This brief presents A 16/32Gb/s dual-mode transmitter including a linearity calibration loop to maintain amplitude linearity of the SST driver. Linearity detection and corresponding master-slave power supply circuits are designed to implement the proposed architecture. The proposed transmitter is manufactured in a 22nm FD-SOI process. The linearity calibration loop reduces the peak INL errors of the transmitter by 50%, and the RLM rises from 92.4% to 98.5% when the transmitter is in PAM4 mode. The chip area of the transmitter is 0.067mm2, while the proposed linearity enhanced part is 0.05×0.02mm2 and the total power consumption is 64.6mW with a 1.1V power supply. The linearity calibration loop can be detached from the circuit without consuming extra power.

In order to increase communication capacity, the use of millimeter-wave and terahertz-wave bands are being actively explored. Non-radiative dielectric waveguide known as NRD guide is one of promising platform of millimeter-wave integrated circuits thanks to its non-radiative and low loss nature. In order to develop millimeter wave circuits with various functions, various circuit components have to be efficiently designed to meet requirements from application side. In this paper, for efficient design of NRD guide devices, we develop a topology optimal design technique based on function-expansion-method which can express arbitrary structure with arbitrary geometric topology. In the present approach, recently developed two-dimensional full-vectorial finite element method (2D-FVFEM) for NRD guide devices is employed to improve computational efficiency and several evolutionary approaches, which do not require appropriate initial structure depending on a given design problem, are used to optimize design variables, thus, NRD guide devices having desired functions are efficiently obtained without requiring designer's special knowledge.

With the popularity of smart devices, mobile crowdsensing, in which the crowdsensing platform gathers useful data from users of smart devices, e.g., smartphones, has become a prevalent paradigm. Various incentive mechanisms have been extensively adopted for the crowdsensing platform to incentivize users of smart devices to offer sensing data. Existing works have concentrated on rewarding smart-device users for their short term effort to provide data without considering the long-term factors of smart-device users and the quality of data. Our previous work has considered the quality of data of smart-device users by incorporating the long-term reputation of smart-device users. However, our previous work only considered a quality maximization problem with budget constraints on one location. In this paper, multiple locations are considered. Stackelberg game is utilized to solve a two-stage optimization problem. In the first stage, the crowdsensing platform allocates the budget to different locations and sets price as incentives for users to maximize the total data quality. In the second stage, the users make efforts to provide data to maximize its utility. Extensive numerical simulations are conducted to evaluate proposed algorithm.

A novel compact 5-pole bandpass filter (BPF) using two different types of resonators, one is coaxial TEM-mode resonator and the other dielectric triple-mode resonator, is proposed in this paper. The coaxial resonator is a simple single-mode resonator, while the triple-mode dielectric resonator (DR) includes one TM01δ mode and two degenerate HE11 modes. An excellent spurious performance of the BPF is obtained due to the different resonant behaviors of these two types of resonators used in the BPF. The coupling scheme of the 5-pole BPF includes two cascade triplets (CTs) which produce two transmission zeros (TZs) and a sharp skirt of the passband. Behaviors of the resonances, the inter-resonance couplings, as well as their tuning methods are investigated in detail. A procedure of mapping the coupling matrix of the BPF to its physical dimensions is developed, and an optimization of these physical dimensions is implemented to achieve best performance of the filter. The designed BPF is operated at 1.84GHz with a bandwidth of 51MHz. The stopband rejection is better than 20dB up to 9.7GHz (about 5.39×f0) except 7.85GHz. Good agreement between the designed and theoretically synthesized responses of the BPF is reached, verifying well the proposed configuration of the BPF and its design method.

Towards realization of a noise-induced synchronization in a natural environment, an experimental study is carried out using the Van der Pol oscillator circuit. We focus on acoustic sounds as a potential source of noise that may exist in nature. To mimic such a natural environment, white noise sounds were generated from a loud speaker and recorded into microphone signals. These signals were then injected into the oscillator circuits. We show that the oscillator circuits spontaneously give rise to synchronized dynamics when the microphone signals are highly correlated with each other. As the correlation among the input microphone signals is decreased, the level of synchrony is lowered monotonously, implying that the input correlation is the key determinant for the noise-induced synchronization. Our study provides an experimental basis for synchronizing clocks in distributed sensor networks as well as other engineering devices in natural environment.

