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Li Wei

Li Wei appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Information Theorymath.ITeess.SPMachine Learningastro-ph.IMComputation and Languagephysics.ins-detArtificial IntelligenceInformation Retrievalmath.CONetworking and Internet Architecture

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Published work

16 published item(s)

preprint2026arXiv

Efficient Context Scaling with LongCat ZigZag Attention

We introduce LongCat ZigZag Attention (LoZA), which is a sparse attention scheme designed to transform any existing full-attention models into sparse versions with rather limited compute budget. In long-context scenarios, LoZA can achieve significant speed-ups both for prefill-intensive (e.g., retrieval-augmented generation) and decode-intensive (e.g., tool-integrated reasoning) cases. Specifically, by applying LoZA to LongCat-Flash during mid-training, we serve LongCat-Flash-Exp as a long-context foundation model that can swiftly process up to 1 million tokens, enabling efficient long-term reasoning and long-horizon agentic capabilities.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

ORBIT: Preserving Foundational Language Capabilities in GenRetrieval via Origin-Regulated Merging

Despite the rapid advancements in large language model (LLM) development, fine-tuning them for specific tasks often results in the catastrophic forgetting of their general, language-based reasoning abilities. This work investigates and addresses this challenge in the context of the Generative Retrieval (GenRetrieval) task. During GenRetrieval fine-tuning, we find this forgetting occurs rapidly and correlates with the distance between the fine-tuned and original model parameters. Given these observations, we propose ORBIT, a novel approach that actively tracks the distance between fine-tuned and initial model weights, and uses a weight averaging strategy to constrain model drift during GenRetrieval fine-tuning when this inter-model distance exceeds a maximum threshold. Our results show that ORBIT retains substantial text and retrieval performance by outperforming both common continual learning baselines and related regularization methods that also employ weight averaging.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the uplink of a RIS-empowered multi-user Multiple Input Single Output (MISO) uplink communication systems and propose a channel estimation framework based on the parallel factor decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cramér-Rao Bound (CRB). We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieves the CRB. It is also demonstrated that the sum rate using the estimated channels always reach that of perfect channels under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Joint Channel Estimation and Signal Recovery for RIS-Empowered Multi-User Communications

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and lowpower configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted wireless communication system and present two joint channel estimation and signal recovery schemes based on message passing algorithms in this paper. Specifically, the proposed bidirectional scheme applies the Taylor series expansion and Gaussian approximation to simplify the sum-product procedure in the formulated problem. In addition, the inner iteration that adopts two variants of approximate message passing algorithms is incorporated to ensure robustness and convergence. Two ambiguities removal methods are also discussed in this paper. Our simulation results show that the proposed schemes show the superiority over the state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design

Wireless communication in the TeraHertz band (0.1--10 THz) is envisioned as one of the key enabling technologies for the future sixth generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology. However, very high propagation attenuations and molecular absorptions of THz frequencies often limit the signal transmission distance and coverage range. Benefited from the recent breakthrough on the reconfigurable intelligent surfaces (RIS) for realizing smart radio propagation environment, we propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies. Particularly, multiple passive and controllable RISs are deployed to assist the transmissions between the base station (BS) and multiple single-antenna users. We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss. To improve the convergence of the proposed DRL-based algorithm, two algorithms are then designed to initialize the digital beamforming and the analog beamforming matrices utilizing the alternating optimization technique. Simulation results show that our proposed scheme is able to improve 50\% more coverage range of THz communications compared with the benchmarks. Furthermore, it is also shown that our proposed DRL-based method is a state-of-the-art method to solve the NP-hard beamforming problem, especially when the signals at RIS-assisted THz communication networks experience multiple hops.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi-User Holographic MIMO Surfaces: Channel Modeling and Spectral Efficiency Analysis

The multi-user Holographic Multiple-Input and Multiple-Output Surface (MU-HMIMOS) paradigm, which is capable of realizing large continuous apertures with minimal power consumption, has been recently considered as an energyefficient solution for future wireless networks, offering increased flexibility in impacting electromagnetic (EM) wave propagation according to the desired communication, localization, and sensing objectives. The tractable channel modeling in MU-HMIMOS wireless systems is one of the most critical research challenges, mainly due to the coupling effect induced by the excessively large number of closely spaced patch antennas. In this paper, we focus on this challenge for the downlink of multi-user MIMO communications and extend an EM-compliant channel model to multiuser case, which is expressed in the wavenumber domain using the Fourier plane wave approximation. Based on the presented channel model, we investigate the spectral efficiency of maximumratio transmission and Zero-Forcing (ZF) precoding schemes. We also introduce a novel hardware efficient ZF precoder, leveraging Neumann series (NS) expansion to replace the required matrix inversion operation, which is very hard to be computed in the conventional way due to the extremely large number of patch antennas in the envisioned MU-HMIMOS communication systems. In comparison with the conventional independent and identical Rayleigh fading channels that ignore antenna coupling effects, the proposed EM-compliant channel model captures the mutual couplings induced by the very small antenna spacing. Our extensive performance evaluation results demonstrate that our theoretical performance expressions approximate sufficiently well ...

