IGCC for PAPR Reduction in OFDM Systems Over the Nonlinearity of SSPA and Wireless Fading Channels

High peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems seriously impacts power efficiency in radio frequency section due to the nonlinearity of high-power amplifiers. In this article, an improved gamma correction companding (IGCC) is proposed for PAPR reduction and investigated under multipath fading channels. It is shown that the proposed IGCC provides a significant PAPR reduction while improving power spectral levels and error performances when compared with the previous gamma correction companding. IGCC outperforms existing companding methods when a nonlinear solid-state power amplifier (SSPA) is considered. Additionally, with the introduction of \(\alpha , \beta , \gamma \), and \(\varDelta \) parameters, the improved companding can offer more flexibility in the PAPR reduction and therefore achieves a better trade-off among the PAPR gain, bit error rate (BER), and power spectral density (PSD) performance. Moreover, IGCC improves the BER and PSD performances by minimizing the nonlinear companding distortion. Further, IGCC improves signal-to-noise ratio (SNR) degradation (\(\varDelta _{\mathrm{SNR}}\)) and total degradation performances by 12.2 and 12.8 dB, respectively, considering an SSPA with input power back-off of 3.0 dB. Computer simulation reveals that the performances of IGCC are independent of the modulation schemes and works with arbitrary number of subcarriers (N), while it does not increase computational complexity when compared with the existing companding schemes used for PAPR reduction in OFDM systems.     

Adaptive Filtering Techniques using Cyclic Prefix in OFDM Systems for Multipath Fading Channel Prediction

This paper presents adaptive channel prediction techniques for wireless orthogonal frequency division multiplexing (OFDM) systems using cyclic prefix (CP). The CP not only combats intersymbol interference, but also precludes requirement of additional training symbols. The proposed adaptive algorithms exploit the channel state information contained in CP of received OFDM symbol, under the time-invariant and time-variant wireless multipath Rayleigh fading channels. For channel prediction, the convergence and tracking characteristics of conventional recursive least squares (RLS) algorithm, numeric variable forgetting factor RLS (NVFF-RLS) algorithm, Kalman filtering (KF) algorithm and reduced Kalman least mean squares (RK-LMS) algorithm are compared. The simulation results are presented to demonstrate that KF algorithm is the best available technique as compared to RK-LMS, RLS and NVFF-RLS algorithms by providing low mean square channel prediction error. But RK-LMS and NVFF-RLS algorithms exhibit lower computational complexity than KF algorithm. Under typical conditions, the tracking performance of RK-LMS is comparable to RLS algorithm. However, RK-LMS algorithm fails to perform well in convergence mode. For time-variant multipath fading channel prediction, the presented NVFF-RLS algorithm supersedes RLS algorithm in the channel tracking mode under moderately high fade rate conditions. However, under appropriate parameter setting in \(2\times 1\) space–time block-coded OFDM system, NVFF-RLS algorithm bestows enhanced channel tracking performance than RLS algorithm under static as well as dynamic environment, which leads to significant reduction in symbol error rate.     

Off-Grid DOA Estimation Via Real-Valued Sparse Bayesian Method in Compressed Sensing

A novel real-valued sparse Bayesian method for the off-grid direction-of-arrival (DOA) estimation is proposed in compressed sensing (CS). The off-grid model is reformulated by the second-order Taylor expansion to reduce modeling error caused by mismatch. To apply the Bayesian perspective in CS conveniently, complex data are addressed to yield a real-valued problem by utilizing a unitary transformation. By assuming that sources among snapshots are independent and share the same sparse prior, joint sparsity is exploited for DOA estimation. Specifically, a full posterior density function can be provided in the Bayesian framework. The convergence rate and convergence stability of the proposed method can be guaranteed in the iterative procedure. Simulation results show superior performance of the proposed method as compared with existing methods.     

