Polarimetric synthetic aperture radar (PolSAR) contains rich data that is potentially exploited to detect hidden features, e.g. archeological artifacts in the forest. In this paper, further analysis is conducted on post-polarization decomposition by exploiting the surrounding area of the archeological sites. This research proposed the double-bounce over surface scattering area (DoS) and double-bounce over volume scattering area (DoV) as novel descriptors to extract information in PolSAR images. These novel descriptors acted as filters to gain the presence of double bounce scattering from the dihedral-shaped archeological objects, distinguishing it from the surrounding surface scattering and volume scattering. Further, three variants of the surrounding area are examined, i.e. fixed area, area with homogeneity, and area with both homogeneity and connectivity. The proposed descriptors were validated using the scale model of the manmade structure or model of archeological objects under the vegetation canopy. Then the proposed descriptors are applied to the ALOS-2 PALSAR-2 image in the archaeological site in Muaro Jambi, Sumatera island, Indonesia to investigate the buried and vegetation-covered structures. Quantitatively, the scattering mechanism is analyzed on both scale model and satellite data. The proposed descriptors were evaluated for classifying: restored site, unrestored sites and vegetation in Muaro Jambi site. The information quality and its ability to classify between oriented structures and natural targets are evaluated. The information quality shows that the proposed descriptors have richer information up to 204%, especially in vegetation areas, and potentially indicate hidden features. For classification, the DoS descriptor provides a better classification of oblique buildings versus natural targets. With further research, the proposed descriptors could be employed to detect other hidden archeological artifacts in vegetation-covered areas.

The increasing demand for safe and efficient air traffic management (ATM) requires the modernization of aeronautical communication systems. This study explores the concept of seamless air-to-ground access to an aeronautical information-sharing platform called system-wide information management (SWIM) using a seamless air-to-ground communication system for ATM. To realize this concept, an enhanced media-independent aircraft messaging (e-MIAM) protocol is proposed in this study. The e-MIAM protocol enables Internet protocol (IP) communication, improves security over the legacy aircraft communication addressing and reporting system (ACARS). The e-MIAM protocol integrates different aeronautical communication media, such as legacy ACARS which is a non-IP, low-speed, point-to-point digital communication system operating on VHF, aeronautical mobile airport communication system (AeroMACS) which is a IP based communication system covering airport area, and IP based world-wide satellite communications system (SATCOM). Moreover, we propose a strategic media selection function for the e-MIAM protocol to realize efficient and seamless air-to-ground access to SWIM throughout all flight phases. Flight experiments were conducted to demonstrate the feasibility of the proposed system. Our findings from the flight experiments revealed the coverage, success rates, and delays associated with each aeronautical communication medium during various flight phases. Furthermore, a SWIM demonstration using the proposed e-MIAM protocol demonstrates its capability to facilitate real-time trajectory negotiation and enhance the overall ATM efficiency.

This paper considers the problem of localization of a receiver using a sequence of Frequency Of Arrival (FOA) of the received signals, which are transmitted from the Low Earth Orbit (LEO) satellites. Especially, we focus on the problem of a biased frequency offset, which remains after the down-conversion of the received signals at the front end, to remove the carrier frequency components. This removal error causes a large localization error. In this case, the FOA localization with simultaneous frequency offset estimation (FOA-SFOE) should be applied. On the other hand, if FOA-SFOE is applied while there is no frequency offset, the localization accuracy degrades compared with the FOA localization with no frequency offset (FOA-WNFO). For this reason, we propose a localization method that switches the above localization algorithms depending on the value of the estimated frequency offset so that the appropriate method is selected regardless of the value of the frequency offset. To verify the effectiveness of the proposed method, the numerical simulation results are shown, and the results indicate a smaller localization RMSE of the proposed method compared with the above two localization methods.

To understand detectability of a radar target such as a vehicle, vessel, aircraft, etc., it is important to obtain a radar cross-section (RCS) of the whole target and its components. For an electrically large target whose dimension is several hundred times the wavelength, the measurement distance satisfying the far-field (FF) condition cannot be obtained. Scaling model measurement is one approach to satisfy the FF condition in an anechoic chamber. However, the FF condition cannot be satisfied even in the scaling model for the too large target. The image-based near-field to far-field transformation (IB-NFFFT) can predict the RCS of the large scaling model from the measured near-field (NF) samples, and in addition, this method can also predict the RCS of the components. It makes us possible to discuss and improve accuracy of the RCS prediction through component analysis and filtering. This paper experimentally presents the RCS prediction of the large target and its components by using the scaling model and the IB-NFFFT. The size of the scaling model in this paper is 472 times the wavelength at 50 GHz frequency. The IB-NFFFT can predict the RCS pattern of the electrically large scaling model, obtain the inverse synthetic aperture radar (ISAR) image of the model, and briefly predict the RCS of the components of the model. This paper finds out the ability of the IB-NFFFT to obtain the RCS of the electrically large target and its components in the anechoic chamber. The finding is important because the large target has its unique features such as the rapid fluctuation of the RCS pattern and a lot of components contributing to the RCS.

