Goal-Oriented Semantics-Aware Communication

• Goal-oriented semantics-aware communication is a framework that embeds semantic relevance and task-driven objectives into every communication layer to optimize decision-making and control.
• The framework employs the Goal-oriented Tensor (GoT) model to unify semantic extraction, adaptive coding, and sparse dissemination, enhancing system efficiency.
• By transmitting only task-relevant data, the approach reduces bandwidth, energy consumption, and latency, enabling robust performance in 6G and cyber-physical systems.

Goal-oriented semantics-aware communication integrates semantic meaning and goal-driven objectives directly into communication system design, prioritizing the transmission and processing of information that is most relevant for end-task performance rather than reconstructing source data bit-for-bit. This paradigm explicitly departs from the classical Shannon-centric view, which focuses exclusively on symbol transmission reliability, by recognizing that the utility of information—its value for subsequent decision-making, control, or action—can and should shape the entire communication pipeline, from sensing through dissemination, coding, and ultimately control. The approach is motivated by the distinct requirements of emerging 6G scenarios, such as cyber-physical systems, AI-native edge networks, industrial automation, UAV control, and AR/VR, where efficiency, timeliness, and effectiveness take precedence over raw data fidelity (Li et al., 2023, Zhou et al., 2022, Getu et al., 2023).

1. Conceptual Foundations and Motivation

Traditional communication theory (Shannon, 1948) is formulated around three abstract levels: (i) the technical level (bit transmission), (ii) the semantic level (meaning transmission), and (iii) the effectiveness level (action or goal). While 5G systems have approached Shannon capacity, they remain agnostic to semantic relevance and task objectives, transmitting all bits equally regardless of their end-task utility. The forecasted data explosion in 6G—due to tactile IoT, AR/VR, multi-modal sensing, and networked AI—renders this approach unsustainable. Goal-oriented semantics-aware communication instead:

• Extracts the meaning most relevant to an ultimate task, discarding task-irrelevant data.
• Incorporates the actual system objective into all layers (sampling, coding, acts/decisions).
• Embeds context, urgency, and action costs into the communication design, thus optimizing both resource expenditure and operational effectiveness (Li et al., 2023, Kountouris et al., 2020, Zhou et al., 2022).

2. Unifying Metrics: The Goal-Oriented Tensor (GoT) Model

A pivotal advance is the introduction of the Goal-oriented Tensor (GoT), which unifies a spectrum of traditional and semantics-aware metrics within a single framework. Formally, for a system state $X(t)\in S$ (semantic states), an estimated state $\hat X(t)\in S$, and a context $\Phi(t)\in V$ (e.g., urgency or actuation cost), the one-slot GoT value is:

$$\mathcal{T}\bigl(X(t),\,\hat X(t),\,\Phi(t)\bigr) = \bigl[C_{1}\bigl(X(t),\Phi(t)\bigr)\;-\;C_{2}\bigl(\pi(\hat X(t))\bigr)\bigr]^{+} + C_{3}\bigl(\pi(\hat X(t))\bigr)$$

with $C_1$ capturing the raw cost of being in state $X(t)$ under context $\Phi(t)$, $C_2$ the benefit of correctly selected actions, $C_3$ the direct resource/actuation cost, and $\pi$ the decision rule mapping receiver estimates to actions. The 3D tensor $\mathcal{T}_{i,j,k}$ over $S\times S\times V$ recovers classic metrics such as Age of Information (AoI), Value of Information (VoI), Urgency of Information (UoI), age-weighted MSE, and the cost of actuation error as special cases under appropriate choices of $C_1, C_2, C_3, \pi$ and the state/context/cost spaces (Li et al., 2023, Li et al., 2023).

3. System Architecture: GoT-Driven Design Cycle

A typical goal-oriented, semantics-aware architecture includes the following stages:

1. Information Perception & Semantic Extraction: Sensing hardware generates raw data, which is then mapped by a semantic quantizer into a finite semantic alphabet $S$ and associated context features $V$ (e.g., urgency, environment).
2. Semantics-Aware Sparse Dissemination: A sampling policy, informed by the GoT, triggers updates only when the expected reduction in GoT-based cost outweighs the communication/sampling cost. This produces content-aware and context-aware sparse message flows.
3. Goal-Oriented Channel Coding & Transmission: Channel codes are designed such that semantically critical pairs of symbols (tuples with high GoT cost if misclassified) get increased protection—larger minimum distances in the codebook, adaptive power or retransmission allocation (Li et al., 2023, Kountouris et al., 2020).
4. Control-Plane Decision Making: Upon each reception, the receiver’s action rule $\pi$ selects an action, closing the loop between transmitted semantic information and real-world actuation or inference.
5. Joint Optimization: The design objective is to select a joint policy $\mu$ (covering sampling, coding, decision) to minimize the long-term average GoT cost, subject to constraints on sampling rate, resource budget, or latency. The optimization is typically cast as a Markov Decision Process (MDP) or Decentralized POMDP for more complex, distributed settings (Li et al., 2023, Li et al., 2023).

