When confirming the ACLR (adjacent channel leakage power ratio), which are representative indicators of distortion in the design of PA (power amplifier), it is well known how to calculate the AM-AM/PM characteristics of PA, input time series data of modulated signals, and analyze the output by Fourier analysis. In 5G (5th generation) mobile phones, not only QPSK (quadrature phase shift keying) modulation but also 16QAM (quadrature modulation), 64QAM, and 256QAM are becoming more multivalued as modulation signals. In addition, the modulation band may exceed 100MHz, and the amount of time series data increases, and the increase in calculation time becomes a problem. In order to shorten the calculation time, calculating the total amount of distortion generated by PA from the probability density of the modulation signal and the AM (amplitude modulation)-AM/PM (phase modulation) characteristics of PA is considered. For the AM-AM characteristics of PA, in this paper, IMD3 (inter modulation distortion 3) obtained from probability density and IMD3 by Fourier analysis, which are often used so long, are compared. As a result, it was confirmed that the result of probability density analysis is similar to that of Fourier analysis, when the nonlinearity is somewhat small. In addition, the agreement between the proposed method and the conventional method was confirmed with an error of about 2.0dB of ACLR using the modulation waves with a bandwidth of 5MHz, RB (resource block) being 25, and QPSK modulation.

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.

High-speed flash ADCs are useful in high-speed applications such as communication receivers. Due to offset voltage variation in the sub-micron processes, the power consumption and the area increase significantly to suppress variation. As an alternative to suppressing the variation, we have developed a flash ADC architecture that selects the comparators based on offset voltage ranking for reference generation. Specifically, with the order statistics as a basis, our method selects the minimum number of comparators to obtain equally spaced reference values. Because the proposed ADC utilizes offset voltages as references, no resistor ladder is required. We also developed a time-domain sorting mechanism for the offset voltages to achieve on-chip comparator selection. We first perform a detailed analysis of the order statistics based selection method and then design a 4-bit ADC in a commercial 65-nm process and perform transistor-level simulation. When using 127 comparators, INLs of 20 virtual chips are in the range of -0.34LSB/+0.29LSB to -0.83LSB/+0.74LSB, and DNLs are in the range of -0.33LSB/+0.24LSB to -0.77LSB/+1.18LSB at 1-GS/s operation. Our ADC achieves the SNDR of 20.9dB at Nyquist-frequency input and the power consumption of 0.84mW.

This paper addresses a distributed filter over wireless sensor networks for optimal estimation. A distributed filter over the networks allows all local estimators to calculate optimal estimates with a scalable communication cost. Outputs of the distributed filter for the optimal estimation can be denoted as a solution of a consensus optimization problem. Thus, the distributed filter is designed based on distributed alternating direction method of multipliers (ADMM). The remarkable points of the distributed filter based on the ADMM are that: the distributed filter has a faster convergence rate than distributed subgradient projection algorithm; the weight, which is optimized by a semidefinite programming problem, accelerates the convergence rate of the proposed method.

In this paper, a passive multiple-input multiple-output (MIMO) radar system with widely separated antennas that estimates the positions and velocities of multiple moving targets by utilizing time delay (TD) and doppler shift (DS) measurements is proposed. Passive radar systems can detect targets by using multiple uncoordinated and un-synchronized illuminators and we assume that all the measurements including TD and DS have been known by a preprocessing method. In this study, the algorithm can be divided into three stages. First, based on location information within a certain range and utilizing the DBSCAN cluster algorithm we can obtain the initial position of each target. In the second stage according to the correlation between the TD measurements of each target in a specific receiver and the DSs, we can find the set of DS measurements for each target. Therefore, the initial speed estimated values can be obtained employing the least squares (LS) method. Finally, maximum likelihood (ML) estimation of a first-order Taylor expansion joint TD and DS is applied for a better solution. Extensive simulations show that the proposed algorithm has a good estimation performance and can achieve the Cramér-Rao lower bound (CRLB) under the condition of moderate measurement errors.

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.

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.

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.

To mitigate the interference caused by range sidelobes in multiple-input multiple-output (MIMO) radar, we propose a new method to construct Doppler resilient complementary waveforms from complete complementary code (CCC). By jointly designing the transmit pulse train and the receive pulse weights, the range sidelobes can vanish within a specified Doppler interval. In addition, the output signal-to-noise ratio (SNR) is maximized subject to the Doppler resilience constraint. Numerical results show that the designed waveforms have better Doppler resilience than the previous works.

The Euclidean projection operation is the most complex and time-consuming of the alternating direction method of multipliers (ADMM) decoding algorithms, resulting in a large number of resources when deployed on hardware platforms. We propose a simplified line segment projection algorithm (SLSA) and present the hardware design and the quantization scheme of the SLSA. In simulation results, the proposed SLSA module has a better performance than the original algorithm with the same fixed bitwidths due to the centrosymmetric structure of SLSA. Furthermore, the proposed SLSA module with a simpler structure without hypercube projection can reduce time consuming by up to 72.2% and reduce hardware resource usage by more than 87% compared to other Euclidean projection modules in the experiments.

Codebooks with small maximal cross-correlation amplitudes have important applications in code division multiple access (CDMA) communication, coding theory and compressed sensing. In this letter, we design a new codebook based on a construction of Ramanujan graphs over finite abelian groups. We prove that the new codebook with length K=q+1 and size N=q²+2q+2 is asymptotically optimal with nearly achieving the Levenshtein bound when n=3, where q is a prime power. The parameters of the new codebook are new.

Adaptive multilooking is a critical processing step in multi-temporal interferometric synthetic aperture radar (InSAR) measurement, especially in small temporal baseline subsets. Various amplitude-based adaptive multilook approaches have been proposed for the improvement of interferometric processing. However, the phase signal, which is fundamental in interferometric systems, is typically ignored in these methods. To fully exploit the information in complex SAR images, a nonlocal adaptive multilooking is proposed based on complex covariance matrix in this work. The complex signal is here exploited for the similiarity measurement between two pixels. Given the complexity of objects in SAR images, structure feature detection is introduced to adaptively estimate covariance matrix. The effectiveness and reliability of the proposed approach are demonstrated with experiments both on simulated and real data.