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Signal-to-noise ratio (SNR) analysis is a crucial component of optical system development for space remote sensing instruments. It serves as a quantitative assessment of the imaging quality and radiometric characteristics of space remote sensing. This paper utilizes the working principles and energy transfer principles of space remote sensing instruments to conduct SNR analysis and model development for commonly used spaceborne imagers, spaceborne imaging spectrometers, micro-optical remote sensing instruments, and point-source spatial targets. Additionally, the paper also examines the impact of the presence and width of slits in different space environments on the SNR of space remote sensing instruments. The calculation results indicate that the analysis of Signal-to-Noise Ratio (SNR) for different space remote sensing instruments requires the establishment of distinct SNR models. The magnitude of SNR primarily depends on crucial factors such as optical system quality, detector performance, and the space environment. Therefore, in the instrument design and data processing processes, it is essential to consider how to maximize SNR and establish more accurate corresponding SNR models to provide high-quality remote sensing data.

The number of degrees of freedom (NDF) of the electromagnetic far-fields generated by current sheets under certain noise level in the environment is evaluated numerically by directly sampling on the constellation of the propagation modes in free space. The electric fields in the far region are efficiently calculated by using the far-field approximation, and the effective numbers of degrees of freedom (ENDFs) are obtained through singular value decomposition (SVD) incorporating noises. The results show that ENDF is proportional to the area of the current sheets. Due to the effect of noise, when two current sheets in parallel are closer within a threshold, the differential mode fields will be submerged by the noise, so the ENDF is only determined by the common modes. Although the threshold is signal-to-noise ratio (SNR) dependent, the ENDF provides a clear physical meaning. When two current sheets are located in the same plane, numerical results suggest that the ENDF is mainly determined by the total area of the two current sheets, and the spacing between them almost does not affect the ENDF.

Long-Range networks are increasingly used in smart spaces due to their ability to provide longer communication range while consuming low energy. To facilitate communication among different Long-Range nodes, a gateway is used. For instance, in smart buildings such as airports, railway stations, indoor stadiums, and auditoriums, sensory data from multiple sites are transferred to a base station through a Long-Range gateway. However, when multiple nodes transmit data simultaneously to the gateway, it generates network interference, especially in high-ceiling smart buildings where Long-Range nodes with sensors are attached to monitor the building’s health. In this paper, we present a new method for identifying interfering Long-Range Nodes (LNs) in high-ceiling smart buildings using a classification model. Our approach involves gathering and analyzing network parameters, such as signal-to-noise ratio and received signal strength indicator, from the signals to extract features that the classifier uses for interference classification. The approach categorizes interference based on the number of interfering LNs, with each class representing a distinct number of interfering LNs. We also introduce a push-based mechanism to detect and adjust the power levels of faulty LNs, reducing interference. Our method is cost-effective as it is hardware-independent, making it feasible to implement on the LG platform. Finally, we present a dataset of network interference generated by varying the number of nodes, obstacles, and other parameters. We train the model on the generated dataset and evaluate its effectiveness using a test bed. The experimental results demonstrate that the approach can successfully identify interference nodes in a complex network.

We propose an improved convergence analysis technique that characterizes the distributed learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink communications. Such imperfect communication scenarios arise in the practical deployment of FL in emerging communication systems and protocols. The analysis developed in this paper demonstrates, for the first time, that there is an asymmetry in the detrimental effects of uplink and downlink communications in FL. In particular, the adverse effect of the downlink noise is more severe on the convergence of FL algorithms. Using this insight, we propose improved Signal-to-Noise (SNR) control strategies that, discarding the negligible higher-order terms, lead to a similar convergence rate for FL as in the case of a perfect, noise-free communication channel while incurring significantly less power resources compared to existing solutions. In particular, we establish that to maintain the $\mathcal {O}\left({\frac {1}{\sqrt {K}}}\right)$ rate of convergence like in the case of noise-free FL, we need to scale down the uplink and downlink noise by $\Omega ({\sqrt {k}})$ and $\Omega ({k})$ respectively, where $k$ denotes the communication round, $k=1, {\dots }, K$ . Our theoretical result is further characterized by two major benefits: firstly, it does not assume the somewhat unrealistic assumption of bounded client dissimilarity, and secondly, it only requires smooth non-convex loss functions, a function class better suited for modern machine learning and deep learning models. We also perform extensive empirical analysis to verify the validity of our theoretical findings.

In this paper, a joint decision tree and visual feature optimization rate control scheme for ultrahigh-definition (UHD) versatile video coding (VVC) is proposed. First, we design a new rate-distortion (R-D) model for UHD videos, and we establish a decision-tree-based multiclass classification scheme to improve the prediction accuracy of the R-D model by fully considering visual features. Second, based on the proposed R-D model, the globally optimal solution is obtained through convex optimization. Finally, we embed our algorithm into the latest VVC reference software, VTM 10.2. According to our experimental results, compared with the latest algorithm in VTM 10.2 and other state-of-the-art algorithms, our method can achieve significant bit rate reductions while maintaining a given peak signal-to-noise ratio (PSNR) or structural similarity index measure (SSIM). [ABSTRACT FROM AUTHOR]

Steady-state visual evoked potential (SSVEP) is one of the most popular brain-computer interface (BCI) paradigms, with high information transmission rate and signal-to-noise ratio. Many calibration-free and calibration-based approaches have been proposed to improve the performance of SSVEP-based BCIs. This paper considers a quick calibration scenario, where there are plenty of data from multiple source subjects, but only a small number of calibration trials from a subset of stimulus frequencies for the new subject. We propose small data least-squares transformation (sd-LST) to solve this problem. Experiments on three publicly available SSVEP datasets demonstrated that sd-LST outperformed several classical or state-of-the-art approaches, with only about 10 calibration trials for 40-target SSVEP-based BCI spellers. [ABSTRACT FROM AUTHOR]

As an important means of environmental reconnaissance and regional security protection, sound target detection (STD) has been widely studied in the field of machine learning for a long time. Considering the shortcomings of the robustness and generalization performance of existing methods based on machine learning, we proposed a target detection method by an auditory brain-computer interface (BCI). We designed the experimental paradigm according to the actual application scenarios of STD, recorded the changes in Electroencephalogram (EEG) signals during the process of detecting target sound, and further extracted the features used to decode EEG signals through the analysis of neural representations, including Event-Related Potential (ERP) and Event-Related Spectral Perturbation (ERSP). Experimental results showed that the proposed method achieved good detection performance under noisy environment. As the first study of BCI applied to STD, this study shows the feasibility of this scheme in BCI and can serve as the foundation for future related applications. [ABSTRACT FROM AUTHOR]

