Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Fast Transmission Control Adaptation for URLLC via Channel Knowledge Map and Meta-Learning

Hongsen Peng, Tobias Kallehauge, Meixia Tao, Petar Popovski

TL;DR

This work tackles URLLC under unknown channel distributions by leveraging a Channel Knowledge Map built from historical samples and two learning-based strategies. It first formulates location-specific and area-wide transmission control problems, using non-parametric quantile estimation and a PPO-based DRL policy. It then introduces a CKM-driven power-scaling approach with improved clustering and a meta-reinforcement learning framework (MAML-inspired) to rapidly adapt to new environments with minimal retraining. Simulations in stationary and QuaDRiGa-based synthetic areas show that the meta-DRL approach delivers the best power efficiency and availability, with the CKM-based method providing strong cross-location transfer when channel statistics are similar.

Abstract

This paper considers methods for delivering ultra reliable low latency communication (URLLC) to enable mission-critical Internet of Things (IoT) services in wireless environments with unknown channel distribution. The methods rely upon the historical channel gain samples of a few locations in a target area. We formulate a non-trivial transmission control adaptation problem across the target area under the URLLC constraints. Then we propose two solutions to solve this problem. The first is a power scaling scheme in conjunction with the deep reinforcement learning (DRL) algorithm with the help of the channel knowledge map (CKM) without retraining, where the CKM employs the spatial correlation of the channel characteristics from the historical channel gain samples. The second solution is model agnostic meta-learning (MAML) based metareinforcement learning algorithm that is trained from the known channel gain samples following distinct channel distributions and can quickly adapt to the new environment within a few steps of gradient update. Simulation results indicate that the DRL-based algorithm can effectively meet the reliability requirement of URLLC under various quality-of-service (QoS) constraints. Then the adaptation capabilities of the power scaling scheme and meta-reinforcement learning algorithm are also validated.

Fast Transmission Control Adaptation for URLLC via Channel Knowledge Map and Meta-Learning

TL;DR

This work tackles URLLC under unknown channel distributions by leveraging a Channel Knowledge Map built from historical samples and two learning-based strategies. It first formulates location-specific and area-wide transmission control problems, using non-parametric quantile estimation and a PPO-based DRL policy. It then introduces a CKM-driven power-scaling approach with improved clustering and a meta-reinforcement learning framework (MAML-inspired) to rapidly adapt to new environments with minimal retraining. Simulations in stationary and QuaDRiGa-based synthetic areas show that the meta-DRL approach delivers the best power efficiency and availability, with the CKM-based method providing strong cross-location transfer when channel statistics are similar.

Abstract

This paper considers methods for delivering ultra reliable low latency communication (URLLC) to enable mission-critical Internet of Things (IoT) services in wireless environments with unknown channel distribution. The methods rely upon the historical channel gain samples of a few locations in a target area. We formulate a non-trivial transmission control adaptation problem across the target area under the URLLC constraints. Then we propose two solutions to solve this problem. The first is a power scaling scheme in conjunction with the deep reinforcement learning (DRL) algorithm with the help of the channel knowledge map (CKM) without retraining, where the CKM employs the spatial correlation of the channel characteristics from the historical channel gain samples. The second solution is model agnostic meta-learning (MAML) based metareinforcement learning algorithm that is trained from the known channel gain samples following distinct channel distributions and can quickly adapt to the new environment within a few steps of gradient update. Simulation results indicate that the DRL-based algorithm can effectively meet the reliability requirement of URLLC under various quality-of-service (QoS) constraints. Then the adaptation capabilities of the power scaling scheme and meta-reinforcement learning algorithm are also validated.

Paper Structure

This paper contains 22 sections, 1 theorem, 29 equations, 7 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulations
  3. Physical Layer Model
  4. Link Layer Model
  5. Problem Formulations
  6. Location-Specific Transmission Control
  7. Non-parametric quantile estimation
  8. MDP formulation and algorithm
  9. Policy Adaptation via Channel Knowledge Map
  10. Channel Knowledge Map
  11. Power Scaling Scheme based on
  12. Transmission Control Adaptation via Meta-DRL
  13. Meta-Reinforcement Learning Elements
  14. Meta-Reinforcement Learning Algorithm
  15. Deployment and Overhead of the Meta-DRL algorithm
  16. ...and 7 more sections

Key Result

Proposition 1

If the $\varepsilon$-quantiles of the source domain and the target domain satisfy $F_{ \gamma_s}^{-1}(\varepsilon)=\kappa F_{ \gamma_t}^{-1}(\varepsilon), ~ \forall \varepsilon \in \mathscr E$, $\kappa$ is a constant, the optimal policy trained from the source domain after power scaling is also opt

Figures (7)

  • Figure 1: Queueing diagram of the cross-layer transmission model
  • Figure 2: Learning curves of the PPO algorithm, target $\xi=10^{-3}$ and $D_{\rm max} =5$ slots
  • Figure 3: Impact of reliability and latency.
  • Figure 4: Average channel gain and the $\varepsilon$-quantile comparison of the target area, where $\varepsilon=0.1$
  • Figure 5: Adaptability convergence performance of the meta-reinforcement learning
  • ...and 2 more figures

Theorems & Definitions (2)

  • Proposition 1
  • proof