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Semantics-Empowered Communication for Networked Intelligent Systems

Marios Kountouris, Nikolaos Pappas

arXiv:2007.11579v3cs.ITcs.NIeess.SP

TL;DR

Emerging intelligent systems must handle excessive real-time data that can become stale or useless, creating communication bottlenecks, latency, and safety issues. The paper proposes goal-oriented semantic communication unifying information generation, transmission, and usage, and applies it to real-time source reconstruction for remote actuation. Semantics-empowered policies reduce reconstruction error, cost of actuation error, and uninformative samples.

  • Problem

    Excessive distributed real-time data can become stale or useless in networked intelligent systems, causing communication bottlenecks, increased latency, and safety issues.

  • Method

    The paper unifies information generation, transmission, and usage through semantics-empowered sampling and communication for real-time source reconstruction and remote actuation.

  • Results

    Semantics-empowered policies reduce reconstruction error, cost of actuation error, and the number of uninformative samples generated.

  • Takeaways & Limitations

    Goal-oriented semantic communication carries information selected for its timeliness, usefulness, and value in achieving application goals.

Abstract

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Wireless connectivity has traditionally been regarded as an opaque data pipe carrying messages, whose context-dependent meaning and effectiveness have been ignored. Nevertheless, in emerging cyber-physical and autonomous networked systems, acquiring, processing, and sending excessive amounts of distributed real-time data, which ends up being stale or useless to the end user, will cause communication bottlenecks, increased latency, and safety issues. We envision a communication paradigm shift, which makes the Semantics of Information, i.e., the significance and the usefulness of messages with respect to the goal of data exchange, the underpinning of the entire communication process. This entails a goal-oriented unification of information generation, transmission, and usage, by taking into account process dynamics, signal sparsity, data correlation, and semantic information attributes. We apply this structurally new, synergetic approach to a communication scenario where the destination is tasked with real-time source reconstruction for the purpose of remote actuation. Capitalizing on semantics-empowered sampling and communication policies, we show significant reduction in both reconstruction error and cost of actuation error, as well as in the number of uninformative samples generated.

I. INTRODUCTION

Emerging networked intelligent systems generate and exchange massive real-time data, while conventional content-agnostic communication overlooks message significance. Sending outdated or irrelevant traffic can create bottlenecks, congestion, resource waste, and excessive energy consumption.

  • Autonomous cars can generate 750 MB per second, while mobile-robot swarms may transmit 1 GB aggregated data per second.
  • Cyber-physical systems require reliable real-time communication, autonomous interaction, and automated decision making over massive multimodal distributed data.
  • Outdated or irrelevant traffic causes communication bottlenecks that can lead to congestion, wasteful resource utilization, and excessive energy consumption.
  • Content-agnostic systems optimize conventional metrics while setting aside the significance and effectiveness of transmitted messages.
  • Age of information, value of information, and quality of information represent initial efforts to address data prioritization in status-update and networked-control systems.

B. Towards Goal-oriented Semantic Communication

The paper treats communication as a means to achieve application goals and defines information semantics as message usefulness relative to those goals. It proposes unifying data generation, transmission, and usage around contextual information attributes.

  • Communication is framed as a means to achieving specific goals rather than an end in itself.
  • Information semantics means the significance or usefulness of messages with respect to the goal of data exchange.
  • The proposed system unifies data generation, information transmission, and usage while exploiting innate and contextual information attributes.
  • Semantic value is assessed at source, link, and system granularity levels.

1) Microscopic scale:

At microscopic and mesoscopic scales, semantics captures source-event importance and link-level information attributes, then maps them to application-dependent value. The framework distinguishes intrinsic quality from context-dependent utility.

  • 1) Microscopic scale:: Source-level semantics assigns relative importance to stochastic-source events, outcomes, or observations according to their utility for a specific goal.
  • 1) Microscopic scale:: A context-dependent entropy weights outcomes through ϕ(y), allowing semantic importance to differ even when events have equal conventional information content.
  • 2) Mesoscopic scale:: Link-level semantics is a composite nonlinear multivariate function of innate and contextual information attributes.
  • 2) Mesoscopic scale:: Innate attributes include freshness or AoI and precision, whereas contextual attributes include timeliness and completeness.
  • 2) Mesoscopic scale:: Information semantics is modeled as S_t = ν(ψ(I, C)), where ν maps multidimensional attributes to context-dependent, cost-aware semantic value.
  • 2) Mesoscopic scale:: Information has both intrinsic value and utilitarian context-dependent value, so the same measurement can matter differently across applications.

