Scalable Bandwidth Research Articles

Fully Dynamic Zoom ADC with Energy-Efficient Residue Feedforward and Two-Step Summation

This paper proposes a fully dynamic zoom ADC based on residue feedforward and correlated level shifting (CLS)-assisted floating inverter amplifier (FIA) technique with 200× bandwidth/power scalability, by only changing the clock frequency. A CLS-assisted FIA which achieves 65 dB DC gain is employed to reduce errors from finite FIA gain. An energy-efficient residue feedforward path extracted from the input of the SAR ADC’s comparator minimizes the leakage of the SAR ADC’s quantization noise into the band. A novel two-step summation approach is proposed to minimize capacitor areas compared to a traditional passive switched-capacitor adder. The post-simulated results show the prototype ADC with near-constant energy efficiency, which scales power from 5μW to 822μW, achieves high resolution (>100dB) during the scalable bandwidth. At 20kHz BW, it achieves 106.2dB DR, 102.0dB SNDR, leading to FoMDR of 180.0dB and FoMSNDR of 175.8dB.

Optimization control of wastewater treatment based on neural network and multi-objective optimization algorithm

In wastewater treatment, energy consumption and effluent quality are conflicting objectives. Improving effluent quality while reducing energy consumption has become a pressing issue. This paper proposes a dynamic optimal control method for the wastewater treatment process, utilizing neural networks and multi-objective dragonfly algorithm. A regression prediction model, based on a Self-Attention mechanism of a multi-scale convolutional neural network-bidirectional gated recurrent unit, is used to establish a soft measurement model for effluent quality and energy consumption, serving as the optimization objective function. The multi-objective dragonfly algorithm then optimizes this objective to determine the optimal set values for control variables. A Linear Active Disturbance Rejection Control, based on a scalable bandwidth linear extended state observer, is designed to track these optimal set values. The proposed method is tested using Benchmark Simulation Model No. 1 (BSM1). Results demonstrate that the method effectively obtains and tracks optimal set values of control variables, ensuring effluent quality meets standards and significantly reducing energy consumption compared to four other methods.

Metamaterial Broadband Absorber Induced by Synergistic Regulation of Temperature and Electric Field and Its Optical Switching Application.

Nowadays, metamaterial absorbers still suffer from limited bandwidth, poor bandwidth scalability, and insufficient modulation depth. In order to solve this series of problems, we propose a metamaterial absorber based on graphene, VO2, gallium silver sulfide, and gold-silver alloy composites with dual-control modulation of temperature and electric field. Then we further investigate the optical switching performance of this absorber in this work. Our proposed metamaterial absorber has the advantages of broad absorption bandwidth, sufficient modulation depth, and good bandwidth scalability all together. Unlike the single inspired layer of previous designs, we innovatively adopted a multi-layer excitation structure, which can realize the purpose of absorption and bandwidth width regulation by a variety of means. Combined with the finite element analysis method, our proposed metamaterial absorber has excellent bandwidth scalability, which can be tuned from 2.7 THz bandwidth to 12.1 THz bandwidth by external electrothermal excitation. Meanwhile, the metamaterial absorber can also dynamically modulate the absorption from 3.8% to 99.8% at a wide incidence angle over the entire range of polarization angles, suggesting important potential applications in the field of optical switching in the terahertz range.

Hybrid deep learning and optimized clustering mechanism for load balancing and fault tolerance in cloud computing

ABSTRACT Cloud services are one of the most quickly developing technologies. Furthermore, load balancing is recognized as a fundamental challenge for achieving energy efficiency. The primary function of load balancing is to deliver optimal services by releasing the load over multiple resources. Fault tolerance is being used to improve the reliability and accessibility of the network. In this paper, a hybrid Deep Learning-based load balancing algorithm is developed. Initially, tasks are allocated to all VMs in a round-robin method. Furthermore, the Deep Embedding Cluster (DEC) utilizes the Central Processing Unit (CPU), bandwidth, memory, processing elements, and frequency scaling factors while determining if a VM is overloaded or underloaded. The task performed on the overloaded VM is valued and the tasks accomplished on the overloaded VM are assigned to the underloaded VM for cloud load balancing. In addition, the Deep Q Recurrent Neural Network (DQRNN) is proposed to balance the load based on numerous factors such as supply, demand, capacity, load, resource utilization, and fault tolerance. Furthermore, the effectiveness of this model is assessed by load, capacity, resource consumption, and success rate, with ideal values of 0.147, 0.726, 0.527, and 0.895 are achieved.