In recent years, the development of transparent antennas for fifth-generation mobile communication systems has progressed, in which transparent conductive films are mounted on glass. To investigate transparent antenna applications apart from telecommunications, we have studied algorithms to realize noncontact control panels using transparent antennas. In this study, we report the development of an algorithm that can estimate the finger position three-dimensionally using multitask Gaussian process regression, which is a machine learning method based on multidimensional Gaussian inference. This algorithm takes advantage of the antenna frequency response to a finger approaching transparent antennas arranged in a two-dimensional manner within a noncontact control panel. The effectiveness of the proposed method was verified by analyzing simulation and experimental data acquired using a dedicated testbed developed for this study.

Cognitive radio network (CRN) is considered to be an effective means of improving spectrum utilization. As a crucial technology in CRN, spectrum sensing detects spectrum holes to achieve efficient frequency band utilization without interfering with licensed users. However, practical scenarios face challenges like wireless channel impairments, such as shadowing and path loss over long distance, which degrade secondary user (SU) reception of primary user (PU) signals. Reconfigurable intelligent surfaces (RIS) offer a promising solution by dynamically adjusting channel conditions to enhance wireless communication efficiency. This paper introduces a RIS-assisted cooperative spectrum sensing scheme to bolster PU signal reception by SUs, thereby improving spectrum sensing reliability. Leveraging deep reinforcement learning, our proposed approach optimizes cooperative spectrum sensing by efficiently coordinating SU actions. Through numerical simulations, we demonstrate the effectiveness of our method, which outperforms existing approaches in terms of detection performance. In particular, the number of SUs required by our proposed efficient spectrum sensing algorithm is fewer than the total number of cooperative spectrum sensing.

Federated learning is a collaborative model training method that iterates model updates by multiple clients and aggregation of the updates by a central server. Device and statistical heterogeneity of participating clients cause significant performance degradation so that an appropriate aggregation weight should be assigned to each client in the aggregation phase of the server. To adjust the aggregation weights, this paper employs deep unfolding, which is known as the parameter tuning method that leverages both learning capability using training data like deep learning and domain knowledge. This enables us to directly incorporate the heterogeneity of the data and environment of interest into the tuning of the aggregation weights. The proposed approach can be combined with various federated learning algorithms. The results of numerical experiments indicate that a higher test accuracy for unknown class-balanced data can be obtained with the proposed method than that with conventional heuristic weighting methods. The proposed method can handle large-scale learning models with the aid of pretrained models such that it can perform practical real-world tasks. Convergence rate of federated learning algorithms with the proposed method is also provided in this paper.

This paper investigates the terminal voltage response of non-uniform two-conductor transmission line by uniform plane wave illumination. The presence of the insulating medium leads to non-uniform dielectric constant around the conductor in the case of non-uniform two-conductor transmission line formed by a wire wrapped in insulating medium and ground, resulting in the difficulty in distributed capacitance calculation. A method is proposed to calculate the distributed capacitance of non-uniform two-conductor transmission line based on S-parameters measurement in this paper. Subsequently, the proposed method for distributed parameter calculation is combined with the terminal response theory of two-conductor transmission line by external field illumination. The terminal response of non-uniform two-conductor transmission line by uniform plane wave illumination can be estimated according to S-parameters measurement results through this approach. The proposed method is implemented and analyzed using numerical computation software and validated with CST simulation results. Finally, the accuracy of the proposed method is verified by field line coupling experiment using GTEM cell. The results show that the terminal voltage responses of the non-uniform two-conductor transmission line by uniform plane wave illumination can be estimated by the S-parameters measurement.