To enable wireless MRI receive arrays, per-channel power consumption must be reduced by a significant factor. To address this, a low-power SiGe alternative to industry standard MRI pre-amplifier blocks has been proposed and its impact on imaging performance evaluated in a benchtop environment. The SiGe amplifier reduces power consumption 28x, but exhibits increased non-linearity and reduced dynamic range relative to industry standard amplifiers. This distorts the images, causing reduced contrast and a blurring of fine features. In conjunction with the amplifier, a semi-blind calibration and compensation framework has been proposed to remove artifacts caused by this non-linearity. Requiring the knowledge of the calibration signal bandwidth, the associated peak transmit powers, and the distorted baseband signals, a second non-linearity is constructed that when cascaded with the receive chain produces a linear response. This method was evaluated for both knee and phantom image datasets of peak input power −20dBm with a −40dBm peak input power image as reference. In the benchtop environment, industry standard amplifiers produced input normalized RMSEs of 0.0199 and 0.0310 for phantom and knee datasets, respectively. The low-power SiGe amplifier resulted in RMSEs of 0.0869 and 0.1130 which were reduced to 0.0158 and 0.0168 following compensation, for phantom and knee images respectively. The ability to effectively compensate for this reduced dynamic range encourages further investigation of low-power SiGe amplifiers for power limited MRI receive arrays. [ABSTRACT FROM AUTHOR]

Acoustic resolution photoacoustic micros- copy (AR-PAM) can achieve deeper imaging depth in biological tissue, with the sacrifice of imaging resolution compared with optical resolution photoacoustic microscopy (OR-PAM). Here we aim to enhance the AR-PAM image quality towards OR-PAM image, which specifically includes the enhancement of imaging resolution, restoration of micro-vasculatures, and reduction of artifacts. To address this issue, a network (MultiResU-Net) is first trained as generative model with simulated AR-OR image pairs, which are synthesized with physical transducer model. Moderate enhancement results can already be obtained when applying this model to in vivo AR imaging data. Nevertheless, the perceptual quality is unsatisfactory due to domain shift. Further, domain transfer learning technique under generative adversarial network (GAN) framework is proposed to drive the enhanced image’s manifold towards that of real OR image. In this way, perceptually convincing AR to OR enhancement result is obtained, which can also be supported by quantitative analysis. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values are significantly increased from 14.74 dB to 19.01 dB and from 0.1974 to 0.2937, respectively, validating the improvement of reconstruction correctness and overall perceptual quality. The proposed algorithm has also been validated across different imaging depths with experiments conducted in both shallow and deep tissue. The above AR to OR domain transfer learning with GAN (AODTL-GAN) framework has enabled the enhancement target with limited amount of matched in vivo AR-OR imaging data. [ABSTRACT FROM AUTHOR]

Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a calibration scan to measure the system matrix (SM), which is then used to set up an inverse problem to reconstruct images of the MNP distribution during subsequent scans. This calibration enables the reconstruction to sensitively account for various system imperfections. Yet time-consuming SM measurements have to be repeated under notable changes in system properties. Here, we introduce a novel deep learning approach for accelerated MPI calibration based on Transformers for SM super-resolution (TranSMS). Low-resolution SM measurements are performed using large MNP samples for improved signal-to-noise ratio efficiency, and the high-resolution SM is super-resolved via model-based deep learning. TranSMS leverages a vision transformer module to capture contextual relationships in low-resolution input images, a dense convolutional module for localizing high-resolution image features, and a data-consistency module to ensure measurement fidelity. Demonstrations on simulated and experimental data indicate that TranSMS significantly improves SM recovery and MPI reconstruction for up to 64-fold acceleration in two-dimensional imaging. [ABSTRACT FROM AUTHOR]

Minimum energy required to achieve a distortion-noise profile, i.e., a function indicating the maximum allowed distortion value for each channel noise level, is studied for transmission of Gaussian sources over Gaussian channels. A general coding scheme is proposed to upper bound the minimum energy needed for any distortion-noise profile. Conversely, utilizing a family of lower bounds originally derived for broadcast channels with power constraints, the minimum required energy is lower bounded for any profile. As examples, linear, exponential, square-law, and staircase profiles are studied. It is shown that for the linear profile, as well as for any concave profile, uncoded transmission is optimal. As a negative result, it is shown that exponential profiles are not achievable with finite energy. For square-law and staircase profiles, upper bounds and lower bounds are derived and the gaps between them are studied. [ABSTRACT FROM AUTHOR]

In this paper, we study the estimation of a rank-one spiked tensor in the presence of heavy tailed noise. Our results highlight some of the fundamental similarities and differences in the tradeoff between statistical and computational efficiencies under heavy tailed and Gaussian noise. In particular, we show that, for $p$ th order tensors, the tradeoff manifests in an identical fashion as the Gaussian case when the noise has finite $4(p-1)$ th moment. The difference in signal strength requirements, with or without computational constraints, for us to estimate the singular vectors at the optimal rate, interestingly, narrows for noise with heavier tails and vanishes when the noise only has finite fourth moment. Moreover, if the noise has less than fourth moment, tensor SVD, perhaps the most natural approach, is suboptimal even though it is computationally intractable. Our analysis exploits a close connection between estimating the rank-one spikes and the spectral norm of a random tensor with iid entries. In particular, we show that the order of the spectral norm of a random tensor can be precisely characterized by the moment of its entries, generalizing classical results for random matrices. In addition to the theoretical guarantees, we propose estimation procedures for the heavy tailed regime, which are easy to implement and efficient to run. Numerical experiments are presented to demonstrate their practical merits. [ABSTRACT FROM AUTHOR]

