arXiv subjects

Mian Li

Publications and source records attributed to Mian Li.

At least 19 recordsLinked to original sources

arXiv2026

Baseband-Efficient WMMSE Precoding: From a Signal Weighting Cost Perspective

For downlink transmission in massive multi-user multiple-input multiple-output (MU-MIMO) systems, conventional precoding research heavily focuses on reducing the computational complexity of precoding matrix design, while largely overlooking another critical bottleneck: the substantial signal weighting cost incurred by repeatedly applying the precoder to high-speed data streams. To address both challenges simultaneously, this paper proposes a novel sparse precoding framework tailored for fully-digital architectures. Within this framework, from the sum-rate maximization perspective, we design two sparse precoding architectures: a common-support row-sparse architecture and a user-specific row-sparse architecture, so as to reduce the number of multiplication operations required in baseband signal weighting without sacrificing system capacity. For the formulated mixed-integer non-linear programming (MINLP) problem, we rigorously prove, for the first time, that the optimal precoder under both sparse architectures strictly resides in a specific low-dimensional subspace determined by the channel matrices, thereby reducing the dimensionality of the optimization variables. Based on this insight, an alternating optimization algorithm is developed within the weighted minimum mean square error (WMMSE) framework to jointly optimize sparse beam selection and low-dimensional precoding coefficients. The combinatorial beam selection problem is handled using an efficient penalty-based majorize-minimization (MM) method, yielding a low-complexity closed-form solution. Simulation results demonstrate that the proposed scheme achieves near-optimal sum-rate performance while substantially reducing both the precoding computation complexity and the overall signal weighting cost.

eess.SP↗

arXiv2026

Optimal Low-Dimensional Structures of ISAC Beamforming: Theory and Efficient Algorithms

Transmit beamforming design is a fundamental problem in integrated sensing and communication (ISAC) systems. Numerous methods have been proposed to jointly optimize key performance metrics such as the signal-to-interference-plus-noise ratio and Cram\'er-Rao bound. However, the computational complexity of these methods often grows rapidly with the number of transmit antennas at the base station (BS). To tackle this challenge, we prove a fundamental structural property of the ISAC beamforming problem, i.e., there exists an optimal solution exhibiting a low-dimensional structure. This leads to an equivalent reformulation of the problem with dimension related to the number of users rather than the number of BS antennas, thereby enabling the development of low-complexity algorithms. When applying the interior-point method to the reformulated problem, we achieve up to six orders of magnitude in complexity reduction when the number of antennas exceeds the number of users by an order of magnitude. To further reduce the complexity, we develop a balanced augmented Lagrangian method to solve the reformulated problem. The proposed algorithm maintains optimality while achieving a computational complexity that scales quartically with the number of users. Our simulation results demonstrate that the proposed R-BAL method can achieve a speedup of more than 10000$\times$ over the conventional IPM in massive MIMO scenarios.

eess.SP↗

arXiv2026

From inconsistency to decision: explainable operation and maintenance of battery energy storage systems

Battery Energy Storage Systems (BESSs) are increasingly critical to power-system stability, yet their operation and maintenance remain dominated by reactive, expert-dependent diagnostics. While cell-level inconsistencies provide early warning signals of degradation and safety risks, the lack of scalable and interpretable decision-support frameworks prevents these signals from being effectively translated into operational actions. Here we introduce an inconsistency-driven operation and maintenance paradigm for large-scale BESSs that systematically transforms routine monitoring data into explainable, decision-oriented guidance. The proposed framework integrates multi-dimensional inconsistency evaluation with large language model-based semantic reasoning to bridge the gap between quantitative diagnostics and practical maintenance decisions. Using eight months of field data from an in-service battery system comprising 3,564 cells, we demonstrate how electrical, thermal, and aging-related inconsistencies can be distilled into structured operational records and converted into actionable maintenance insights through a multi-agent framework. The proposed approach enables accurate and explainable responses to real-world operation and maintenance queries, reducing response time and operational cost by over 80% compared with conventional expert-driven practices. These results establish a scalable pathway for intelligent operation and maintenance of battery energy storage systems, with direct implications for reliability, safety, and cost-effective integration of energy storage into modern power systems.