Orthogonal Time Frequency Space (OTFS) modulation, a two-dimensional delay-Doppler (DD) domain modulation technique, has been widely studied in recent years to address challenges in high-speed mobile communication environments. In this work, we propose an optimized design for OTFS systems, in which a Kaiser (KS) window is employed in the time domain of the transmitter (Tx), to significantly enhances the sparsity of the DD domain channel and effectively suppresses fractional inter-Doppler interference (IDI) without requiring additional spectral overhead. Furthermore, the impacts of the roll-off factor of the Root Raised Cosine (RRC) pulse shaping on the system’s Bit Error Rate (BER) in fractional delay channels is investigated, and a detailed matrix derivation of the input-output relationship of the proposed system in the context of fractional delay and Doppler shift channels is given. Simulation results show that, compared to traditional OTFS systems using a Dolph-Chebyshev (DC) window in the time-frequency (TF) domain, the proposed design not only reduces computational complexity by 2N M log₂ M but also achieves an approximate performance gain of 3dB.

Quantum secure communication networks based on quantum key distribution (QKD) technology are gradually improving, which can provide remote and networked key services for multiple users. This paper proposes a key distribution solution for latency and resource optimization in trusted relay QKD networks. In this scheme, the key distribution process between trusted relay nodes is presented as a joint optimization problem by considering the constraints of service delay, bandwidth, path resource consumption, quality loss and remaining quantum key quantity. In this paper, we treat key distribution as a bi-objective minimization problem, maintaining a trade-off between service delay and path resource consumption. Specifically, we propose a dynamic key distribution framework based on linear programming and particle swarm optimization. Simulation results show that the key distribution path selection in this scheme can be adjusted according to the service delay, key quality, and path resource consumption. Compared with the Random and customized OSPF policies, this scheme has higher performance. More specifically, the solution reduced average service latency by 27% while reducing path resource consumption by 56%.

Recently, skepticism has surrounded the ability of mobile network operators (MNOs) to achieve a timely mass rollout of 5G mobile network infrastructure. This is mainly due to the staggering number of antennas required and the unforeseen complexities involved, leading to a considerable disparity between total ownership cost (TCO) and return on investment (ROI), which is the primary concern from MNOs perspective. This amplifies significantly when contemplating universal 5G and beyond (5GB) coverage, pivotal for unlocking a myriad of innovative use cases and applications. Amidst these challenges, the concept of micro-operators represents a potential solution to augment the role of traditional MNOs in expediting the deployment of widespread 5GB infrastructure. Particularly, the newly emerging neutral host business model, wherein a third party assumes responsibility for providing coverage across multiple MNOs, stands as a compelling micro-operator solution, offering the sough-after cost-effectiveness and reliability. Our proposal in this paper relies on the dynamics observed in tidal traffic patterns of multi-tenant venues and citywide deployments to outline a cost-optimized design for a citywide neutral host micro-operator. Leveraging network slicing and statistical multiplexing techniques, our design approach enables real-time dynamic resource allocation. Additionally, the design integrates radio over Ethernet (RoE) and high availability seamless redundancy (HSR) protocols to meet the diverse service quality demands of 5GB applications. Simulation results demonstrate the scalability of our proposed design, meeting diverse 5GB QoS requirements across a spectrum of real-world citywide deployment scenarios, and cost-effectiveness by driving the TCO down by over 67%.

In smart IoT environments such as smart factory ones, periodically generated data packets as well as aperiodically generated ones with various sizes and generation time intervals must be transferred at low costs from the viewpoints of network installation and operation. Using Wireless Sensor Networks (WSNs) based on IEEE 802.11 is desirable for the low cost nature, but the channel access mechanism with Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) suffers from packet collision as packet transfer load increases. In this paper, we establish a new transmission time scheduling method for avoiding packet collision among wireless stations (STAs) that generate and transmit packets to their access point at various time periods, give the detailed system design, and verify its operation through prototype implementation and experimental studies. In the proposed method, transmission durations during which each STA is permitted to transmit packets are assigned in a discrete-time manner on the time axis, so that the overlap of transmission durations from different STAs is minimized based on the periodicity of packet generation. The transmission permission control is realized with a software buffer provided just before packets are passed from the upper IP layer to the MAC layer. This has the advantage that no modifications and replacements of existing IEEE 802.11 devices are required. The discrete-time assignment of the transmission durations ensures the low cost nature and feasibility of the transmission timing control for STAs whose computation resource is assumed to be limited as wireless sensor nodes. We implement the proposed method as a prototype system and conduct several experiments to verify its operation and evaluate its performance.