A Scalable Bandwidth and Frequency-Dependent DPD Linearizer for User Equipment Power Amplifiers With Nonflat Frequency Response

A Scalable Bandwidth and Frequency-Dependent DPD Linearizer for User Equipment Power Amplifiers With Nonflat Frequency Response

Out‐of‐band transaction pool sync for large dynamic blockchain networks

AbstractSynchronization of transaction pools (mempools) has shown potential for improving the performance and block propagation delay of state‐of‐the‐art blockchains. Indeed, various heuristics have been proposed in the literature to incorporate early exchanges of unconfirmed transactions into the block propagation protocol. In this work, we take a different approach, maintaining transaction synchronization externally (and independently) of the block propagation channel. In the process, we formalize the synchronization problem within a graph theoretic framework and introduce a novel algorithm (SREP—set reconciliation‐enhanced propagation) with quantifiable guarantees. We analyze the algorithm's performance for various realistic network topologies and show that it converges on static connected graphs in a time bounded by the diameter of the graph. In graphs with dynamic edges, SREP converges in an expected time that is linear in the number of nodes. We confirm our analytical findings through extensive simulations that include comparisons with MempoolSync, a recent approach from the literature. Our simulations show that SREP incurs reasonable bandwidth overhead and scales gracefully with the size of the network (unlike MempoolSync).

A Lumped-Element Directional Coupler for Bandwidth Enhancement, Impedance Matching, and Harmonic Suppressions

This paper presents a lumped-element wideband directional coupler that enables arbitrary impedance matching and harmonic suppressions from 2f0. The coupler consists of four filtering networks (FNs) and an asymmetric branch-line hybrid serving as the matching network. The use of lumped-element topology helps reduce circuit size and improve harmonic suppressions. Each FN, composed of two lumped-element resonators, contributes two additional transmission poles, enabling bandwidth scaling, bandwidth enhancement, and harmonic suppressions. To demonstrate the effectiveness of the proposed design, a wideband coupler with various terminal impedances operating at 0.5 GHz is designed, simulated, and fabricated. The measured size is 0.039×0.039 λg2, the fractional bandwidth for –14.3 dB return loss is 46%, and the harmonic suppression is more than –30 dB from 0.69 to 4.5 GHz.

(Invited, Digital Presentation) Overview of Current and Future 3D Heterogeneous Integration Architectures

High performance computing architectures such as high core count servers and AI accelerators have increasing demands on performance and energy efficiency. This requires power efficient and high bandwidth integration of multiple functions such as processing, memory and high speed circuits in a single package. Current trend is to separate these functions into chiplets with optimized technology then integrating them using advanced packaging technologies that target ease of integration, low overhead and maximum flexibility. In this talk, we will provide an overview of current and future 3D packaging technologies. We will start with solder silicon interposers which enable die stacking to support improved logic density/mm2 of package area and cover their design considerations and applications. Next, we will cover local silicon interconnect architectures which bridge the flexibility gap between 2.5D architectures and 3D interposer architectures. Afterwards, we will discuss the next generation architectures which are based on hybrid bonding. These architectures offer much higher interconnect density up to >100,000 connections/mm2 and enable new types of integration. Finally, we will discuss some future directions to continue to support performance and bandwidth scaling.

Stochastic gradient geographical weighted regression (sgGWR): scalable bandwidth optimization for geographically weighted regression

GWR (Geographical Weighted Regression) is a widely accepted regression method under spatial dependency. Since the calibration of GWR is computationally intensive, some efficient methods for calibration were proposed. However, these methods require extensive computation environments or limit the class of kernel functions, limiting the applicability of GWR. To improve the applicability, we propose sgGWR (stochastic gradient GWR), an optimization approach for GWR based on stochastic gradient, which stochastically approximates cross-validation errors and applies gradient-based optimization methods. To achieve this, we show the analytical derivate of the GWR cross-validation error. sgGWR can handle a broader class of kernels than that by the existing scalable method, and we can benefit from it even high-performance computers cannot be accessed. Therefore, sgGWR fills in the gap that existing scalable methods do not cover. We examine the performances of sgGWR and the existing methods by simulation studies. Additionally, we apply sgGWR for the land price analysis for Tokyo, Japan. As a result, a spatio-temporal version of GWR has the best prediction performance, and it captures the spatio-temporal heterogeneity of regression coefficients.

