The increasing threat of malicious unmanned aerial vehicles (UAVs) necessitates robust anti-UAV systems. However, their performance is often degraded by bird misclassification caused by low-resolution imagery and unseen UAV types. This study proposes a motion-based 3D convolutional neural network (3D-CNN) trained on image sequences acquired from a radar-camera integrated anti-UAV solution. The proposed method effectively distinguishes UAVs from birds, even under low-resolution conditions and when encountering previously unseen UAV types.

For future applications such as sensor sharing, computation task offloading for autonomous driving, and remote driving, robust real-time video transmission with low latency via cellular vehicle-to-everything (C-V2X) communication is essential to ensure operational reliability. While advanced communication services require sensor sharing, yet there is a lack of comprehensive latency analysis for high-volume data between vehicles and remote users or servers. Existing literature predominantly focuses on vehicle-to-vehicle communication or basic device status messages with low volume, which are insufficient for supporting advanced services such as autonomous driving. Consequently, it is crucial to analyze the latency involved in sensor sharing between vehicle and remote entity over different channel states. In this paper, the latency of sensor data transmission using 5G C-V2X Uu interface is investigated in consideration of various channel states and modulation and coding schemes. Additionally, we analyzed the latency depending on the resolution and encoding of the camera video image. Simulation results show the feasible frame rates and video resolutions for both raw and compressed video transmissions within these communication systems, and highlight the effects of channel state and multi-user scenarios on the feasibility of real-time camera video sharing.

To enhance the performance of autonomous driving, recent studies have been incorporating various tasks that require increasingly more computation. As computational demands increase, it is often difficult to achieve timely execution with the limited performance of onboard computing units alone. To address this issue, Vehicle Edge Computing (VEC), which offloads computational workloads to the edge and retrieves the results back to the vehicle, is gaining significant attention. To achieve efficient offloaded analytics via VEC, it is crucial to comprehensively consider both of the computing and network conditions of the V2X systems, as well as the vehicle energy consumption and timely execution. However, current studies have not sufficiently addressed the comprehensive modeling of computational and network loads in these V2X systems. To deal with this, we propose a Cooperative Network-Computation Load Balancing Simulator for VEC.

This paper proposes an integrated simulator combining WiLabV2Xsim, a MATLAB-based open-source simulator, and Virtual Test Drive (VTD), a traffic generation and vehicle dynamics simulator developed by HEXAGON, to evaluate Vehicle to Everything (V2X) communication performance in real road and driving environments. WiLabV2Xsim is a system-level simulator that implements Cellular-V2X and New Radio-V2X communication protocol stacks. Through VTD, it is possible to implement usecases standardized in 3GPP Technical Report (TR) 22.886 or evaluate V2X communication performance in real environments using virtual environments based on the Association for Standardization of Automation and Measuring Systems (ASAM) Open Scenario. Future research plans include evaluating various scenarios based on the 5G Automotive Association (5GAA) TR documents through the integrated simulator and conducting studies on wireless resource allocation and decentralized congestion control to enhance V2X communication performance.

Security threats increase as connectivity among vehicles increases. In particular, a lack of authentication, integrity, and confidentiality makes the controller area network (CAN) protocol, which is used in critical domains such as vehicle body and powertrain, vulnerable to threats. In this paper, we propose methods for CAN security enhancement that use a support vector machine (SVM) and the autocorrelation of the received signal to detect a malicious node. Robustness to temperature variation is also considered because autocorrelation is affected by temperature variation. There are two methods based on the degree of uniformity of the temperature distribution. If the temperature is uniformly distributed over the vehicle and the temperature sensor is embedded in the secure node, the first scheme (temperature measurement system) trains data in each segmented temperature range more precisely using multiple classifiers. If not (i.e., a nonuniform temperature distribution or an absence of a temperature sensor), the alternative scheme (all-temperature training system) trains data in all temperature ranges with a single classifier. The performances of the proposed systems are evaluated on a testbed. The proposed method can operate without modifying the CAN protocol because it is based on the characteristics of the physical layer. In addition, security can be enhanced redundantly by the system running independently without authentication protocols.

In this paper, we suggest a vehicle speeding enforcement system based on V2X (Vehicle-to-Everything). Existing camera-based enforcement, which can measure in one direction, has a narrow measurement range and it is difficult to analyze vehicle speed in real time. To overcome these shortcomings, we measure vehicle speed in real time by extracting speed data from the GPS (Global Positioning System) and the IVN (In-Vehicle Network). Also, we aim to expand the scope of enforcement by using a V2X-based system. Under the V2X-based system, we show a wider enforcement range and the instantaneous/average speed of the vehicle in real time. We also analyze the difference in performance between DSRC (Dedicated Short Range Communication) and C-V2X (Cellular-V2X) in the proposed V2X-based system.

