In this study, a contextual multi-armed bandit (CMAB)-based decentralized channel exploration framework disentangling a channel utility function (i.e., reward) with respect to contending neighboring access points (APs) is proposed. The proposed framework enables APs to evaluate observed rewards compositionally for contending APs, allowing both robustness against reward fluctuation due to neighboring APs' varying channels and assessment of even unexplored channels. To realize this framework, we propose contention-driven feature extraction (CDFE), which extracts the adjacency relation among APs under contention and forms the basis for expressing reward functions in disentangled form, that is, a linear combination of parameters associated with neighboring APs under contention). This allows the CMAB to be leveraged with a joint linear upper confidence bound (JLinUCB) exploration and to delve into the effectiveness of the proposed framework. Moreover, we address the problem of non-convergence — the channel exploration cycle — by proposing a penalized JLinUCB (P-JLinUCB) based on the key idea of introducing a discount parameter to the reward for exploiting a different channel before and after the learning round. Numerical evaluations confirm that the proposed method allows APs to assess the channel quality robustly against reward fluctuations by CDFE and achieves better convergence properties by P-JLinUCB.

In this paper, we propose new radio access network (RAN) architecture for reliable millimeter-wave (mmWave) communications, which has the flexibility to meet users' diverse and fluctuating requirements in terms of communication quality. This architecture is composed of multiple radio units (RUs) connected to a common distributed unit (DU) via fronthaul links to virtually enlarge its coverage. We further present grant-free non-orthogonal multiple access (GF-NOMA) for low-latency uplink communications with a massive number of users and robust coordinated multi-point (CoMP) transmission using blockage prediction for uplink/downlink communications with a high data rate and a guaranteed minimum data rate as the technical pillars of the proposed RAN. The numerical results indicate that our proposed architecture can meet completely different user requirements and realize a user-centric design of the RAN for beyond 5G/6G.

For intelligent vision measurement, the geometric image feature extraction is an essential issue. Contour primitive of interest (CPI) means a regular-shaped contour feature lying on a target object, which is widely used for geometric calculation in vision measurement and servoing. To realize that the CPI extraction model can be flexibly applied to different novel objects, the one-shot learning based CPI extraction can be implemented with deep convolutional neural network, by using only one annotated support image to guide the CPI extraction process. In this paper, we propose a multi-stage contour primitives of interest extraction network (MS-CPieNet), which uses the multi-stage strategy to improve the discrimination ability of CPI and complex background. Second, the spatial non-local attention module is utilized to enhance the deep features, by globally fusing the image features with both short and long ranges. Moreover, the dense 4-direction classification is designed to obtain the normal direction of the contour, and the directions can be further used for the contour thinning post-process. The effectiveness of the proposed methods is validated by the experiments with the OCP and ROCM datasets. A 2-D measurement experiments are conducted to demonstrate the convenient application of the proposed MS-CPieNet.

Recently, multi-modal fusion methods based on remote sensing data and social sensing data have been widely used in the field of urban region function recognition. However, due to the high complexity of noise problem, most of the existing methods are not robust enough when applied in real-world scenes, which seriously affect their application value in urban planning and management. In addition, how to extract valuable periodic feature from social sensing data still needs to be further study. To this end, we propose a multi-modal fusion network guided by feature co-occurrence for urban region function recognition, which leverages the co-occurrence relationship between multi-modal features to identify abnormal noise feature, so as to guide the fusion network to suppress noise feature and focus on clean feature. Furthermore, we employ a graph convolutional network that incorporates node weighting layer and interactive update layer to effectively extract valuable periodic feature from social sensing data. Lastly, experimental results on public available datasets indicate that our proposed method yeilds promising improvements of both accuracy and robustness over several state-of-the-art methods.

This paper proposes a reference-clock-less quick-start-up CDR that resumes from a stand-by state only with a 4-bit preamble utilizing a phase generator with an embedded Time-to-Digital Converter (TDC). The phase generator detects 1-UI time interval by using its internal TDC and works as a self-tunable digitally-controlled delay line. Once the phase generator coarsely tunes the recovered clock period, then the residual time difference is finely tuned by a fine Digital-to-Time Converter (DTC). Since the tuning resolution of the fine DTC is matched by design with the time resolution of the TDC that is used as a phase detector, the fine tuning completes instantaneously. After the initial coarse and fine delay tuning, the feedback loop for frequency tracking is activated in order to improve Consecutive Identical Digits (CID) tolerance of the CDR. By applying the frequency tracking architecture, the proposed CDR achieves more than 100bits of CID tolerance. A prototype implemented in a 65nm bulk CMOS process operates at a 0.9-2.15Gbps continuous rate. It consumes 5.1-8.4mA in its active state and 42μA leakage current in its stand-by state from a 1.0V supply.

