Energy-Efficient Spectrum Sharing for 6G Ubiquitous IoT Networks Through Blockchain

Sun, Zhengzhong; Qi, Fei; Liu, Lei; Xing, Yanxia; Xie, Weiliang

doi:10.1109/jiot.2022.3224849

Cited by 12 publications

(2 citation statements)

References 18 publications

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“…Furthermore, the combination of blockchains or specifically SCs with 6G hybrid cloud architectures, as explored by [146], is investigated for ensuring stability, security, and energy efficiency in spectrum sharing among Ubiquitous Internet of Things (UIoT) devices. The study demonstrates that SCs can be instrumental in preventing spectrum violations throughout the spectrum transition process.…”

Section: ) Possible Blockchain-based Solutionsmentioning

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 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: ) Possible Blockchain-based Solutionsmentioning

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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“…Few studies have specifically addressed the shortcomings of power consumption, SE, and achievable sum rates for HAP networks, in contrast to the extensive research that has been undertaken in the spectrum sharing and theoretical analysis of HAP [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Inspired Hybrid Precoding for HAP Massive MIMO Systems with Limited RF Chains

et al. 2023

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Energy efficiency (EE) is the main target of wireless communication nowadays. In this paper, we investigate hybrid precoding (HP) and massive multiple-input multiple-output (MIMO) systems for a high-altitude platform (HAP). The HAP is an emerging solution operating in the stratosphere at an amplitude of up to 20–40 km to provide communication facilities that can achieve the best features of both terrestrial and satellite systems. The existing hybrid beamforming solution on a HAP requires a large number of high-resolution phase shifters (PSs) to realize analog beamforming and radio frequency (RF) chains associated with each antenna and achieve better performance. This leads to enormous power consumption, high costs, and high hardware complexity. To address such issues, one possible solution that has to be tweaked is to minimize the number of PSs and RFs or reduce their power consumption. This study proposes an HP sub-connected low-resolution bit PSs to address these challenges while lowering overall power consumption and achieving EE. To significantly reduce the RF chain in a massive MIMO system, HP is a suitable solution. This study further examined adaptive cross-entropy (ACE), a machine learning-based optimization that optimizes the achievable sum rate and energy efficiency in the Rician fading channel for HAP massive MIMO systems. ACE randomly generates several candidate solutions according to the probability distribution (PD) of the elements in HP. According to their sum rate, it adaptively weights these candidates’ HP and improves the PD in HP systems by minimizing the cross-entropy. Furthermore, this work suggests energy consumption analysis performance evaluation to unveil the fact that the proposed technique based on a sub-connected low-bit PS architecture can achieve near-optimum EE and sum rates compared with the previously reported methods.

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