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi-User Wireless Communications with Holographic MIMO Surfaces: A Convenient Channel Model and Spectral Efficiency Analysis

The multi-user Holographic Multiple-Input and Multiple-Output Surface (MU-HMIMOS) paradigm, which is capable of realizing large continuous apertures with minimal power consumption and of shaping radio wave propagation at will, has been recently considered as an energy-efficient solution for future wireless networks. The tractable channel modeling of MU-HMIMOS signal propagation is one of the most critical challenges, mainly due to the coupling effect induced by the excessively large number of closely spaced patch antennas. In this paper, we focus on this challenge for downlink communications and model the electromagnetic channel in the wavenumber domain using the Fourier plane wave representation. Based on the proposed model, we devise a Zero-Forcing (ZF) precoding scheme, capitalizing on the sampled channel variance that depends on the number and spacing of the HMIMOS patch antennas, and perform a spectral efficiency analysis. Our simulation results showcase that the more patch antennas and the larger their spacing is, the performance of the considered MU-HMIMOS system improves. In addition, it is demonstrated that our theoretical performance expressions approximate sufficiently well the simulated spectral efficiency, even for the highly correlated cases, thus verifying the effectiveness and robustness of the presented analytical framework.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Hybrid Beamforming for RIS-Empowered Multi-hop Terahertz Communications: A DRL-based Method

Wireless communication in the TeraHertz band (0.1--10 THz) is envisioned as one of the key enabling technologies for the future six generation (6G) wireless communication systems. However, very high propagation attenuations and molecular absorptions of THz frequencies often limit the signal transmission distance and coverage range. Benefited from the recent breakthrough on the reconfigurable intelligent surfaces (RIS) for realizing smart radio propagation environment, we propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies. We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss. Simulation results show that our proposed scheme is able to improve 50\% more coverage range of THz communications compared with the benchmarks. Furthermore, it is also shown that our proposed DRL-based method is a state-of-the-art method to solve the NP-bard beamforming problem, especially when the signals at RIS-empowered THz communication networks experience multiple hops.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Parallel Factor Decomposition Channel Estimation in RIS-Assisted Multi-User MISO Communication

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low power configuration enabling massive connectivity and low latency communications. Channel estimation in RIS-based systems is one of the most critical challenges due to the large number of reflecting unit elements and their distinctive hardware constraints. In this paper, we focus on the downlink of a RIS-assisted multi-user Multiple Input Single Output (MISO) communication system and present a method based on the PARAllel FACtor (PARAFAC) decomposition to unfold the resulting cascaded channel model. The proposed method includes an alternating least squares algorithm to iteratively estimate the channel between the base station and RIS, as well as the channels between RIS and users. Our selective simulation results show that the proposed iterative channel estimation method outperforms a benchmark scheme using genie-aided information. We also provide insights on the impact of different RIS settings on the proposed algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

TEAM: An Taylor Expansion-Based Method for Generating Adversarial Examples

Although Deep Neural Networks(DNNs) have achieved successful applications in many fields, they are vulnerable to adversarial examples.Adversarial training is one of the most effective methods to improve the robustness of DNNs, and it is generally considered as solving a saddle point problem that minimizes risk and maximizes perturbation.Therefore, powerful adversarial examples can effectively replicate the situation of perturbation maximization to solve the saddle point problem.The method proposed in this paper approximates the output of DNNs in the input neighborhood by using the Taylor expansion, and then optimizes it by using the Lagrange multiplier method to generate adversarial examples. If it is used for adversarial training, the DNNs can be effectively regularized and the defects of the model can be improved.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Full Duplex Joint Sensing for Opportunistic Access in Spectrum-Heterogeneous Cognitive Radio Networks

