Optimizing Latency-Distortion Tradeoffs in Communicating Classifier Decisions over Noisy Channels

Core Concepts

There is an interesting interplay between source distortion (distortion for the probability vector measured via f-divergence) and the subsequent channel encoding/decoding parameters, and a joint design of these parameters is crucial to navigate the latency-distortion tradeoff when communicating classifier decisions over noisy channels.

Abstract

The paper considers the problem of communicating the decisions of a classifier (represented as a probability vector) over a noisy channel. The goal is to study the tradeoff between transmission latency and the distortion between the original probability vector and the reconstructed one at the receiver, where the distortion is measured using f-divergence. The key highlights and insights are: The authors analyze this tradeoff using uniform, lattice, and sparse lattice-based quantization techniques to encode the probability vector. They first characterize the bit budgets for each technique given a requirement on the allowed source distortion. These bounds are then combined with results from finite-blocklength literature to provide a framework for analyzing the effects of both quantization distortion and distortion due to decoding error probability (i.e., channel effects) on the incurred transmission latency. The results show that there is an interesting interplay between source distortion and the subsequent channel encoding/decoding parameters, and indicate that a joint design of these parameters is crucial to navigate the latency-distortion tradeoff. The impact of changing different parameters (e.g., number of classes, SNR, source distortion) on the latency-distortion tradeoff is studied, and experiments are performed on AWGN and fading channels. The authors find that sparse lattice-based quantization is the most effective at minimizing latency for low end-to-end distortion requirements across different parameters and works best for sparse, high-dimensional probability vectors (i.e., high number of classes).

Stats

"In recent years, machine learning (ML) has been increasingly applied to time-sensitive applications, including Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications." "These applications require reliable and rapid data transmission for tasks such as trajectory prediction [2] and lane change detection [3]."

Quotes

"Semantic communication generally focuses on sending context dependent features/decisions dependent on the data to the receiver (rather than the entire raw message) [7]." "URLLC is to design protocols in order to transmit low-data rate (short packets) with high reliability (low probability of error) within a small latency [9]."

Key Insights Distilled From

Latency-Distortion Tradeoffs in Communicating Classification Results over Noisy Channels

by Noel Teku,Su... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14586.pdf

Deeper Inquiries

How can the proposed framework be extended to handle more complex distortion measures beyond f-divergence, such as task-specific distortion metrics

The proposed framework can be extended to handle more complex distortion measures beyond f-divergence by incorporating task-specific distortion metrics. This extension would involve customizing the distortion measure based on the specific requirements of the application or task at hand. For instance, in safety-critical applications where certain errors may have higher consequences, the distortion metric can be tailored to prioritize minimizing those errors. By defining task-specific distortion metrics, the framework can be adapted to optimize the communication of classifier decisions based on the unique needs of the system. This customization would involve a deeper analysis of the impact of different types of errors on the overall system performance and adjusting the distortion measure accordingly.

What are the potential implications of the latency-distortion tradeoffs on the design of real-time machine learning systems deployed in safety-critical applications

The latency-distortion tradeoffs can have significant implications on the design of real-time machine learning systems deployed in safety-critical applications. In safety-critical scenarios, such as autonomous driving or medical diagnosis, the timely and accurate communication of classifier decisions is crucial for ensuring the reliability and safety of the system. The tradeoff between latency and distortion directly affects the decision-making process of the machine learning system. A system with low latency but high distortion may lead to incorrect decisions being made, potentially endangering lives or causing critical errors. On the other hand, a system with low distortion but high latency may not be able to respond quickly to changing conditions, leading to missed opportunities or delayed actions. Therefore, in safety-critical applications, finding the right balance between latency and distortion is paramount to guaranteeing the system's performance and reliability under varying conditions.

How can the insights from this work be leveraged to develop efficient semantic communication protocols for a broader range of applications beyond just classifier decisions

The insights from this work can be leveraged to develop efficient semantic communication protocols for a broader range of applications beyond just classifier decisions by focusing on optimizing the communication of context-dependent features or decisions. Semantic communication protocols aim to transmit meaningful information that captures the essence of the data being communicated. By applying the principles of latency-distortion tradeoffs and quantization techniques to semantic communication, it is possible to design protocols that minimize latency while preserving the essential information required for decision-making. This approach can be beneficial in applications such as IoT, edge computing, and smart systems where real-time decision-making based on contextual information is essential. By incorporating the findings from this research, developers can create efficient and reliable communication protocols that enhance the performance of various systems reliant on semantic information exchange.