Compressed sensing is a rapidly growing research field in signal and image processing, machine learning, statistics, and systems control. In this survey paper, we provide a review of the theoretical foundations of compressed sensing and present state-of-the-art algorithms for solving the corresponding optimization problems. Additionally, we discuss several practical applications of compressed sensing, such as group testing, sparse system identification, and sparse feedback gain design, and demonstrate their effectiveness through Python programs. This survey paper aims to contribute to the advancement of compressed sensing research and its practical applications in various scientific disciplines.

In the beyond 5G and 6G networks, the number of connected devices and their types will greatly increase including not only user devices such as smartphones but also the Internet of Things (IoT). Moreover, Non-terrestrial networks (NTN) introduce dynamic changes in the types of connected devices as base stations or access points are moving objects. Therefore, continuous network capacity design is required to fulfill the network requirements of each device. However, continuous optimization of network capacity design for each device within a short time span becomes difficult because of the heavy calculation amount. We introduce device types as groups of devices whose traffic characteristics resemble and optimize network capacity per device type for efficient network capacity design. This paper proposes a method to classify device types by analyzing only encrypted traffic behavior without using payload and packets of specific protocols. In the first stage, general device types, such as IoT and non-IoT, are classified by analyzing packet header statistics using machine learning. Then, in the second stage, connected devices classified as IoT in the first stage are classified into IoT device types, by analyzing a time series of traffic behavior using deep learning. We demonstrate that the proposed method classifies device types by analyzing traffic datasets and outperforms the existing IoT-only device classification methods in terms of the number of types and the accuracy. In addition, the proposed model performs comparable as a state-of-the-art model of traffic classification, ResNet 1D model. The proposed method is suitable to grasp device types in terms of traffic characteristics toward efficient network capacity design in networks where massive devices for various services are connected and the connected devices continuously change.

Recent embedded systems require both traditional machinery control and information processing, such as network and GUI handling. A dual-OS platform consolidates a real-time OS (RTOS) and general-purpose OS (GPOS) to realize efficient software development on one physical processor. Although the dual-OS platform attracts increasing attention, it often suffers from energy inefficiency in the GPOS for guaranteeing real-time responses of the RTOS. This paper proposes an energy minimization method called DVFS virtualization, which allows running multiple DVFS policies dedicated to the RTOS and GPOS, respectively. The experimental evaluation using a commercial microcontroller showed that the proposed hardware could change the supply voltage within 500 ns and reduce the energy consumption of typical applications by 60 % in the best case compared to conventional dual-OS platforms. Furthermore, evaluation using a commercial microprocessor achieved a 15 % energy reduction of practical open-source software at best.

An output voltage estimation and regulation system for a wireless power transfer (WPT) circuit is proposed. Since the fluctuation of a coupling condition and/or a load may vary the voltage supplied with WPT resulting in a malfunction of wireless-powered devices, the output voltage regulation is needed. If the output voltage is regulated by a voltage regulator in a secondary side of the WPT circuit with fixed input power, the voltage regulator wastes the power to regulate the voltage. Therefore the output voltage regulation using a primary-side control, which adjusts the input power depending on the load and/or the coupling condition, is a promising approach for efficient regulation. In addition, it is desirable to eliminate feedback loop from the secondary side to the primary side from the viewpoint of reducing power dissipation and system complexity. The proposed system can estimate and regulate the output voltage independent of both the coupling and the load variation without the feedback loop. An usable range of the coupling coefficient and the load is improved compared to previous works. The validity of the proposed system is confirmed by the SPICE simulator.

The Volterra filter is one of the digital filters that can describe nonlinearity. In this paper, we analyze the dynamic behaviors of an adaptive signal processing system with the Volterra filter for nonwhite input signals by a statistical-mechanical method. Assuming the self-averaging property with an infinitely long tapped-delay line, we derive simultaneous differential equations that describe the behaviors of macroscopic variables in a deterministic and closed form. We analytically solve the derived equations to reveal the effect of the nonwhiteness of the input signal on the adaptation process. The results for the second-order Volterra filter show that the nonwhiteness decreases the mean-square error (MSE) in the early stages of the adaptation process and increases the MSE in the later stages.

The fingerprint verification system is widely used in mobile devices because of fingerprint's distinctive features and ease of capture. Typically, mobile devices utilize small sensors, which have limited area, to capture fingerprint. Meanwhile, conventional fingerprint feature extraction methods need detailed fingerprint information, which is unsuitable for those small sensors. This paper proposes a novel fingerprint verification method for small area sensors based on deep learning. A systematic method combines deep convolutional neural network (DCNN) in a Siamese network for feature extraction and XGBoost for fingerprint similarity training. In addition, a padding technique also introduced to avoid wraparound error problem. Experimental results show that the method achieves an improved accuracy of 66.6% and 22.6% in the FingerPassDB7 and FVC2006DB1B dataset, respectively, compared to the existing methods.

In this paper, we investigate the evolution of an optical network architecture and discuss the future direction of research on optical network design and control. We review existing research on optical network design and control and present some open challenges. One of the important open challenges lies in multilayer resource optimization including IT and optical network resources. We propose an adaptive joint optimization method of IT resources and optical spectrum under time-varying traffic demand in optical networks while avoiding an increase in operation cost. We formulate the problem as mixed integer linear programming and then quantitatively evaluate the trade-off relationship between the optimality of reconfiguration and operation cost. We demonstrate that we can achieve sufficient network performance through the adaptive joint optimization while suppressing an increase in operation cost.