Dynamic positron emission tomography (dPET) is currently a widely used medical imaging technique for the clinical diagnosis, staging and therapy guidance of all kinds of human cancers. Higher temporal imaging resolution for the early stage of radiotracer metabolism is desired; however, in this case, the reconstructed images with short frame durations always suffer from a limited image signal-to-noise ratio (SNR) and unsatisfactory image spatial resolution. The appearance of uEXPLORER (United Imaging Healthcare, Inc.) with higher PET imaging sensitivity and resolution may help solving this problem. In this work, based on dynamic PET data acquired by uEXPLORER, we proposed a dPET processing method that denoises images with short frame durations via pixel-level time-activity curve (TAC) correction based on third-order Hermite interpolation (Pitch-In). The proposed method was validated and compared to several state-of-the-art methods to demonstrate its superior performance in terms of high temporal resolution dPET image noise reduction and imaging accuracy. Higher stability and feasibility of the proposed Pitch-In method for future clinical application with high temporal resolution (HTR) dPET imaging can be expected. [ABSTRACT FROM AUTHOR]

Powerful solar storms emit plasma that may travel towards the earth. Interactions between the plasma and the earth magnetic field cause geomagnetic disturbances (GMDs), which in turn induce quasi-dc voltage along long conductors in power systems. The induced electric field can be calculated by magnetic field data measured by magnetometers at several stations. In this paper, a set of online processing methods are proposed to improve signal to noise ratio (SNR) of the calculated electric field signals. In the first step, magnetic field signals are denoised inside a sliding window by a wavelet transform. The sliding window width is optimized and the introduced transients are compensated. Time derivative of magnetic field signals are estimated by a continuous wavelet transform in the next step, and the size of the sliding window is minimized for optimal computational speed. Ultimately, a method is proposed to further enhance the SNR by updating the previously processed data points. The magnetic and electric field signals obtained by the online and offline processing methods are compared with each other. It is demonstrated that the online processing methods can decrease the noises to a level comparable to that obtained by the offline methods. The results of this study can be implemented to protect key elements of power systems against severe solar storms. [ABSTRACT FROM AUTHOR]

Here, we presented a novel near-infrared (NIR) photodetectors based on Si micro-holes array/graphene (SiMHs/Gr) Schottky barrier diode (SBD) for monitoring heart rate. The SBD exhibited a unique self-filtered NIR response in a range from 900 nm to 1160 nm with a responsivity of 122 mA/W at zero bias voltage. Moreover, our device possessed an ultrahigh signal to noise ratio (SNR) of more than $10^{{5}}$ between visible and NIR light. The presented self-filtered NIR photodetector was used as a non-contact transmission-type photoplethysmography sensor for real-time heart rate monitoring. Due to the suppression of disturbances caused by ambient light, our device performed more accurate heart rate monitoring than those extracted from commercial broadband photodetectors. [ABSTRACT FROM AUTHOR]

This article presents a 5-GS/s continuous-time (CT) multi-stage noise-shaping (MASH) analog-to-digital converter (ADC). The ADC consists of three first-order modulators with a 3-bit quantizer/digital-to-analog converter (DAC) per stage. An RC-hybrid stabilization DAC is used to compensate for the excess loop delay and excess phase shift. A delay matching all-pass input filter with a low-pass feedforward filter is employed to suppress input signal leakage. As a result, inter-stage DACs are waived in residue generation, and low-power, area-saving Gm-C integrators are enabled in the back-end stages. The MASH ADC was implemented in 40-nm CMOS and occupies 0.21 mm2. The ADC achieves 68-dB dynamic range (DR) and 65-dB signal-to-noise and distortion ratio (SNDR) over a 360-MHz bandwidth (BW). The ADC consumes 158 mW from 1/1.1/1.8 V supplies, yielding 159-dB Schreier figure-of-merit (FOM) and 151-fJ/Conv. Walden FOM. [ABSTRACT FROM AUTHOR]

In this article, a 6-GHz, 2-bit, fourth-order continuous-time delta–sigma (CT $\Delta \Sigma $) analog-to-digital converter (ADC) fabricated in 28-nm CMOS is presented. It achieves −101- and −105-dBc total harmonic distortion (THD)/third-order inter-modulation (IM3) typically and 72.3-dB signal to noise and distortion ratio (SNDR) in 120-MHz bandwidth (BW). The ADC comprises four cascaded integrators with inverter-based amplifiers, an offset compensated 2-bit quantizer, and calibrated 2-bit feedback (FB) digital-to-analog converter (DAC). The DAC and quantizer employ blind digital calibration techniques enabling the wideband linearity performance. The ADC does not require any external test signal during calibration. The power dissipation of the modulator core, including demultiplexer, is 108.8 mW. [ABSTRACT FROM AUTHOR]

This article reports an ultra-low-power (ULP) Bluetooth low-energy (BLE) receiver with an improved spurious-free dynamic range (SFDR). It features two passive-intensive RF techniques: an $N$ -path passive balun-LNA and a pipeline down-mixing baseband (BB)-extraction scheme. They together offer a high- $Q$ bandpass response at RF, and a high passive gain to suppress the noise of the BB hybrid complex filter. Specifically, the balun-LNA is a step-up triple-coil transformer aided by an $N$ -path switched-capacitor (SC) network to perform in-band voltage amplification, high- $Q$ bandpass filtering, $I/Q$ down-mixing, and input-impedance matching. Instead of using active amplifiers as the first-BB gain stage, we passively extract the four-phase ($I/Q$ and differential) BB signals using a pipeline of passive-SC networks that can stack up the voltage gain. Prototyped in TSMC 28-nm CMOS, the BLE receiver consumes only 266 $\mu \text{W}$ , of which 75 $\mu \text{W}$ in the BB hybrid filter at 1 V, and 191 $\mu \text{W}$ in the LO divider + buffer at 0.6 V. Measured at the maximum RF-to-BB gain of 61 dB, the receiver exhibits a noise figure (NF) of 6.1 dB and an out-of-band (OOB)-IIP3 of 22.5 dBm. The corresponding SFDR is 77 dB for a 1-MHz BLE channel and a 10-dB minimum signal-to-noise ratio (SNR $_{\mathrm {min}}$). The OOB-B−1 dB is −3 dBm. [ABSTRACT FROM AUTHOR]

