From a practical point of view, quantization is necessary for the design of any low-complexity LDPC decoder. However, the quantization scheme with the minimum computational complexity called all-integer quantization has not been well investigated since, intuitively, it seems to significantly degrade the decoding performance. It is found in this work that the utilization of 4-bit all-integer quantization for MSA-based LDPC decoder is adequate to achieve the decoding performance of the nonquantized case which can be considered as the lower bound on the decoding performance. Moreover, this simple quantization scheme can also be applied to the hardware and cost-efficient rate-compatible LDPC codes.

It is widely acknowledged in communication theory that polar codes have been proven to achieve channel capacity across a range of communication channels. However, their exceptional performance is usually evaluated through simulations or analyses conducted under the assumption of infinite precision, i.e., floating-point arithmetic, which represents an ideal numerical computation. To address this implementation challenge, this work proposes a min-sum successive cancellation (MS-SC) polar decoder employing all-integer quantization to improve practicality in real-world scenarios. To balance the trade-off between practicality and decoding performance, we investigate whether 5-bit all-integer quantization is the optimal choice for the MS-SC polar decoder. Moreover, the simulation results over fading channels show that the proposed decoder achieves a performance almost equivalent to the high-precision successive cancellation (SC) decoder. The integer-based calculation for the MS-SC polar decoder reduces computational complexity by $75\%$ compared to the conventional SC decoding algorithm with infinite-precision computation.

The scintillation in radio wave propagation, particularly in regions near the magnetic equator, is found to be introduced by the ionospheric irregularities causing unsatisfactory performance in satellite based applications. In order to mitigate this effect, we design a long short-term memory (LSTM) model to forecast amplitude scintillation at one-minute resolution. Additionally, the upsampling-based feature preprocessing is introduced to improve forecasting performance, especially for short-term severe scintillation events. In terms of R$^{2}$ which is a popular forecast evaluation metric, our proposed model exhibits about 20% improvement over the same LSTM model without upsampling. Furthermore, although existing studies achieve good forecasting accuracy up to 4 hours ahead, the proposed model sets a benchmark with one-day-ahead forecasting, but at the cost of longer training time due to upsampling.

Polar codes are fundamental to the control channels of fifth-generation (5G) systems. Owing to their excellent block error rate (BLER) performance, polar coding is considered a strong candidate for sixth-generation (6G) networks. In scenarios where both computational complexity and ultra-high data rates are critical, we investigate integer quantization in successive cancellation (SC) decoding as a replacement for double-precision calculations. A 4096-quadrature amplitude modulation (QAM) system with orthogonal frequency-division multiplexing (OFDM) is employed to emulate high-data-rate transmission conditions. Our work shows that integer quantization influences decoding performance across multiple dimensions, depending on system parameters such as code length, rate, and required precision. Our evaluation indicates that a 5-bit integer representation can closely match the ideal BLER performance. The proposed integer-based SC decoding achieves a 14–16% reduction in computational complexity compared to fixed-point implementations with fractional precision, while maintaining decoding performance. This offers a simple and effective solution for polar-coded 6G communications.

The practicality of the densely packed and spectrally efficient 4096-Quadrature Amplitude Modulation (4K-QAM) is obstructed by the ultra-high computational complexity of its soft demapper, which is essential for generating the soft outputs required by channel decoders. In this paper, we propose an efficient training scheme to build a highly effective neural network based 4K-QAM soft demapper that can offer significantly lower computational complexity. The results demonstrate that this alternative demapper can achieve comparable decoding performance in coded 4K-QAM systems, while reducing computational complexity by up to 20% compared with the well-known low-complexity max-logarithm of maximum a posteriori (log-MAP) demapper.

This work aimed to develop a computer-aided diagnostic system that utilizes radiomics and artificial intelligence (AI) to differentiate between spinal tuberculosis and spinal metastasis, which are difficult to distinguish due to their similar visual characteristics. A total of 50 cases, comprising 25 rare cases of spinal tuberculosis and 25 cases of spinal metastasis, were included in the study cohort. Expert radiologists and orthopedic specialists manually segmented the regions of interest from the corresponding MRI images. PyRadiomics was used to extract 788 radiomic features from each segmented image. XGBoost was then applied for feature selection and SHAP values confirmed their importance. The subset of features from the previous machine learning stage were used to train a deep neural network, resulting in a radiomics-powered AI classification model. Due to the visual challenges in classifying these two diseases, human doctors achieve an average accuracy of 64%, while our well-trained model demonstrates an impressive accuracy of 93%. Along with its strong performance in external validation, this study suggests that this AI model can offer valuable assistance in achieving early and accurate differential diagnoses of spinal tuberculosis and spinal metastasis.