Zero-shot slot filling is a domain adaptation approach to handle unseen slots in new domains without training instances. Previous studies implemented zero-shot slot filling by predicting both slot entities and slot types. Because of the lack of knowledge about new domains, the existing methods often fail to predict slot entities for new domains as well as cannot effectively predict unseen slot types even when slot entities are correctly identified. Moreover, for some seen slot types, those methods may suffer from the domain shift problem, because the unseen context in new domains may change the explanations of the slots. In this study, we propose intrinsic representations to alleviate the domain shift problems above. Specifically, we propose a multi-relation-based representation to capture both the general and specific characteristics of slot entities, and an ontology-based representation to provide complementary knowledge on the relationships between slots and values across domains, for handling both unseen slot types and unseen contexts. We constructed a two-step pipeline model using the proposed representations to solve the domain shift problem. Experimental results in terms of the F1 score on three large datasets—Snips, SGD, and MultiWOZ 2.3—showed that our model outperformed state-of-the-art baselines by 29.62, 10.38, and 3.89, respectively. The detailed analysis with the average slot F1 score showed that our model improved the prediction by 25.82 for unseen slot types and by 10.51 for seen slot types. The results demonstrated that the proposed intrinsic representations can effectively alleviate the domain shift problem for both unseen slot types and seen slot types with unseen contexts.

Federated learning is a promising strategy for indoor localization that can reduce the labor cost of constructing a fingerprint dataset in a distributed training manner without privacy disclosure. However, the traffic generated during the whole training process of federated learning is a burden on the up-and-down link, which leads to huge energy consumption for mobile devices. Moreover, the non-independent and identically distributed (Non-IID) problem impairs the global localization performance during the federated learning. This paper proposes a communication-efficient FedAvg method for federated indoor localization which is improved by the layerwise asynchronous aggregation strategy and layerwise swapping training strategy. Energy efficiency can be improved by performing asynchronous aggregation between the model layers to reduce the traffic cost in the training process. Moreover, the impact of the Non-IID problem on the localization performance can be mitigated by performing swapping training on the deep layers. Extensive experimental results show that the proposed methods reduce communication traffic and improve energy efficiency significantly while mitigating the impact of the Non-IID problem on the precision of localization.

Service chaining is attracting attention as a promising technology for providing a variety of network services by applying virtual network functions (VNFs) that can be instantiated on commercial off-the-shelf servers. The data transmission for each service chain has to satisfy the quality of service (QoS) requirements in terms of the loss probability and transmission delay, and hence the amount of resources for each VNF is expected to be sufficient for satisfying the QoS. However, the increase in the amount of VNF resources results in a high cost for improving the QoS. To reduce the cost of utilizing a VNF, sharing VNF instances through multiple service chains is an effective approach. However, the number of packets arriving at the VNF instance is increased, resulting in a degradation of the QoS. It is therefore important to select VNF instances shared by multiple service chains and to determine the amount of resources for the selected VNFs. In this paper, we propose a cost-effective service chain construction with a VNF sharing model. In the proposed method, each VNF is modeled as an M/M/1/K queueing model to evaluate the relationship between the amount of resources and the loss probability. The proposed method determines the VNF sharing, the VNF placement, the amount of resources for each VNF, and the transmission route of each service chain. For the optimization problem, these are applied according to our proposed heuristic algorithm. We evaluate the performance of the proposed method through a simulation. From the numerical examples, we show the effectiveness of the proposed method under certain network topologies.

Design of distributed energy management systems composed of several agents such as factories and buildings is important for realizing smart cities. In addition, demand response for saving the power consumption is also important. In this paper, we propose a design method of distributed energy management systems with real-time demand response, in which both electrical energy and thermal energy are considered. Here, we use ADMM (Alternating Direction Method of Multipliers), which is well known as one of the powerful methods in distributed optimization. In the proposed method, demand response is performed in real-time, based on the difference between the planned demand and the actual value. Furthermore, utilizing a blockchain is also discussed. The effectiveness of the proposed method is presented by a numerical example. The importance of introducing a blockchain is pointed out by presenting the adverse effect of tampering the actual value.

