). "F1 Car" Technology: This class is a wrapper around the zoo::rl::Policy engine, which provides a hyper-optimized, low-latency implementation of the MLP or small Transformer that typically constitutes an RL policy. This allows the control loop to run at the millisecond scale required for real-time network management.