With the growth of internet of things (IoT) devices, cyberattacks, such as distributed denial of service, that exploit vulnerable devices infected with malware have increased. Therefore, vendors and users must keep their device firmware updated to eliminate vulnerabilities and quickly handle unknown cyberattacks. However, it is difficult for both vendors and users to continually keep the devices safe because vendors must provide updates quickly and the users must continuously manage the conditions of all deployed devices. Therefore, to ensure security, it is necessary for a system to adapt autonomously to changes in cyberattacks. In addition, it is important to consider network-side security that detects and filters anomalous traffic at the gateway to comprehensively protect those devices. This paper proposes a self-adaptive anomaly detection system for IoT traffic, including unknown attacks. The proposed system comprises a honeypot server and a gateway. The honeypot server continuously captures traffic and adaptively generates an anomaly detection model using real-time captured traffic. Thereafter, the gateway uses the generated model to detect anomalous traffic. Thus, the proposed system can adapt to unknown attacks to reflect pattern changes in anomalous traffic based on real-time captured traffic. Three experiments were conducted to evaluate the proposed system: a virtual experiment using pre-captured traffic from various regions across the world, a demonstration experiment using real-time captured traffic, and a virtual experiment using a public dataset containing the traffic generated by malware. The results of all experiments showed that the detection model with the dynamic update method achieved higher accuracy for traffic anomaly detection than the pre-generated detection model. The experimental results indicate that a system adaptable in real time to evolving cyberattacks is a novel approach for ensuring the comprehensive security of IoT devices against both known and unknown attacks.

Mode-division multiplexing (MDM) transmission using few-mode fibers (FMFs) has been studied as a promising approach to expand transmission capacity per fiber. In MDM transmission system, the computational load of the system increases with the differential modal group delay (DMD) between the propagation modes. Therefore, evaluating the DMD of FMF is necessary to implement MDM transmission systems. In this paper, we propose a method to simultaneously measure the DMDs for all linearly polarized (LP) modes in FMFs. The method involves using a commercially available optical time domain reflectometer (OTDR). It measures the round-trip impulse responses for all LP modes simultaneously using delay lines of different lengths and a mode-selective coupler. The DMDs for all LP modes are obtained from these round-trip impulse responses. By using commercially available OTDR and delay lines, the proposed method can measure the DMD easily and reasonably compared with the conventional method. We use delay lines for each mode to distinguish the signal of each mode and crosstalk component. We also clarify the conditions for the length of delay lines given to each mode. We measured the DMD for two types of FMFs to verify the applicability of the proposed method.

We investigated the performance of a nonlinear equalizer based on complex-valued reservoir computing (CVRC) for compensation of nonlinear waveform distortion caused by fiber nonlinearity in optical fiber communication systems. Reservoir computing (RC) is a kind of neuromorphic signal-processing algorithm. One important advantage of RC is the high-speed training capability compared to feedforward artificial neural networks (ANNs) with multiple (three or more) layers. CVRC can lower the computational complexity and directly deal with the complex amplitude of the IQ-modulated optical signals. We analyzed the impact of various parameters in CVRC on the equalization performance. The parameters include the number of reservoir units, the spectral radius, the timing of the training signal, and sparse connectivity. By optimizing these parameters, we achieved efficient operation of the CVRC-based nonlinear equalizers, making them applicable to various optical transmission distances. A comparative performance evaluation between CVRC and conventional real-valued RC (RVRC) was conducted through numerical simulations and experiments. We clarified that the CVRC-based nonlinear equalizer requires only about half the number of reservoir units compared to the RVRC-based nonlinear equalizer and reduces the computational complexity to approximately one-quarter.