Instantaneous Frequency Estimation of Nonlinear Frequency-Modulated Signals Under Strong Noise Environment

Instantaneous frequency (IF) is the most important parameter of a signal, which is an important representation of non-stationary signals, such as frequency-modulated signals. Usually, signals are received with noises. Under noise environment, the conventional IF estimation methods for nonlinear frequency-modulated (NLFM) signal cannot work. In this paper, we focus on how to extract IF of NLFM signal under strong noise environment. First, a modified S-method (SM) is proposed to represent the time–frequency (TF) characteristic. The modified SM uses an adaptive smooth window. The symmetric window is used for multi-component signals and asymmetric window for mono-component signals. The modified SM enhances the TF energy concentration and suppresses the cross-terms effectively. Then, the Viterbi algorithm is used to extract the IF from the TF plane. Viterbi algorithm is a hidden Markov chain approach, which is proposed here as the IF estimator. The proposed method is utilized for various types of NLFM signals. Simulation results demonstrate the efficiency and validity of the proposed method under strong noise environment.     

An Evolutionary Method for Synthesizing Low-Sensitivity Lossless Matching Networks

Whereas the present practice of designing matching networks for antennas is limited to conventional topologies, requiring a significant amount of domain knowledge, evolutionary algorithms can be used for automatically identifying unconventional designs that are more effective than would otherwise be developed. In this work, an automatic method to design lossless matching networks driven by an evolutionary algorithm (EA) that considers the sensitivities of the network parameters during the synthesis process is presented. To this end, a closed-form expression for the transducer power gain (TPG) sensitivity with respect to the component values is employed in such a way that the effects of the components tolerance on the matching network performance can easily be quantified. A 3D data structure based on the adjacency matrix is conveniently used to represent any type of network topologies. The proposed EA employs a novel set of topology variation operators, tailored for changing the circuit topology, and an association step, with the aim of reducing the number of nodes of the matching circuit. The efficiency of the proposed EA is tested in the synthesis of an impedance matching network for a VHF monopole whip antenna. This study’s results indicate a matching bandwidth improvement, a more uniformly distributed TPG along the operation frequency band and a more stable TPG regarding the components tolerance compared to the results obtained by previous approaches.     

A Novel SAO-based Filtering Technique for Reduction in Temporal Flickering Artifacts in H.265/HEVC

This paper presents a novel filtering technique based on sample adaptive offset (SAO) in H.265/high-efficiency video coding (HEVC) for reduction in the temporal flickering artifacts and improving the coding performance. SAO is a newly introduced technique for in-loop filtering in H.265/HEVC, which derives the offsets independently for each frame in the spatial domain without considering temporal frame correlation. As a result, the temporal distortion artifacts which will have a negative effect on the subjective quality, such as flickering artifacts, cannot be effectively addressed. In this paper, the rate-distortion optimization of the newly developed SAO method, referred to as Inter-SAO, is performed on the residual samples between adjacent frames. Inter-SAO and SAO in the reference software of H.265/HEVC (i.e., the test model HM) are then combined to form the novel in-loop filter-based method, denoted as 3D-SAO filtering method, where both spatial information and temporal information are effectively utilized to reduce the overall distortion in reconstructed videos. Compared with the SAO in HM, 3D-SAO has demonstrated its advanced performance for flickering artifacts suppression. Furthermore, 3D-SAO improves the coding efficiency compared with the SAO in HM with a performance gain of up to 0.91 dB in \(\Delta PSNR\), 1.74 dB in \(\Delta PSPNR\) and 7.33 % in BD-rate reduction.     

