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AdaSem: Adaptive Goal-Oriented Semantic Communications for End-to-End Camera Relocalization

Qi Liao, Tze-Yang Tung

TL;DR

AdaSem tackles end-to-end latency in remote camera relocalization by introducing a channel-aware variational information bottleneck and a lightweight, adaptive semantic codec. The system jointly optimizes encoder/decoder modules and an adaptation policy to dynamically adjust the symbol budget based on channel feedback, achieving low latency without sacrificing accuracy. Experimental results against a real-world Android-edge baseline show substantial gains: end-to-end latency reductions of about 75% and pose-error reductions around 64–87%, with robust performance across varying channel conditions. This work demonstrates the practicality of adaptive semantic communications for latency-sensitive edge-enabled vision tasks like camera relocalization.

Abstract

Recently, deep autoencoders have gained traction as a powerful method for implementing goal-oriented semantic communications systems. The idea is to train a mapping from the source domain directly to channel symbols, and vice versa. However, prior studies often focused on rate-distortion tradeoff and transmission delay, at the cost of increasing end-to-end complexity and thus latency. Moreover, the datasets used are often not reflective of real-world environments, and the results were not validated against real-world baseline systems, leading to an unfair comparison. In this paper, we study the problem of remote camera pose estimation and propose AdaSem, an adaptive semantic communications approach that optimizes the tradeoff between inference accuracy and end-to-end latency. We develop an adaptive semantic codec model, which encodes the source data into a dynamic number of symbols, based on the latent space distribution and the channel state feedback. We utilize a lightweight model for both transmitter and receiver to ensure comparable complexity to the baseline implemented in a real-world system. Extensive experiments on real-environment data show the effectiveness of our approach. When compared to a real implementation of a client-server camera relocalization service, AdaSem outperforms the baseline by reducing the end-to-end delay and estimation error by over 75% and 63%, respectively.

AdaSem: Adaptive Goal-Oriented Semantic Communications for End-to-End Camera Relocalization

TL;DR

AdaSem tackles end-to-end latency in remote camera relocalization by introducing a channel-aware variational information bottleneck and a lightweight, adaptive semantic codec. The system jointly optimizes encoder/decoder modules and an adaptation policy to dynamically adjust the symbol budget based on channel feedback, achieving low latency without sacrificing accuracy. Experimental results against a real-world Android-edge baseline show substantial gains: end-to-end latency reductions of about 75% and pose-error reductions around 64–87%, with robust performance across varying channel conditions. This work demonstrates the practicality of adaptive semantic communications for latency-sensitive edge-enabled vision tasks like camera relocalization.

Abstract

Recently, deep autoencoders have gained traction as a powerful method for implementing goal-oriented semantic communications systems. The idea is to train a mapping from the source domain directly to channel symbols, and vice versa. However, prior studies often focused on rate-distortion tradeoff and transmission delay, at the cost of increasing end-to-end complexity and thus latency. Moreover, the datasets used are often not reflective of real-world environments, and the results were not validated against real-world baseline systems, leading to an unfair comparison. In this paper, we study the problem of remote camera pose estimation and propose AdaSem, an adaptive semantic communications approach that optimizes the tradeoff between inference accuracy and end-to-end latency. We develop an adaptive semantic codec model, which encodes the source data into a dynamic number of symbols, based on the latent space distribution and the channel state feedback. We utilize a lightweight model for both transmitter and receiver to ensure comparable complexity to the baseline implemented in a real-world system. Extensive experiments on real-environment data show the effectiveness of our approach. When compared to a real implementation of a client-server camera relocalization service, AdaSem outperforms the baseline by reducing the end-to-end delay and estimation error by over 75% and 63%, respectively.
Paper Structure (17 sections, 1 theorem, 16 equations, 12 figures, 3 tables, 2 algorithms)

This paper contains 17 sections, 1 theorem, 16 equations, 12 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. System Model and Problem Statement
  4. Proposed AdaSem Solution
  5. Channel-Aware variational information bottleneck
  6. AdaSem model
  7. Adaptation policy
  8. Dynamic neural network architecture
  9. Experimental Results
  10. Baseline system setup and data collection
  11. Neural network architecture and training hyperparameters
  12. Performance comparison
  13. Interpretation of the extracted features and encoded symbols
  14. Evaluation on adaptation policy
  15. Conclusion
  16. ...and 2 more sections

Key Result

Corollary 1

Assume the Markov chain in Figure fig:MarkovAssumption, let $q_{\phi}(\boldsymbol{\mathbf{y}}|\hat{\boldsymbol{\mathbf{s}}})$ (the decoder) be a variational approximation to $p(\boldsymbol{\mathbf{y}}|\hat{\boldsymbol{\mathbf{s}}})$. Let $p_{\theta}(\hat{\boldsymbol{\mathbf{s}}}|\boldsymbol{\mathbf{ where $\mathcal{D}_{\mathcal{K}\mathcal{L}}(p||q)$ denote the KL divergence between the two distrib

Figures (12)

  • Figure 1: Comparison of conventional and semantic communications schemes.
  • Figure 2: Assumed Markov chain of Problem \ref{['prob:EC_Camloc']}.
  • Figure 3: Proposed AdaSem model consisting of 1) encoder $f_e=f_s\circ f_z$, 2) variational encoder $g = f_{\hat{s}}\circ f_z$, 3) adaptation policy $\pi$, and 4) decoder $f_d$.
  • Figure 4: Basic architecture of the multi-input encoder $f_e$.
  • Figure 5: Multi-head encoder. E.g., if $k^{\ast}=k_1$, then, only the head corresponding to $k_1$ is activated.
  • ...and 7 more figures

Theorems & Definitions (3)

  • Corollary 1
  • proof
  • proof