To reduce network traffic and support environments with limited resources, a method for transmitting images with minimal transmission data is required. Several machine learning-based image compression methods, which compress the data size of images while maintaining their features, have been proposed. However, in certain situations, reconstructing only the semantic information of images at the receiver end may be sufficient. To realize this concept, semantic-information-based communication, called semantic communication, has been proposed, along with an image transmission method using semantic communication. This method transmits only the semantic information of an image, and the receiver reconstructs it using an image-generation model. This method utilizes a single type of semantic information for image reconstruction, but reconstructing images similar to the original image using only this information is challenging. This study proposes a multi-modal image transmission method that leverages various types of semantic information for efficient semantic communication. The proposed method extracts multi-modal semantic information from an original image and transmits only that to a receiver. Subsequently, the receiver generates multiple images using an image-generation model and selects an output image based on semantic similarity. The receiver must select the result based only on the received features; however, evaluating semantic similarity using conventional metrics is challenging. Therefore, this study explores new metrics to evaluate the similarity between semantic features of images and proposes two scoring procedures for evaluating semantic similarity between images based on multiple semantic features. The results indicate that the proposed procedures can compare semantic similarities, such as position and composition, between the semantic features of the original and generated images.

A reflector antenna excited by TM₀₁ circular waveguide mode is studied. Since the conventional reflector antenna with steps cancels out half of the polarized components in the main beam direction, the polarization loss increases, theoretically by 3 dB. Therefore, we propose an antenna to improve the gain by loading a metal grid on the step reflector surface, as compared to the conventional antenna design. Prototype verification shows the effectiveness of the antenna.

In cooperative systems for multi-antenna techniques, multiple-input multiple-output (MIMO) is essential to enhance the system capacity. For the transmission technique of MIMO systems, there is the combination of the space division multiplexing (SD) and the spatial modulation (SM), and the bit error rate (BER) performance is improved by using the channel ranking QR decomposition with M algorithm maximum likelihood detection (CR-QRM-MLD). And then, the transmission for the phase rotation SM on-off keying (PR-SM-OOK) and the rectangular SD-8QAM is also effective by using the CR-QRM-MLD with the simultaneous and separated searches. On the other hand, there are several assignments of the constellation for the 8QAM, but they are not evaluated in the SD-SM-MIMO. Therefore, in this paper, we propose the combination of the PR-SM-OOK and the rectangular, cross, octagonal SD-8QAMs for MIMO systems.

This paper proposes iterative decision feedback channel estimation (IDFCE) using a time-division multiplexing based reference signal that is incorporated into a turbo frequency domain equalizer (FDE) for discrete Fourier transform (DFT)-Spread Orthogonal Frequency Division Multiplexing (OFDM) (DFT-S-OFDM hereafter). In the proposed IDFCE, estimated channel responses, which are used for weights in feed-forward and decision-feedback FDEs, are iteratively updated multiple times at each iteration of the turbo FDE. We present the average block error rate (BLER) performance of IDFCE using weighted coherent averaging employing empirically optimized fixed weights and the 2-dimensional (2D) linear minimum mean square error (LMMSE) algorithm for the turbo FDE in DFT-S-OFDM. Computer simulation results indicate the best window size for the 2D LMMSE algorithm assuming the 9-path Extended Typical Urban (ETU) channel model and the maximum Doppler frequency of up to approximately 220 Hz. The computer simulations also show that the 2D LMMSE based IDFCE is effective in decreasing the required average received signal-to-noise power ratio (SNR) that satisfies the target average BLER for the turbo FDE in a frequency-selective Rayleigh fading channel with low-to-high maximum Doppler frequencies and a wide range of root mean square delay spread values for DFT-S-OFDM.

The achievement of optimum performance of a co-existing radar and communication (CRC) system is a challenging task when the channel state information (CSI) for the interference channels is imperfect, particularly when the power of the CRC system is restricted. To address this issue, this article proposes a robust CRC system that considers CSI uncertainty. Specifically, the objective is to design a robust beamforming scheme to maximize the radar performance subject to the user signal-to-interference-plus-noise ratio (SINR) and the transmit power of the CRC system. Due to the infinite number of constraints that result from the CSI uncertainty of the interference channels, a loosely bound robust approach is developed, which employs the shrinkage method to convert the infinite number of constraints into a finite number of constraints. However, the performance of the loosely bound robust approach is not satisfactory. Therefore, this paper proposes a sub-optimal robust approach that employs the S-procedure to transform the infinite number of inequality constraints into equivalent finite linear matrix inequalities (LMIs). The simulation results indicate that the design of the robust CRC system can significantly improve the system performance compared to the traditional design approach.