4. Holistic Optimization and Policy Derivation

In the GoT framework, the system seeks to solve:

$$J(\mu) = \limsup_{T\to\infty} \frac{1}{T} \sum_{t=1}^T \mathbb{E}_\mu[\, \mathcal{T}(X(t), \hat X(t), \Phi(t)) \,]$$

subject to resource constraints. This leads to various analysis/optimization tools:

• MDP/Dynamic Programming: Enumerate or approximate optimal sampling, coding, and decision rules over state space $S\times S\times V$, using Bellman/Fixed-Point equations (Li et al., 2023, Li et al., 2023).
• Decentralized/Partially Observable MDPs (Dec-POMDPs) for joint sampler–decision-maker systems, with computationally tractable sub-optimal algorithms based on Nash Equilibrium search and policy iteration (Li et al., 2023).
• Reinforcement Learning: Where system models are inaccessible or too complex, goal-oriented cost learned via data-driven methods.

Benchmarks show that joint design driven by the GoT yields substantial reduction in overall cost and sampling rate compared to classical schemes based on AoI or MSE alone, particularly under stringent channel or resource-constrained regimes (Li et al., 2023).

5. Domain-Specific Framework Instantiations

Goal-oriented semantics-aware communication subsumes and extends existing frameworks:

• Semantics-Empowered Communication: Designs joint sampling and communication so that actions are triggered only by events that materially affect control performance or inference accuracy, cutting actuation errors and unnecessary traffic (Kountouris et al., 2020).
• AR/VR and Metaverse: Goal-oriented frameworks, e.g., TSAR and GSCM, transmit only task-relevant semantic features (e.g., avatar skeleton, joint positions, custom object metadata) and leverage shared base knowledge or scene priors for virtual world reconstruction, achieving multi-fold resource savings and improved latency/fidelity (Wang et al., 2023, Wang et al., 7 Aug 2024).
• Pull-based/Query Scheduling: In systems with multiple sensing and actuation agents, effect-aware scheduling (using a Grade of Effectiveness, GoE) maximizes cumulative task utility by selectively querying only those system attributes likely to produce the greatest impact, under risk-aware cost constraints (Agheli et al., 9 Mar 2025).
• Common-Language and Curriculum Frameworks: Speaker-listener pairs can learn a minimal belief hierarchy for effective semantic description, drive down communication cost and improve reliability via curriculum learning (Farshbafan et al., 2022).

6. Open Challenges and Future Research Directions

Outstanding research challenges in goal-oriented semantics-aware communication include:

• Scalability and Heterogeneity: Supporting large-scale, multi-agent networks with coexisting, possibly conflicting, task goals each characterized by distinct GoT tensors, and resource division across these streams (Li et al., 2023).
• Physical Layer Integration: Extending GoT-based optimization to include modulation, waveform design, multi-user scheduling, and direct minimization of semantic/goal cost, not just bit or symbol error (Li et al., 2023).
• Perception–Communication–Computation–Control Co-Design: Higher-order tensors and tightly integrated algorithms coordinating across these modules to optimize end-to-end task utility (Li et al., 2023).
• Data-Driven/Tunable GoT Learning: Inferring or adapting cost functions online from real system performance and implicit user/task feedback; learning to synthesize or update goal representations under distribution shift.
• Game-Theoretic and Multi-Agent Extensions: Managing information exchange and cooperation/competition in decentralized systems where agents may have strategic incentives (Li et al., 2023, Li et al., 2023).
• Standardization and Metrics: Establishing unified performance metrics that can objectively benchmark reasoning, effectiveness, and semantic fidelity across data types and tasks.

7. Broader Impact and Significance

By restructuring communication and inference around task-specific utility—rather than undifferentiated data fidelity—goal-oriented semantics-aware communication enables ultra-efficient, context-sensitive, and robust operation of future AI-centric and cyber-physical systems. It is anticipated to substantially reduce bandwidth, energy, and latency, unlock new operating regimes for 6G wireless, and underpin the realization of smart cities, decentralized autonomy, and immersive media applications. The GoT model unifies a rapidly diversifying field and is expected to serve as a backbone for both theoretical developments and practical deployments in the decades ahead (Li et al., 2023, Strinati et al., 12 Feb 2024, Getu et al., 2023).

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References

• Towards Goal-Oriented Semantic Communications: New Metrics, Open Challenges, and Future Research Directions (Li et al., 2023)
• Goal-oriented Tensor: Beyond Age of Information Towards Semantics-Empowered Goal-Oriented Communications (Li et al., 2023)
• Semantics-Empowered Communication for Networked Intelligent Systems (Kountouris et al., 2020)
• Goal-orientated Semantic Communications for Avatar-centric Augmented Reality (Wang et al., 2023)
• Pull-Based Query Scheduling for Goal-Oriented Semantic Communication (Agheli et al., 9 Mar 2025)
• Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language (Farshbafan et al., 2022)
• Goal-Oriented Semantic Communications for 6G Networks (Zhou et al., 2022)
• Tutorial-Cum-Survey on Semantic and Goal- Oriented Communication: Research Landscape, Challenges, and Future Directions (Getu et al., 2023)
• Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach (Strinati et al., 12 Feb 2024)