Short-Packet Communications in Wireless-Powered Cognitive IoT Networks: Performance Analysis and Deep Learning Evaluation

Ho, Chung Duc; Nguyen, Toan-Van; Huynh‐The, Thien; Nguyen, Sang Quang; Costa, Daniel Benevides da; An, Beongku

doi:10.1109/tvt.2021.3061157

Cited by 48 publications

(33 citation statements)

References 18 publications

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“…In [18], a cooperative automatic-repeatrequest sharing strategy for URLLC in multi-hop networks was studied, where the reliability communication was improved without compromising on the latency requirement. Recently, SPCs with WET were studied in multi-hop transmissions [19] and in dual-hop transmissions [20], where a deep neural network (DNN) was designed to evaluate the BLER performance. However, simple designs of DNN with one output having high root-mean-square-error (RMSE) may reduce the accuracy of performance prediction when deploying complex network scenarios.…”

Section: A Literature Surveymentioning

confidence: 99%

“…Most of aforementioned works mainly focused on performance analysis of multi-hop networks with WET under infinite blocklength (IBL) regimes [4], [17] or devoted to studying SPC in dual-hop cooperative networks [11], [13], [20]. In finite blocklength systems, the outage probability obtained by using the traditional Shannon capacity overestimates the communication reliability because it is strictly lower than the average BLER at finite blocklength regimes.…”

Section: B Motivation and Contributionsmentioning

confidence: 99%

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Short-Packet Communications in Multihop Networks With WET: Performance Analysis and Deep Learning-Aided Optimization

Nguyen

Costa

et al. 2023

IEEE Trans. Wireless Commun.

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In this paper, we study short-packet communications in multi-hop networks with wireless energy transfer, where relay nodes harvest energy from power beacons to transmit short packets to multiple destinations. It is proposed a novel cooperative beamforming relay selection (CRS) scheme which incorporates partial relay selection and distributed multiuser beamforming to achieve a high-reliable transmission in two consecutive hops. A closed-form expression for the average block error rate (BLER) of the CRS scheme is derived, based on which an asymptotic analysis is also carried out. To achieve optimal channel uses allocation, we formulate a fairness end-toend throughput maximization problem which is generally NPhard due to the non-concavity of the objective function and mixed-integer constraints. To solve this challenging problem efficiently, we first relax channel uses to be continuous and transform the relaxed problem into an equivalent non-convex one, but with a more tractable form. We then develop a lowcomplexity iterative algorithm relying on inner approximation framework to convexify non-convex parts that converges to at least a locally optimal solution. Towards real-time settings, we design an efficient deep convolutional neural network (CNN) with multiscale-accumulation connections to achieve the suboptimal solution of the relaxed problem via real-time inference processes. Numerical results are presented to verify the analytical derivations and to demonstrate performance improvements of the CRS scheme over the benchmark ones in terms of BLER, reliability, latency, and throughput in various settings. Moreover, the designed CNN provides the lowest root-mean-square error compared to the state-of-the-art deep learning approaches while the CNN-aided optimization framework estimates accurately the optimal channel uses allocation with low execution time.

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Section: A Literature Surveymentioning

confidence: 99%

Section: B Motivation and Contributionsmentioning

confidence: 99%

Short-Packet Communications in Multihop Networks With WET: Performance Analysis and Deep Learning-Aided Optimization

Nguyen

Costa

et al. 2023

IEEE Trans. Wireless Commun.

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“…2) Advanced Techniques: DL, a subset of AI and ML that develops multi-layered artificial neural networks to attain state-of-the-art accuracy in many classification and regression tasks, has been exploited for various applications in multiple domains [11]- [13]. Unlike traditional ML techniques, DL can automatically learn underlying features of unstructured data without human intervention or human domain knowledge.…”

Section: A Categorization Of Aimentioning

confidence: 99%

Artificial Intelligence for the Metaverse: A Survey

Huynh‐The¹,

Pham²,

Pham³

et al. 2022

Preprint

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Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse, a term formed by combining meta and universe, has been introduced as a shared virtual world that is fueled by many emerging technologies, such as fifth-generation networks and beyond, virtual reality, and artificial intelligence (AI). Among such technologies, AI has shown the great importance of processing big data to enhance immersive experience and enable humanlike intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI in the foundation and development of the metaverse. We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse: natural language processing, machine vision, blockchain, networking, digital twin, and neural interface, and being potential for the metaverse. Subsequently, several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds. Finally, we conclude the key contribution of this survey and open some future research directions in AI for the metaverse.

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“…The interleaver scrambles the sequence order through interleaving and then converts the burst error in the transmission process into random error, which is the error type "preferred" by the classical decoding algorithm and has a high error correction probability for this type of error. However, in the production process of an intelligent factory, the control instruction is short packet transmission (Ho et al, 2021). For the interleaving of short packets, the interleaved sequence may experience the same channel condition (Barac et al, 2013;Zhan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Research on Efficient Channel Decoding Algorithm for Memory Channel and Short Packet Transmission in Smart Grid

Qin

Gao²,

Hou³

et al. 2022

Front. Energy Res.

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Many smart factories use the smart grid for power system automation, and its wireless control technology requires low-time-delay and high-reliability communication. Guessing random additive noise decoding algorithm has outstanding short packet error correction performance. In the decoding process, the order of noise parameter combination affects the decoding delay. Aiming at the communication problem of the smart grid in the process of factory power supply and distribution, this study analyzes the characteristics of the original noise parameter ranking algorithm. When the steady-state flip probability is large, more search times are required to obtain the correct combination of noise parameters, which means that greater delay is required for decoding in the time-varying channel. To solve the aforementioned problems, this study optimizes the noise parameter ranking before the noise error mode arrangement and proposes a noise parameter ranking algorithm for predicting the symbol string. First, the channel perception is completed by edge computing. Then, the algorithm uses the obtained soft information to rank the channel noise parameters. Simulation results show that the proposed algorithm has better search performance than the original sorting algorithm, especially when the channel parameter b is greater than 0.5. Finally, by comparing the BM Decoding Algorithm of BCH with different noise parameter ranking algorithms of decoding, the results show that the noise parameter ranking algorithm proposed in this study has better decoding performance in the environmental channel of the smart factory, so as to improve the reliability of the smart grid in the process of factory power supply and distribution.

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Short-Packet Communications in Wireless-Powered Cognitive IoT Networks: Performance Analysis and Deep Learning Evaluation

Cited by 48 publications

References 18 publications

Short-Packet Communications in Multihop Networks With WET: Performance Analysis and Deep Learning-Aided Optimization

Short-Packet Communications in Multihop Networks With WET: Performance Analysis and Deep Learning-Aided Optimization

Artificial Intelligence for the Metaverse: A Survey

Research on Efficient Channel Decoding Algorithm for Memory Channel and Short Packet Transmission in Smart Grid

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