Optimal decision making in multi-channel RF-powered cognitive radio networks with ambient backscatter capability

Zakariya, Ahmed Y.; Rabia, Sherif I.; Zahra, Waheed K.

doi:10.1016/j.comnet.2021.107907

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Authors in [25] propose an ABC system using OFDMbased structure, investigating phase shift keying (PSK) and delay shift keying (DSK) modulation schemes to realize higher data rates relative to the conventional ABC. The author in [26] investigated a multi-channel RF-powered cognitive backscatter network with one SU which can actively transmit its data packets if the selected channel is idle, if the channel is busy, it can harvest energy or backscatter data. Markov decision process is proposed to describe the action determination process, problem-based Markov decision process MDP is formulated to obtain the optimal channel-mode pair for the SU that maximizes the achieved average throughput.…”

Section: A Literature Reviewmentioning

confidence: 99%

Ambient Backscatter Communication System Empowered by Matching Game and Machine Learning for Enabling Massive IoT Over 6G HetNets

Khalifa,

El-Haleem,

Elmesalawy

et al. 2024

IEEE Access

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Ambient backscatter communication (ABC) is considered as a promising paradigm for meeting the 6G massive Internet of Things (IoT) requirements which is expected to revolutionize our world. In this paper, a new multimode matching game and machine learning-based IoT ambient backscatter communication scheme is proposed to maximize the ABC system rate and capacity over the LTE and Wi-Fi multi-RAT heterogeneous network, thereby supporting the 6G green massive IoT communication. The proposed algorithm is designed to support different rate and capacity requirements for different massive Machine Type Communication (mMTC) use cases such as sensor networks, smart grid, agriculture and low data rate Ultra Reliable Low Latency Communication (URLLC) use cases such as Tactile Interaction. The proposed optimization algorithm runs into two phases, the first one is a matching game-based algorithm that selects the optimum association between the IoT tags and the primary users (PU) downlink signals from a specific base station which maximizes the IoT tags rate while minimizing the resulting interference to the PU. Each IoT tag can ride the PU downlink signal using one of three different riding modes according to the required IoT ABC system rate and capacity, whereas mode 1 allows multiple IoT tags to ride the whole PU downlink signal resource blocks, in mode 2 each IoT tag can ride only one subcarrier from the PU downlink signal resource blocks, while in mode 3 multiple IoT tags can ride the same subcarrier from the PU downlink signal resource blocks. In addition, unmanned aerial vehicles (UAVs) flying HetNodes equipped with LTE and Wi-Fi receivers are used as backscatter receivers to receive the IoT tags uplink backscattered signals, so the second optimization phase is formulated to maximize the total sum rate of the ABC system by dividing its service area into clusters using the enhanced unsupervised k-means algorithm, also the enhanced k-means algorithm finds the optimum location of each cluster's serving UAV flying HetNode that maximizes the channels gain between the IoT tags and the serving UAV flying HetNode in order to maximize the total system rate. The system model was implemented within the MATLAB environment where simulations across the various scenarios are conducted to assess the effectiveness of the proposed algorithm. Simulation results and the performance analysis demonstrated that the proposed algorithm can support the required rate for the most mMTC and low data rate URLLC IoT applications with average IoT tag rates in the range of 15 Kbps to 115 Kbps, and outperforms the algorithm-free riding technique in the case of massive IoT applications. The proposed mode 2 (first enhanced mode) achieves the best performance in terms of the average IoT tags rate and the total system rate with the lowest interference to the primary system users, on the other hand, mode 3 (second enhanced mode) improves the system capacity with maximum IoT tags satisfaction ratio. The capacity and satisfaction ratio of the proposed mode 3 outp...

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Section: A Literature Reviewmentioning

confidence: 99%

Ambient Backscatter Communication System Empowered by Matching Game and Machine Learning for Enabling Massive IoT Over 6G HetNets

Khalifa,

El-Haleem,

Elmesalawy

et al. 2024

IEEE Access

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“…Modeling the energy harvesting (EH) process as a discrete process helps to find a tractable solution to these MDP problems. The commonly used discrete EH models in literature (e.g., the Poisson and Bernoulli models 4,[12][13][14][15] ) help reduce the performance analysis complexity of the wireless communication systems. However, these models are not practical.…”

Section: Motivationmentioning

confidence: 99%

“…The amount of RF harvested energy is generally modelled as a stochastic process with a sequence of independent and identically distributed (iid) random variables (e.g., References 4,12‐15). In References 4,12, a cognitive radio network is assumed where the secondary user (SU) harvests energy from the primary user (PU) transmission according to a Bernoulli distribution. The SU is assumed to harvest a unit of energy when the channel is busy with the PU transmission with a certain successful RF‐EH probability.…”

Section: Introductionmentioning

confidence: 99%

Discrete practical RF energy harvesting models for wireless communication networks

Tayel

Zakariya

Rabia

et al. 2023

Trans Emerging Tel Tech

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Optimal resource management in wireless communication networks is usually modeled in discrete state space, for example, using Markov decision process. This requires the energy harvesting (EH) model to be discrete. Since the existing discrete EH models in the literature use simple discrete distributions, for example, Bernoulli and Poisson, with no regard to the properties of the EH circuits, the proposed work handles this dilemma by deriving discrete EH models based on the practical continuous radio frequency to direct current (RF‐to‐DC) power conversion models. General closed‐form expressions are derived for the probability density functions of the harvested energy in the case of linear, piecewise linear, and non‐linear EH models. These general closed‐form expressions are applied to some special fading models, for example, the Nakagami‐m$$ m $$ fading model. Simulation results verify the derived models. Moreover, statistical analysis is performed to compare the derived models to other discrete models in the literature. Finally, the derived models are applied to a case study where the effect of the number of discrete EH levels and the importance of the derived models compared to other models in the literature are presented. These comparisons show the deviation in the performance of the discrete models commonly used in the literature from the actual performance when the proposed piecewise linear EH model is used. Based on these comparisons, the proposed models, especially the piecewise linear EH model with an adequate number of segments, are recommended to be used for accurate discrete modeling of the EH process.

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Age of information minimization in hybrid cognitive radio networks under a timely throughput constraint

El-Sherif,

Rabia,

El-Malek

et al. 2024

Performance Evaluation

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Optimal decision making in multi-channel RF-powered cognitive radio networks with ambient backscatter capability

Cited by 3 publications

References 25 publications

Ambient Backscatter Communication System Empowered by Matching Game and Machine Learning for Enabling Massive IoT Over 6G HetNets

Ambient Backscatter Communication System Empowered by Matching Game and Machine Learning for Enabling Massive IoT Over 6G HetNets

Discrete practical RF energy harvesting models for wireless communication networks

Age of information minimization in hybrid cognitive radio networks under a timely throughput constraint

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