Monitoring for Rare Events in a Wireless Powered Communication mmWave Sensor Network

Koutsioumpos, Michael; Zervas, E.; Hadjiefthymiades, Stathes; Merakos, Lazaros

doi:10.3390/s20123341

Cited by 4 publications

(4 citation statements)

References 28 publications

(31 reference statements)

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“…In addition, the use of UAV as an energy transmitter and data collector in WSNs was studied in [11]. Energy harvesting (EH) to obtain energy from RF signals was proposed as a technology capable of expanding the battery lifetime of devices [12]. The feasibility of the optimization issue and the presence of a simple solution were examined in [13], two-user multiple-input single-output (MISO) for wireless powered communication networks where user devices are equipped with nonlinear EH circuits.…”

Section: Related Workmentioning

confidence: 99%

Optimal Power Allocation with Sectored Cells for Sum-Throughput Maximization in Wireless-Powered Communication Networks Based on Hybrid SDMA/NOMA

2022

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Wireless-powered communication networks (WPCNs) consist of wireless devices (WDs) that transmit information to the hybrid access point (HAP). In this situation, there is interference among WDs that is considered to be noise and causes information loss because of adjacent signals. Moreover, power is limited and can be lost if transmission distance is long. This paper studies sum-throughput maximization with sectored cells for WPCN. We designed a downlink (DL) energy beamforming by sector based on the hybrid space division multiple access (SDMA) and nonorthogonal multiple access (NOMA) approach to maximize the sum throughput. First, a cell is divided into several sectors, and signals from each sector are transmitted to each antenna of the HAP, so that the signals are not adjacent. Further, the HAP decodes the overlapping information of each sector. Next, power allocation is optimized by sector. To optimize power allocation, a constrained optimization problem is formulated and then converted into a nonconstraint optimization problem using the interior penalty method. The optimal solution derives the maximal value to the problem. Power for each sector is optimally allocated according to this optimal solution. Under this consideration, sum-throughput maximization is performed by optimally allocating DL energy beamforming by sector. We analyzed sum throughput and fairness, and then compared them according to the number of sectors. Performance results show that the proposed optimal power allocation by sector using hybrid SDMA/NOMA outperforms the existing equal power allocation by sector in terms of the sum throughput while fairness is also maintained. Moreover, the performance difference between the hybrid approach and SDMA, which optimally allocates power by sector, was about 1.4 times that of sum throughput on average, and the hybrid approach was dominant. There was also no difference in fairness performance.

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Section: Related Workmentioning

confidence: 99%

Optimal Power Allocation with Sectored Cells for Sum-Throughput Maximization in Wireless-Powered Communication Networks Based on Hybrid SDMA/NOMA

2022

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“…The authors propose a solution that balances the two criteria. The authors in [ 8 ] introduce a reporting scheme that allows sensor nodes to inform the AP about their energy levels. Based on these reports, efficient energy-beamforming strategies are devised so that, acting proactively, all sensors are sufficiently charged most of the time and are capable of successfully transmitting their information upon detection of an event.…”

Section: Related Workmentioning

confidence: 99%

“…The second time slot format was proposed in [ 8 ] to facilitate sensors’ feedback to the AP. This was achieved by introducing an additional field, called the reporting period, during which the sensors report their energy levels to the AP.…”

Section: System Model and Analysismentioning

confidence: 99%

A Comprehensive Study of Event Detection in WPCN Networks with Noisy Measurements

Koutsioumpos

Zervas

Hadjiefthymiades

et al. 2022

Sensors

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Various aspects of the detection of events in wireless powered communication networks (WPCN) are studied and analyzed under the assumption of highly noisy sensor measurements. In WPCN, networks sensor nodes’ stored energy is a scarce resource and must be treated sparingly. Frequent false alarm detections force superfluous transmissions, thus depleting nodes’ energy storage. This has an adverse effect on the probability of successful transmission of the information message and its delay in case of a true positive detection. In this work, the detection problem is approached using an optimal stopping framework, where the involved likelihoods are highly unstable due to the noisy measurements. A classical AR filter is adopted in order to smooth the posterior likelihoods prior to their usage in the detection phase and its performance is contrasted to that of a novel Beta Particle Filter smoother. The effects of the smoothing filters on the achieved false alarm rate and detection delay are examined using numerical and simulation results. Moreover, the assessment of the detection process takes into account critical WPCN parameters, such as the charging efficiency and the location of the sensors, thus aiding the system design.

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“…DL embedding wireless network and mobile is well utilized for various cases. In wireless mobile environment the exact channel information and the overall model of evolution environment are unknown 18 . So therefore, the DL technique is used model-free to solve stochastic optimization issue in WN.…”

Section: Introductionmentioning

confidence: 99%

Hybrid cascaded attention-guided refinement network-based optimal power allocation in MIMO-NOMA system

Kumar

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2

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The multiple input multiple output (MIMO) plays an integral role in improving the energy efficiency for enhancing the QoS of all users. For the 5G wireless communication (WC), NOMA strategy is used with MIMO system to improve the sum rate as well as the computational complexity of the system. This developed work mainly focuses on emphasizing deep learning based optimal power allocation and user pairing techniques in MIMO-NOMA technology to enhance the sum rate and to reduce computational complexity. Normally, NOMA system is used to improve the spectrum in 5G mobile communication. When NOMA is hybridizing with MIMO system, the spectral efficiency (SE) is highly improved. But user pairing problems arise in NOMA system. To overcome this, deep reinforcement learning approach (DRL) is established to receive data from the user equipment (UE) about CSI and position. To allocate the power in UE, cascaded attention guided refinement network (CAGR-Net) is proposed. In addition to this, tiki taka algorithm (TTA) is manipulated to perform clustering as well as to vary the power levels for optimizing the system overall performance of the proposed method. The performance measures such as energy efficiency (EE), spectral efficiency (SE), channel gain, ergodic sum capacity, MSE and error rate are evaluated. In experimental scenario, the error between user 1 and user 2 attained about 0.1044 and 0.5642 respectively with the SNR of 16dB. The performance of the developed model shows better outcome compared to other existing techniques.

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Monitoring for Rare Events in a Wireless Powered Communication mmWave Sensor Network

Cited by 4 publications

References 28 publications

Optimal Power Allocation with Sectored Cells for Sum-Throughput Maximization in Wireless-Powered Communication Networks Based on Hybrid SDMA/NOMA

Optimal Power Allocation with Sectored Cells for Sum-Throughput Maximization in Wireless-Powered Communication Networks Based on Hybrid SDMA/NOMA

A Comprehensive Study of Event Detection in WPCN Networks with Noisy Measurements

Hybrid cascaded attention-guided refinement network-based optimal power allocation in MIMO-NOMA system

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