This letter proposes a new partial transmit sequence (PTS) scheme to reduce envelope fluctuations in orthogonal frequency division multiple access (OFDMA) uplink systems. We found that the conventional PTS can be seriously degraded in the non-contiguous allocation of OFDMA uplink systems due to the linearity in the conventional phase rotations. To overcome this problem, we propose a new phase rotation using the complex conjugate, which can be implemented without multiplication like the conventional phase rotations. Simulation results show that the proposed PTS outperforms the conventional PTS in terms of cubic metric (CM) and bit error rate (BER).

In this paper, we study channel estimation methods with various curve fitting techniques for mobile WiMAX system and find that they cannot support high mobility sufficiently. We also propose a channel estimation method which is based on piecewise linear estimation to overcome the problem of high mobility. The performances including mean squared error (MSE) and bit error rate (BER) are compared and we find that the proposed method has better performance than others at higher speeds.

In this study, the authors propose robust automatic modulation classifiers using convolutional neural networks (CNNs). CNNs can identify features directly from a received signal when appropriately trained. They trained the classifier by taking the magnitude, phase, real, and imaginary parts of the received signal for CNN‐based automatic modulation classification, which is insensitive to phase and frequency deviation. Also, in order to make the proposed classifier more robust, they generated signal‐to‐noise ratio, phase offset, and frequency offset in the training signal set. They compared the proposed methods with conventional automatic modulation classification methods using higher‐order cumulants, order statistics, and a CNN‐based method. The simulation results show that the proposed methods outperform the conventional methods not only in the additive white Gaussian noise channel but also in the channels having severe phase or frequency offsets.

Background/Objectives: This study systematically compared the performance of U-Net and variants for automatic analysis of carotid intima-media thickness (CIMT) in ultrasound images, focusing on segmentation accuracy and real-time efficiency. Methods: Ten models were trained and evaluated using a publicly available Carotid Ultrasound Boundary Study (CUBS) dataset (2176 images from 1088 subjects). Images were preprocessed using histogram-based smoothing and resized to a resolution of 256 × 256 pixels. Model training was conducted using identical hyperparameters (50 epochs, batch size 8, Adam optimizer with a learning rate of 1 × 10⁻⁴, and binary cross-entropy loss). Segmentation accuracy was assessed using Dice, Intersection over Union (IoU), Precision, Recall, and Accuracy metrics, while real-time performance was evaluated based on training/inference times and the model parameter counts. Results: All models achieved high accuracy, with Dice/IoU scores above 0.80/0.67. Attention U-Net achieved the highest segmentation accuracy, while UNeXt demonstrated the fastest training/inference speeds (approximately 420,000 parameters). Qualitatively, UNet++ produced smooth and natural boundaries, highlighting its strength in boundary reconstruction. Additionally, the relationship between the model parameter count and Dice performance was visualized to illustrate the tradeoff between accuracy and efficiency. Conclusions: This study provides a quantitative/qualitative evaluation of the accuracy, efficiency, and boundary reconstruction characteristics of U-Net-based models for CIMT segmentation, offering guidance for model selection according to clinical requirements (accuracy vs. real-time performance).