In this paper, we suggest a physical layer security transmission scheme which can be employed when an eavesdropper is located near a receiver and channels are correlated. When the receiver-eavesdropper distance is short, it is difficult to obtain a security gain because the eavesdropper can have a similar channel compared to the receiver. To mitigate this problem, the receiver may transmit a jamming signal to prevent data acquisition from the eavesdropper. On this occasion to lessen the outgoing interference caused by the jamming signal to external communication networks, we assume that the sum of the transmission power of the transmitter and the jamming power of the receiver is kept constant. Under this restricted power consumption condition, we evaluate the security performance of our proposed transmission scheme using two metrics, such as threshold outage probability and secrecy capacity. By simulation, we find the optimal power consumption ratio between transmission power and jamming power which maximizes the threshold outage probability. We also analyze the secrecy capacity by simulation and numerical results.

In this paper, we develop an eavesdropping method for uplink wideband code division multiple access (WCDMA) system. In order to achieve effective eavesdropping of uplink WCDMA message, one of the most demanding systematic characteristics is the scrambling code information. While the previous works of identifying the scrambling code use only one antenna and cannot operate in practical environments, we propose an improved method for robust identification performance in more practical scenarios by using multiple antennas. The proposed method exploits not only multiple antennas but also non-coherent combining (NCC) which enables combining the received signals of each antenna without any channel state information (CSI). Through numerical simulation, we demonstrate that our method is effective for both slow and fast fading environments and evaluates the performance for various system parameters.

Lattice reduction (LR) has been researched a lot in conjunction with ordered successive interference cancellation (OSIC), contributing large enhancement of the performance with relatively small complexity. However, the performance gap between the LR-aided OSIC (LR-OSIC) and maximum likelihood (ML) still exists. This paper suggests lattice reduction aided all-ordering SIC (LR-AOSIC) as an optimum SIC detector showing near-ML performance almost unaffected by channel correlation and modulation order. In addition, LR-aided ordered SIC using modified local search algorithm (LR-MLS-OSIC) is also proposed as a sub-optimum SIC detector to reduce the complexity of LR-AOSIC affordably, nearly maintaining the performance.

This paper investigates the effect of imperfect channel state information (CSI) on the outage performance of cooperative distributed beamforming (DBF). The acquisition of perfect CSI is disturbed by outdated CSI and channel estimation errors. Decode-and-forward relays are considered with individual power constraints at each relay. Considering a training-and-feedback strategy to acquire CSI, a closed-form approximate expression for outage probability and the achievable diversity gain are first provided under Rayleigh fading channels. Numerical observations verify that the approximation is very tight. It is shown that outdated CSI seriously degrades the performance, and therefore, the outage probability is bounded and no diversity is achieved. Conversely, channel estimation errors cause slight performance degradation, and full diversity order is still achievable. However, even when CSI is outdated, it is shown that the asymptotic outage decreases as the number of relays K increases, where the logarithm of the outage probability decreases at the rate of K log K. This paper shows that, when the destination re-estimates the current CSI for decoding, the effect of outdated CSI is greatly mitigated and consequently achieves a diversity gain of one. To re-estimate the current CSI with small overhead, the beamformed training sequence (BFTS) strategy is proposed, in which a new closed-form formula for minimum-mean-square-error estimation is presented using Gaussian approximation. Numerical results verify that the outage performance with the proposed BFTS scheme is close to that of a perfect estimation. A closed-form outage expression of DBF with the proposed BFTS scheme is also presented under an assumption of high-signal-to-noise ratio (SNR), but the expression is very tight even in low-to-moderate SNR regimes.

In wireless peer-to-peer networks that serve various proximity-based applications, peer discovery is the key to identifying other peers with which a peer can communicate and an understanding of its performance is fundamental to the design of an efficient discovery operation. This paper analyzes the performance of wireless peer discovery through comprehensively considering the wireless channel, spatial distribution of peers, and discovery operation parameters. The average numbers of successfully discovered peers are expressed in closed forms for two widely used channel models, i.e, the interference limited Nakagami-m fading model and the Rayleigh fading model with nonzero noise, when peers are spatially distributed according to a homogeneous Poisson point process. These insightful expressions lead to the design principles for the key operation parameters including the transmission probability, required amount of wireless resources, level of modulation and coding scheme (MCS), and transmit power. Furthermore, the impact of shadowing on the spatial performance and suggested design principles is evaluated using mathematical analysis and simulations.