Constraining the FRB mechanism from scintillation in the host galaxy

ABSTRACT Most fast radio burst (FRB) models can be divided into two groups based on the distance of the radio emission region from the central engine. The first group of models, the so-called ‘nearby’ or magnetospheric models, invoke FRB emission at distances of 109 cm or less from the central engine, while the second ‘far-away’ models involve emission from distances of 1011 cm or greater. The lateral size for the emission region for the former class of models (≲107 cm) is much smaller than the second class of models (≳109 cm). We propose that an interstellar scattering screen in the host galaxy is well-suited to differentiate between the two classes of models, particularly based on the level of modulations in the observed intensity with frequency, in the regime of strong diffractive scintillation. This is because the diffractive length scale for the host galaxy’s interstellar medium scattering screen is expected to lie between the transverse emission-region sizes for the ‘nearby’ and the ‘far-away’ class of models. Determining the strength of flux modulation caused by scintillation (scintillation modulation index) across the scintillation bandwidth (∼1/2πδts) would provide a strong constraint on the FRB radiation mechanism when the scatter broadening (δts) is shown to be from the FRB host galaxy. The scaling of the scintillation bandwidth as ∼ν4.4 may make it easier to determine the modulation index at ≳ 1 GHz.

Checkerboard and interrupted speech: Critical intelligibility differences observed in factor-analysis-based checkerboard speech stimuli

It has been shown that the intelligibility of checkerboard speech stimuli, in which speech signals were periodically interrupted in time and frequency, drastically varied according to the combination of the number of frequency bands (2–20) and segment duration (20–320 ms). However, the effects of the number of frequency bands between 4 and 20 and the ways of frequency divisions on intelligibility have been basically unknown. Here we show that 8-band checkerboard speech stimuli were more intelligible than temporally interrupted speech stimuli and 4-band checkerboard speech stimuli, irrespective of segment duration (n = 19 and 20). At the same time, U-shaped intelligibility curves were observed for 4-band and possibly 8-band checkerboard speech stimuli. Furthermore, when the frequency divisions were determined according to regular intervals on a critical bandwidth scale rather than factor analysis results of speech power fluctuations, intelligibility improved at the 160- and 320-ms segment duration. These results suggest that the factor-analysis-based frequency bands perform as speech cue channels that are essential for speech perception and that a probability summation model based on a perceptual unit of speech perception may account for the U-shaped intelligibility curves if the model is modified to include the speech cue channels.

Spectrally Efficient UWB Pulse Shaping Based on Polynomially Weighted Gaussian Pulses With Maximally Flat Amplitude Spectra

This letter presents a new class of symmetric waveforms that is suitable for spectrally efficient ultra-wideband (UWB) pulse shaping. The waveforms are based on polynomially weighted Gaussian, where polynomials are constructed by imposing maximum flatness at the peak of the pulse’s amplitude spectrum. To perform pulse shaping, frequency shift and bandwidth scaling are applied. The proposed approach is illustrated with the design of UWB pulses that fill the FCC masks and provide high spectral efficiency. Compared to other Gaussian-based UWB waveforms, the proposed pulses have significantly higher spectral efficiency and slightly lower energy concentration.

