This paper presents the design of a wideband pipelined multi-stage noise shaping (MASH) continuous time (CT) delta-sigma modulator (DSM). The quantization error of the overall$1^{\mathrm {st}}$-stage DSM is extracted as the input of the$2^{\mathrm {nd}}$stage, while the outputs of both stages are simply combined without using any digital filters. Overall, different shaping functions are generated for both QN without requiring any digital QN cancellation. Therefore, the pipelined MASH (PMASH) significantly mitigates QN leakage while retaining the decent loop stability of a traditional MASH. Additionally, several analyses have been made for the PMASH topology, e.g. the design guideline, the signal transfer function (STF), the robustness, etc. Clocked at 800MHz and enabling on-chip DAC calibration, the 65nm CMOS prototype with an exemplary 2-2 topology using multi-bit quantizers achieves 82.6 dB SNDR, 98.8 dB SFDR over 15.4 MHz BW at −0.5 dBFS 1.8 MHz input. The power consumption is 16.9 mW with 1.2V/1.5V supplies. It results in a competitive FoM${}_{\mathrm {S\vert SNDR}}$of 172.2 dB, while it avoids any off-chip calibrations.

Accurate localized wireless channel modeling is a cornerstone of cellular network optimization, enabling reliable prediction of network performance during parameter tuning. Localized statistical channel modeling (LSCM) is the state-of the-art channel modeling framework tailored for cellular network optimization. However, traditional LSCM methods, which infer the channel's angular power spectrum (APS) from reference signal received power (RSRP) measurements, suffer from critical limitations: they are typically confined to single-cell, single grid and single-carrier frequency analysis and fail to capture complex cross-domain interactions. To overcome these challenges, we propose RF-LSCM, a novel framework that models the channel APS by jointly representing large-scale signal attenuation and multipath components within a radiance field. RF-LSCM introduces a multi-domain LSCM formulation with a physics informed frequency-dependent attenuation model (FDAM) to facilitate the cross frequency generalization as well as a point cloud-aided environment enhanced method to enable multi-cell and multi-grid channel modeling. Furthermore, to address the computational inefficiency of typical neural radiance fields, RF LSCMleverages a low-rank tensor representation, complemented by a novel hierarchical tensor angular modeling (HiTAM) algo rithm. This efficient design significantly reduces GPU memory requirements and training time while preserving fine-grained accuracy. Extensive experiments on real-world multi-cell datasets demonstrate that RF-LSCM significantly outperforms state-of the-art methods, achieving up to a 30% reduction in mean absolute error (MAE) for coverage prediction and a 22% MAE improvement by effectively fusing multi-frequency data.

Localized statistical channel modeling (LSCM), a key enabler for digital twin networks, traditionally relies on costly and spatially limited drive test data to estimate the channel angular power spectrum (APS) from reference signal received power measurements. This paper proposes a measurement report (MR) data-driven LSCM framework (MR-LSCM) to leverage low-cost and ubiquitous MR data. However, integrating MR data presents critical challenges: the prevalent lack of location labels required for LSCM, and the mismatch between uniform geographic grids in LSCM and spatially non-uniform MR data in complex propagation environments. To address these issues, our MR-LSCM framework introduces two specialized modules. First, a semi-supervised hypergraph neural network is proposed for MR localization, which exploits multimodal information to achieve robust performance even with scarce labels. Second, we unify grid construction and APS estimation into a joint clustering and sparse recovery problem where the two tasks mutually reinforce each other. An improved sparse recovery algorithm tailored to the ill-conditioned measurement matrix and incomplete observation is developed by incorporating physical priors. Through comprehensive experiments on a real-world MR dataset, we demonstrate the superior performance and robustness of our framework in localization and channel modeling.

Accurate prediction of time-to-event outcomes, commonly known as survival analysis, is vital in high-stakes domains such as healthcare and business, where timely and trustworthy insights can have profound implications. Traditional survival analysis methods either provide predictions without clear explanations or offer interpretability without a mechanism to integrate expert insights. In many real-world scenarios, decision-makers require models that are both transparent and capable of interactively incorporating domain knowledge to refine predictions. The key challenge we address is how to simultaneously achieve accurate time-to-event forecasting, clear interpretability, and interactive integration of expert feedback. We propose the Interpretable and Interactive Deep Discrete-Time Survival Analysis framework, an approach that is both data-driven and knowledge-driven. Furthermore, it embeds expert knowledge into the model, dynamically aligns feature contributions with evolving risk patterns, and actively engages experts to guide the learning process interactively. This interactive strategy not only enhances predictive performance but also produces explanations that clearly reflect the critical factors identified by domain experts. Extensive evaluations on diverse clinical and business datasets demonstrate that our method captures feature importance and yields robust and reliable predictions that stand in contrast to conventional black-box models. These results indicate that bridging interpretability with interactive expert engagement can significantly improve decision support systems. By integrating accurate forecasting with human-aligned, interactive explanations, our framework offers a promising direction for developing more transparent and trusted models across a wide range of disciplines.

