Reinforcement Learning-Based Resource Allocation for Adaptive Transmission and Retransmission Scheme for URLLC in 5G

Pradhan, Annapurna; Das, Susmita

doi:10.1007/978-981-15-5243-4_13

Cited by 3 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…He noted that the proposed approach minimizes normalized energy consumption with less computational complexity better than existing approaches". [26] propose "The reinforcement learning RL for resource reservation in highly reliable low-latency communication for 5G network. RL is used to outperform the base method in terms of packet drop probability and resource usage".…”

Section: Literature Reviewmentioning

confidence: 99%

Unveiling the Potential of Artificial Intelligence and Machine Learning in the 5G Network Landscape: A Comprehensive Review

Rizvi

2023

AJRCoS

View full text Add to dashboard Cite

Exploring successful case studies, scholars and industry experts concur that artificial intelligence (AI) and 5G technology, as holistic solutions, exhibit remarkable efficiency. The integration of AI has notably resolved wireless communication dilemmas that defy traditional modeling approaches, substantially diminishing technological uncertainties. Furthermore, 5G technology is poised to amalgamate communication, computation, sensing, and control across diverse industries. However, the convergence of these capabilities also introduces complexity, a challenge that can be effectively addressed through the application of artificial intelligence and machine learning functionalities. These cutting-edge technologies not only ensure data security but also satisfy stringent latency requirements while minimizing the burden on both communication and computation resources. In this context, a thorough examination of machine learning and artificial intelligence applications geared towards optimizing communication, computation, and resource allocation within the realm of 5G technology holds paramount significance. In pursuit of this objective, the study endeavors to offer a comprehensive outlook on the current landscape of artificial intelligence research within the 5G domain. By scrutinizing recent studies, we aim to encapsulate the contributions and prevailing trends associated with these technologies. Ultimately, our aim is to empower researchers and industry practitioners with insights that will facilitate informed decision-making when selecting the most suitable machine learning and artificial intelligence approaches for their endeavors.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Unveiling the Potential of Artificial Intelligence and Machine Learning in the 5G Network Landscape: A Comprehensive Review

Rizvi

2023

AJRCoS

View full text Add to dashboard Cite

show abstract

“…Pradhan and Das [48] proposed RL for resource reservation in ultrareliable low latency communication for 5G network. It is found that the RL performs better than the baseline method in terms of the packet drop probability and resource utilization.…”

Section: E Applications Of the Deep Reinforcement Learning In 5g Wire...mentioning

confidence: 99%

“…e algorithm is able to change the demand of network at a fast rate of response in the processing of resource allocation. Pradhan and Das [48] proposed RL for resource reservation in ultrareliable low-latency communication for 5G network. It is found that RL performs better than the baseline method in terms of the f packet drop probability and resource utilization.…”

