A Deep-Q Learning Scheme for Secure Spectrum Allocation and Resource Management in 6G Environment

Bhattacharya, Pronaya; Patel, Farnazbanu; Alabdulatif, Abdulatif; Gupta, Rajesh; Tanwar, Sudeep; Kumar, Neeraj; Sharma, Ravi

doi:10.1109/tnsm.2022.3186725

Cited by 12 publications

(3 citation statements)

References 35 publications

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“…Integrating AI into DSM involves the analysis of data patterns stored in BCs through ML algorithms for early detection of malicious users and optimized spectrum access, enabling intelligent decision-making [111], [131], [133]. RL and DRL approaches like Q-learning gain prominence due to their unsupervised nature [128], [134], while supervised schemes are phased out. The synergistic integration of BC and AI technologies marks a transformative leap in enhancing the efficiency, security, and adaptability of DSM systems.…”

Section: Blockchain Based Services 1) Lessons Learnedmentioning

confidence: 99%

“…Addressing dynamic spectrum allocation, the DeepBlocks scheme [134] introduces a Deep-Q-Network (DQN) to minimize the search state explosion through a reward penalty framework, utilizing blockchain architecture to record transactions related to the dynamic allocation of unallocated spectrum resources to mobile units. SCs are employed to track resource usage and automate spectrum assignments based on DSA profits.…”

Section: E Support For Ai Integration 1) Introductionmentioning

confidence: 99%

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A Survey on Blockchain for Dynamic Spectrum Sharing

Perera,

Ranaweera,

Kusaladharma

et al. 2024

IEEE Open J. Commun. Soc.

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The rapid increase in mobile users, the IoT, and data-hungry applications have brought forth unprecedented demand on the spectrum, which is scarce; on top of that, the existing static spectrum allocation schemes have resulted in a heavily underutilized spectrum which can be mitigated with a Dynamic Spectrum Access (DSA) scheme with unlicensed users gaining access to the idle spectrum bands of licensed spectrum users opportunistically. Such a DSA and Dynamic Spectrum Management (DSM) scheme would significantly increase spectral efficiency while facilitating new services and applications beyond 5G (B5G) networks. Even with access to new spectrum bands like terahertz (THz) and Visible light communication and enabling technologies such as Software Defined Networks (SDN) and Cognitive Radio (CR), implementing a fully realized DSM requires rapid sensing, coordination, and management, and sharing of idle spectrum bands in a fair manner while preserving the security and privacy aspects, limiting interferences. With their decentralized, immutable nature, blockchains promise the execution of spectrum access and sharing in a fully transparent, fair manner while preserving privacy and security. Furthermore, blockchain-based Smart Contracts (SCs) allow automation of DSM, cryptocurrencies, and tokens to facilitate the trading of spectrum and related resources. In addition to that, blockchains act as an interface for integrating AI and Machine Learning (ML) techniques into DSM, which provides a certain level of intelligence to the underlying architecture. Although several attempts have been carried out to analyze the research gaps in DSM, a comprehensive analysis addressing the blockchains as the primary solution to address DSM has not been carried out. In this survey, we address the potential of a blockchainbased approach toward realizing a decentralized DSM while presenting future directives to improve the use of blockchains for DSM.

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Section: Blockchain Based Services 1) Lessons Learnedmentioning

confidence: 99%

Section: E Support For Ai Integration 1) Introductionmentioning

confidence: 99%

A Survey on Blockchain for Dynamic Spectrum Sharing

Perera,

Ranaweera,

Kusaladharma

et al. 2024

IEEE Open J. Commun. Soc.

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“…The high-level DRL agent is responsible for selecting the optimal beam among the available beams based on the communication environment state. It employs a deep Qnetwork (DQN) to approximate the optimal action-value function [22].…”

Section: Hierarchical Deep Reinforcement Learning Frameworkmentioning

confidence: 99%

Deep Reinforcement Learning-Based Adaptive Beam Tracking and Resource Allocation in 6G Vehicular Networks with Switched Beam Antennas

et al. 2023

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In this paper, we propose a novel switched beam antenna system model integrated with deep reinforcement learning (DRL) for 6G vehicle-to-vehicle (V2V) communications. The proposed system model aims to address the challenges of highly dynamic V2V environments, including rapid changes in channel conditions, interference, and Doppler effects. By leveraging the beam-switching capabilities of switched beam antennas and the intelligent decision making of DRL, the proposed approach enhances the performance of 6G V2V communications in terms of throughput, latency, reliability, and spectral efficiency. The proposed work develops a comprehensive mathematical model that accounts for 6G channel modeling, beam-switching, and beam-alignment errors. The Proposed DRL framework is designed to learn optimal beam-switching decisions in real time, adapting to the complex and varying V2V communication scenarios. The integration of the proposed antenna system and DRL model results in a robust solution that is capable of maintaining reliable communication links in a highly dynamic environment. To validate the proposed approach, extensive simulations were conducted and performance analysis using various performance metrics, such as throughput, latency, reliability, energy efficiency, resource utilization, and network scalability, was analyzed. Results demonstrate that the proposed system model significantly outperforms conventional V2V communication systems and other state-of-the-art techniques. Furthermore, the proposed approach shows that the beam-switching capabilities of the switched beam antenna system and the intelligent decision making of the DRL model are essential in addressing the challenges of 6G V2V communications.

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Evolution Towards 6G Wireless Networks: A Resource Allocation Perspective with Deep Learning Approach - A Review

Kamble¹,

Shaikh²

2022

Advancements in Smart Computing and Information Security

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A Deep-Q Learning Scheme for Secure Spectrum Allocation and Resource Management in 6G Environment

Cited by 12 publications

References 35 publications

A Survey on Blockchain for Dynamic Spectrum Sharing

A Survey on Blockchain for Dynamic Spectrum Sharing

Deep Reinforcement Learning-Based Adaptive Beam Tracking and Resource Allocation in 6G Vehicular Networks with Switched Beam Antennas

Evolution Towards 6G Wireless Networks: A Resource Allocation Perspective with Deep Learning Approach - A Review

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