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... consider a multiuser network in which a transmitter observes a physical phenomenon/event and sends status updates (samples) to two monitors (named 1 and 2, c.f. Fig. 1). Samples from the information source/process are generated in the form of packets carrying different realizations from a finite set X = {x i | i ∈ I n }, I n = {1, 2, ..., n}, each having a probability of occurrence˜poccurrence˜ occurrence˜p i = P X (x i ) where P X (·) is a known probability mass function (pmf). The observation ...
Citations
... Shao et al. [48] used the semantic distortion and the semantic cost to define the achievable region for semantic coding. The semantic distortion reflects the Agheli [49] et al. explored semantic coding within a multi-user context. They introduced an approach where observations from an information source are filtered and sent to two monitors depending on their importance for each user's specific objectives. ...
In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be established. In particular, finding the fundamental limits of semantic communication, exploring the capabilities of semantic-aware networks, or utilizing theoretical guidance for deep learning in semantic communication are very important yet still unresolved issues. In general, the mathematical theory of semantic communication and the mathematical representation of semantics are referred to as semantic information theory. In this paper, we introduce the pertinent advancements in semantic information theory. Grounded in the foundational work of Claude Shannon, we present the latest developments in semantic entropy, semantic rate-distortion, and semantic channel capacity. Additionally, we analyze some open problems in semantic information measurement and semantic coding, providing a theoretical basis for the design of a semantic communication system. Furthermore, we carefully review several mathematical theories and tools and evaluate their applicability in the context of semantic communication. Finally, we shed light on the challenges encountered in both semantic communication and semantic information theory.
... Agheli [49] et al. explored semantic coding within a multi-user context. They introduced an approach where observations from an information source are filtered and sent to two monitors depending on their importance for each user's specific objectives. ...
In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be established. In particular, finding the fundamental limits of semantic communication, exploring the capabilities of semantic-aware networks, or utilizing theoretical guidance for deep learning in semantic communication are very important yet still unresolved issues. In general, the mathematical theory of semantic communication and the mathematical representation of semantics are referred to as semantic information theory. In this paper, we introduce the pertinent advancements in semantic information theory. Grounded in the foundational work of Claude Shannon, we present the latest developments in semantic entropy, semantic rate-distortion, and semantic channel capacity. Additionally, we analyze some open problems in semantic information measurement and semantic coding, providing a theoretical basis for the design of a semantic communication system. Furthermore, we carefully review several mathematical theories and tools and evaluate their applicability in the context of semantic communication. Finally, we shed light on the challenges encountered in both semantic communication and semantic information theory.
... This calls for a fresh examination into the design of communication systems that have been engineered with reliability as one of their ultimate goals [16]. The emerging literature regarding SC [17] as well as goal/task-oriented communications [11] is attempting to take the first steps towards the above-mentioned goal, i.e., incorporating these semantics [18], [19], together with the goal of message exchange, into the design of communication systems. The everincreasing growth of machine-to-machine communications is the major motivating factor behind the accelerating research interest in the task-oriented design of communications. ...
The emergence of the AI era signifies a shift in the future landscape of global communication networks, wherein robots are expected to play a more prominent role compared to humans. The establishment of a novel paradigm for the development of next-generation 6G communication is of utmost importance for semantics task-oriented empowered communications. The goal of semantic communication lies in the integration of collaborative efforts between the intelligence of the transmission source and the joint design of source coding and channel coding. This characteristic represents a significant benefit of joint source-channel coding (JSCC), as it enables the generation of source alphabets with diverse lengths and achieves a code rate of unity. Therefore, we leverage not only quasi-cyclic (QC) characteristics to facilitate the utilization of flexible structural hardware design but also Unequal Error Protection (UEP) to ensure the recovery of semantic importance. In this study, the feasibility of using a semantic encoder/decoder that is aware of UEP can be explored based on the existing JSCC system. This approach is aimed at protecting the significance of semantic task-oriented information. Additionally, the deployment of a JSCC system can be facilitated by employing QC-Low-Density Parity-Check (LDPC) codes on a reconfigurable device. The QC-LDPC layered decoding technique, which has been specifically optimized for hardware parallelism and tailored for channel decoding applications, can be suitably adapted to accommodate the JSCC system. The performance of the proposed system is evaluated by conducting BER measurements using both floating-point and 6-bit quantization. This is done to assess the extent of performance deterioration in a fair manner. The fixed-point system is synthesized and subsequently used as a semantic feature transmission and reception system across a noisy channel, with the aim of presenting a prototype for semantic communications. This study concludes with some insights and potential research avenues for the JSCC prototype in the context of future communication.