eess.SY↗

arXiv2025

Si-Substituted MAX Phases and In-Situ Formation of Si-coated MXene Composites via Chlorosilane Etching

Silicon-based MAX phases are a promising class of layered ceramics with superior thermal and chemical stability. However, their synthesis remains challenging due to inherent thermodynamic instability at high temperatures. Herein, we develop a general top-down strategy to synthesize a broad family of Si-substituted MAX phases (M = Ti, V, Nb, Ta, Cr; X = C, N) by reacting Al-based MAX precursors with SiCl4 vapor. This approach not only circumvents traditional high-temperature limitations but also enables precise A-site defect engineering, resulting in phases with controlled vacancy concentrations (e.g., Nb2Si3/4C and Nb2Si1/2C). Furthermore, we introduce a redox potential-based model that rationalizes the reaction pathway. Using Tin+1AlXn etched with SiCl4 as an example, the process simultaneously forms Cl-terminated MXene (Mn+1XnCl2) and amorphous nano-Si, enabling the one-step synthesis of Si-coated MXene composites. This methodology provides new avenues for designing advanced MAX phases and MXene-based hybrids with tailored functionalities for applications in energy storage and catalysis.

cond-mat.mtrl-sci↗

arXiv2025

Phase stabilization of In2Se3 by disordered Ni intercalation and its enhanced thermoelectrical performance

Van der Waals (vdW) layered materials have gained significant attention owing to their distinctive structure and unique properties. The weak interlayer bonding in vdW layered materials enables guest atom intercalation, allowing precise tuning of their physical and chemical properties. In this work, a ternary compound, NixIn2Se3 (x = 0-0.3), with Ni randomly occupying the interlayers of In2Se3, was synthesized via an intercalation route driven by electron injection. The intercalated Ni atoms act as anchor points within the interlayer of In2Se3, which effectively suppresses the phase transition of In2Se3 at elevated temperatures. Furthermore, the disordered Ni intercalation significantly enhanced the electrical conductivity of In2Se3 through electron injection, while reducing the thermal conductivity due to the interlayer phonon scattering, leading to an improved thermoelectric performance. For instance, the thermoelectric figure of merit (ZT) of Ni0.3In2Se3 increased by 86% (in-plane) and 222% (out-of-plane) compared to In2Se3 at 500 oC. These findings not only provide an effective strategy to enhance the performance of layered thermoelectric materials, but also demonstrate the potential of intercalation chemistry for expanding the application scope of van der Waals (vdW) layered materials.

cond-mat.mtrl-sci↗

arXiv2025

The 2D Materials Roadmap

Over the past two decades, 2D materials have rapidly evolved into a diverse and expanding family of material platforms. Many members of this materials class have demonstrated their potential to deliver transformative impact on fundamental research and technological applications across different fields. In this roadmap, we provide an overview of the key aspects of 2D material research and development, spanning synthesis, properties and commercial applications. We specifically present roadmaps for high impact 2D materials, including graphene and its derivatives, transition metal dichalcogenides, MXenes as well as their heterostructures and moir\'e systems. The discussions are organized into thematic sections covering emerging research areas (e.g., twisted electronics, moir\'e nano-optoelectronics, polaritronics, quantum photonics, and neuromorphic computing), breakthrough applications in key technologies (e.g., 2D transistors, energy storage, electrocatalysis, filtration and separation, thermal management, flexible electronics, sensing, electromagnetic interference shielding, and composites) and other important topics (computational discovery of novel materials, commercialization and standardization). This roadmap focuses on the current research landscape, future challenges and scientific and technological advances required to address, with the intent to provide useful references for promoting the development of 2D materials.