3) Macroscopic scale:

At the system scale, semantics concerns end-to-end distortion and timing mismatch, while the proposed architecture controls sampling and carries communication through reconstruction to application use. Its blocks connect source observation, semantic transmission, and actionable intelligence.

  • 3) Macroscopic scale:: System-level semantics evaluates effective distortion and timing mismatch between generated information and its reconstructed version across space, time, and processing stages.
  • B. Semantics-empowered Communication Model: The architecture begins with information generation and acquisition instead of assuming uncontrolled exogenous traffic arrivals.
  • B. Semantics-empowered Communication Model: Distributed devices observe continuous or discrete, possibly correlated stochastic processes representing time-varying physical phenomena.
  • B. Semantics-empowered Communication Model: Process-aware active sampling generates and prioritizes only valuable samples according to source variability, communication characteristics, and application requirements.
  • B. Semantics-empowered Communication Model: Samples may be quantized, compressed, or feature-extracted before semantic-value-based scheduling over noisy, delay- and error-prone channels.
  • B. Semantics-empowered Communication Model: Destinations reconstruct source signals from received samples for collision avoidance, estimation, control, actuation, situation awareness, or learning.

C. Joint sampling, communication, and reconstruction under real-time constraints

The proposed paradigm jointly redesigns information generation, transmission, and reconstruction around semantic information for real-time networked applications.

  • C. Joint sampling, communication, and reconstruction under real-time constraints: Sampling and communication are structurally linked because source-triggered samples may become stale, while timely samples may contain no useful information.The example concerns real-time causal reconstruction or remote estimation of a continuous stochastic process for networked robotics.
  • C. Joint sampling, communication, and reconstruction under real-time constraints: The approach extends cohesion across information generation, transmission, and reconstruction to efficiently meet real-time application requirements.

III. AN ILLUSTRATIVE EXAMPLE

The illustrative example studies real-time reconstruction of a two-state Markovian source for remote actuation through a digital twin.

  • III. AN ILLUSTRATIVE EXAMPLE: A monitoring device samples a two-state Markovian source and transmits status updates over an i.i.d. wireless erasure channel to a remote actuator.The source initiates actions for a robotic object at the transmitter, while the receiver maintains a digital twin.
  • III. AN ILLUSTRATIVE EXAMPLE: Real-time source reconstruction occurs at the endpoint when status updates are received, supporting the digital twin’s real-time actuation goal.
  • III. AN ILLUSTRATIVE EXAMPLE: Fig. 2 presents the setup for this source-monitoring, wireless-update, and remote-actuation example.

A. Sampling and Transmission Policies

Four policies coordinate information generation and transmission differently, ranging from source-agnostic periodic sampling to end-to-end discrepancy-triggered updates.

  • A. Sampling and Transmission Policies: Uniform sampling occurs periodically without regard to source evolution and retransmits the latest acquired measurement after failure.Rapidly varying sources may undergo several state transitions between periodic samples.
  • A. Sampling and Transmission Policies: Age-aware sampling triggers a new sample when receiver-side AoI reaches a threshold and predicts the next state after transmission failure.The prediction uses known or learnable source transition probabilities.
  • A. Sampling and Transmission Policies: Semantics-aware sampling generates and transmits an update whenever the transmitter observes a source-state change since the previous sample.This policy accounts only for changes tracked at the source side.
  • A. Sampling and Transmission Policies: E2E Semantics triggers sampling whenever source and destination states differ, extending source-side semantics-aware sampling to end-to-end discrepancy tracking.

B. Metrics and Performance Evaluation

The example evaluates reconstruction and actuation errors under different source variability and communication conditions, while also measuring uninformative samples and communication-resource use.