This article presents an 8-bit time-domain analog-to-digital converter (ADC) achieving ten-GS/s conversion speed with only two time-interleaved (TI) channels. A successive approximation register (SAR) time-to-digital converter (TDC) is implemented for the subpicosecond resolution time quantization with high power/area efficiency and low jitter. The throughput of the SAR TDC is enhanced by a unique delay-tracking pipelining technique to enable a 5-GS/s single-channel conversion. On the circuit level, the reference time generation for the SAR TDC is realized by the proposed selective delay tuning (SDT) cell for high efficiency and small reference time variation. Fabricated in the 14-nm FinFet CMOS technology, this ADC achieves a 37.2-dB signal-to-noise and distortion ratio (SNDR) and a 50.6-dB spurious-free dynamic range (SFDR) at the Nyquist input frequency, leading to a 24.8-fJ/conv-step Walden figure of merit with an active area of only 2850 $\mu \text{m}^{2}$. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate simultaneously without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns. In this paper, we extensively measure the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction based on interference-resolving approaches are insufficient. Motivated by the findings, we propose MDSR, which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction, MDSR takes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfection and reflections. Using the conflict graph, MDSR improves the spatial reuse from three dimensions: AP association, user scheduling, and beam selection, which can determine the optimal AP-user-beam combination and minimize interference in each scheduling cycle. We prototype and evaluate MDSR on the testbed using commodity mmWave radios. The evaluation results demonstrate that MDSR improves network throughput by multi-folds compared with the state-of-the-art one. [ABSTRACT FROM AUTHOR]

To alleviate the extremely intrinsical ill-posedness and nonlinearity of electromagnetic inverse scattering under high contrast and low signal to noise ratio (SNR), we propose a deep unfolding network based on generative adversarial network (GAN) under contrast source inversion (CSI) framework, termed UCSI-GAN. The method solves inverse scattering problems (ISPs) using end-to-end generating confrontation way by incorporating a physical model together with its iterative updating formulation into the internal architecture of GAN. First, the nonlinear iterative scheme is extended to a deep unfolding generator network, and the contrast source and contrast updates are mapped to each module of the generator network. Second, to stabilize the imaging process, we add a refinement network to each variable update. Finally, the discriminator network is employed to ensure the authenticity of reconstructed images. The generator network and discriminator network are alternately trained with a generative adversarial learning strategy to reconstruct the properties of the medium object. Numerical experiments demonstrated that the performance of UCSI-GAN is better than traditional CSI and state-of-the-art learning approaches under high contrast and low SNR condition. [ABSTRACT FROM AUTHOR]

We consider the joint constellation design problem for the noncoherent multiple-input multiple-output multiple-access channel (MAC). By analyzing the noncoherent maximum-likelihood detection error, we propose novel design criteria so as to minimize the error probability. As a baseline approach, we adapt several existing design criteria for the point-to-point channel to the MAC. Furthermore, we propose new design criteria. Our first proposed design metric is the dominating term in nonasymptotic lower and upper bounds on the pairwise error probability exponent. We give a geometric interpretation of the bound using Riemannian distance in the manifold of Hermitian positive definite matrices. From an analysis of this metric at high signal-to-noise ratio, we obtain further simplified metrics. For any given set of constellation sizes, the proposed metrics can be optimized over the set of constellation symbols. Motivated by the simplified metric, we propose a simple constellation construction consisting in partitioning a single-user constellation. We also provide a generalization of our previously proposed construction based on precoding individual constellations of lower dimensions. For a fixed joint constellation, the design metrics can be further optimized over the per-user transmit power, especially when the users transmit at different rates. Considering unitary space-time modulation, we investigate the option of building each individual constellation as a set of truncated unitary matrices scaled by the respective transmit power. Numerical results show that our proposed metrics are meaningful, and can be used as objectives to generate constellations through numerical optimization that perform better, for the same transmission rate and power constraint, than a common pilot-based scheme and the constellations optimized with existing metrics. [ABSTRACT FROM AUTHOR]

We consider the weak detection problem in a rank-one spiked Wigner data matrix where the signal-to-noise ratio is small so that reliable detection is impossible. We prove a central limit theorem for the linear spectral statistics of general rank-one spiked Wigner matrices, and based on the central limit theorem, we propose a hypothesis test on the presence of the signal by utilizing the linear spectral statistics of the data matrix. The test is data-driven and does not require prior knowledge about the distribution of the signal or the noise. When the noise is Gaussian, the proposed test is optimal in the sense that its error matches that of the likelihood ratio test, which minimizes the sum of the Type-I and Type-II errors. If the density of the noise is known and non-Gaussian, the error of the test can be lowered by applying an entrywise transformation to the data matrix. [ABSTRACT FROM AUTHOR]

In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes. [ABSTRACT FROM AUTHOR]

The use of 1-bit analog-to-digital converters (ADCs) is seen as a promising approach to significantly reduce the power consumption and hardware cost of multiple-input multiple-output (MIMO) receivers. However, the nonlinear distortion due to 1-bit quantization fundamentally changes the optimal communication strategy and also imposes a capacity penalty to the system. In this paper, the capacity of a Gaussian MIMO channel in which the antenna outputs are processed by an analog linear combiner and then quantized by a set of zero threshold ADCs is studied. A new capacity upper bound for the zero threshold case is established that is tighter than the bounds available in the literature. In addition, we propose an achievability scheme which configures the analog combiner to create parallel Gaussian channels with phase quantization at the output. Under this class of analog combiners, an algorithm is presented that identifies the analog combiner and input distribution that maximize the achievable rate. Numerical results are provided showing that the rate of the achievability scheme is tight in the low signal-to-noise ratio (SNR) regime. Finally, a new 1-bit MIMO receiver architecture which employs analog temporal and spatial processing is proposed. The proposed receiver attains the capacity in the high SNR regime. [ABSTRACT FROM AUTHOR]

An online shaping technique for high performance communication over Gaussian channels with Inter-Symbol Interference (ISI) and receiver Analog to Digital Converter (ADC) noise is presented. The technique uses online transmitter precoding over Pulse Amplitude Modulation (PAM) constellation, designed to shape the symbols distribution so that peak power constraint at the channel output is satisfied. An iterative decoder shares information between a modified M-BCJR module, which computes online the trellis transition probabilities of the shaped distribution, and turbo decoder. The result is a reduction in the required Effective Number Of Bits (ENOB) of the receiver ADC. We show that the transmitter precoding performs channel equalization without using any filter. At the limit of very high Signal to Noise Ratio (SNR) a complete channel inversion is possible so that the receiver signal spectrum becomes almost flat. Theoretical bounds are analytically derived which enable to assess the possible gain using the proposed method. For data rates of 200 Gbps and 400 Gbps over printed circuit board, simulations show that the shaping scheme enables reduction in the ENOB requirement as high as 1.43 bit and 1.78 bit, respectively, compared to uniform 4-PAM transmission with turbo equalization at the receiver side. [ABSTRACT FROM AUTHOR]

