Multimodal semantic communication accelerated bidirectional caching for 6G MEC

Wang, Chaowei; Yu, Xiaofei; Xu, Lexi; Wang, Ziye; Wang, Weidong

doi:10.1016/j.future.2022.10.036

Cited by 47 publications

(18 citation statements)

References 39 publications

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“…Considering the above-mentioned requirements, we propose a semantic-communication-based delay-aware algorithm, which adopts an intelligent routing protocol to extract the semantic level features such as

the selection of the most-optimal or the shortest path to ensure the instant packet delivery of prioritized information and

the accurate prediction of the routing decision by evaluating the subflow’s delivery performance in sending prioritized content with the lowest latency. With the motivation to improve the traffic capacity requirement, which plays an integral role in the 6G era, our proposed approach can be capable of identifying/predicting the accurate route for the transmission of selected information while maintaining a low-latency communication [ 116 ]. With the integration of AI/ML techniques similar to a few existing wireless B5G networks [ 117 , 118 ] to increase the transmission rate beyond Shannon’s capacity, we introduced an AI/ML integrated learning algorithm to enable the proposed end-to-end semantic communication approach.…”

Section: Discussion and Future Scopementioning

confidence: 99%

Towards an Evolved Immersive Experience: Exploring 5G- and Beyond-Enabled Ultra-Low-Latency Communications for Augmented and Virtual Reality

Hazarika

Rahmati

2023

Sensors

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Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of low-latency connectivity, which is defined as the end-to-end delay between the action and the reaction, is very crucial to leverage these technologies for a high-quality immersive experience. This paper provides a comprehensive survey and detailed insight into various advantageous approaches from the hardware and software perspectives, as well as the integration of 5G technology, towards 5GB, in enabling a low-latency environment for AR and VR applications. The contribution of 5GB systems as an outcome of several cutting-edge technologies, such as massive multiple-input, multiple-output (mMIMO) and millimeter wave (mmWave), along with the utilization of artificial intelligence (AI) and machine learning (ML) techniques towards an ultra-low-latency communication system, is also discussed in this paper. The potential of using a visible-light communications (VLC)-guided beam through a learning algorithm for a futuristic, evolved immersive experience of augmented and virtual reality with the ultra-low-latency transmission of multi-sensory tracking information with an optimal scheduling policy is discussed in this paper.

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the selection of the most-optimal or the shortest path to ensure the instant packet delivery of prioritized information and

Section: Discussion and Future Scopementioning

confidence: 99%

Towards an Evolved Immersive Experience: Exploring 5G- and Beyond-Enabled Ultra-Low-Latency Communications for Augmented and Virtual Reality

Hazarika

Rahmati

2023

Sensors

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“…Advancements in DL, in particular, and AI, in general, have spurred a surge in research contributions pertaining to the design and optimization of various DL-enabled wireless SemCom systems. Such SemCom systems constitute the state-of-the-art algorithmic research developments in wireless text SemCom [46], [48], [56], [91]- [99]; wireless audio SemCom [45], [100]- [104]; wireless image SemCom [105]- [115]; wireless video SemCom [116]- [119]; wireless multimodal SemCom [120]; and wireless cross-modal SemCom [121] pertaining to the efficient wireless transmission of text data, audio data, image data, video data, multimedia data, and multimedia and haptic data, respectively. All these wireless SemCom techniques have been demonstrated to outperform traditional/conventional wireless communication schemes, especially in low signal-to-noise ratio (SNR) regimes.…”

Section: A Wireless Semcommentioning

confidence: 99%

“…Semantic metrics that are based on the age of information (AoI) and the value of information (VoI) -also known as effectiveness-level metrics [51] -can be used in the design, analysis, and optimization of many classical Sem-Com systems (in both the wireless and optical domains) [58]. Classical SemCom systems can involve text SemCom [46], [48], [56], [91]- [99]; audio SemCom [45], [100]- [104]; image SemCom [105]- [115]; video SemCom [116]- [119]; multimodal SemCom [120]; or cross-modal SemCom [121]. AoI-and VoI-based semantic metrics also have several applications in goal-oriented SemCom [51].…”