  • B. Metrics and Performance Evaluation: Real-time reconstruction error measures source-versus-reconstruction discrepancy over time, while actuation error weights mismatches by their consequence at the actuator.A mismatch in one direction has cost one, the opposite direction cost five, and matching states have no actuation error.
  • B. Metrics and Performance Evaluation: For slowly varying sources, age-aware sampling outperforms semantics-aware sampling in reconstruction error over poor channels, while semantics-aware sampling beats uniform sampling in actuation cost.The reconstruction result is attributed to failed receiver anticipation of the correct state.
  • B. Metrics and Performance Evaluation: For slow sources, E2E Semantics significantly outperforms semantics-aware sampling by rapidly eliminating source-destination discrepancy despite low channel quality.
  • B. Metrics and Performance Evaluation: For rapidly varying sources, both semantics-empowered policies have similar reconstruction error, while E2E Semantics achieves the lowest actuation error without transmitting uninformative samples.
  • B. Metrics and Performance Evaluation: E2E Semantics generates no redundant samples by definition, whereas uniform and age-aware policies usually generate the highest percentage of uninformative samples.Semantics-aware sampling remains below 10% for good channels with slow sources and below 5% for rapidly varying sources.
  • B. Metrics and Performance Evaluation: Semantics-empowered policies generate samples intended to convey valuable information for real-time reconstruction and actuation, with acquisition timing treated as crucial.Learning source-evolution patterns, for instance through reinforcement learning, could provide additional savings in communication load and samples generated.

IV. SEMANTICS-AWARE NETWORKING

Semantics-aware networking integrates goal-oriented acquisition, representation, reception, and control to prioritize useful information. It supports semantic quality assessment beyond distortion alone and can improve reconstruction, actuation outcomes, and resource utilization.

  • Core functionalities: Semantic networking spans local information acquisition, semantic representation and value inference, prioritization, in-network processing, reception, and control.These functions include active sampling, censoring, sparse representations, approximate reconstruction, information fusion, and resource orchestration.
  • Core functionalities: Semantic filtering generates and transmits only useful information while potentially reducing sampling rates below the Nyquist limit without affecting reconstruction accuracy.Active sampling and censoring avoid unnecessary redundancy during data acquisition and encoding.
  • Core functionalities: Semantic preprocessing converts raw multimodal data into goal-oriented sparse representations, such as local estimates, labels, embeddings, or selected informative samples.Examples include extracting a tracked target’s velocity and location from visual data and transmitting representative learning samples instead of raw data.
  • Core functionalities: Semantic reception supports fast partial or approximate reconstruction and goal-dependent information recovery, fusion, and querying.Conventional reconstruction quality is measured using time-averaged MSE between the estimated and original signals.
  • Semantic quality: Semantic quality indicators can use divergences or distances between input and output signal distributions when distortion does not capture perceptual quality.The paper gives divergence measures and Kantorovich-Wasserstein distance as examples.
  • Semantic quality: Lower reconstruction error can coexist with higher cost of actuation error, showing that distortion alone may not represent remote-actuation performance.The illustrative example distinguishes reconstruction quality from the quality perceived through actuation consequences.

V. FUTURE CHALLENGES

Future semantic-aware networks face challenges in defining metrics, adapting shared-medium access, orchestrating resources, and optimizing multiple semantic objectives. These challenges require models that incorporate dynamics, semantics, application demands, and constrained resources.

  • Semantic metrics: Concrete semantic metrics must incorporate qualitative information attributes alongside source and network dynamics and their potential interdependencies.Establishing such metrics within communication theory is identified as a key challenge.
  • Semantics-aware Multiple Access: Semantics-aware multiple access must adapt device access patterns and transmission attempts to process variability, information semantics, and application demands.Adaptation must also account for traffic arrivals and the status of other nodes when optimizing a shared medium.
  • Goal-oriented Resource Orchestration: Goal-oriented resource orchestration requires scheduling and allocation policies for correlated, multimodal, multi-source information acquired at different quality levels.Online algorithms may choose which information to gather, from where, and when under communication and processing constraints.
  • Multi-objective Stochastic Optimization: Semantics-aware data gathering and prioritization require multi-criteria optimization based on end-user-perceived utilities across information attributes.A risk-sensitive, multi-attribute utility framework based on cumulative prospect theory is presented as a promising direction.

VI. EPILOGUE

The paper proposes semantics-empowered communication as a goal-oriented unification of information generation, transmission, and usage. It aims to carry timely, useful, and valuable samples while improving resource, energy, and computational efficiency for networked intelligent systems.

  • Epilogue: Semantics-empowered communication unifies information generation, transmission, and usage around information semantics and application goals.The approach is intended to support autonomous, real-time, connected intelligence applications and next-generation real-time data networking.
  • Epilogue: Carrying only the most informative samples is presented as a way to convey timely, useful, and valuable information to end users.The paper associates this paradigm with improved network resource usage, energy consumption, and computational efficiency.
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