... To achieve content-aware transmission, a large amount of research focusing on the significance of data has directed much attention [8], [10]- [20], [22]- [24]. These works aim to deliver useful information to the destination at the right time. ...
... To maximize the semantic-aware utility function and minimize the quadratic average length cost, the optimal real codeword lengths is determined, which highlights the performance gains of semantic-aware filtering and source coding. [22] extends the semantic encoding problem to a two-user system with heterogeneous, possibly conflicting, or diverging, goals. [8] is also extended into distributed monitoring systems with multiple sensors and multiple monitors in [20], which have heterogeneous and dissimilar goals. ...
... An adaptive semantic filtering method and importance-aware packet error control mechanism at the packet level are employed to drop arrival packets that are not related to the goal and harness packet transmission failures due to fading channels. This improves the performance of the system compared to fixed filtering adopted in [8], and the importance-agnostic error control proposed in [22]. However, the notion of SoI is actually captured through a metric of timeliness, which is a nonlinear function of AoI. ...
Semantic status update communication (SSUC) is envisioned to provide content-aware and energy efficient information delivery. In this paper, we introduce a new metric in the SSUC system, named Semantic Utility Loss (SUL), which captures both age penalty and estimated error for semantic information. By incorporating the knowledge base (KB)-enabled semantic network into a discrete time Markov chain, we investigate the SUL in a time-slotted status update system. The transmitter samples and extracts the semantic information from the physical process, and transmits the status updates. The receiver can update the local KB to keep semantic match with the transmitter and infer or recover the semantic information from received status updates. To minimize the weighted sum of SUL and energy cost incurred by transmitting status update and updating KB, we formulate an infinite horizon average cost Markov Decision Process. We prove that the joint transmission and updating scheduling policy has optimal threshold structure with respect to SUL. Simulation results show that the optimized policy outperforms the zero-wait and sample-at-change policies. Furthermore, we study a practical SSUC application to validate the superiority of proposed framework over the traditional non-semantic status update system in terms of improving the timeliness and reducing the energy consumption.
... Agheli [49] et al. have explored semantic coding within a multi-user context. They introduced an approach where observations from an information source are filtered and sent to two monitors depending on their importance for each user's specific objectives. ...
In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be established. In particular, finding the fundamental limits of semantic communication, exploring the capabilities of semantic-aware networks, or utilizing theoretical guidance for deep learning in semantic communication are very important but yet still unresolved issues. In this paper, we delve into the pertinent advancements in semantic information theory. Grounded in the foundational work of Claude Shannon, we present the latest developments in semantic entropy, semantic rate distortion, and semantic channel capacity. Additionally, we will analyze some open problems in semantic information measurement and semantic coding, providing a theoretical basis for the design of a semantic communication system. Furthermore, we carefully review several mathematical theories and tools and evaluate their applicability in the context of semantic communication. Finally, we shed light on the challenges encountered in both semantic communication and semantic information theory.
... This calls for a fresh examination into the design of communication systems that have been engineered with reliability as one of their ultimate goals [16]. The emerging literature regarding SC [17] as well as goal/taskoriented communications [11] is attempting to take the first steps towards the above-mentioned goal, i.e., incorporating these semantics [18], [19], together with the goal of message exchange, into the design of communication systems. The ever-increasing growth of machine-to-machine communications is the major motivating factor behind the accelerating research interest in the task-oriented design of communications. ...