In this paper, we propose an improved MIMO successive interference cancellation (SIC) detector taking maximum likelihood (ML) criterion into account. Applying the ML criterion to multiple candidates obtained from all possible orderings of streams in the SIC, the proposed method can provide improved performance with complexity comparable to conventional ordered SIC for moderate number of spatial streams.

This paper presents an architectural approach to the design of low power reconfigurable finite impulse response (FIR) filter. The approach is well suited when the filter order is fixed and not changed for particular applications, and efficient trade-off between power savings and filter performance can be made using the proposed architecture. Generally, FIR filter has large amplitude variations in input data and coefficients. Considering the amplitude of both the filter coefficients and inputs, the proposed FIR filter dynamically changes the filter order. Mathematical analysis on power savings and filter performance degradation and its experimental results show that the proposed approach achieves significant power savings without seriously compromising the filter performance. The power savings is up to 41.9% with minor performance degradation, and the area overhead of the proposed scheme is less than 5.3% compared to the conventional approach.

This paper proposes a dimension reduction maximum likelihood (ML) soft demodulator for multiple-input multiple-output (MIMO) system. The proposed dimension reduction soft demodulator reduces the dimension of the search space for minimum Euclidean distance calculation by exploiting ML hard detection algorithms and can achieve significantly lower complexity than existing optimal soft demodulators based on exhaustive or near-exhaustive search. The dimension reduction can be realized through the use of optimum MIMO hard detectors such as a sphere decoder, resulting in optimal performance. It can also be implemented using suboptimum low-complexity MIMO hard detectors such as various conventional MIMO equalizers, further reducing the complexity with slight performance degradation.

When a preamble signal is repeated multiple times in OFDM systems, we derive joint maximum likelihood (ML) estimation of the frame timing (FT) and carrier frequency offset (CFO). Unlike conventional estimators which use correlation of adjacent repetition patterns only or some specific sets of patterns, the joint ML estimation exploits correlation of any pair of repetition patterns, providing optimized performance. To reduce the implementation complexity involved in the joint ML estimation, we also propose a near-ML estimation method that separates estimation of the FT and the CFO. The performance of the proposed methods is verified by computer simulation.

In this paper, we argue that the lag size of half a moving horizon of the minimum variance finite impulse response (MVFIR) smoother is not generally best in the sense of the estimation error variance even though it has been believed to be optimal for a simple input/output (I/O) model with correlated data. For a more general analysis, a state space model is considered in this paper. It is shown via numerical example that the dependency of the lag size on the minimum estimation error variance is asymmetric around half a moving horizon and varies with model parameters. In the case of a scalar system, the best lag size is represented in a closed-form. Some hints on a numerical method are presented for a more general vector system. Considering the lag size as well as the estimation error, we also propose a new performance criterion that puts a penalty on the large lag size while making the estimation error small.

This paper investigates the effect of inaccurate SINR estimation on the performance of multi-user scheduling in downlink orthogonal frequency division multiplexing (OFDM) systems. Considering path loss, time and frequency selectivity of the channel, the error statistics of SINR estimation is analyzed. Then, the effect of SINR inaccuracy is evaluated in respect to system throughput and fairness when each user has different error statistics according to channel environments.

To maximize the spectral efficiency for a time- varying downlink channel with multiple users, the scheduling order of the users needs to be optimized. The scheduling problem naturally is a combinatorial optimization problem with high complexity, which exponentially increases with the number of users. This paper proposes several low-complexity scheduling algorithms. Computer simulation results show that the proposed algorithms approach optimal performance.

To maximize spectral efficiency for multiple users with time-varying broadcast channel, the scheduling order of the users needs to be optimized. The scheduling problem naturally is a combinatorial optimization problem with high complexity, which exponentially increases with the number of users. In this paper, several scheduling algorithms that have small complexities are proposed. Computer simulation results show that the proposed algorithms achieve performance close to the optimal one.

In this paper, we consider two approximate maximum a posteriori probability (MAP) detectors for code division multiple access (CDMA) systems. We show that if the sum of multiple access interferences (MAI) is Gaussian (with certain mean and variance from the extrinsic information), the (well-known) minimum mean square error (MMSE) detector with soft interference cancellation (SIC), called the MMSE-SIC detector, becomes the MAP detector. Thus, for a large number of users, the MMSE-SIC detector becomes asymptotically the MAP detector. In addition, we consider a correlation based analysis for the iterative detection with the approximate MAP detectors to see the performance limit. Then, the correlation analysis is further generalized to analyze the performance of the iterative receiver consisting of an approximate MAP detectors and channel decoders for coded signals