The successive cancellation (SC) process in which symbols are decoded sequentially by processing some intermediate information is an essential component of various decoding algorithms used for polar codes and their variants. In some decoding schemes, we may need to redo this process from some specific symbol or from the first symbol. This operation is called rewinding. Rewinding the SC process seems trivial if we have access to all intermediate log-likelihood ratios (LLRs) and partial sums. However, as the block length increases, retaining all of the intermediate information becomes inefficient and impractical. Rewinding the SC process in a memory-efficient way is a problem that we address in this paper. As we store a fraction of all the intermediate information in the memory-efficient scheme, we may not be able to rewind the SC process to the target symbol index. The reason is that some of the stored intermediate information needed to decode the target symbol may have been overwritten. To recompute the lost information, we may need to rewind the process further. Before proposing the formal scheme for the rewinding process, we explore the known properties of the SC process based on the binary representation of the bit indices. Then, we introduce a new operator used for grouping the bit indices. This special grouping helps us in finding the closest bit index to the target index for rewinding. We also analytically prove that this approach gives access to the untouched intermediate information stored in the memory which is essential in resuming the SC process. Finally, we adapt the proposed approach to multiple rewinds and apply it to SC-flip decoding and shifted-pruning-based list decoding. The numerical evaluation of the proposed solution shows a significant reduction of ≥50% in the complexity of the additional decoding attempts at medium and high SNR regimes for SC-flip decoding and less for shifted-pruning based list decoding. [ABSTRACT FROM AUTHOR]

In optical fiber communication, due to the random variation of the environment, the state of polarization (SOP) fluctuates randomly with time leading to distortion and performance degradation. The memory-less SOP fluctuations can be regarded as a two-by-two random unitary matrix. In this paper, for what we believe to be the first time, the capacity of the polarization drift channel under an average power constraint with imperfect channel knowledge is characterized. An achievable information rate (AIR) is derived when imperfect channel knowledge is available and is shown to be highly dependent on the channel estimation technique. It is also shown that a tighter lower bound can be achieved when a unitary estimation of the channel is available. However, the conventional estimation algorithms do not guarantee a unitary channel estimation. Therefore, by considering the unitary constraint of the channel, a data-aided channel estimator based on the Kabsch algorithm is proposed, and its performance is numerically evaluated in terms of AIR. Monte Carlo simulations show that Kabsch outperforms the least-square error algorithm. In particular, with complex, Gaussian inputs and eight pilot symbols per block, Kabsch improves the AIR by 0.20 to 0.30 bits/symbol throughout the range of studied signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

The existing relay-assisted terahertz (THz) wireless system is limited to dual-hop transmission with pointing errors and short-term fading without considering the shadowing effect. This paper analyzes the performance of a multihop-assisted backhaul communication mixed with an access link under the shadowed fading with antenna misalignment errors. We derive novel probability density and distribution functions of the signal-to-noise ratio (SNR) of the multihop link employing channel-assisted (CA) and fixed-gain (FG) amplify-and-forward (AF) relaying for each hop by considering independent but not identically distributed (i.ni.d) $\alpha $ - $\mu $ fading channel with pointing errors. We present analytical expressions for the outage probability and average bit-error-rate (ABER) and develop asymptotic analysis in high SNR regime to derive the diversity order for both CA-multihop and FG-multihop backhaul links. We use the derived results of the multihop systems to analyze the statistical performance of the integrated backhaul-access relaying for both uplink and downlink transmission considering the generalized- $K$ shadowed fading model in the access link. We also present exact and asymptotic expressions of the outage probability for the considered mixed system. We perform computer simulations to provide design and deployment aspects of employing multiple relays to enhance THz wireless transmissions. [ABSTRACT FROM AUTHOR]

The relay positions or hop distances in a multi-hop relaying scheme is an important parameter that can be optimized in order to mitigate the angle-of-arrival variance or pointing error in a free-space optical (FSO) backhaul link. In this study, we have optimized the relay positions for amplify-and-forward and decode-and-forward relays in a multi-hop unmanned aerial vehicle-based relaying scheme for FSO. Particularly, we have shown that a significant performance improvement may be achieved by optimizing the outage probability as a function of the hop distance for amplify-and-forward relays when the relays are constrained by a finite power gain. Additionally, we have discovered that for a low signal-to-noise ratio channel, the optimal hop distance of a particular hop for decode-and-forward relays is inversely proportional to angle-of-arrival variance in that hop. [ABSTRACT FROM AUTHOR]

In this article, a linear precoding weight vector is designed to enhance the minimum signal-to-interference plus noise ratio (SINR) of a space-time line code (STLC)-based multicast users. To solve the intractable minimum SINR maximization problem, the optimal weight vector for each STLC user is derived, and then, the individual optimal weight vectors are linearly combined. The designed linear precoding vector improves the minimum and average SINR of the multicast users as verified by numerical results. Numerical results also verify that the STLC-based multicast is a promising scheme, especially, if a transmitter has a large number of transmit antennas and the additive noises are severe at the users. [ABSTRACT FROM AUTHOR]

In a Fog Radio Access Network (F-RAN), fog access points (F-APs) are equipped with caches that can store popular files during off-peak hours. Besides, they are densely deployed to have overlapping radio coverage so that requested files can be delivered cooperatively using beamforming. The bottleneck of the network is typically in the bandwidth-limited wireless fronthaul, which connects a cloud server to the F-APs. This work studies index coding design for cooperative caching and delivery in F-RAN to minimize fronthaul traffic and transmit energy. Index coding algorithms are designed considering the cached content at the F-APs and the possibility of beamforming in the access network under coded and uncoded caching schemes. An optimal polynomial-time index coding algorithm for uncoded and repetition caching and an efficient heuristic for Maximum Distance Separable (MDS) coded caching are designed, and their superior performance is verified by simulations. The study is further extended to consider the tradeoff between the traffic load of the fronthaul link and the transmit energy consumed in the access network. At the expense of more fronthaul traffic, beamforming opportunities can be increased, significantly reducing energy consumption. Algorithms to achieve the tradeoff are crafted, and simulation results show that uncoded caching well balances the tradeoff. [ABSTRACT FROM AUTHOR]

