India's inhabitants rely heavily on agriculture for their livelihood. This research contributes significantly to the country's economy. Soil is essential for agriculture. An important component of agriculture ...
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We propose to use symbol decision based on neural networks to alleviate the impacts of quantization noise, laser phase noise, IQ impairments, and their interactions. Our DSP-assisted transceiver design allows the use ...
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ISBN:
(纸本)9798350323702
We propose to use symbol decision based on neural networks to alleviate the impacts of quantization noise, laser phase noise, IQ impairments, and their interactions. Our DSP-assisted transceiver design allows the use of low-cost transceiver hardware while satisfying the performance requirements. Extensive computer simulations show the effectiveness of neural networks when higher-order QAM signals are contaminated by the above impairments.
THz technology is considered a key element in 6G wireless communication because it provides ultra-high bandwidths, considerable capacities, and significant gains. However, wireless systems operating at high frequencie...
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ISBN:
(纸本)9798350333398
THz technology is considered a key element in 6G wireless communication because it provides ultra-high bandwidths, considerable capacities, and significant gains. However, wireless systems operating at high frequencies are faced with uncertainty and highly dynamic channels. Reflecting intelligent surfaces (RISs) can increase the range of the THz communication links and boost the rate at the receiver. In contrast to the existing literature, we investigate the scenario of multiple access multi-hop (cascaded) RISs uplink THz networks in a correlated channel environment. We show that our inspected cascaded RIS system is over-determined and that the rate maximization optimization problem is non-convex. To this end, we derive a closed-form expression of the received power and derive an analytical solution based on pseudo-inverse to obtain optimum RISs' phase shifts that maximize the received signal power and hence increase the rate. In addition, we utilize deep reinforcement learning (DRL), which is capable of solving non-convex optimization problems, to obtain the optimum cascaded RISs' phase shifts at the receiver taking into account the situation of the spatially correlated channels. Simulation results demonstrate that the DRL algorithm achieves higher rates than the mathematical sub-optimal method and the case of randomized phases.
Diagnosing cervical cancer early is crucial for successful treatment. Early detection proves to be challenging in areas where medical infrastructure is scarce, as is the awareness and information regarding cervical ca...
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Algorithms, Analytics, Modeling, and Knowledge-Based Techniques are used to propose an innovative solution in this research paper to enhance the prognostic health monitoring (PHM) of electrical submersible pumps (ESPs...
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Heart disease illness depicts a scope of conditions that influence your heart. machinelearning (ML) and deep learning end up being powerful in helping to simply decide and forecast from the enormous amount of informa...
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Image classification and interpretation pose significant challenges in the field of artificial intelligence (AI), with the rapid growth of technology and the availability of vast image datasets offering numerous oppor...
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In this study, we address the limitations of traditional methods for stock price prediction by introducing an innovative hybrid machinelearning model that integrates Long Short-Term Memory (LSTM) and Gated Recurrent ...
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Currently, many organizations are widely using intellectual analysis methods to effectively use large-scale data for their economic activities. Predictive analytics models help identify problems and opportunities for ...
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Performance prediction for cloud applications is important due to multiple reasons, including ensuring service level agreements, cost optimization, and resource provisioning. However, accurately predicting performance...
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