Section: Resource Managementmentioning

confidence: 99%

“…Xu et al [38] proposed RGB stream and spatial rich model noise stream for differentiating between adversarial and clean examples. e CNN is used to detect adversarial image, and Caching scheme Dong et al [41] Hybrid 5G service Gante et al [27]; Yu et al [104]; Cheng et al [36]; Kaya and Viswanathan [73]; Zhang et al [74] mmWave Huang et al [68] Traffic loads Huang et al [28] Channel information Kim et al [92] Massive MIMO Kim et al [62] Sparse code multiple access Klautau et al [15] Beam Lei et al [61]; Yu et al [53] Cache Luo et al [67] Channel state information Luo et al [69] Power transmission Maimó et al [76]; Doan and Zhang [34]; Chen et al [83]; Maimó et al [78] Traffic flow Ning et al [42]; Ahmed et al [35] Spectrum Ozturk et al [81]; Klus et al [32] Handover Pang et al [12] Intelligent cache scheme Razaak et al [89] 5G fixed wireless Sadeghi et al [43] Scalable cache Shahriari et al [39] Load balancing Sundqvist et al [79]; Abbas et al [90]; Ali et al [100] Random access network He et al [29] MU-MIMO Li et al [44] Chaining Xia et al [45] Multicell MEC Saetan et al [93] Nonorthogonal multiple access Pradhan and Das [48]; Li and Zhang [52] Ultrareliable low-latency Ho et al [50] 5G-V2X Tang et al [47...…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…erefore, we have this research question for researchers: What is the systematic deep learning approach to conceal data confidentiality before, during, and after modeling? [44] User group DQN Effective chaining Xia et al [45] User group DQN Reduces energy cost and delay experience by users Saetan et al [93] Outdoor DDNN Predicts power factor Pradhan and Das [48] Individual RL Provides packet drop probability and better resource utilization Zhao et al [49] Outdoor RL Fast response to network demand Ho et al [50] Outdoor DQN Solves the problem of base station allocation Yu et al [46] Outdoor DQN Searches for optimal deployment location with high speed Tang et al [47] Outdoor DQN Improved network performance based on throughput and packet drop rate Chergui and Verikoukis [99] Outdoor DDNN Estimates slices resources El Boudani et al [97] Outdoor DDNN Predicts 3D position of mobile station Xie et al [51] Outdoor DQN Improves efficiency of initial window decision Hussain et al [30] Outdoor CNN Detects denial of service with high accuracy Butt et al [95] Outdoor DDN Estimating user equipment positioning Godala et al [31] Outdoor CNN Estimates channel state information Li and Zhang [52] Outdoor DRL Improves quality of service Yu et al [53] Outdoor DRL Improves energy efficiency Yu et al [9] Outdoor LSTM Resource allocation in TV broadband services Liu et al [82] Outdoor LSTM Predicts hotspot for small virtual cell Mismar et al [54] Outdoor DQN Signal to interference plus noise ratio improvement Sim et al [96] Outdoor DDNN Selection of beam Doan and Zhang [34] Outdoor CNN Detects anomaly in 5G network Memon et al [84] Outdoor LSTM Improves power savings and predicts discontinue reception Klus et al [32] Localization CNN Predicts user localization and reduces unnecessary handover Saeidian et al [55] Outdoor DQN Improves data rate at the edge and reduces power transmitted antharate et al [98] Outdoor DDNN Detects security thread 5.10. Complexity.…”

Section: G Ecosystemmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction

Almutairi

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The advent of the 5G mobile network has brought a lot of benefits. However, it prompted new challenges on the 5G network cybersecurity defense system, resource management, energy, cache, and mobile network, therefore making the existing approaches obsolete to tackle the new challenges. As a result of that, research studies were conducted to investigate deep learning approaches in solving problems in 5G network and 5G powered Internet of Vehicles (IoVs). In this article, we present a survey on the applications of deep learning algorithms for solving problems in 5G mobile network and 5G powered IoV. The survey pointed out the recent advances on the adoption of deep learning variants in solving the challenges of 5G mobile network and 5G powered IoV. The deep learning algorithm solutions for security, energy, resource management, 5G-enabled IoV, and mobile network in 5G communication systems were presented including several other applications. New comprehensive taxonomies were created, and new comprehensive taxonomies were suggested, analysed, and presented. The challenges of the approaches are already discussed in the literature, and new perspective for solving the challenges was outlined and discussed. We believed that this article can stimulate new interest in practical applications of deep learning in 5G network and provide clear direction for novel approaches to expert researchers.

show abstract

Reinforcement Learning Model Selection for Resource Allocation and Subcarrier Spacing Optimization in 5G Sliced Spectrum Networks

Samidi,

Radzi,

Aripin

2024

2024 IEEE International Conference on Applied Electronics and Engineering (ICAEE)

View full text Add to dashboard Cite

Reinforcement Learning-Based Resource Allocation for Adaptive Transmission and Retransmission Scheme for URLLC in 5G

Cited by 3 publications

References 9 publications

Unveiling the Potential of Artificial Intelligence and Machine Learning in the 5G Network Landscape: A Comprehensive Review

Unveiling the Potential of Artificial Intelligence and Machine Learning in the 5G Network Landscape: A Comprehensive Review

Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction

Reinforcement Learning Model Selection for Resource Allocation and Subcarrier Spacing Optimization in 5G Sliced Spectrum Networks

Contact Info

Product

Resources

About