Covert communications is critical in many application scenarios for ensuring transmission security and privacy. Two-way protocols are widely adopted in intelligent reflecting surface (IRS) assisted relaying networks to enable covert communications and prevent wireless signals from being overheard. To quantify the performance of covert communications in this special scenario, we derive the closed-form expression of outage probability and its asymptote in this paper. Considering the worst-case of covert communications, the optimal normalized power threshold of warden's detector is analyzed under a complex Gaussian distribution approximation. To meet the requirement of the detection error probability, the ratio of the transmission power between the covert nodes is investigated. Simulation results are provided to validate the theoretical expressions as well as the asymptotic analysis with a small number of reflecting elements. [ABSTRACT FROM AUTHOR]

This paper considers the downlink of an ultra-reliable low-latency communication (URLLC) system in which a base station (BS) serves multiple single-antenna users in the short (finite) blocklength (FBL) regime with the assistance of a reconfigurable intelligent surface (RIS). In the FBL regime, the users' achievable rates are complex functions of the beamforming vectors and of the RIS's programmable reflecting elements (PREs). We propose the joint design of the transmit beamformers and PREs to maximize the geometric mean (GM) of these rates (GM-rate) and show that this approach provides fair rate distribution and thus reliable links to all users. A novel computational algorithm is developed, which is based on closed forms to generate improved feasible points. Simulations show the merit of our solution. [ABSTRACT FROM AUTHOR]

Dual-functional radar-communication (DFRC) has been viewed as a promising component in future networks. The considered two-cell interfering DFRC network involves two multi-antenna base stations (BSs), and each BS serves multiple single-antenna users and receives echo signals to detect the target. An optimization framework for the joint transmit beamforming and receive filters design of a coordinated two-cell network is formulated. Specifically, we minimize the transmit power at two BSs subject to the signal-to-interference-and-noise ratio constraints. To solve the formulated non-convex optimization problem, an alternating optimization-based method is invoked. For the transmit beamforming design, a locally optimal successive inner convex approximation (SICA)-based method with fast convergence is firstly proposed. To further reduce the computational complexity, we also provide three zero-forcing (ZF)-based sub-optimal methods for practical consideration. For the receive filters design, a power iterations-based method is proposed. Simulation results validate the effectiveness of our proposed SICA-based locally optimal and ZF-based sub-optimal schemes. [ABSTRACT FROM AUTHOR]

We consider a cross-layer packet scheduling problem with hybrid ARQ (HARQ) in fading channels in which the channel state information at the transmitter (CSIT) is known only after one slot delay. Packets arrive according to a Bernoulli process at the transmitter, and each packet is required to be timely-delivered at the receiver, within a delay of $d$ time-slots, and is dropped, if the delay deadline is not met. Since the transmitter has only a delayed CSIT, a HARQ with Chase combining is employed for error recovery. The problem is to decide the transmit-energy in each time-slot such that the timely-throughput is maximum for a given average transmit-energy constraint. We pose this problem as a constrained Markov decision process, and provide an optimum policy based on Lagrangian relaxation. The optimum Lagrangian multiplier is obtained using a subgradient method. We obtain the structure of the optimum policy, based on which we propose a computationally simple policy READER that requires no CSIT. We show that for large Doppler spread (or mobility), the timely-throughput of READER is close to the optimum policy. We also provide two more policies: an optimum policy assuming perfect CSIT with zero delay, and a naive randomization policy, and compare the throughput performance of the proposed policies. [ABSTRACT FROM AUTHOR]

Although Unmanned Aerial Vehicles (UAVs) are capable of significantly improving the information security by detecting the eavesdropper's location, their limited energy motivates our research to propose a secure and energy efficient scheme. Thanks to the common-message philosophy introduced by Rate-Splitting (RS), we no longer have to allocate a portion of the transmit power to radiate Artificial Noise (AN), and yet both the Energy Efficiency (EE) and secrecy can be improved. Hence we define and study the Secrecy Energy Efficiency (SEE) of a multi-carrier multi-UAV network, in which Cooperative Rate-Splitting (CRS) is employed by each multi-antenna UAV Base-Station (UAV-BS) for protecting their downlink transmissions against an external eavesdropper ($Eve$). Furthermore, we consider the challenging scenario in which CRS is employed by each multi-antenna UAV-BS to protect their corresponding downlink transmissions against an external $Eve$. We further consider a difficult scenario in terms of security in which only imperfect channel state information of $Eve$ is available at the Tx. Accordingly, we conceive a robust secure resource allocation algorithm, which maximizes the SEE by jointly optimizing both the user association matrix and the network parameter allocation problem, including the RS precoders, time slot sharing and power allocation. Due to the non-convexity of the problem, it is decoupled into a pair of convex sub-problems. Firstly, new two-tier intra-cell optimization problems are formulated for achieving $\xi$ -optimal solutions by iterative block coordinate decent programming. Then, the power of each sub-channel is optimized by formulating the associated power control problem. [ABSTRACT FROM AUTHOR]

Rate-Splitting Multiple Access (RSMA) is an emerging flexible and powerful multiple access for downlink multi-antenna networks. In this paper, we introduce the concept of RSMA into short-packet downlink communications. We design optimal linear precoders that maximize the sum rate with Finite Blocklength (FBL) constraints. The relations between the sum rate and blocklength of RSMA are investigated for a wide range of network loads and user deployments. Numerical results demonstrate that RSMA can achieve the same transmission rate as Non-Orthogonal Multiple Access (NOMA) and Space Division Multiple Access (SDMA) with smaller blocklengths (and therefore lower latency), especially in overloaded multi-antenna networks. Hence, we conclude that RSMA is a promising multiple access for low-latency communications. [ABSTRACT FROM AUTHOR]

With low-cost energy consumption, the reconfigurable intelligent surface (RIS) technique is a potential solution to the real-time data processing for intelligent transportation systems (ITSs). In this paper, an intelligent transmissive surface is introduced into the vehicular communications, enabling vehicle-to-infrastructure (V2I) signals to penetrate the intelligent RIS to access the base station (BS) on the opposite side of the vehicle. Considering that the vehicle-to-vehicle (V2V) communication reuses the spectrum spanned for V2I link, we investigate the ergodic capacity optimization problem for the vehicle performing V2I communications with the assistance of RIS, while meeting the low-latency and high-reliability requirements of the V2V link. The RIS transmission coefficients and power allocation of vehicles are jointly optimized, for the management of the desired and undesired vehicular communication links. Moreover, the expression of optimal phase shifts is derived in a closed-form, which reveals that the performance gain brought by RIS is proportional to the number of intelligent elements, while inversely proportional to the distance from vehicle-to-BS, in a quadratic form. Moreover, in the case of discrete phase shifts, an intelligent algorithm is proposed for the beamforming design at RIS. Afterwards, with the objective to maximize the ergodic capacity of the V2I link, the optimal power allocation is also proposed. Simulation results confirm the accuracy of the proposed resource allocation strategy, and that the system performance in terms of the ergodic V2I capacity can be significantly improved by the RIS. [ABSTRACT FROM AUTHOR]