The transmission performance of an OFDM system can significantly be improved by exploiting the channel characteristics. In this paper, we consider the design of a channel-aware OFDM transceiver whose parameters are adjusted in response to the change of channel condition. To this end, we first estimate the channel state information (CSI), such as the signal to interference power ratio, low order moments of Doppler spectrum and power-delay profile of the channel. The proposed CSI estimator can estimate these CSI parameters altogether in a unique manner by exploiting the autocorrelation properties of the channel impulse response (CIR). Then, we design a CIR estimator and adaptive OFDM modulator that adjust their parameters according to the estimated CSI. Finally, we verify the performance of the proposed OFDM transceiver by computer simulation

Adaptive modulation with coding (AMC) is often employed to enhance the throughput of OFDM based wireless systems. The AMC mode is usually adjusted according to the signal to interference ratio (SIR) of the received signal. Most of the conventional schemes use fixed thresholds for the selection of AMC mode, yielding a large performance variation depending on the channel characteristics. This paper proposes the use of adaptive SIR thresholds for the mode selection in response to the channel condition, providing significant improvement in throughput performance in a wide range of channel environments

The performance of channel estimation can be improved by employing a channel estimation filter (CEF) that can adjust its bandwidth in response to the channel condition. For coherent OFDM receiver, we consider the use of two-dimensional (2-D) moving average (MA) FIR filter with a linear interpolation filter as the CEF that can provide good performance without requiring large implementation complexity. The optimum tap size of 2-D MA FIR CEF is analytically determined in terms of the pilot spacing, the channel spectrum and pilot signal to interference power ratio. The performance of the proposed channel estimator is evaluated by computer simulation.

The performance of channel estimation in orthogonal frequency division multiplexing (OFDM) systems significantly depends on the pilot signal, which is usually scattered in time and frequency domains. For a given pilot density, the authors optimally design the pilot pattern so as to minimize the mean squared error (MSE) of the channel estimate with the use of a general interpolator. The analytic results are verified by computer simulation.

The performance of channel estimation in OFDM systems significantly depends on the pilot signal, which is usually scattered in the time and frequency domains. For a given pilot density, we analytically design the optimum pilot pattern that minimizes the mean square error of the channel estimate obtained with the use of general interpolators. The analytic results are verified by computer simulation.

The receiver performance with the use of space time transmit diversity (STTD) scheme is more susceptible to the accuracy of channel estimate than that without the use of the STTD scheme since the despreading signals suffer from the effect of crosstalk and the transmit power is equally divided into multiple transmit antennas. As a result, the efficiency of channel estimation in the WCDMA STTD system becomes an important issue more than that in the non-STTD system. In this paper, an adaptive channel estimator (ACE) is designed to mitigate the performance degradation due to inaccurate channel estimation. Numerical results show that the performance improvement significantly increases with the use of the proposed ACE, particularly when the channel condition becomes worse.

We design an adaptive channel estimator (ACE) in the DS-CDMA downlink that has a common pilot channel (CPICH). The proposed ACE first estimates the channel condition, including the maximum Doppler frequency, without requiring exact a priori information on the operating condition. Then, it adjusts the impulse response of the channel estimation filter in response to the estimated channel condition. The performance of the proposed ACE is verified by computer simulation. The performance improvement with the use of the proposed ACE increases as the channel experiences severe frequency selective fading or the mobile is getting closer to the cell boundary.

The performance of a coherent RAKE receiver for DS-CDMA systems is significantly affected by the accuracy of channel estimation. The performance of the channel estimator can be improved by employing a channel estimation filter (CEF) whose impulse response is adjusted according to the channel condition. In this paper, we consider the analytic design of an adaptive channel estimator for DS-CDMA systems with a pilot channel. The proposed channel estimator estimates the channel condition including the maximum Doppler frequency, which can be performed without having exact a priori information on the operating condition. The estimated channel condition adjusts the impulse response of the CEF. Numerical results show that the use of the proposed channel estimator can provide BER performance quite robust to a wide range of channel condition.

The characteristics of spreading sequences significantly affect the performance of the forward link DS-CDMA system in a fading channel. The bit error rate (BER) is a function of the signal-to-interference ratio (SIR) which has different characteristics depending upon the spreading sequence. In this paper, the SIR is analyzed when orthogonal codes and pseudo random codes are used as the spreading sequence. The BER performance is analyzed with and without the use of convolutional codes. The use of orthogonal codes can cancel out the interference signals having the same path delay, but the use of random codes cannot, resulting in significant performance degradation. As a result, the BER performance of the orthogonal spreading system is much better than that of the random spreading system. Even in the case of many multipaths, the number of users achieving a fixed BER value in the orthogonal spreading system is twice larger than that in the random spreading system. The analytical results are verified by computer simulation.