A Linear-in-Decibel Automatic Gain Control Amplifier With Dual Mode Continuous Gain Tuning

A reconfigurable fully integrated automatic gain control (AGC) amplifier is presented which is based on a dual mode continuous gain adjustment variable-gain amplifier (VGA). The VGA is realized based on the current-steering structure, achieving a linear-in-dB gain tuned by AGC’s feedback voltage. A current division active load is proposed, which provides additional gain control range and realizes gain robustness to process and supply variations without extra calibration. Simulated with Cadence IC, the maximum gain variations due to process and supply variations are 3.5 dB and 2.6 dB, respectively. By using dual mode gain tuning technique, the VGA achieves a total gain range of 68.2 dB. Both bandwidth and gain of the VGA are adjusted independently. The AGC is realized in UMC 55-nm CMOS technology with 0.026 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> core area. With the current division ratio K of 0.5, the proposed VGA achieves a linear-in-dB gain of 42.2 dB ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> 30.0 to 12.2 dB) with less than 0.79 dB error. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> 3 dB bandwidth can be adjusted from 80 MHz to 140 MHz and is insensitive to gain variations. The power dissipation is from 2.9 mW to 4.5 mW. This design features bandwidth scalability and the gain independent of bandwidth to achieve various applications.

Second spectrum of charge carrier density fluctuations in graphene due to trapping/detrapping processes

We investigate the second spectrum of charge carrier density fluctuations in graphene within the McWorther model, where noise is induced by electron traps in the substrate. Within this simple picture, we obtain a closed-form expression including both Gaussian and non-Gaussian fluctuations. We show that a very extended distribution of switching rates of the electron traps in the substrate leads to a carrier density power spectrum with a non-trivial structure on the scale of the measurement bandwidth. This explains the appearance of a 1/f component in the Gaussian part of the second spectrum, which adds up to the expected frequency-independent term. Finally, we find that the non-Gaussian part of the second spectrum can become quantitatively relevant by approaching extremely low temperatures.

Fully Dynamic Zoom-ADC Based on Improved Swing-Enhanced FIAs Using CLS Technique With 1250× Bandwidth/Power Scalability

This brief proposes a fully dynamic zoom ADC based on improved swing-enhanced floating-inverter amplifiers (SEFIAs) using the correlated-level-shifting (CLS) technique with 1250× bandwidth/power scalability, by only changing the clock frequency. The improved SEFIA with CLS uses a reused capacitor to replace the dynamic biasing capacitor and input coupling capacitor, which simultaneously achieves both high gain and high output swing. Fabricated in a 55nm CMOS process, the prototype ADC with near-constant energy efficiency, which scales power from 453nW to 122μW, achieves high resolution (> 95.2 dB) and high DR (> 96.7 dB) during the scalable bandwidth. At 5kHz BW, it achieves 97.3dB DR, 96.3dB SNDR, and 112.2dB SFDR, leading to FoMDR of 177.1dB and FoMSNDR of 176.1dB.

Wideband Precoding for RIS-Aided THz Communications

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) communication has been considered as a promising technology for enabling future sixth-generation (6G) wireless systems. Due to the exploitation of extremely large bandwidth and large scale of RIS, RIS-aided THz communications would suffer from the beam split effect, where the generated beams cannot be aligned with the target physical direction in the whole bandwidth, so a severe array gain loss will be introduced. In this paper, the beam split effect is first analyzed in the existence of RIS. Then, a novel sub-connected RIS architecture is proposed to mitigate the beam split effect. The crux is to introduce additional time-delay (TD) modules and phase shifters into RIS elements so as to convert the classical phase-only precoding to the joint phase and delay precoding. Accordingly, a wideband precoding design is proposed to compensate for the severe array gain loss, and the performance analysis on the array gain is also provided. After that, we extend our discussions to the emerging scenarios with massive antennas equipped at the base station (BS), where the effect of “double beam split”, i.e., the coupling of beam split at the BS and the RIS, occurs. We prove the decomposability of the array gain, based on which the double beam split effect can be addressed by separately optimizing the wideband precoding at the BS and the RIS. Simulation results demonstrate that our proposed sub-connected RIS significantly alleviates the beam split effect with a small number of TD modules, and it is capable of achieving sub-optimal achievable rate performance with acceptable hardware cost and power consumption.

Artificial Intelligence-Assisted Network Slicing: Network Assurance and Service Provisioning in 6G

6G networks are expected to provide instant global connectivity and enable the transition from “connected things” to “connected intelligence,” where promising network slicing can play an important role in network assurance and service provisioning for various demanding vertical application scenarios. On the basis of diversified massive data, artificial intelligence (AI)-assisted techniques are widely considered more suitable than traditional models and algorithms to deal with challenges faced by complex and dynamic slicing problems in 6G. In view of this, we provide a tutorial on AI-assisted 6G network slicing for network assurance and service provisioning, aiming to show the prospect of 6G slicing and the advantages of applying AI technology. Specifically, we propose six typical characteristics of 6G network slicing, analyze the feasibility of AI from different network domains and technical aspects, propose a case study on AI-assisted bandwidth scaling, and, finally, put forward the main challenges and open issues for its future development.

