Intelligent covert communication design for cooperative cognitive radio network

Li, Zan; Shi, Jia; Wang, Chao; Wang, Dan-Yang; Li, Xiaomeng; Liao, Xiaomin

doi:10.23919/jcc.fa.2022-0514.202307

Cited by 4 publications

(2 citation statements)

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“…Conversely, the efficiency of utilizing the abundant spectrum remains suboptimal. Cognitive radio (CR) emerges as an innovative approach to address the scarcity of spectrum resources [3][4][5][6][7][8]. In CR networks, cognitive users (CUs) are authorized to use frequency bands designated for primary users (PUs) for communication, ensuring minimal interference to PUs.…”

Section: Introductionmentioning

confidence: 99%

Jointly Active/Passive Beamforming Optimization for Intelligent-Reflecting Surface-Assisted Cognitive-IoT Networks

Zhou,

Deng,

2024

Electronics

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To overcome challenges such as limited energy availability for terminal devices, constrained network coverage, and suboptimal spectrum resource utilization, with the overarching objective of establishing a sustainable and efficient interconnection infrastructure, we introduce an innovative Intelligent Reflective Surface (IRS) technology. This cutting-edge IRS technology is employed to architect a wireless and energy-efficient cognitive secure communication network assisted by IRS. To further optimize the overall energy harvesting of this network, we present a cognitive secure resource allocation scheme, aiming to maximize the system’s total collected energy. This scheme carefully considers various constraints, including transmission power constraints for cognitive base stations, power constraints for jammer devices, interference limitations for all primary users, minimum security rate constraints for all cognitive Internet of Things (IoT) devices, and phase shift constraints for IRS. We establish a comprehensive hybrid cognitive secure resource allocation model, encompassing joint cognitive transmission beam design, jammer device transmission beam design, and phase shift design. Given the non-convex nature of the formulated problem and the intricate coupling relationships among variables, we devise an effective block coordinate descent (BCD) iterative algorithm. The realization of joint cognitive/jammer base station transmission beam design and phase shift design employs sophisticated techniques such as continuous convex approximation methods and semi-definite programming. Simulation results underscore the superior performance of the proposed scheme compared to existing resource allocation approaches, particularly in terms of total harvested energy and other critical metrics.

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Section: Introductionmentioning

confidence: 99%

Jointly Active/Passive Beamforming Optimization for Intelligent-Reflecting Surface-Assisted Cognitive-IoT Networks

Zhou,

Deng,

2024

Electronics

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“…Ref. [11] reviews the research of covert communications in cooperative cognitive radio networks via three intelligent covert transmission approaches, namely, intelligent parasitic covert transmission, intelligent jammer-aided covert transmission, and intelligent reflecting surface-assisted covert transmission.…”

Section: Introductionmentioning

confidence: 99%

SPACNet: A Simulation Platform of an Acoustic Cognitive Network

Yang,

Zheng,

Zhao

et al. 2023

JMSE

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Originating from the concept of cognitive networks (CNs), which are becoming popular in wireless terrestrial communication scenarios, underwater acoustic cognitive networks (UACNs) are drawing more and more attention in the field of the Internet of Underwater Things (IoUT). However, as the implementation of cognitive mechanisms in underwater acoustic networks is different from that of wireless scenarios, it is impossible or difficult for traditional simulation platforms to carry out simulations of UACNs. There is a lack of specialized simulation tools in terms of UACNs. To enable the quantitative evaluation of the effectiveness and performance enhancement of a UACNs in an adverse underwater environment, a simulation platform of acoustic cognitive networks (SPACNet) was designed and investigated in this article. First, based on a state machine-based protocol programming framework, the SPACNet is capable of supporting the implementation of different state-transform types associated with cognitive networking protocols. Moreover, to facilitate the realization of cognitive function at comprehensive levels of signal, information, and link, an underwater acoustic channel model with an environmental parameter input is integrated in SPACNet to generate underwater environment-driven multiple-aspect behaviors. Moreover, a simplified collision model consisting of an environment factor, channel response, and node location is used to reduce the complexity of the simulation of UACNs signal reception. A simulation was carried out to verify the effectiveness of SPACNet in evaluating the cognitive capabilities of UACNs. Finally, a field UACNs experiment was performed to validate the general consistency between the conclusion obtained with the SPACNet-based simulation and that from the field test.

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