To facilitate the reuse of environmental waste heat in our society, we have developed high-efficiency flexible thermoelectric power generators (TEPGs). In this study, we investigated the thermoelectromotive force (TEMF) and output power of a prototype device with 50 pairs of Π-type structures using a homemade measurement system for flexible TEPGs in order to evaluate their characteristics along the thickness direction. The prototype device consisted of C fabrics (CAFs) used as p-type materials, NiCu fabrics (NCFs) used as n-type materials, and Ag fabrics (AGFs) used as metal electrodes. Applying a temperature difference of 5K, we obtained a TEMF of 150μV and maximum output power of 6.4pW. The obtained TEMF was smaller than that expected from the Seebeck coefficients of each fabric, which is considered to be mainly because of the influence of contact thermal resistance at the semiconductor-fabric/AGF interfaces.

In this paper, a high-efficiency high-power rectenna with a bridge diode and the diode on antenna (DoA) topology is discussed. First, the topologies of rectifiers and rectennas are discussed to indicate the direction for obtaining highly efficient rectification. Rectifiers with well-matched diode pairs, as double voltage and bridge rectifiers, can reactively terminate even order harmonics, and is suitable for highly efficient operation. A rectenna with the DoA topology is suitable for a direct connection between the highly functional antenna and the rectifier diodes to remove lossy circuit portions. Next, the formulas for the rectification efficiency of the bridge rectifier are demonstrated with the behavioral model. The indicated formulas clarify the fundamental limitation on the rectification efficiency, which is the design goal in case of the DoA topology. Finally, we demonstrate a 5.8 GHz band 1 W rectenna with the bridge diode and the DoA topology. The bridge rectifier that is directly connected to the inductive high-impedance antenna achieved a rectification efficiency of 92.8% at an input power of 1 W. This is close to the fundamental limitation due to the diode performance.

This paper focuses on verification for reactive system specifications. A reactive system is an open system that continuously interacts with an uncontrollable external environment, and it must often be highly safe and reliable. However, realizability checking for a given specification is very costly, so we need effective methods to detect and analyze defects in unrealizable specifications to refine them efficiently. We introduce a systematic characterization on necessary conditions of realizability. This characterization is based on quantifications for inputs and outputs in early and late behaviors and reveals four essential aspects of realizability: exhaustivity, strategizability, preservability and stability. Additionally, the characterization derives new necessary conditions, which enable us to classify unrealizable specifications systematically and hierarchically.

A power-scalable wideband distributed amplifier is proposed. For reducing the power consumption of this power-hungry amplifier, it is efficient to lower the supply voltage. However, there is a hurdle owing to the transistor threshold voltage. In this work, a CMOS deeply depleted channel process is employed to overcome the hurdle.

The characteristics of antenna array, like sensor location, gain and phase response are rarely perfectly known in realistic situations. Location errors usually have a serious impact on the DOA (direction of arrival) estimation. In this paper, a novel array location calibration method of MUSIC (multiple signal classification) algorithm based on the virtual interpolated array is proposed. First, the paper introduces the antenna array positioning scheme. Then, the self-calibration algorithm of FIR-Winner filter based on virtual interpolation array is derived, and its application restriction are also analyzed. Finally, by simulating the different location errors of antenna array, the effectiveness of the proposed method is validated.

In order to obtain a feasible schedule of a hard real-time system, heuristic based techniques are the solution of choice. In the last few years, optimization solvers have gained attention from research communities due to their capability of handling large number of constraints. Recently, some works have used integer linear programming (ILP) for solving mono processor scheduling of real-time systems. In fact, ILP is commonly used for static scheduling of multiprocessor systems. However, two main solvers are used to solve the problem indistinctly. But, which one is the best for obtaining a schedulable system for hard real-time systems? This paper makes a comparison of two well-known optimization software packages (CPLEX and GUROBI) for the problem of finding a feasible schedule on monoprocessor hard real-time systems.

This brief presents a slope analog-digital converter (ADC)-based supply voltage monitor (SVM) for biofuel-cell-powered supply-sensing systems operating in a supply voltage range of 0.18-0.35V. The proposed SVM is designed to utilize the output of energy harvester extracting power from biological reactions, realizing energy-autonomous sensor interfaces. A burst pulse generator uses a dynamic leakage suppression logic oscillator to generate a stable clock signal under the sub-threshold region for pulse counting. A slope-based voltage-to-time converter is employed to generate a pulse width proportional to the supply voltage with high linearity. The test chip of the proposed SVM is implemented in 180-nm CMOS technology with an active area of 0.018mm2. It consumes 2.1nW at 0.3V and achieves a conversion time of 117-673ms at 0.18-0.35V with a nonlinearity error of -5.5/+8.3mV, achieving an energy-efficient biosensing frontend.

In this research, we investigated the metal-ferroelectric-semiconductor field-effect transistors (MFSFETs) with 5nm thick nondoped HfO2 gate insulator by decreasing the sputtering power for Pt gate electrode deposition. The leakage current was effectively reduced to 2.6×10-8A/cm2 at the voltage of -1.5V by the sputtering power of 40W for Pt electrode deposition. Furthermore, the memory window (MW) of 0.53V and retention time over 10 years were realized.