An amplitude weighted cross-correlation detector is proposed for the detection of the Doppler-spread target (DST), based on the frequency modulated continuous waveform (FMCW) radar. The concurrent range-Doppler matrices (RDM) make cross-correlation optimized with higher accumulation gain and significantly reduced computation effort. A novel measurement of signal sparseness is defined. The quantitative analysis of the proposed detector, compared with the energy integration detector, is realized based on the signal sparseness. Two generalizations of proposed detector are discussed for the number of antennas greater than two. Data from a walking pedestrian are got and used to verify the effectiveness of the proposed detector. [ABSTRACT FROM AUTHOR]

The new Fifth generation (5G) of wireless communications and beyond 5G (B5G) networks are required to provide connectivity for massive number of devices at high speed and low latency. Within this context, non-orthogonal multiple access (NOMA) has been involved and emerged in recent research since it is considered as an enabling technology for 5G and B5G networks. NOMA allows several users to share the same resources of time and frequency, making it an excellent candidate to provide impressively large connectivity with high spectral efficiency. In this paper, we study the performance of NOMA systems assuming independent but not necessarily to be identical $N$ -Nakagami-m multipath fading channels. This channel model subsumes the double Nakagami-m, which was investigated in the context of intervehicular communications and keyhole multiple input multiple output systems, as special cases. To this end, we derive exact analytical and asymptotic expressions for the probability density function of the overall signal to noise ratio, system capacity, and the pairwise error probability (PEP) for each user. Numerical results are also provided herein to show the effects of fading parameters and number of users on the capacity and the PEP of each user. [ABSTRACT FROM AUTHOR]

This article presents a wireless neural implant with body-coupled (BC) data transmission and power delivery for freely behaving animals and incorporates a precision front end for high-quality neural recordings. The neural implant utilizes the body as a wireless transmission medium where it only needs small electrodes for data transmission and power delivery. An external device with patch electrodes can then be placed far away from the implant without the need for precise alignment. Furthermore, a four-channel continuous-time delta–sigma modulator (CT- $\Delta \Sigma \text{M}$) is integrated into the system for precision neural recordings. Each neural recording CT- $\Delta \Sigma \text{M}$ achieves an 82.3-dB signal-to-noise and distortion ratio (SNDR) and an 83.3-dB dynamic range (DR) while consuming only 8.6- $\mu \text{W}$ at a signal bandwidth of 10 kHz. The neural implant integrated circuit (IC) is fabricated in a 0.11- $\mu \text{m}$ CMOS with a high-density capacitor option, and the BC data receiver (RX) IC is implemented in a 0.18- $\mu \text{m}$ CMOS. The implant IC occupies a chip area of 4 $\text {mm}^{2}$ , including a 5-nF on-chip capacitor, and draws 280 $\mu \text{A}$ from a 2.3-V supply with a working data transmitter (TX) electrode. By exploiting direct-digital signaling for data transmission, the neural implant achieves a data rate of 20.48 Mb/s and a wireless power recovery of 644 $\mu \text{W}$ , resulting in an energy efficiency of 32 pJ/b. The entire neural implant system has been successfully verified by both electrical and in vivo measurements, while the wirelessly recorded electrocorticography (ECoG) signals with the prototype neural implant inside a rat demonstrate the end-to-end functionality of the proposed system. [ABSTRACT FROM AUTHOR]

We present design techniques for single-bit continuous-time delta–sigma modulators that attain high resolution (>16 bits) over a bandwidth (BW) that is more than ten times the audio range. We introduce the zapped, virtual-ground-switched dual return-to-open DAC which is immune to ISI and other transition-dependent errors. FIR feedback facilitates chopping, improves clock-jitter sensitivity and the loop filter’s linearity. We show that the compensation FIR DAC, which is typically bulky, can be implemented in an extremely power- and area-efficient manner in a single-bit modulator using a capacitive DAC and passive summation. Thanks to these techniques, the fabricated prototype achieves 103.2-/104.3-dB signal to noise and distortion ratio (SNDR)/signal to noise ratio (SNR) in a 250-kHz bandwidth while operating at 48 MS/s. Consuming 17.7 mW from a 1.8-V supply, the modulator occupies 1.1 mm 2 in a 180-nm CMOS process. The Schreier (SNDR) figure of merit (FoM) is 174.7 dB. [ABSTRACT FROM AUTHOR]

Nowadays transceiver-free (also referred to as device-free) localization using Received Signal Strength (RSS) is a hot topic for researchers due to its widespread applicability. However, RSS is easily affected by the indoor environment, resulting in a dense deployment of reference nodes. Some hybrid systems have already been proposed to help RSS localization, but most of them require additional hardware support. In order to solve this problem, in this paper, we propose two algorithms, which leverage the Packet Received Rate (PRR) to help RSS localization without additional hardware support. Moreover, we take the environment noise information into consideration by utilizing the Signal-to-Noise Ratio (SNR) which is based on the RSS and Noise Floor (NF) information instead of pure RSS. Thus, we can alleviate the noise effect in the environment and make our system more sensitive to the target. Specifically, when reference nodes are sparsely deployed and RSS is very weak, PRR and SNR can help in performing localization more accurately. Our BEYOND RSS system is based on sparse wireless sensor networks, wherein the experimental results show that the average localization error of our approach outperforms the pure RSS based approach by about 15.19%. [ABSTRACT FROM AUTHOR]

