Generative Artificial Intelligence (AI) techniques have become integral part in advancing next generation wireless communication systems by enabling sophisticated data modeling and feature extraction for enhanced netw...
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This paper provides the first real-world evaluation of using visual (RGB camera) data and machine learning for proactively predicting millimeter wave (mmWave) dynamic link blockages before they happen. Proactively pre...
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This paper presents an implicit reactive power flow representation to support energy scheduling of distributed energy resources (DER) in distribution networks. An approximation previously used in transmission models, ...
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The introduction of massive MIMO (Multiple Input Multiple Output) communication systems enables base stations (BS) to perform beamforming for enhancing communication reliability. A typical key assumption, however, is ...
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ISBN:
(数字)9798331508050
ISBN:
(纸本)9798331508067
The introduction of massive MIMO (Multiple Input Multiple Output) communication systems enables base stations (BS) to perform beamforming for enhancing communication reliability. A typical key assumption, however, is the availability of accurate downlink channel state information (CSI). In practice, CSI estimation and reporting delays coupled with the process of channel aging result in the BS receiving outdated CSI information, which in turn impacts the system's spectral efficiency. To combat this latency, this paper develops efficient methods of CSI prediction that preemptively predict future downlink CSI based on historical data. We leverage the spatial and temporal correlation properties of the channel and use explicit feature extraction frameworks for both dimensions to accurately predict future CSI. We analyze combinations of spatial and temporal feature extractors in terms of a tradeoff between performance and latency. We evaluate the performance of the proposed prediction model in terms of proximity to the ground truth, prediction latency, and model footprint. Our experiments show that our method outperforms classical statistical methods as well as existing CSI prediction baselines.
In this paper, we propose a Convolutional-Transformer speech codec which utilizes stacks of convolutions and self-attention layers to remove redundant information at the downsampling and upsampling blocks of a U-Net-s...
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In high-stakes domains such as healthcare and hiring, the role of machine learning (ML) in decision-making raises significant fairness concerns. This work focuses on Counterfactual Fairness (CF), which posits that an ...
ISBN:
(纸本)9798331314385
In high-stakes domains such as healthcare and hiring, the role of machine learning (ML) in decision-making raises significant fairness concerns. This work focuses on Counterfactual Fairness (CF), which posits that an ML model's outcome on any individual should remain unchanged if they had belonged to a different demographic group. Previous works have proposed methods that guarantee CF. Notwithstanding, their effects on the model's predictive performance remain largely unclear. To fill this gap, we provide a theoretical study on the inherent trade-off between CF and predictive performance in a model-agnostic manner. We first propose a simple but effective method to cast an optimal but potentially unfair predictor into a fair one with minimal performance degradation. By analyzing the excess risk incurred by perfect CF, we quantify this inherent trade-off. Further analysis on our method's performance with access to only incomplete causal knowledge is also conducted. Built upon this, we propose a practical algorithm that can be applied in such scenarios. Experiments on both synthetic and semi-synthetic datasets demonstrate the validity of our analysis and methods.
With the rapid advancement of blockchain technology, a significant trend is the adoption of Directed Acyclic Graphs (DAGs) as an alternative to traditional chain-based architectures for organizing ledger records. Syst...
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This paper presents a novel high-gain, non-isolated step-up DC-DC converter to enhance the performance of photovoltaic (PV) systems in DC microgrids. The converter addresses the challenges faced by PV systems by emplo...
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ISBN:
(数字)9798331533946
ISBN:
(纸本)9798331533953
This paper presents a novel high-gain, non-isolated step-up DC-DC converter to enhance the performance of photovoltaic (PV) systems in DC microgrids. The converter addresses the challenges faced by PV systems by employing a parallel topology, where each PV module is paired with a dedicated converter for improved flexibility and energy production. The proposed converter achieves high voltage gain with a lower duty cycle, thereby reducing semiconductor losses and increasing efficiency. This design also simplifies the switch driver by using a single switch and provides continuous input current, which is crucial for renewable energy applications. A comprehensive analysis of the converter’s operation in Continuous Conduction Mode (CCM) is provided, including both ideal and non-ideal conditions. The performance is validated through MATLAB/Simulink simulations, along with an adaptive Perturb and Observe (P&O) MPPT algorithm, confirming the converter’s effectiveness for grid-connected, parallel Solar Power Optimization (SPO) systems.
By applying Adaptive Optics in Structured Illumination Microscopy, we developed an adaptive three-dimensional super-resolution microscopy system and demonstrate three-dimensional super-resolution aberration-free imagi...
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The world is transitioning to utilise distributed generation (DG) to reduce social, economic and environmental effects. As DG penetration is increasing, the need of determining the associated impacts on distribution n...
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