An intelligent reflective surface (IRS) comprises numerous intelligent reflective elements made of passive materials. This can change the propagation environment of the signals in wireless channels. With the development of intelligent reflector theory, channel estimation based on IRS has become a critical task. This study uses a Bayesian estimator to estimate massive multiple-input multiple-output (MIMO) channels based on intelligent-reflector-assisted conditions. First, a reflection phase shift selection scheme for maximizing the line-of-sight (LOS) intelligent reflector channel strength is proposed. This scheme utilizes the orthogonal characteristics of the channel to reduce channel interference. Second, this study provides three antenna selection schemes for the uplink with the assistance of an intelligent reflector. Solution 1 is to maximizemaximise the strength of the intelligent reflective surface line of sight channel and obtain closed form solutions in the algorithm via QR decomposition. Solution 2 is to maximize the strength of the cascaded channels to solve the problem of obtaining only suboptimal solutions in solution 1. Solution 3 is to maximize the channel capacity to further improve the spectral efficiency of the system. Finally, the intelligent phase shift under antenna selection is optimized, and the channel is estimated based on the coupling characteristics of the base station antenna and the reflection phase shift. The proposed methods exploit the spatial correlation characteristics at both the base station and planar IRSs as well as other statistical characteristics of multi-specular fading in a mobile environment. The simulation results show that compared with channel estimation in large-scale MIMO systems, the proposed channel estimation framework based on antenna selection improves the uplink spectrum efficiency of mobile user devices at the edge of a cell with the assistance of the uplink IRS. We simulated and compared channel estimation methods based on antenna selection with non-antenna selection channel estimation methods to obtain optimal solutions under different conditions.

Cognitive radio networks (CRNs) represent a promising technology for efficiently utilizing spectrum resources in next-generation wireless communication systems. Cooperative spectrum sensing (CSS) plays a pivotal role in CRNs, involving multiple secondary users (SUs) to detect the primary users (PUs). However, in conventional CSS systems, the PU signals received by SUs can be too weak for reliable detection due to the uncontrollable wireless environment. Furthermore, energy detection relies on accurate knowledge of noise power and is therefore susceptible to noise uncertainty, which can significantly degrade performance. To address these issues, this paper proposes a robust spectrum sensing method with the assistance of the reconfigurable intelligent surface (RIS), where the RIS is employed to enhance the signal power gain at the SUs to improve sensing performance. Specifically, we introduce an energy ratio detection (ERD) algorithm that leverages energy ratios from any two distinct SUs. We derive closed-form expressions for the decision threshold and detection probability of the proposed ERD algorithm. Notably, our approach is robust to noise uncertainty as it does not require prior knowledge of noise power. Furthermore, we optimize the RIS phase shifts in closed-form by maximizing the total signal-to-noise ratio across all SUs, resulting in significant detection performance improvements. Our optimization framework considers both instantaneous channel state information (CSI) and statistical CSI. Simulation results validate the effectiveness of the proposed algorithm, confirming the theoretical calculations and demonstrating that configuring the RIS under both instantaneous and statistical CSI substantially improves detection performance.

The demand for large-capacity, latency sensitive applications such as ultra-high-definition video transmission is increasing in wireless communication systems. In the next-generation wireless local area network (LAN), multi-access point (AP) coordination technique is attracting attention for the purpose of reducing collision probability and improving throughput. In high-demand applications such as ultra-high-definition video transmission, there is a problem that increasing the capacity of wireless communication does not necessarily lead directly to the realization of such applications. Therefore, to improve the number of applications that can be accommodated, the authors have studied application proportional fairness (APF), which is a radio resource control technique that improves the video throughput calculated based on whether the requirements for ultra-high-definition video transmission are satisfied. The joint use of the approach of APF and multi-AP coordination can provide the further performance improvement. In this paper, we modify APF to take radio quality into account to support multi-AP coordination and demonstrate its effectiveness through computer simulations.

This paper designs Bayesian iterative detection and decoding (IDD) schemes incorporating a blind channel estimator in the physical layer of long-range wide-area network (LoRaWAN). The Bayesian IDD exchanges log-likelihood ratios (LLRs) as soft decision values between the LoRa demodulator and Hamming decoder to enhance the signal detection capability while obtaining the iterative gain. The data rate of LoRa is notably slow, consequently resulting in prolonged frame transmission times. In time-varying fast Rayleigh fading channels, the channel during the prolonged time experiences significant fluctuations due to the Doppler effect. Typical LoRa modulation uses non-coherent demodulation without channel estimation to overcome the Doppler problem, at the expense of detection accuracy. This paper investigates LLR for coherent demodulation, supported by an iterative blind channel estimator for tracking the time-varying channel states, in order to maximize the iterative gain in the IDD process. Finally, computer simulation results explicitly demonstrate the efficacy of the proposed Bayesian IDD in terms of bit error rate (BER) performance.

