This paper presents a unified software and hardware wireless AI platform (USHWAP) for developing and evaluating machine learning in wireless systems. The platform integrates multi-software development such as MATLAB and Python with hardware platforms like FPGA and SDR, allowing for flexible and scalable device and edge computing application development. The USHWAP is implemented and validated using FPGAs and SDRs. Wireless signal classification, wireless LAN sensing, and rate adaptation are used as examples to showcase the platform's capabilities. The platform enables versatile development, including software simulation and real-time hardware implementation, offering flexibility and scalability for multiple applications. It is intended to be used by wireless-AI researchers to develop and evaluate intelligent algorithms in a laboratory environment.

This paper focuses on a pseudorandom number generator called an NTU sequence for use in cryptography. The generator is defined with an m-sequence and Legendre symbol over an odd characteristic field. Since the previous researches have shown that the generator has maximum complexity; however, its bit distribution property is not balanced. To address this drawback, the author introduces dynamic mapping for the generation process and evaluates the period and some distribution properties in this paper.

Even correlation and odd correlation of sequences are two kinds of measures for their similarities. Both kinds of correlation have important applications in communication and radar. Compared with vast knowledge on sequences with good even correlation, relatively little is known on sequences with preferable odd correlation. In this paper, a generic construction of sequences with low odd correlation is proposed via interleaving technique. Notably, it can generate new sets of binary sequences with optimal odd correlation asymptotically meeting the Sarwate bound.

A group signature scheme allows us to anonymously sign a message on behalf of a group. One of important issues in the group signatures is user revocation, and thus lots of revocable group signature (RGS) schemes have been proposed so far. One of the applications suitable to the group signature is privacy-enhancing crowdsensing, where the group signature allows mobile sensing users to be anonymously authenticated to hide the location. In the mobile environment, verifier-local revocation (VLR) type of RGS schemes are suitable, since revocation list (RL) is not needed in the user side. However, in the conventional VLR-RGS schemes, the revocation check in the verifier needs O(R) cryptographic operations for the number R of revoked users. On this background, VLR-RGS schemes with efficient revocation check have been recently proposed, where the revocation check is just (bit-string) matching. However, in the existing schemes, signatures are linkable in the same interval or in the same application-independent task with a public index. The linkability is useful in some scenarios, but users want the unlinkability for the stronger anonymity. In this paper, by introducing a property that at most K unlinkable signatures can be issued by a signer during each interval for a fixed integer K, we propose a VLR-RGS scheme with the revocation token matching. In our scheme, even the signatures during the same interval are unlinkable. Furthermore, since used indexes are hidden, the strong anonymity remains. The overheads are the computational costs of the revocation algorithm and the RL size. We show that the overheads are practical in use cases of crowdsensing.

Many nodes in Internet of Things (IoT) rely on batteries for power. Additionally, the demand for executing compute-intensive and latency-sensitive tasks is increasing for IoT nodes. In some practical scenarios, the computation tasks of WDs have the non-separable characteristic, that is, binary offloading strategies should be used. In this paper, we focus on the design of an efficient binary offloading algorithm that minimizes system energy consumption (EC) for TDMA-based wireless-powered multi-access edge computing networks, where WDs either compute tasks locally or offload them to hybrid access points (H-APs). We formulate the EC minimization problem which is a non-convex problem and decompose it into a master problem optimizing binary offloading decision and a subproblem optimizing WPT duration and task offloading transmission durations. For the master problem, a DRL based method is applied to obtain the near-optimal offloading decision. For the subproblem, we firstly consider the scenario where the nodes do not have completion time constraints and obtain the optimal analytical solution. Then we consider the scenario with the constraints. By jointly using the Golden Section Method and bisection method, the optimal solution can be obtained due to the convexity of the constraint function. Simulation results show that the proposed offloading algorithm based on DRL can achieve the near-minimal EC.

Similarity search for data streams has attracted much attention for information recommendation. In this context, recent leading works regard the latest W items in a data stream as an evolving set and reduce similarity search for data streams to set similarity search. Whereas they consider standard sets composed of items, this paper uniquely studies similarity search for text streams and treats evolving sets whose elements are texts. Specifically, we formulate a new continuous range search problem named the CTS problem (Continuous similarity search for Text Sets). The task of the CTS problem is to find all the text streams from the database whose similarity to the query becomes larger than a threshold ε. It abstracts a scenario in which a user-based recommendation system searches similar users from social networking services. The CTS is important because it allows both the query and the database to change dynamically. We develop a fast pruning-based algorithm for the CTS. Moreover, we discuss how to speed up it with the inverted index.