cond-mat.mtrl-sci↗

arXiv2025

Organometallic-Inorganic Hybrid MXenes with Tunable Superconductivity

Ti-based two-dimensional transition-metal carbides (MXenes) have attracted attention due to their superior properties and are being explored across various applications1,2. Despite their versatile properties, superconductivity has never been demonstrated, not even predicted, for this important group of 2D materials. In this work, we have introduced an electrochemical intercalation protocol to construct versatile organometallic-inorganic hybrid MXenes and achieved tunable superconductivity in the metallocene-modified layered crystals. Through structural editing of MXene matrix at atomic scale and meticulously modulated intercalation route, Ti3C2Tx intercalated with metallocene species exhibits a superconductive transition temperature (Tc) of 10.2 K. Guest intercalation induced electron filling and strain engineering are responsible for the emerging superconductivity in this intrinsically non-superconducting material. Theoretically, simulated electron-phonon interaction effects further elucidate the nature of the changes in Tc. Furthermore, the Tc of crafted artificial superlattices beyond Ti-based MXenes have been predicted, offering a general strategy for engineering superconductivity and magnetism in layered hybrid materials.

cond-mat.supr-con↗

arXiv2024

Sublayers Editing of Covalent MAX Phase for Nanolaminated Early Transition Metal Compounds

Two-dimensional transition metal carbides and nitrides (MXenes) have gained popularity in fields such as energy storage, catalysis, and electromagnetic interference due to their diverse elemental compositions and variable surface terminations (T). Generally, the synthesis of MXene materials involves etching the weak M-A metallic bonds in the ternary layered transition metal carbides and nitrides (MAX phase) using HF acid or Lewis acid molten salts, while the strong M-X covalent bonds preserve the two-dimensional framework structure of MXenes. On the other hand, the MAX phase material family also includes a significant class of members where the A site is occupied by non-metal main group elements (such as sulfur and phosphorus), in which both M-A and M-X are covalent bond-type sublayers. The aforementioned etching methods cannot be used to synthesize MXene materials from these parent phases. In this work, we discovered that the covalent bond-type M-A and M-X sublayers exhibit different reactivity with some inorganic materials in a high-temperature molten state. By utilizing this difference in reactivity, we can structurally modify these covalent sublayers, allowing for the substitution of elements at the X site (from B to Se, S, P, C) and converting non-metal A site atoms in non-van der Waals (non-vdW) MAX phases into surface atoms in vdW layered materials. This results in a family of early transition metal Xide chalcogenides (TMXCs) that exhibit lattice characteristics of both MXenes and transition metal chalcogenides. Using electron-donor chemical scissors, these TMXC layered materials can be further exfoliated into monolayer nanosheets. The atomic configurations of each atom in these monolayer TMXCs are the same as those of conventional MXenes, but the oxidation states of the M-site atoms can be regulated by both X-site atoms and intercalated cations.

cond-mat.mtrl-sci↗

arXiv2024

Semiconductive and Ferromagnetic Lanthanide MXenes Derived from Carbon Intercalated Two-dimensional Halides