The ARM TrustZone architecture, which provides hardware-assisted isolation, is widely adopted in mobile and IoT devices. The security of ARM TrustZone relies on the idea of splitting system-on-chip hardware and software into two worlds, namely normal world and secure world. There are legitimate channels at the hardware level that the normal world and the secure world can use to communicate with each other. To protect these channels from being abused, research efforts were invested on restricting the access to these channels from normal world components. Therefore, only predefined and legitimate normal world components can use cross-world communication channels. In this work, we present a study on data covert channels that can bypass such protection mechanisms and smuggle sensitive information. We first analyze causes of the noise in the covert channel between two worlds. Then, we evaluate the accuracy and bandwidth of covert channels built by our PRIME+COUNT method with one built by PRIME+PROBE method. Our results demonstrate that PRIME+COUNT is an effective technique for enabling cross-world covert channels in the ARM TrustZone.

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.

An asymmetric zero correlation zone (A-ZCZ) sequence set can be regarded as a special type of ZCZ sequence set, which consists of multiple sequence subsets. Each subset is a ZCZ sequence set, and have a common zero cross-correlation zone (ZCCZ) between sequences from different subsets. This paper supplements an existing construction of A-ZCZ sequence sets and further improves the research results. Besides, a new construction of A-ZCZ sequence sets is proposed by matrices transformation. The obtained sequence sets are optimal with respect to theoretical bound, and the parameters can be chosen more flexibly, such as the number of subsets and the lengths of ZCCZ between sequences from different subsets. Moreover, as the diversity of the orthogonal matrices and the flexibility of initial matrix, more A-ZCZ sequence sets can be obtained. The resultant sequence sets presented in this paper can be applied to multi-cell quasi-synchronous code-division multiple-access (QS-CDMA) systems, to eliminate the interference not only from the same cell but also from adjacent cells.

The phenomenon known as social polarization, in which a social group splits into two or more groups, can cause division of the society by causing the radicalization of opinions and the spread of misinformation, is particularly significant in online communities. To develop technologies to mitigate the effects of polarization in online social networks, it is necessary to understand the mechanism driving its occurrence. There are some models of social polarization in which network structure and users' opinions change, based on the quantified opinions held by the users of online social networks. However, they are based on the interaction between users connected by online social networks. Current recommendation systems offer information from unknown users who are deemed to have similar interests. We can interpret this situation as being yielded non-local effects brought on by the network system, it is not based on local interactions between users. In this paper, based on the spectral graph theory, which can describe non-local effects in online social networks mathematically, we propose a model of polarization that user behavior and network structure change while influencing each other including non-local effects. We investigate the characteristics of the proposed model. Simultaneously, we propose an index to evaluate the degree of network polarization quantitatively, which is needed for our investigations.

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.

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.

S-parameters of InGaAs/InAlAs triple-barrier resonant tunneling diodes (TBRTDs) were measured up to 67 GHz with various mesa areas and various bias voltages. Admittance data of bare TBRTDs are deembedded and evaluated by getting rid of parasitic components with help of electromagnetic simulations for particular fabricated device structures. Admittance spectroscopy up to 67 GHz is applied for bare TBRTDs for the first time and a Kramers-Kronig relation with Lorentzian function is found to be a consistent model for the admittance especially in cases of low bias conditions. Relaxation time included in the Lorentzian function are tentatively evaluated as the order of several pico second.

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.

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.

Multibeam massive multiple-input multiple-output (MIMO) configuration has been proposed that selects high-power beams in an analog part and uses a blind algorithm, such as the constant-modulus algorithm (CMA), in the digital part. The CMA does not require channel state information. However, when least-squares CMA (LS-CMA) is applied to a quadrature amplitude modulation signal whose amplitude changes, the interference cancellation effect decreases as the modulation order increases. In this paper, a variable-step-size-based CMA (VS-CMA), which modifies the step size of the steepest-descent CMA, is proposed as a blind adaptive algorithm to replace LS-CMA. The basic performance of VS-CMA, its success in cancelling interference, and its effectiveness in multibeam massive MIMO transmission are verified via simulation and compared with other blind algorithms such as independent component analysis, particularly when the data smoothing size is small.