In this letter, we study the adaptive regulation problem for a chain of integrators in which there are different individual delays in measured feedback states for a controller. These delays are considered to be unknown and time-varying, and they can be arbitrarily fast-varying. We analytically show that a feedback controller with a dynamic gain can adaptively regulate a chain of integrators in the presence of unknown individual state delays. A simulation result is given for illustration.

For an [n, k, d] (r, δ)-locally repairable codes ((r, δ)-LRCs), its minimum distance d satisfies the Singleton-like bound. The construction of optimal (r, δ)-LRC, attaining this Singleton-like bound, is an important research problem in recent years for thier applications in distributed storage systems. In this letter, we use Reed-Solomon codes to construct two classes of optimal (r, δ)-LRCs. The optimal LRCs are given by the evaluations of multiple polynomials of degree at most r - 1 at some points in Fq. The first class gives the [(r + δ - 1)t, rt - s, δ + s] optimal (r, δ)-LRC over Fq provided that r + δ + s - 1≤q, s≤δ, s

Recently, there has been a surge of interest in Artificial Intelligence (AI) and its applications have been considered in various fields. Mobile networks are becoming an indispensable part of our society, and are considered as one of the promising applications of AI. In the Beyond 5G/6G era, AI will continue to penetrate networks and AI will become an integral part of mobile networks. This paper provides an overview of the collaborations between networks and AI from two categories, “AI for Network” and “Network for AI,” and predicts mobile networks in the B5G/6G era. It is expected that the future mobile network will be an integrated infrastructure, which will not only be a mere application of AI, but also provide as the process infrastructure for AI applications. This integration requires a driving application, and the network operation is one of the leading candidates. Furthermore, the paper describes the latest research and standardization trends in the autonomous networks, which aims to fully automate network operation, as a future network operation concept with AI, and discusses research issues in the future mobile networks.

It has become increasingly important for industries to promote digital transformation by utilizing 5G and industrial internet of things (IIoT) to improve productivity. To protect IIoT application performance (work speed, productivity, etc.), it is often necessary to satisfy quality of service (QoS) requirements precisely. For this purpose, there is an increasing need to automatically identify the root causes of radio-quality deterioration in order to take prompt measures when the QoS deteriorates. In this paper, a method for identifying the root cause of 5G radio-quality deterioration is proposed that uses machine learning. This Random Forest based method detects the root cause, such as distance attenuation, shielding, fading, or their combination, by analyzing the coefficients of a quadratic polynomial approximation in addition to the mean values of time-series data of radio quality indicators. The detection accuracy of the proposed method was evaluated in a simulation using the MATLAB 5G Toolbox. The detection accuracy of the proposed method was found to be 98.30% when any of the root causes occurs independently, and 83.13% when the multiple root causes occur simultaneously. The proposed method was compared with deep-learning methods, including bidirectional long short-term memory (bidirectional-LSTM) or one-dimensional convolutional neural network (1D-CNN), that directly analyze the time-series data of the radio quality, and the proposed method was found to be more accurate than those methods.

Zero Trust Networking (ZTN) is a security model where no default trust is given to entities in a network infrastructure. The first bastion of security for achieving ZTN is strong identity verification. Several standard methods for assuring a robust identity exist (E.g., OAuth2.0, OpenID Connect). These standards employ JSON Web Tokens (JWT) during the authentication process. However, the use of JWT for One Time Token (OTT) enrollment has a latent security issue. A third party can intercept a JWT, and the payload information can be exposed, revealing the details of the enrollment server. Furthermore, an intercepted JWT could be used for enrollment by an impersonator as long as the JWT remains active. Our proposed mechanism aims to secure the ownership of the OTT by including the JWT as encrypted metadata into a Non-Fungible Token (NFT). The mechanism uses the blockchain Public Key of the intended owner for encrypting the JWT. The blockchain assures the JWT ownership by mapping it to the intended owner's blockchain public address. Our proposed mechanism is applied to an emerging Zero Trust framework (OpenZiti) alongside a permissioned Ethereum blockchain using Hyperledger Besu. The Zero Trust Framework provides enrollment functionality. At the same time, our proposed mechanism based on blockchain and NFT assures the secure distribution of OTTs that is used for the enrollment of identities.