Concurrent Knowledge Extraction in Public-Key Models

Knowledge extraction is a fundamental notion, modeling machine possession of values (witnesses) in a computational complexity sense and enabling one to argue about the internal state of a party in a protocol without probing its internal secret state. However, when transactions are concurrent, say over the Internet, with players possessing public keys (as is common in cryptography), assuring that entities “know” what they claim to know, where adversaries may be well coordinated across different transactions, turns out to be much more subtle and in need of re-examination. In such settings, mixing the public-key structure as part of the language and statements is a natural adversarial strategy. Here, we investigate how to formally treat knowledge possession by parties interacting concurrently in the public-key model. More technically, we look into the relative power of the notion of “concurrent knowledge extraction” (CKE) for concurrent zero knowledge (CZK) in the bare public-key (BPK) model, where the language and statements being proved can be dynamically and adaptively chosen by the prover and may be possibly based on verifiers’ public keys. By concrete attacks against some existing natural protocols, we first show that concurrent soundness and normal arguments of knowledge do not guarantee concurrent verifier security in the public-key setting. Here, roughly speaking, concurrent verifier security says that the malicious concurrent prover should “know" all the witnesses to all the possibly public-key-related statements adaptively chosen and successfully proved in the concurrent sessions. These concrete attacks serve as a good motivation for understanding “possession of knowledge” for concurrent transactions with registered public keys, i.e., the subtleties of concurrent knowledge extraction in the public-key model. This motivates us to introduce and formalize the notion of CKE, along with clarifications of various subtleties. Two implementations are then presented for constant-round concurrently knowledge extractable concurrent zero-knowledge (CZK–CKE) argument for \(\mathcal {NP}\) in the BPK model: One protocol is generic and based on standard polynomial-time assumptions, whereas the other protocol is computationally efficient and employs complexity leveraging in a novel way. Both protocols can be practically instantiated for some specific number-theoretic languages without going through general \(\mathcal {NP}\)-reductions. Of independent interest are the discussions about the subtleties surrounding the fundamental structure of Feige–Shamir zero knowledge in the BPK model.     

Linearity of Air-Biased Coherent Detection for Terahertz Time-Domain Spectroscopy

The performance of air-biased coherent detection (ABCD) in a broadband two-color laser-induced air plasma system for terahertz time-domain spectroscopy (THz-TDS) has been investigated. Fundamental parameters of the ABCD detection, including signal-to-noise ratio (SNR), dynamic range (DR), and linearity of detection have been characterized. Moreover, the performance of a photomultiplier tube (PMT) and an avalanche photodiode (APD) as photodetector in the ABCD have been compared. We have observed nonlinear behavior of PMT detector, which leads to artificial gain factor in TDS spectroscopy. The APD turns out to have superior linearity and three times higher dynamic compared to the PMT.     

Generation of Intense Narrow-Band Tunable Terahertz Radiation from Highly Bunched Electron Pulse Train

We present the analysis and start-to-end simulation of an intense narrow-band terahertz (THz) source with a broad tuning range of radiation frequency, using a single-pass free electron laser (FEL) driven by a THz-pulse-train photoinjector. The fundamental radiation frequency, corresponding to the spacing between the electron microbunches, can be easily tuned by varying the spacing time between the laser micropulses. Since the prebunched electron beam is highly bunched at the first several harmonics, with the harmonic generation technique, the radiation frequency range can be further enlarged by several times. The start-to-end simulation results show that this FEL is capable of generating a few tens megawatts power, several tens micro-joules pulse energy, and a few percent bandwidth at the frequencies of 0.5–5 THz. In addition, several practical issues are considered.     

Further Characterization of 394-GHz Gyrotron FU CW GII with Additional PID Control System for 600-MHz DNP-SSNMR Spectroscopy

A 394-GHz gyrotron, FU CW GII, has been designed at the University of Fukui, Japan, for dynamic nuclear polarization (DNP)-enhanced solid-state nuclear magnetic resonance (SSNMR) experiments at 600-MHz ¹H resonant frequency. After installation at the Institute for Protein Research (IPR), Osaka University, Japan, a PID feedback control system was equipped to regulate the electron gun heater current for stabilization of the electron beam current, which ultimately achieved stabilization of output power when operating in continuous wave (CW) mode. During exploration to further optimize operating conditions, a continuous tuning bandwidth of approximately 1 GHz was observed by varying the operating voltage at a fixed magnetic field. In the frequency range required for positive DNP enhancement, the output power was improved by increasing the magnetic field and the operating voltage from their initial operational settings. In addition, fine tuning of output frequency by varying the cavity cooling water temperature was demonstrated. These operating conditions and ancillary enhancements are expected to contribute to further enhancement of SSNMR signal.