The novel optical path routing architecture named flexible waveband routing networks is reviewed in this paper. The nodes adopt a two-stage path routing scheme where wavelength selective switches (WSSs) bundle optical paths and form a small number of path groups and then optical switches without wavelength selectivity route these groups to desired outputs. Substantial hardware scale reduction can be achieved as the scheme enables us to use small scale WSSs, and even more, share a WSS by multiple input cores/fibers through the use of spatially-joint-switching. Furthermore, path groups distributed over multiple bands can be switched by these optical switches and thus the adaptation to multi-band transmission is straightforward. Network-wide numerical simulations and transmission experiments that assume multi-band transmission demonstrate the validity of flexible waveband routing.

To cope with the drastic increase in traffic, space division multiplexing elastic optical networks (SDM-EONs) have been investigated. In multicore fiber environments that realize SDM-EONs, crosstalk (XT) occurs between optical paths transmitted in the same frequency slots of adjacent cores, and the quality of the optical paths is degraded by the mutual influence of XT. To solve this problem, we propose a core and spectrum assignment method that introduces the concept of prohibited frequency slots to protect the degraded optical paths. First-fit-based spectrum resource allocation algorithms, including our previous study, have the problem that only some frequency slots are used at low loads, and XT occurs even though sufficient frequency slots are available. In this study, we propose a core and spectrum assignment method that introduces the concepts of “adjacency criterion” and “XT budget” to suppress XT at low and middle loads without worsening the path blocking rate at high loads. We demonstrate the effectiveness of the proposed method in terms of the path blocking rate using computer simulations.

In this paper we obtain a new method to compute the correlation values of two arbitrary sequences defined by a mapping from F4n to F4. We apply this method to demonstrate that the usual nonbinary maximal length sequences have almost ideal correlation under the canonical complex correlation definition and investigate some decimations giving good cross correlation. The techniques we develop are of independent interest for future investigation of sequence design and related problems, including Boolean functions.

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.

The robust recursive identification method of ARX models is proposed using the beta divergence. The proposed parameter update law suppresses the effect of outliers using a weight function that is automatically determined by minimizing the beta divergence. A numerical example illustrates the efficacy of the proposed method.

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.

Beyond 5G is the next generation mobile communication system expected to be used from around 2030. Services in the 2030s will be composed of multiple systems provided by not only the conventional networking industry but also a wide range of industries. However, the current mobile communication system architecture is designed with a focus on networking performance and not oriented to accommodate and optimize potential systems including service management and applications, though total resource optimizations and service level performance enhancement among the systems are required. In this paper, a new concept of the Beyond 5G cross-industry service platform (B5G-XISP) is presented on which multiple systems from different industries are appropriately organized and optimized for service providers. Then, an architecture of the B5G-XISP is proposed based on requirements revealed from issues of current mobile communication systems. The proposed architecture is compared with other architectures along with use cases of an assumed future supply chain business.

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.

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.

In this paper, we investigate a wireless-powered relays assisted batteryless IoT network based on the non-linear energy harvesting model, where there exists an energy service provider constituted by the hybrid access point (HAP) and an IoT service provider constituted by multiple clusters. The HAP provides energy signals to the batteryless devices for information backscattering and the wireless-powered relays for energy harvesting. The relays are deployed to assist the batteryless devices with the information transmission to the HAP by using the harvested energy. To model the energy interactions between the energy service provider and IoT service provider, we propose a Stackelberg game based framework. We aim to maximize the respective utility values of the two providers. Since the utility maximization problem of the IoT service provider is non-convex, we employ the fractional programming theory and propose a block coordinate descent (BCD) based algorithm with successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques to solve it. Numerical simulation results confirm that compared to the benchmark schemes, our proposed scheme can achieve larger utility values for both the energy service provider and IoT service provider.

Frame synchronization detection before data transmission is an important module which directly affects the lifetime and coexistence of underwater acoustic communication (UAC) networks, where linear frequency modulation (LFM) is a frame preamble signal commonly used for synchronization. Unlike terrestrial wireless communications, strong bursty noise frequently appears in UAC. Due to the long transmission distance and the low signal-to-noise ratio, strong short-distance bursty noise will greatly reduce the accuracy of conventional fractional fourier transform (FrFT) detection. We propose a multi-segment verification fractional fourier transform (MFrFT) preamble detection algorithm to address this challenge. In the proposed algorithm, 4 times of adjacent FrFT operations are carried out. And the LFM signal identifies by observing the linear correlation between two lines connected in pair among three adjacent peak points, called ‘dual-line-correlation mechanism’. The accurate starting time of the LFM signal can be found according to the peak frequency of the adjacent FrFT. More importantly, MFrFT do not result in an increase in computational complexity. Compared with the conventional FrFT detection method, experimental results show that the proposed algorithm can effectively distinguish between signal starting points and bursty noise with much lower error detection rate, which in turn minimizes the cost of retransmission.

Physical contact (PC) optical connectors realize long-term stability by maintaining contact with the optical fiber even during temperature fluctuations caused by the microscopic displacement of the ferrule endface. With multicore fiber (MCF) connectors, stable PC connection conditions need to be newly investigated because MCFs have cores other than at the center. In this work, we investigated the microscopic displacement of connected ferrule endfaces using the finite element method (FEM). As a result, by using MCF connectors with an apex offset, we found that the allowable fiber undercut where all the cores make contact is slightly smaller than that of single-mode fiber (SMF) connectors. Therefore, we propose a new equation for determining the allowable fiber undercut of MCF connectors. We also fabricated MCF connectors with an allowable fiber undercut and confirmed their reliability using the composite temperature/humidity cyclic test.