We have developed an all-optical fiber link antenna measurement system for a millimeter wave 5th generation mobile communication frequency band around 28 GHz. Our developed system consists of an optical fiber link an electrical signal transmission system, an antenna-coupled-electrode electric-field (EO) sensor system for 28GHz-band as an electrical signal receiving system, and a 6-axis vertically articulated robot with an arm length of 1m. Our developed optical fiber link electrical signal transmission system can transmit the electrical signal of more than 40GHz with more than -30dBm output level. Our developed EO sensor can receive the electrical signal from 27GHz to 30GHz. In addition, we have estimated a far field antenna factor of the EO sensor system for the 28GHz-band using an amplitude center modified antenna factor estimation equation. The estimated far field antenna factor of the sensor system is 83.2dB/m at 28GHz.

An ESL-cancelling circuit for a shunt-connected film capacitor filter using vertically stacked coupled square loops is reported in this paper. The circuit is applicable for a shunt-connected capacitor filter whose equivalent series inductance (ESL) of the shunt-path causes deterioration of filter performance at frequencies above the self-resonant frequency. Two pairs of vertically stacked magnetically coupled square loops are used in the circuit those can equivalently add negative inductance in series to the shunt-path to cancel ESL for improvement of the filter performance. The ESL-cancelling circuit for a 1-μF film capacitor was designed according to the Biot-Savart law and electromagnetic (EM)-analysis, and the prototype was fabricated with an FR4 substrate. The measured result showed 20-dB improvement of the filter performance above the self-resonant frequency as designed, satisfying Sdd21 less than -40dB at 1MHz to 100MHz. This result is almost equivalent to reduce ESL of the shunt-path to less than 1nH at 100MHz and is also difficult to realize using any kind of a single bulky film capacitor without cancelling ESL.

In this paper, a concept of non-contact PIM evaluation method using balanced transmission lines is proposed for impedance-matched PIM measurement systems. In order to evaluate the PIM characteristics of a MSL by using its image model, measurement system using balanced transmission line is introduced. In non-contact PIM measurement, to reduce undesirable PIM generation by metallic contact and the PIM-degradation in repeated measurements, a non-contact connector which is applicable without any design changes in DUT is introduce. The three-dimensional balun composed of U-balun and balanced transmission line is also proposed so that it can be applicable to conventional unbalanced PIM measurement systems. In order to validate the concept of the proposed system, a sample using nickel producing high PIM is introduced. In order to avoid the effect of the non-contact connection part on observed PIM, a sample-configuration that PIM-source exists outside of the non-contact connection part is introduced. It is also shown using a sample using copper that, nickel-sample can be clearly differentiated in PIM characteristics while it is equivalent to low-PIM sample in scattering-parameter characteristics. Finally, by introducing the TRL-calibration and by extracting inherent DUT-characteristics from whole-system characteristics, a method to estimate the PIM characteristics of DUT which cannot be taken directly in measurement is proposed.

With the rapid developments in the Internet of Things (IoT), low power wide area networks (LPWAN) framework, which is a low-power, long-distance communication method, is attracting attention. However, in LPWAN, the access time is limited by Duty Cycle (DC) to avoid mutual interference. Packet-level index modulation (PLIM) is a modulation scheme that uses a combination of the transmission time and frequency channel of a packet as an index, enabling throughput expansion even under DC constraints. The indexes used in PLIM are transmitted according to the mapping. However, when many sensors access the same index, packet collisions occur owing to selecting the same index. Therefore, we propose a mapping design for PLIM using mathematical optimization. The mapping was designed and modeled as a quadratic integer programming problem. The results of the computer simulation evaluations were used to realize the design of PLIM, which achieved excellent sensor information aggregation in terms of environmental monitoring accuracy.