In cognitive radio networks (CRN), secondary users (SUs) can share spectrum with licensed primary users (PUs). Because an SU receiver (SU-Rx) does not always share the same view of spectrum availability as the corresponding SU transmitter (SU-Tx), spectrum sensing conducted only by an SU transmitter tends to be overly sensitive to guarantee safe spectrum access at the price of SU inefficiency. In this letter, we propose a joint spectrum sensing mechanism, named Full-Duplex Joint Sensing (FJDS), to relax sensitivity of SU detection and improve SU throughput. FDJS employs instantaneous feedback enabled by in-band full duplex communication to facilitate the sharing of spectral information from SU-Rx to SU-Tx. The joint detection problem in FDJS is modeled as non-linear optimization and solved by a binary searching algorithm. Simulation results show that FDJS could improve SU throughput as well as meeting PU interruption constraints in a wide range of parameter settings.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Robust frequency offset estimator for OFDM over fast varying multipath channel

This paper presents a robust carrier frequency offset(CFO) estimation algorithm suitable for fast varying multipath channels. The proposed algorithm estimates CFO both in time-domain and frequency-domain using two carefully designed sequences. This novel technique possesses high accuracy as well as large estimation range and works well in fast varying channels.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

A linear calibration method on DNL error for energy spectrum

A calibration method aimed for the differential nonlinearity (DNL) error of the Low Energy X-ray Instrument (LE) onboard the Hard X-ray Modulation Telescope (HXMT) is presented, which is independent with electronic systems used as testing platform and is only determined by the analog-to-digital converter (ADC) itself. Exploring this method, ADCs that are used within the flight model phase of HXMT-LE can be calibrated individually and independently by a non-destructive and low-cost way, greatly alleviating the complexity of the problem. As a result, the performance of the energy spectrum can be significantly improved, further more, noise reduced and resolution enhanced.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Measurements of Charge Transfer Efficiency in a Proton-irradiated Swept Charge Device

Charge Coupled Devices (CCDs) have been successfully used in several low energy X-ray astronomical satellite over the past two decades. Their high energy resolution and high spatial resolution make them an perfect tool for low energy astronomy, such as formation of galaxy clusters and environment of black holes. The Low Energy X-ray Telescope (LE) group is developing Swept Charge Device (SCD) for the Hard X-ray Modulation Telescope (HXMT) satellite. SCD is a special low energy X-ray CCD, which could be read out a thousand times faster than traditional CCDs, simultaneously keeping excellent energy resolution. A test method for measuring the charge transfer efficiency (CTE) of a prototype SCD has been set up. Studies of the charge transfer inefficiency (CTI) have been performed at a temperature range of operation, with a proton-irradiated SCD.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Optimal sequencing of a set of positive numbers with the variance of the sequence's partial sums maximized

We consider the problem of sequencing a set of positive numbers. We try to find the optimal sequence to maximize the variance of its partial sums. The optimal sequence is shown to have a beautiful structure. It is interesting to note that the symmetric problem which aims at minimizing the variance of the same partial sums is proved to be NP-complete in the literature.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Optimization in Multi-Frequency Interferometry Ranging: Theory and Experiment

Multi-frequency interferometry (MFI) is well known as an accurate phase-based measurement scheme. The paper reveals the inherent relationship of the unambiguous measurement range (UMR), the outlier probability, the MSE performance with the frequency pattern in MFI system, and then provides the corresponding criterion for choosing the frequency pattern. We point out that the theoretical rigorous UMR of MFI deduced in the literature is usually optimistic for practical application and derive a more practical expression . It is found that the least-square (LS) estimator of MFI has a distinguished "double threshold effect". Distinct difference is observed for the MSE in moderate and high signal-to-noise ratio (SNR) region (denoted by MMSE and HMSE respectively) and the second threshold effect occurs during the rapid transition from MMSE to HMSE with increasing SNR. The closed-form expressions for the MMSE, HMSE and Cramer-Rao bound (CRB) are further derived, with HMSE coinciding with CRB. Since the HMSE is insensitive to frequency pattern, we focus on MMSE minimization by proper frequency optimization. We show that a prime-based frequency interval can be exploited for the purpose of both outlier suppression and UMR extension and design a special optimal rearrangement for any set of frequency interval, in the sense of MMSE minimization. An extremely simple frequency design method is finally developed. Simulation and field experiment verified that the proposed scheme considerably outperforms the existing method in UMR as well as MSE performance, especially in the transition from MMSE to HMSE, for Gaussian and non-Gaussian channel.

0 citations0 reviewsNo code linkedOpen access