This paper presents the detection performance of New Radio (NR) downlink initial access in the physical layer including physical-layer cell ID (PCID) detection, detection of the demodulation reference signal (DMRS) in the physical broadcast channel (PBCH), and demodulation and decoding of the PBCH payload using the synchronization signal block (SSB) in the millimeter (mm)-wave bands. In this paper, we take into account carrier frequency offset (CFO) due to the low frequency stability of a local oscillator of a set of user equipment (UE) and time-varying phase noise that occurs chiefly in a UE local oscillator, which are major impairments in the mm-wave bands. Link-level simulation results show that the detection error of the primary synchronization signal has a large impact on the detection probability of the PCID, the detection probability of the PBCH DMRS sequence, and on the resultant detection error of the PBCH payload using the Polar code. We also show that the residual CFO after CFO compensation affects the detection probability of the PBCH payload. We show, however, that the high detection probability of 90% for the correct radio frame timing based on the PBCH payload information is achieved at the average received signal-to-noise ratios (SNRs) of approximately -3 dB and -5 dB for the 3GPP Tapped Delay Line (TDL) - C and E channel models, respectively, indicating an effective SSB configuration of the NR radio interface for intermittent reception at a UE.

The resource allocation in high throughput satellites (HTSs) has been extensively investigated due to their payload ability to provide high-capacity and high-speed communication. To date, research has been conducted on variable beam arrangement using digital beamforming technology and on illumination patterns using beam hopping. In addition, there has been a great deal of research on flexible frequency resource allocation realized by digital channelizers. However, limited attention has been paid to a coordination of system throughput and fairness among user equipments (UEs) in these previous studies. In our previous paper, we proposed a frequency resource allocation model based on a beam-indexes-series finite Markov decision process. The policies of the resource allocation determined by a network operation center were only focused on maximizing either the number of allocated UEs or the system throughput. In this paper, we introduce a combined evaluation function that considers both the system throughput and the number of allocated UEs. This allows for the simultaneous optimization of communication capacity and fairness of communication opportunities. The superiority of the proposed resource allocation scheme and combined evaluation function is demonstrated through computational simulation in the comparison of the allocation scheme in the prior research. The operator is then able to coordinate the trade-off between system throughput and fairness among UEs.

We propose seven velocity-estimation methods for fast moving targets beyond the Nyquist-limit velocity for pulse Doppler radars. The Doppler-shift frequency beyond the Nyquist limit, which equals one-half the pulse-repetition frequency, cannot be correctly measured as the frequency is folded back within the Nyquist interval. Our methods use pulse compression with divided reference signals of varying center frequencies to detect the difference among the Doppler-shift frequencies of the pulse-compressed signals and increase sensitivity through signal integration, all without modifying the transmitted signals. These methods are either grid-search-type or Newton-type methods, each possessing distinct characteristics. The Cramér-Rao lower bounds on the variance of estimated velocity are derived, and the upper bounds on the probability of correct ambiguity resolution are shown. The results of our numerical simulation indicate that our methods can estimate target velocity beyond the Nyquist-limit velocity and that the method of independently estimating the Doppler frequencies at both end parts of the signal bandwidth and estimating the target velocity from the difference exhibits a lower decrease than the other methods regarding the success probability of Doppler ambiguity resolution even for high target velocities. We also confirmed that the upper bounds closely match the probability of corresponding methods for when the target velocity is lower than several times the Nyquist velocity and the signal-to-noise ratio on the range-Doppler map is above approximately 20 dB.

With the development of drone technology, concerns have been raised regarding the potential application of drones in terrorism and other crimes. Accordingly, a drone detection system that can classify incoming drones is needed to contain potential drone threats. In this study, we generate the inverse synthetic aperture radar (ISAR) imagery of various drones using millimeter-wave (mmW) fast chirp modulation (FCM) multiple-input and multiple-output (MIMO) radar and propose a drone classification method to distinguish the generated ISAR imagery using convolutional neural networks (CNNs). Two experimental cases were investigated to demonstrate the effectiveness of our proposed method. In case 1, we tested five types of drones (3DR Solo, DJI Phantom 3, DJI Mavic Pro, Parrot Anafi, and DJI Mavic Mini) moving under ideal conditions in the laboratory and generated the ISAR images of the drones. The models of five drones could be classified with high accuracy by learning the features of the ISAR images. In case 2, we classified the same models of flying drones using trained CNN models based on their ISAR images. Notably, its classification accuracy was comparable to that of other studies in drone classification. The experimental results indicated that ISAR imagery features are valid for drone classification.