Analyzing unintentional electromagnetic (EM) emissions from contemporary devices remains a significant challenge due to the difficulty of identifying potential sources of vulnerability within modern integrated circuit design and the limited research on critical leakage points. These challenges are particularly pressing in today’s information-driven society, where such emissions pose substantial security risks. To address this issue, this paper focuses on spread spectrum clocking (SSC) schemes employed in information visualization devices (IVDs), offering an in-depth analysis of the diverse characteristics of EM leakage and highlighting their associated risks. To convey our contributions, we introduce a novel model based on a modified Fourier series that accurately captures SSC-induced EM waves, enhancing the understanding of emissions from SSC-based devices. Additionally, we propose complete coherent demodulation (CCD), an advancement of the periodic nonuniform sampling (PNS) framework that resolves byproduct-phase terms. Our work further integrates parameterized demodulation with singular value decomposition (PD-SVD) to refine the analysis of modulated instantaneous frequency terms within EM leakage observed over significant distances. These contributions advance security assessments and strengthen electromagnetic resilience.

Vehicle-to-everything (V2X) technology can facilitate platoon driving, which can improve traffic efficiency in cooperative-intelligent transportation systems (C-ITS). In platoon driving, vehicles share their driving information with each other, including their location and speed. However, in congested traffic situations, there is a higher likelihood of packet collision, as the vehicles in the same platoon are using the same resources. This paper analyzes the communication QoS degradation factors that occur when operating the cooperative adaptive cruise control (CACC)-based platoon driving service. In order to improve the reliability characteristics of the dynamic information exchange vehicle-to-vehicle (V2V) link for the CACC-based platoon driving service, this paper proposed the proactive V2X PC5 resource allocation algorithm using the extended 1-stage SCI. This paper finally showed the improved communication QoS performance of the system in which the CACC-based platoon driving service is operated through the proposed resource allocation algorithm.

This study analyzes the phenomenon of electromagnetic (EM) leakage that occurs through cables and explores the potential for information forensics using deep learning-based image-processing algorithms. We focus on the transition-minimized differential signaling (TMDS) interface to analyze information leakage caused by the inherent differential signal synchronization errors in video graphics controllers (VGC). Our analysis includes detailed mathematical modeling of the EM leakage phenomena from the video display unit (VDU) interface that uses the TMDS protocol. Furthermore, this study presents mathematical models for distortions and alterations caused by the VDU characteristics and its associated RF front-end system. Utilizing mathematical models of EM phenomena, this paper presents a method for creating training datasets for deep learning-based signal processing algorithms by generating and augmenting pseudo leakage signals (PLS) that closely resemble actual leakage signals. This study confirms the practical utility of signal enhancement models trained with generated and augmented PLS in real-world scenarios. Validation involves applying the trained model to measured actual VDU leakage signals and evaluating the results using image quality metrics: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and the structural similarity index measure (SSIM). Ultimately, this study demonstrates the potential to develop deep learning models using theoretically generated PLS for VDU-targeted side-channel attacks, where collecting real training data poses a challenge. This suggests the potential for expanding into high-performance deep learning algorithms in future developments.

Joint transmission across the cluster of radio remote heads (RRHs) by exploiting the centralized processing of the cloud-radio access network (C-RAN) is a promising technique to overcome the severe interference problems in ultra-dense small cell networks. In practical considerations, local clustering of networks is preferred over global clustering as the number of RRHs that can be jointly transmitted on the network is finite. In this study, we attempt to revisit the principles of the well-known affinity propagation (AP) clustering algorithms, especially for the naive method of determining the exemplar with a fixed threshold. To further explain the decision method, we propose a method to easily determine the threshold using the network map of converged value of messages generated by AP algorithm, by combining Otsu's threshold and density peak searching method with the existing AP clustering algorithm. The proposed algorithm provides higher spectral efficiency than the conventional AP algorithms as well as statically coordinated multi-point (CoMP) techniques, although it has similar execution time as the traditional AP algorithms.

In this paper, we propose a channel estimation method based on a complex valued regression of the neural network for the IEEE 802.11p standard. It consists of the complex weighted summation optimized by feedforward neural network with backpropagation algorithm using initial estimated channel of the pilot and the long preamble. It also exploits the shift matrix in order to mitigate the effect from a systemic problems in IEEE 802.11p standards. The major problems of IEEE 802.11p standard are wide bandwidth of 10 MHz consisting of 64 subcarriers and relatively insufficient four pilot subcarriers at single ODFM symbol, which are unsuitable for a channel of vehicular environment. Despite these problems, the proposed method performs better than the conventional channel estimation methods. The performance of proposed scheme is provided with the comparison between constructed data pilots (CDP), Spectral Temporal Averaging (STA), and proposed scheme. The proposed channel estimation scheme has low mean square error (MSE) and bit error rate (BER) throughout the whole SNR region. It is the result from properly trained weight. At the low SNR region, especially, the performance of proposed scheme is much better than CDP and STA scheme. It is because of the noise suppression effect caused by a weighted summation algorithm.

The optimal number of clusters (K) differs depending on the radio remote head (RRH) density. This paper verifies that the K values cannot be met by the conventional affinity propagation (AP) clustering algorithm. In an ultra-dense network (UDN) environment, the density of RRH is a very important factor for the bender because it is directly related to the cost of configuring the wireless communication network. Likewise, in order to provide the optimal communication environment to the user in the UDN environment, it is necessary to enable flexible clustering according to changing channel environment by utilizing semi-dynamic clustering technology. As a result, we propose an AP algorithm that finds a better K value than the conventional method. To this end, the proposed algorithm additionally utilizes a non-coordinated multi-point (CoMP) interference power that varies depending on the RRH density, user position, and the variations in propagation channel. The simulation results show that the proposed algorithm shows a better average capacity than the conventional algorithm.

