Multi-Device Task-Oriented Communication via Maximal Coding Rate Reduction
arXiv (Cornell University)(2023)
摘要
In task-oriented communications, most existing work designed the
physical-layer communication modules and learning based codecs with distinct
objectives: learning is targeted at accurate execution of specific tasks, while
communication aims at optimizing conventional communication metrics, such as
throughput maximization, delay minimization, or bit error rate minimization.
The inconsistency between the design objectives may hinder the exploitation of
the full benefits of task-oriented communications. In this paper, we consider a
task-oriented multi-device edge inference system over a multiple-input
multiple-output (MIMO) multiple-access channel, where the learning (i.e.,
feature encoding and classification) and communication (i.e., precoding)
modules are designed with the same goal of inference accuracy maximization.
Instead of end-to-end learning which involves both the task dataset and
wireless channel during training, we advocate a separate design of learning and
communication to achieve the consistent goal. Specifically, we leverage the
maximal coding rate reduction (MCR2) objective as a surrogate to represent the
inference accuracy, which allows us to explicitly formulate the precoding
optimization problem. We cast valuable insights into this formulation and
develop a block coordinate descent (BCD) algorithm for efficient
problem-solving. Moreover, the MCR2 objective also serves the loss function for
feature encoding and guides the classification design. Simulation results on
synthetic features explain the mechanism of MCR2 precoding at different SNRs.
We also validate on the CIFAR-10 and ModelNet10 datasets that the proposed
design achieves a better latency-accuracy tradeoff compared to various
baselines. As such, our work paves the way for further exploration into the
synergistic alignment of learning and communication objectives in task-oriented
communication systems.
更多查看译文
关键词
maximal coding rate
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要