Graphene has been expected as an alternative material for copper interconnects in which resistance increases and reliability deteriorates in nanoscale. While the principle advantages are verified by simulations and experiments, they have not been put into practical use due to the immaturity of the manufacturing process leading to mass production. On the other hand, recent steady progress in the fabrication process has increased the possibility of practical application. In this paper, I will review the recent advances and the latest prospects for conductor applications of graphene centered on interconnects. The possibility of further application utilizing the unique characteristics of graphene is discussed.

In this letter, we give a new construction of a family of sequences of period pk-1 with low correlation value by using additive and multiplicative characters over Galois rings. The new constructed sequence family has family size (M-1)(pk-1)rpkr(e-1) and alphabet size Mpe. Based on the characters sum over Galois rings, an upper bound on the correlation of this sequence family is presented.

This paper presents a high-resolution and low-insertion-loss CMOS hybrid phase shifter with a nonuniform matching technique for satellite communication (SATCOM). The proposed hybrid phase shifter includes three 45° coarse phase-shifting stages and one 45° fine phase-tuning stage. The coarse stages are realized by bridged-T switch-type phase shifters (STPS) with 45° phase steps. The fine-tuning stage is based on a reflective-type phase shifter (RTPS) with two identical LC load tanks for phase tuning. A 0.8° phase resolution is realized by this work to support fine beam steering for the SATCOM. To further reduce the chain insertion loss, a nonuniform matching technique is utilized at the coarse stages. For the coarse and fine stages, the measured RMS gain errors at 29GHz are 0.7dB and 0.3dB, respectively. The measured RMS phase errors are 0.8° and 0.4°, respectively. The proposed hybrid phase shifter maintains return losses of all phase states less than -12dB from 24GHz to 34GHz. The presented hybrid phase shifter is fabricated in a standard 65-nm CMOS technology with a 0.14mm2 active area.

Stance prediction on social media aims to infer the stances of users towards a specific topic or event, which are not expressed explicitly. It is of great significance for public opinion analysis to extract and determine users' stances using user-generated content on social media. Existing research makes use of various signals, ranging from text content to online network connections of users on these platforms. However, it lacks joint modeling of the heterogeneous information for stance prediction. In this paper, we propose a self-supervised heterogeneous graph contrastive learning framework for stance prediction in online debate forums. Firstly, we perform data augmentation on the original heterogeneous information network to generate an augmented view. The original view and augmented view are learned from a meta-path based graph encoder respectively. Then, the contrastive learning among the two views is conducted to obtain high-quality representations of users and issues. Finally, the stance prediction is accomplished by matrix factorization between users and issues. The experimental results on an online debate forum dataset show that our model outperforms other competitive baseline methods significantly.

Unique spatial eigenmodes for the spherical coordinate system are shown to be successfully synthesized by properly allocated combinations of current distributions along θ' and φ' on a spherical conformal array. The allocation ratios are analytically found in a closed form with a matrix that relates the expansion coefficients of the current to its radiated field. The coefficients are obtained by general Fourier expansion of the current and the mode expansion of the field, respectively. The validity of the obtained formulas is numerically confirmed, and important effects of the sphere radius and the degrees of the currents on the radiated fields are numerically explained. The formulas are used to design six current distributions that synthesize six unique eigenmodes. The accuracy of the synthesized fields is quantitatively investigated, and the accuracy is shown to be remarkably improved by more than 27dB with two additional kinds of current distributions.

A sub-Terahertz band is envisioned to play a great role in 6G to achieve extreme high data-rate communication. In addition to very wide band transmission, we need spatial multiplexing using a hybrid MIMO system. A recently presented paper, however, reveals that the number of observed multipath components in a sub-Terahertz band is very few in indoor environments. A channel with few multipath components is called sparse. The number of layers (streams), i.e. multiplexing gain in a MIMO system does not exceed the number of multipaths. The sparsity may restrict the spatial multiplexing gain of sub-Terahertz systems, and the poor multiplexing gain may limit the data rate of communication systems. This paper describes fundamental considerations on sub-Terahertz MIMO spatial multiplexing in indoor environments. We examined how we should steer analog beams to multipath components to achieve higher channel capacity. Furthermore, for different beam allocation schemes, we investigated eigenvalue distributions of a channel Gram matrix, power allocation to each layer, and correlations between analog beams. Through simulation results, we have revealed that the analog beams should be steered to all the multipath components to lower correlations and to achieve higher channel capacity.

5G service has been launched in various countries, and research for the beyond 5G is already underway actively around the world. In beyond 5G, it is expected to expand the various capabilities of communication technologies to cover further wide use cases from 5G. As a candidate elemental technology, cell free massive MIMO has been widely researched and shown its potential to enhance the capabilities from various aspects. However, for deploying this technology in reality, there are still many technical issues such as a cost of distributing antenna and installing fronthaul, and also the scalability aspects. This paper surveys research trends of cell free massive MIMO, especially focusing on the deployment challenges with an introduction to our specific related research activities including some numerical examples.