Optimization of Image Transmission in Cooperative Semantic Communication Networks

Zhang, Wenjing; Wang, Y.; Chen, Mingzhe; Luo, Tao; Niyato, Dusit

doi:10.1109/twc.2023.3282906

Cited by 17 publications

(4 citation statements)

References 31 publications

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“…VSRAA-SM can be used to optimize vehicle-to-infrastructure semantic understanding tasks and vehicle-to-vehicle information transmission tasks [283]. Furthermore, the authors of [284] devise an image SemCom framework that enables a set of servers to collaboratively transmit images to their respective users using SemCom techniques. In their framework, all servers must jointly decide -subject to limited wireless resource constraints -which semantic information to transmit and which corresponding resource block (RB) allocation scheme o use [284].…”

Section: Algorithmic Developments In Semcom Resource Allocationmentioning

confidence: 99%

“…Furthermore, the authors of [284] devise an image SemCom framework that enables a set of servers to collaboratively transmit images to their respective users using SemCom techniques. In their framework, all servers must jointly decide -subject to limited wireless resource constraints -which semantic information to transmit and which corresponding resource block (RB) allocation scheme o use [284]. The authors of [284] formulate this problem as an optimization problem whose target is minimizing the average transmission latency while satisfying the image-tograph semantic similarity (ISS) requirement -ISS is the authors' proposed semantic metric.…”

Section: Algorithmic Developments In Semcom Resource Allocationmentioning

confidence: 99%

“…In their framework, all servers must jointly decide -subject to limited wireless resource constraints -which semantic information to transmit and which corresponding resource block (RB) allocation scheme o use [284]. The authors of [284] formulate this problem as an optimization problem whose target is minimizing the average transmission latency while satisfying the image-tograph semantic similarity (ISS) requirement -ISS is the authors' proposed semantic metric. To solve this problem, the authors develop a value decomposition-based entropymaximized multi-agent RL algorithm.…”

Section: Algorithmic Developments In Semcom Resource Allocationmentioning

confidence: 99%

“…To solve this problem, the authors develop a value decomposition-based entropymaximized multi-agent RL algorithm. This algorithm is shown to considerably reduce transmission latency and improve convergence speed compared with traditional multi-agent RL algorithms [284].…”

Section: Algorithmic Developments In Semcom Resource Allocationmentioning

confidence: 99%

See 3 more Smart Citations

Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions

Getu,

Kaddoum,

Bennis

2023

Preprint

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<p> Amid the global rollout of fifth-generation (5G) services, researchers in academia, industry, and national labo?ratories have been developing proposals for the sixth generation (6G). Despite the many 6G proposals, the materialization of 6G as presently envisaged is fraught with many fundamental interdisciplinary, multidisciplinary, and transdisciplinary (IMT) challenges. To alleviate some of these challenges, semantic com?munication (SemCom) has emerged as promising 6G technology enabler. SemCom is designed to transmit only semantically?relevant information and hence help to minimize power usage, bandwidth consumption, and transmission delay. Thus, SemCom embodies a paradigm shift that can change the status quo that wireless connectivity is an opaque data pipe carrying messages whose context-dependent meaning have been ignored. On the other hand, 6G is critical for the materialization of major SemCom use cases. These paradigms of 6G for SemCom and SemCom for 6G call for a tighter integration and marriage of 6G and SemCom. To facilitate this integration and marriage, this comprehensive survey paper first provides the fundamentals of semantics and semantic information, semantic representation, semantic information, and semantic entropy. It then builds on this understanding and details the state-of-the-art research landscape of SemCom; exposes the fundamental and major challenges of SemCom; and offers promising future research directions for SemCom theories, algorithms, and realization. Accordingly, this survey article stimulates major streams of research on SemCom theories, algorithms, and implementation. </p>

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Section: Algorithmic Developments In Semcom Resource Allocationmentioning

confidence: 99%