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV systems have evolved from single machine to multi-machine cooperation. Among the core technologies of UAV swarm systems, UAV swarm communication technology is increasingly receiving attention. The current modeling of UAV channels only considers communication between receiving and transmitting nodes, typically overlooking the objective situation where non-communication nodes in UAV swarm communication may interfere with the channel between two communicating nodes as they pass through. Therefore, to address this complex scenario,this paper proposes a three-dimensional wideband non-stationary channel model based on binary spheres and cylinders of heterogeneous scattering sources, incorporating line-of-sight, single-bounce, and double-bounce reflections. In this model, parameters such as the speed of the UAV swarm, distances between UAVs, departure, and arrival angles are treated as time-varying. Furthermore, statistical characteristics of the channel such as time-varying spatial-temporal-frequency correlation function and time-varying Doppler power spectral density are derived. This model not only takes into account the impact of the mobility parameters of transmitting nodes on the channel but also analyzes the influence of the motion states of non-communication nodes within the swarm on the communication channel. The simulation results demonstrate that the statistical characteristics of the wideband channel model for UAV swarms are generally similar to existing models. However, the influence of non-communication nodes on statistical characteristics cannot be ignored, with the maximum change in correlation coefficient reaching 0.29. This model provides a new practical approach for modeling UAV swarm communication channels, which can be utilized for performance evaluation and validation of UAV swarm communication systems.

This paper proposes physical random access channel (PRACH) transmission employing carrier frequency offset (CFO) pre-compensation that is measured at a set of user equipment (UE) using the synchronization signal block for the New Radio uplink. Computer simulation results show that by using cyclic-prefix (CP)-based fractional-frequency offset (FFO) estimation and joint integer-frequency offset (IFO) estimation with the primary synchronization signal, the correct FFO and IFO estimation probabilities of 90% are achieved at the average received signal-to-noise ratio (SNR) of approximately -4 dB and -8 dB, respectively, at a UE receiver. Accordingly, we show that when the average received SNR at the UE receiver is higher than approximately -5 dB, the miss-detection probability (MDP) of the proposed PRACH transmission with CFO compensation at UE transmission is almost identical to that without CFO. We also show that the PRACH transmission with CFO compensation at UE transmission achieves almost the same PRACH MDP as that without CFO for the frequency stability of a UE local oscillator of up to 10 ppm when the average received SNR at the UE receiver is higher than approximately 0 dB.

For high-resolution inverse synthetic aperture radar (ISAR) imaging situations, the continuous sparse recovery (SR) approach is very appropriate as it can accomplish high-precision reconstruction of missing signals. Existing high-resolution sparse ISAR imaging method based on the reweighted atomic norm (RAM) can avoid the grid mismatch problem, but it come with a lengthy calculation time, due to the high dimensionality of the ISAR echo matrix. To address this problem, a sparse ISAR imaging method based on the modified reweighted atomic norm (MRAM) was proposed in this paper. Firstly, the atomic representation model of ISAR signal was built, and utilizing the semi-definite property of atomic norm to transform the problem of minimizing atomic norm into a semi-definite programming (SDP) problem. Subsequently, a new non-convex surrogate function was introduced to reduce the number of iterations of the RAM method. Secondly, to swiftly handle SDP problem, the alternating direction multiplier method (ADMM) was used. Finally, Vandermonde decomposition was used to obtain amplitude and frequency information of scattering points, and imaging was completed through fast Fourier transform. The effectiveness of the proposed method was verified through experiments with real data.