Synthetic aperture radar (SAR) is a device for observing the ground surface and is one of the important technologies in the field of microwave remote sensing. In SAR observation, a platform equipped with a small-aperture antenna flies in a straight line and continuously radiates pulse waves to the ground during the flight. After that, by synthesizing the series of observation data obtained during the flight, one realize high-resolution ground surface observation. In SAR observation, there are two spatial resolutions defined in the range and azimuth directions and they are limited by the bandwidth of the SAR system. The purpose of this study is to improve the resolution of SAR by sparse reconstruction. In particular, we aim to improve the resolution of SAR without changing the frequency parameters. In this paper, we propose to improve the resolution of SAR using the deconvolution iterative shrinkage-thresholding algorithm (ISTA) and verify the proposed method by carrying out an experimental analysis using an actual SAR dataset. Experimental results show that the proposed method can improve the resolution of SAR with low computational complexity.

The rise of next-generation logistics systems featuring autonomous vehicles and drones has brought to light the severe problem of Global navigation satellite system (GNSS) location data spoofing. While signal-based anti-spoofing techniques have been studied, they can be challenging to apply to current commercial GNSS modules in many cases. In this study, we explore using multiple sensing devices and machine learning techniques such as decision tree classifiers and Long short-term memory (LSTM) networks for detecting GNSS location data spoofing. We acquire sensing data from six trajectories and generate spoofing data based on the Software-defined radio (SDR) behavior for evaluation. We define multiple features using GNSS, beacons, and Inertial measurement unit (IMU) data and develop models to detect spoofing. Our experimental results indicate that LSTM networks using ten-sequential past data exhibit higher performance, with the accuracy F1 scores above 0.92 using appropriate features including beacons and generalization ability for untrained test data. Additionally, our results suggest that distance from beacons is a valuable metric for detecting GNSS spoofing and demonstrate the potential for beacon installation along future drone highways.

Network function virtualization (NFV) technology significantly changes the traditional communication network environments by providing network functions as virtual network functions (VNFs) on commercial off-the-shelf (COTS) servers. Moreover, for using VNFs in a pre-determined sequence to provide each network service, service chaining is essential. A VNF can provide multiple service chains with the corresponding network function, reducing the number of VNFs. However, VNFs might be the source or the target of a cyberattack. If the node where the VNF is installed is attacked, the VNF would also be easily attacked because of its security vulnerabilities. Contrarily, a malicious VNF may attack the node where it is installed, and other VNFs installed on the node may also be attacked. Few studies have been done on the security of VNFs and nodes for service chaining. This study proposes a service chain construction with security-level management. The security-level management concept is introduced to built many service chains. Moreover, the cost optimization problem for service chaining is formulated and the heuristic algorithm is proposed. We demonstrate the effectiveness of the proposed method under certain network topologies using numerical examples.

Modulated signal detection has been rapidly advancing in various wireless communication systems as it's a core technology of spectrum sensing. To address the non-Gaussian statistical of noise in radio channels, especially its pulse characteristics in the time/frequency domain, this paper proposes a method based on Information Geometric Difference Mapping (IGDM) to solve the signal detection problem under Alpha-stable distribution (α-stable) noise and improve performance under low Generalized Signal-to-Noise Ratio (GSNR). Scale Mixtures of Gaussians is used to approximate the probability density function (PDF) of signals and model the statistical moments of observed data. Drawing on the principles of information geometry, we map the PDF of different types of data into manifold space. Through the application of statistical moment models, the signal is projected as coordinate points within the manifold structure. We then design a dual-threshold mechanism based on the geometric mean and use Kullback-Leibler divergence (KLD) to measure the information distance between coordinates. Numerical simulations and experiments were conducted to prove the superiority of IGDM for detecting multiple modulated signals in non-Gaussian noise, the results show that IGDM has adaptability and effectiveness under extremely low GSNR.

Non-orthogonal multipe access based multiple-input multiple-output system (MIMO-NOMA) has been widely used in improving user's achievable rate of millimeter wave (mmWave) communication. To meet different requirements of each user in multi-user beams, this paper proposes a power allocation algorithm to satisfy the quality of service (QoS) of head user while maximizing the minimum rate of edge users from the perspective of max-min fairness. Suppose that the user who is closest to the base station (BS) is the head user and the other users are the edge users in each beam in this paper. Then, an optimization problem model of max-min fairness criterion is developed under the constraints of users' minimum rate requirements and the total transmitting power of the BS. The bisection method and Karush-Kuhn-Tucher (KKT) conditions are used to solve this complex non-convex problem, and simulation results show that both the minimum achievable rates of edge users and the average rate of all users are greatly improved significantly compared with the traditional MIMO-NOMA, which only consider max-min fairness of users.