This article obtains fundamental limits on the computational precision of in-memory computing architectures (IMCs). An IMC noise model and associated signal-to-noise ratio (SNR) metrics are defined and their interrelationships analyzed to show that the accuracy of IMCs is fundamentally limited by the compute SNR (${\mathrm {SNR}}_{a}$) of its analog core, and that activation, weight, and output (ADC) precision needs to be assigned appropriately for the final output SNR (${\mathrm {SNR}}_{T}$) to approach ${\mathrm {SNR}}_{a}$. The minimum precision criterion (MPC) is proposed to minimize the analog-to-digital converter (ADC) precision and hence its overhead. Three in-memory compute models—charge summing (QS), current summing (IS), and charge redistribution (QR)—are shown to underlie most known IMCs. Noise, energy, and delay expressions for the compute models are developed and employed to derive expressions for the SNR, ADC precision, energy, and latency of IMCs. The compute SNR expressions are validated via Monte Carlo simulations in a 65 nm CMOS process. For a 512 row SRAM array, it is shown that: 1) IMCs have an upper bound on their maximum achievable ${\mathrm {SNR}}_{a}$ due to constraints on energy, area and voltage swing, and this upper bound reduces with technology scaling for QS-based architectures; 2) MPC enables ${\mathrm {SNR}}_{T}$ to approach ${\mathrm {SNR}}_{a}$ to be realized with minimal ADC precision; and 3) QS-based (QR-based) architectures are preferred for low (high) compute SNR scenarios. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) have been often proposed as a means for restoring nonline-of-sight links in wireless communications. Because the path loss changes with the relative distance of the RIS from the transmitter and the receiver and their relative orientations, the quality of the communication is expected to depend strongly on the RIS placement in an RIS-aided link. In this work, the optimal placement of the RIS is studied with respect to both position and orientation and it is shown that even for an ideal RIS, i.e., lossless, polarization preserving, and with a uniform response with respect to the incident angle, the quality of the communication is greatly affected by the RIS orientation. To provide insight into the optimal choice, the analysis is based on an analytical model that treats the RIS as a continuous surface of finite size that steers the incident beam toward a prescribed direction. By imposing a desired minimum threshold on the received power, it is studied how the efficiency of the RIS depends on both its position and orientation. The decisions that have to be made concerning the placement of the RIS are illustrated with examples of D-band indoor scenarios. [ABSTRACT FROM AUTHOR]

A cost-effective and accurate method for predicting over-the-air (OTA) data throughput of multiple-input and multiple-output (MIMO) wireless devices in the antenna design stage is proposed. The method incorporates passive characteristics of MIMO antennas, realistic channel model, and conductive measurement results in a computer simulation framework. With extracted relation between received power and signal-to-noise ratio (SNR) of a wireless device at antenna ports by a conductive test and measured passive characteristics of MIMO antennas, OTA throughput versus received power in a realistic channel model is obtained by the Monte Carlo simulation of Shannon’s law. The unique features of this method include: no channel emulator equipment is required; a wide variety of precoding schemes can be applied; passive characteristics of antennas are fully incorporated; and frequency-selective fading channel can be adopted. The method has been intensively validated by comparing the calculated OTA throughputs with those measured by a multiprobe anechoic chamber method under spatial channel model extension (SCME) in three aspects: spatial correlation, radiation efficiency, and mutual coupling, showing as low as 0.2–0.3 dB discrepancy in power level in most cases. The method can be used to evaluate a MIMO array antenna design of a wireless device with respect to OTA performance. [ABSTRACT FROM AUTHOR]

We present a novel method to enhance the SNR for multi-TE MR spectroscopic imaging (MRSI) data by integrating learned nonlinear low-dimensional model and spatial constraints. A deep complex convolutional autoencoder (DCCAE) was developed to learn a nonlinear low-dimensional representation of the high-dimensional multi-TE $^{1}$ H spectroscopy signals. The learned model significantly reduces the data dimension thus serving as an effective constraint for noise reduction. A reconstruction formulation was proposed to integrate the spatiospectral encoding model, the learned model, and a spatial constraint for an SNR-enhancing reconstruction from multi-TE data. The proposed method has been evaluated using both numerical simulations and in vivo brain MRSI experiments. The superior denoising performance of the proposed over alternative methods was demonstrated, both qualitatively and quantitatively. In vivo multi-TE data was used to assess the improved metabolite quantification reproducibility and accuracy achieved by the proposed method. We expect the proposed SNR-enhancing reconstruction to enable faster and/or higher-resolution multi-TE $^{1}$ H-MRSI of the brain, potentially useful for various clinical applications. [ABSTRACT FROM AUTHOR]

Metamaterials have attracted enormous interest in the perfect absorber. In sharp contrast to most previous studies focused on improving absorption coefficient and expanding absorption bandwidth, this article presents an optimal design approach that enhances the robustness of absorption bandwidth to several hard-to-control variables. The split ring resonator (SRR) was used as an optimizable object to illustrate this approach. Unlike the traditional design method for metamaterial absorbers, which pays attention to the construction of equivalent circuit models and the effect of lumped parameters on the performance index, this article uses the decoupling analysis to establish the mapping between structural parameters and the performance index. The concept of factor effect and interaction effect is introduced to describe the decoupling analysis, and the impact of each factor on the signal-to-noise ratio (SNR) of absorption bandwidth is analyzed. Compared to the original design, the robustness of the optimal design is improved. The predicted result obtained by decoupling analysis and the simulated result shows a good agreement. [ABSTRACT FROM AUTHOR]

To find out which kind of mode-locked fiber oscillator is superior, six kinds of typical mode-locked Yb:fiber oscillators, including nonlinear polarization evolution (NPE)-based oscillators (i.e. conventional NPE-based oscillator, polarization maintaining (PM) NPE-based oscillator), nonlinear amplifying loop mirror (NALM)-based oscillators (i.e. figure-8 oscillator, all-fiber figure-9 oscillator, free-space figure-9 oscillator), and semiconductor saturable absorption mirror (SESAM)-based oscillator, were presented and crosswise compared. Except for the conventional NPE-based one, all the others were constructed with all-PM fiber components. Experimentally, detailed crosswise comparisons were carried out quantitatively and qualitatively for different oscillators in terms of spectral bandwidth, spectral shape, pulse repetition rate, signal-to-noise ratio (SNR), mode-locking threshold, self-starting mode-locking pump power, robustness, component life, construction simplicity, and so on. In addition, subjective sensations on each oscillator were also provided, which could be helpful for decision-making when one wants to make or purchase an oscillator. Finally, the all-fiber figure-9 oscillator was suggested to be the best choice even it needs higher pumping power, which could be an ideal seed for high power ultrafast laser amplifiers. [ABSTRACT FROM AUTHOR]