A minimum mean square error estimation (MMSE) using a virtual pilot channel estimation scheme was proposed by our researchers for improving the error performance of the IEEE 802.11p. However, compared to the STA channel estimation scheme, the MMSE scheme cannot provide an acceptable error performance in a low SNR region. To alleviate this, we propose an adaptive channel estimation scheme, which selectively uses a channel estimation scheme that has an error performance in-between that of the STA and the MMSE, through a decision method using a long preamble. Simulation results demonstrate that the proposed scheme can select a better channel estimation scheme, thereby demonstrating an excellent performance over the entire SNR range of interest.

Polarization division multiple access (PDMA) is a method to simultaneously transmit two independent signals by using vertical and horizontal polarization in the same frequency band. In PDMA systems, the received signal can experience significant interference from cross-polarized signals if the transmit and receive antennas are misaligned in terms of polarization state. In this paper, a precoding technique to solve this problem is discussed and a channel model to disentangle vertical and horizontal components from the received signal and include the rotation of receive antenna is proposed. The precoding technique is designed and analyzed using a channel model assuming a line-of-sight environment. To measure the performance of the precoding technique, a testbed is implemented using a software-defined radio platform. Simulation and measurement show that enhanced channel gain is achieved even when the polarization state between the transmit and receive antennas is mismatched. Comparison between simulation and measurements proves a basic and crucial principle concerning the capability of controlling the polarization state of a transmitted signal.

This paper analyzes the effects of polarization-mismatch and space-correlation to a multiple-input and single-output (MISO) channel which is observable in the near future cellular communications environments such as large-scale antenna arrays and small cells. The analysis is based on a polarization-mismatched and space-correlated MISO channel which is modeled from the conventional dual-polarized channel. In the MISO channel, polarization-mismatch is described by the polarization-mismatch angle which is uniformly distributed from 0 to the maximum polarization-mismatch angle and space-correlation is described by the exponential correlation model. Assuming high SNR, approximate expressions of the ergodic capacity are derived as a function of the transmit power, number of transmit antennas, maximum polarization-mismatch angle, and space-correlation coefficient in four representative environments: narrowly or widely spread polarization-mismatch angles and slightly or highly correlated channels. Further, the capacity loss introduced by polarization-mismatch and space-correlation is derived with respect to the maximum polarization-mismatch angle and space-correlation coefficient. It is shown that the capacity loss introduced by polarization-mismatch is upper bounded by 2 bit/s/Hz. Whereas, the capacity loss introduced by space-correlation increase with the number of transmit antennas and is upper bounded by 0.832 bit/s/Hz. Required resources to compensate for the capacity loss is derived as well.

In broadcast channels, network utility maximization formulates a scheduling problem in order to maximize a sum of given utility functions. It is known that the network utility maximization is achieved if a gradient method is used. In this paper, however, we show that the network utility maximization is achieved only if a gradient method is used. That proves the equivalence between a problem formulated by the network utility maximization and a problem with a gradient method. The gradient method simplifies the object function of a scheduling problem by modifying utility functions to a gradient form, so that it makes easy to deal with the problem. We apply the gradient method to a problem with utility functions given by generalized proportional fairness. It is revealed that using the gradient method for the generalized proportional fairness is equivalent to applying a binomial approximation. Simulation results are presented with various scheduling parameters.

This paper proposes a self-optimized coverage coordination scheme for two-tier femtocell networks, in which a femtocell base station adjusts the transmit power based on the statistics of the signal and the interference power that is measured at a femtocell downlink. Furthermore, an analytic expression is derived for the coverage leakage probability that a femtocell coverage area leaks into an outdoor macrocell. The coverage analysis is verified by simulation, which shows that the proposed scheme provides sufficient indoor femtocell coverage and that the femtocell coverage does not leak into an outdoor macrocell.

This paper proposes two interference mitigation strategies that adjust the maximum transmit power of femtocell users to suppress the cross-tier interference at a macrocell base station (BS). The open-loop and the closed-loop control suppress the cross-tier interference less than a fixed threshold and an adaptive threshold based on the noise and interference (NI) level at the macrocell BS, respectively. Simulation results show that both schemes effectively compensate the uplink throughput degradation of the macrocell BS due to the cross-tier interference and that the closed-loop control provides better femtocell throughput than the open-loop control at a minimal cost of macrocell throughput.

Since reciprocal channels can be found in time division duplex (TDD) systems, TDD code division multiple access (CDMA) systems are capable of implementing a pre-RAKE structure as a pre-distortion technique. We propose a singular value decomposition (SVD) pre/post-RAKE in which the RAKE combiner is utilized at both the transmitter and receiver. If the combining weights of the pre-RAKE and post-RAKE are determined by the singular value decomposition, then parallel sub-channels can be established in the eigen-space. We obtain the optimal information capacity by applying the water-filling solution to the SVD pre/post-RAKE. The SVD pre/post-RAKE provides remarkable capacity improvement without increasing the average transmit power or frequency bandwidth, since multiple data symbols can be reliably transmitted through the parallel sub-channels.