Speculative execution attacks such as Spectre and Meltdown exploit the wrong execution patch to leak private data. In current state-of-the-art defense strategies, executions of all memory accesses that use speculatively-loaded addresses are blocked, resulting in high overhead. Our key observation is that these blocked memory accesses can be executed without operand-dependent hardware resource usage through value prediction. Therefore, we propose a novel hardware defense framework, named Speculative Value Prediction (SVP), to safely and efficiently execute the potentially unsafe memory accesses earlier. We build SVP on the cycle-accurate Gem5 simulator and its performance improvement is positively correlated with the coverage of value predictors. Experiments show that when using the value predictor with 30%/60%/100% coverage, SVP outperforms the state-of-the-art defense mechanism STT in the Spectre model by 21.5%/50.3%/107.7% respectively, and in the Futuristic model by 28.7%/55.4%/105.7% respectively.

This work introduces a two-channel time-interleaved (TI) continuous-time (CT) 3rd-order delta-sigma modulator (DSM). It uses the information from one complete channel to predict the other channel based on the extrapolation principle. Note that, Cascaded Integrator of Distributed Feedforward (CIFF) topology is selected for the loop filter for the following reasons: 1) it could reduce the number of required feedback DACs as much as possible; 2) it allows to implement the zero optimization for the TI DSM such that the performance could be further improved. Furthermore, we employ the technique of error correction to address the issue regarding the delay-free feedback path, which originates from the extrapolating TI DSM. We present the derivations of the target TI CT DSM starting from a single-channel discrete-time (DT) DSM, while the compensation for excess loop delay (ELD) is considered. Fabricated in 65nm CMOS process, this modulator achieves an equivalent output sampling rate of 800MS/s, while the analog channel operates at 400MHz. It exhibits a signal-to-noise and distortion ratio (SNDR) /spurious-free dynamic range (SFDR)/dynamic range (DR) of 75.5dB/89.7dB/79dB over a 10MHz bandwidth. The total power consumption is 33.73mW from 1.2v/1.8v power supplies. It results in a Schreier Figure of Merit (FoM) of 163.7dB based on DR.

This paper presents a continuous-time (CT) 0–4 dual-stage Multi-stAge Noise-sHaping (MASH) Delta-Sigma Modulator (DSM), exhibiting +5dBFS maximum stable amplitude (MSA). In the context of 0–4 MASH topology, the 4-bit CT DSM employed as the second stage only processes 4-bit quantization noise (QN) of the front-end. Though the input signal exceeds the full scale (FS), the second stage still stays stable as long as the signal leakage does not overload it. Such feature guarantees the improved stability over a wider signal input range. In addition, to address the well-known QN leakage issue of MASH topology, we propose to combine the feedforward topology with proportional-integral-based excess loop delay compensation. It ensures high robustness of the proposed 0–4 MASH DSM without requiring any calibration. Additionally, we present an analysis of the anti-aliasing filtering (AAF) for the 0-X MASH DSM. It is found that the overall AAF of the 0-X MASH DSM is contributed from the second stage. Sampled at 400MHz, the 65nm CMOS experimental prototype measures signal-to-noise and distortion ratio (SNDR)/spurious-free dynamic range (SFDR) of 76.7dB/87.3dB over a 10MHz bandwidth with 15.1mW power consumption. Moreover, with achieving +5dBFS MSA, the dynamic range (DR) is extended to be as high as 85dB, resulting in a state-of-the-art Scherier Figure of Merit (FoM) of 173.2dB based on DR.

This paper presents a discrete-time multi-stage noise shaping (MASH) delta-sigma modulator (DSM) with second-order digital noise coupling for wideband highresolution applications. By directly injecting the output of the second loop into the quantizer input of the first loop while choosing an appropriate signal transfer function of the second loop, a second-order digital noise coupling can be easily constructed without almost imposing any hardware complexity. With the help of the second-order digital noise coupling, the inherent quantization noise leakage in the MASH topology is significantly mitigated, thus resulting in less DC gain requirement for the integrators. Mathematical analysis and further simulation results are presented to demonstrate the effectiveness of the proposed MASH structure.