Section: Age Of Information-and Value Of Information-based Semantic M...mentioning

confidence: 99%

“…The optimization of usually scarce resources -for optimality and efficiency across one or more networks -across wireless or optical SemCom networks is one of the key problems facing classical SemCom systems. Resource allocation semantic metrics are therefore crucial to optimize several types of classical SemCom systems -in either the wireless or optical domain -such as text SemCom [46], [48], [56], [91]- [99]; audio SemCom [45], [100]- [104]; image SemCom [105]- [115]; video SemCom [116]- [119]; multimodal SemCom [120]; and cross-modal SemCom [121]. The following resource allocation semantic metrics are largely relevant to optimize the mentioned SemCom systems in either a wireless or an optical network setting: the metric of semantic similarity (MSS), semantic transmission rate (S-R), semantic spectral efficiency (S-SE), quality-of-experience (QoE), and system throughput in message (STM).…”

Section: Resource Allocation Semantic Metricsmentioning

confidence: 99%

“…The generic semantic metrics of SemCom that we discuss in this section are metrics that are applicable to the design, analysis, and optimization of a wide variety of classical SemCom systems -in either the wireless or optical domain -including text SemCom [46], [48], [56], [91]- [99]; audio SemCom [45], [100]- [104]; image SemCom [105]- [115]; video SemCom [116]- [119]; multimodal SemCom [120]; and cross-modal SemCom [121]. We present below the following generic semantic metrics of SemCom that have inspired the materialization of the mentioned SemCom systems: the general quality index of semantic service, triplet drop probability (TDP), semantic mutual information (SMI), the semantic impact, the communication symmetry index, and reasoning capacity.…”

Section: Generic Semantic Metrics Of Semcommentioning

confidence: 99%

See 2 more Smart Citations

Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication

2023

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Semantic communication (SemCom) aims to convey the meaning behind a transmitted message by transmitting only semantically-relevant information. This semantic-centric design helps to minimize power usage, bandwidth consumption, and transmission delay. SemCom and goal-oriented SemCom (or effectiveness-level SemCom) are therefore promising enablers of 6G and developing rapidly. Despite the surge in their swift development, the design, analysis, optimization, and realization of robust and intelligent SemCom as well as goal-oriented SemCom are fraught with many fundamental challenges. One of the challenges is that the lack of unified/universal metrics of SemCom and goal-oriented SemCom can stifle research progress on their respective algorithmic, theoretical, and implementation frontiers. Consequently, this survey paper documents the existing metrics -scattered in many references -of wireless SemCom, optical SemCom, quantum SemCom, and goal-oriented wireless SemCom. By doing so, this paper aims to inspire the design, analysis, and optimization of a wide variety of SemCom and goal-oriented SemCom systems. This article also stimulates the development of unified/universal performance assessment metrics of SemCom and goal-oriented SemCom, as the existing metrics are purely statistical and hardly applicable to reasoning-type tasks that constitute the heart of 6G and beyond.

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Reliable information delivery and dynamic link utilization in MANET cloud using deep reinforcement learning

Kuang,

Zhang,

Mohajer

2024

Trans Emerging Tel Tech

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Modern networking demands efficient and reliable information delivery within Mobile Ad‐hoc Network (MANET) and cloud environments. This paper introduces a novel approach that employs Multi‐Agent Deep Learning (MADL) for adaptive resource allocation, addressing the challenges of optimizing traffic and ensuring dependable information delivery while adhering to Service Level Agreement (SLA) constraints. Our method dynamically allocates resources across nodes, leveraging the synergy between Advanced Cloud Computing and Edge Computing to balance centralized processing and localized adaptability. The integration of Graph Neural Networks (GNNs) further enhances this process by adapting resource allocation decisions based on network topology. Through iterative learning, our algorithm fine‐tunes continuous‐time resource optimization policies, resulting in substantial improvements in throughput and latency minimization. Simulations validate the effectiveness of our approach, demonstrating its potential to contribute to the advancement of MANET cloud networks by offering adaptability, efficiency, and real‐time optimization for reliable information delivery and dynamic link utilization.

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Multimodal semantic communication accelerated bidirectional caching for 6G MEC

Cited by 47 publications

References 39 publications

Towards an Evolved Immersive Experience: Exploring 5G- and Beyond-Enabled Ultra-Low-Latency Communications for Augmented and Virtual Reality

Towards an Evolved Immersive Experience: Exploring 5G- and Beyond-Enabled Ultra-Low-Latency Communications for Augmented and Virtual Reality

Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication

Reliable information delivery and dynamic link utilization in MANET cloud using deep reinforcement learning

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