The emergence of the AI era signifies a shift in the future landscape of global communication networks, wherein robots are expected to play a more prominent role compared to humans. The establishment of a novel paradigm for the development of next-generation 6G communication is of utmost importance for semantics task-oriented empowered communications. This paper begins by examining the historical development of advanced communications, focusing specifically on the incorporation of semantics and task-oriented features. The goal of semantic communication is to surpass optimal Shannon's criterion regarding a notable problem for future communication which lies in the integration of collaborative efforts between the intelligence of the transmission source and the joint design of source coding and channel coding. The convergence of scholarly investigation and applicable products in the field of semantic communication is facilitated by the utilization of flexible structural hardware design, which is constrained by the computational capabilities of edge devices. This characteristic represents a significant benefit of joint source-channel coding (JSCC), as it enables the generation of source alphabets with diverse lengths and achieves a code rate of unity. Moreover, JSCC exhibits near-capacity performance while maintaining low complexity. Therefore, we leverage not only quasi-cyclic (QC) characteristics to propose a QC-LDPC code-based JSCC scheme but also Unequal Error Protection (UEP) to ensure the recovery of semantic importance. In this study, the feasibility for using a semantic encoder/decoder that is aware of UEP can be explored based on the existing JSCC system. This approach is aimed at protecting the significance of semantic task-oriented information. Additionally, the deployment of a JSCC system can be facilitated by employing Low-Density Parity-Check (LDPC) codes on a reconfigurable device. This is achieved by reconstructing the LDPC codes as QC-LDPC codes. The QC-LDPC layered decoding technique, which has been specifically optimized for hardware parallelism and tailored for channel decoding applications, can be suitably adapted to accommodate the JSCC system. The performance of the proposed system is evaluated by conducting BER measurements using both floating-point and 6-bit quantization. This is done to assess the extent of performance deterioration in a fair manner. The fixed-point system is synthesized and subsequently used to a semantic feature transmission and reception system across a noisy channel, with the aim of presenting a prototype for semantic communications. This study concludes with some insights and potential research avenues for the JSCC prototype in the context of future communication.
As the process of envisioning, researching, and strategizing for 6G communications has commenced, we present our vision on the role of semantic communications (SemCom) in the beyond 5G era. In the following discourse, we delve into the factors that may impede the progress of research and implementation of 6G with the objective of catering to the semantically enriched communication requirements of the 2030s. In addition, we proceed to establish the fundamental attributes of SemCom and engage in an examination of the requisite technological modalities. In order to bolster this overarching vision, we introduce a semantic networking architecture that encapsulates and characterizes various application scenarios of SemCom. Expanding on the concept of point-to-point SemCom systems the semantic networking architecture focuses on extracting and filtering goal specific semantic information at the source before transmitting signals. It also involves decoding and post processing semantics at the destination. What sets this networking architecture apart is its transformation into a multi-user distributed, edge-to-cloud network with deadline constraint traffic. Given its complexity, implementing such an architecture necessitates frameworks for extracting and representing knowledge theoretical models to predict and manage multiple time varying deadline/delay constrained traffic flows that may cause significant congestion in the network as well, as innovative metrics infused with semantics to measure performance while encapsulating its inherent relevance. Finally, we provide research considerations and future directions towards the integration of semantics in the forthcoming 6G wireless systems.
The problem of goal-oriented semantic filtering and timely source coding in multiuser communication systems is considered here. We study a distributed monitoring system in which multiple information sources, each observing a physical process, provide status update packets to multiple monitors having heterogeneous goals. Two semantic filtering schemes are first proposed as a means to admit or drop arrival packets based on their goal-dependent importance, which is a function of the intrinsic and extrinsic attributes of information and the probability of occurrence of each realization. Admitted packets at each sensor are then encoded and transmitted over block-fading wireless channels so that served monitors can timely fulfill their goals. A truncated error control scheme is derived, which allows transmitters to drop or retransmit undelivered packets based on their significance. Then, we formulate the timely source encoding optimization problem and analytically derive the optimal codeword lengths assigned to the admitted packets which maximize a weighted sum of semantic utility functions for all pairs of communicating sensors and monitors. Our analytical and numerical results provide the optimal design parameters for different arrival rates and highlight the improvement in timely status update delivery using the proposed semantic filtering, source coding, and error control schemes.