Intercoupling of Cascaded Metasurfaces for Broadband Spectral Scalability

Electromagnetic metasurfaces have been intensively used as ultra-compact and easy-to-integrate platforms for versatile wave manipulations from optical to terahertz (THz) and millimeter wave (MMW) ranges. In this paper, the less investigated effects of the interlayer coupling of multiple metasurfaces cascaded in parallel are intensively exploited and leveraged for scalable broadband spectral regulations. The hybridized resonant modes of cascaded metasurfaces with interlayer couplings are well interpreted and simply modeled by the transmission line lumped equivalent circuits, which are used in return to guide the design of the tunable spectral response. In particular, the interlayer gaps and other parameters of double or triple metasurfaces are deliberately leveraged to tune the inter-couplings for as-required spectral properties, i.e., the bandwidth scaling and central frequency shift. As a proof of concept, the scalable broadband transmissive spectra are demonstrated in the millimeter wave (MMW) range by cascading multilayers of metasurfaces sandwiched together in parallel with low-loss dielectrics (Rogers 3003). Finally, both the numerical and experimental results confirm the effectiveness of our cascaded model of multiple metasurfaces for broadband spectral tuning from a narrow band centered at 50 GHz to a broadened range of 40~55 GHz with ideal side steepness, respectively.

Beyond CPO: A Motivation and Approach for Bringing Optics Onto the Silicon Interposer

Co-packaged optics (CPO) technology is well positioned to break through the bottlenecks that impede efficient bandwidth scaling in key near-term commercial integrated circuits. We begin by providing some historical context for this important sea change in the optical communications industry. Then, motivated by GPU-based accelerated computing requirements, we investigate the next pain points that are poised to constrain bandwidth and efficiency in future CPO-based systems. We identify 2.5D integrated optics (i.e., bringing optics onto the interposer) as a promising solution that can enable continued scaling for these systems due to the dense wiring available which facilitates more efficient slow-and-wide electrical interfaces. We explore the benefits, challenges, and requirements associated with such tight coupling of the processors and optical engines by considering high-level photonic link design, technology, and packaging. We demonstrate the viability of a control loop which can adequately regulate temperature within the aggressive thermal environment. Then, we introduce a custom simulation framework that allows quantified comparisons of detailed design decisions; the simulations validate the feasibility of the general approach while also providing key guidance to designers on best directions to pursue for efficient optimization.

Two stages of bandwidth scaling drives efficient neural coding of natural sounds.

Theories of efficient coding propose that the auditory system is optimized for the statistical structure of natural sounds, yet the transformations underlying optimal acoustic representations are not well understood. Using a database of natural sounds including human speech and a physiologically-inspired auditory model, we explore the consequences of peripheral (cochlear) and mid-level (auditory midbrain) filter tuning transformations on the representation of natural sound spectra and modulation statistics. Whereas Fourier-based sound decompositions have constant time-frequency resolution at all frequencies, cochlear and auditory midbrain filters bandwidths increase proportional to the filter center frequency. This form of bandwidth scaling produces a systematic decrease in spectral resolution and increase in temporal resolution with increasing frequency. Here we demonstrate that cochlear bandwidth scaling produces a frequency-dependent gain that counteracts the tendency of natural sound power to decrease with frequency, resulting in a whitened output representation. Similarly, bandwidth scaling in mid-level auditory filters further enhances the representation of natural sounds by producing a whitened modulation power spectrum (MPS) with higher modulation entropy than both the cochlear outputs and the conventional Fourier MPS. These findings suggest that the tuning characteristics of the peripheral and mid-level auditory system together produce a whitened output representation in three dimensions (frequency, temporal and spectral modulation) that reduces redundancies and allows for a more efficient use of neural resources. This hierarchical multi-stage tuning strategy is thus likely optimized to extract available information and may underlies perceptual sensitivity to natural sounds.