Two-dimensional (2D) magnetic semiconductors are a key focus in developing next-generation information storage technologies. MXenes, as emerging 2D early transition metal carbides and nitrides, offer versatile compositions and tunable chemical structures. Incorporating lanthanide metals, with their unique role of 4f-electrons in engineering physical properties, into MXenes holds potential for advancing technological applications. However, the scarcity of lanthanide-containing ternary MAX phase precursors and the propensity of lanthanides to oxidize pose significant challenges to obtain lanthanide MXenes (Ln2CT2) via the top-down etching method. Here, we propose a general bottom-up methodology for lanthanide MXenes, that derive from carbon intercalated van der Waals building blocks of 2D halides. Compared to conventional MXenes conductors, the synthesized Ln2CT2 exhibit tunable band gaps spanning 0.32 eV to 1.22 eV that cover typical semiconductors such as Si (1.12 eV) and Ge (0.67 eV). Additionally, the presence of unpaired f-electrons endows Ln2CT2 with intrinsic ferromagnetism, with Curie temperatures ranging between 36 K and 60 K. Theoretical calculations reveal that, in contrast to traditional MXenes, the number of d-electrons states around the Fermi level are largely diminishes in bare Ln2C MXenes, and the halogen terminals can further exhaust these electrons to open band gaps. Meanwhile, the Ln-4f electrons in Ln2CT2 are highly localized and stay away from the Fermi level, contributing to the spin splitting for the observed ferromagnetic behavior. Lanthanide MXenes hold immense promise for revolutionizing future applications in spintronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Merging Parameter Estimation and Classification Using LASSO

Soft sensing is a way to indirectly obtain information of signals for which direct sensing is difficult or prohibitively expensive. It may not \textit{a priori} be evident which sensors provide useful information about the target signal, and various operating conditions often necessitate different models. In this paper, we provide a systematic method to construct a soft sensor that can deal with these issues. We propose a single estimation criterion, where the objectives are encoded in terms of model fit, model sparsity (reducing the number of different models), and model parameter coefficient sparsity (to exclude irrelevant sensors). The proposed method is tested on real-world scenarios involving prototype vehicles, demonstrating its effectiveness.

eess.SY↗

arXiv2023

Structure maps for MAX phases formability revisited

The extraordinary chemical diversity of MAX phases raises the question of how many and which novel ones are yet to be discovered. The conventional schemes rely either on executions of well designed experiments or elaborately crafted calculations; both of which have been key tactics within the past several decades that have yielded many of important new materials we are studying and using today. However, these approaches are expensive despite the emergence of high throughput automations or evolution of high speed computers. In this work, we have revisited the in prior proposed light duty strategy, i.e. structure mapping, for describing the genomic conditions under which one MAX phase could form; that allow us to make successful formability and non formability separation of MAX phases with a fidelity of 95.5%. Our results suggest that the proposed coordinates, and further the developed structure maps, are able to offer a useful initial guiding principles for systematic screenings of potential MAX phases and provide untapped opportunities for their structure prediction and materials design.

cond-mat.mtrl-sci↗

arXiv2023

Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples

Recently, the decentralized baseband processing (DBP) paradigm and relevant detection methods have been proposed to enable extremely large-scale massive multiple-input multiple-output technology. Under the DBP architecture, base station antennas are divided into several independent clusters, each connected to a local computing fabric. However, current detection methods tailored to DBP only consider ideal white Gaussian noise scenarios, while in practice, the noise is often colored due to interference from neighboring cells. Moreover, in the DBP architecture, linear minimum mean-square error (LMMSE) detection methods rely on the estimation of the noise covariance matrix through averaging distributedly stored noise samples. This presents a significant challenge for decentralized LMMSE-based equalizer design. To address this issue, this paper proposes decentralized LMMSE equalization methods under colored noise scenarios for both star and daisy chain DBP architectures. Specifically, we first propose two decentralized equalizers for the star DBP architecture based on dimensionality reduction techniques. Then, we derive an optimal decentralized equalizer using the block coordinate descent (BCD) method for the daisy chain DBP architecture with a bandwidth reduction enhancement scheme based on decentralized low-rank decomposition. Finally, simulation results demonstrate that our proposed methods can achieve excellent detection performance while requiring much less communication bandwidth.

eess.SP↗

arXiv2023

Intercalation of metal into transition metal dichalcogenides in molten salts

Van der Waals (vdW) layered materials have drawn tremendous interests due to their unique properties. Atom intercalation in the vdW gap of layered materials can tune their electronic structure and generate unexpected properties. Here we report a chemical-scissor mediated method that enables metal intercalation into transition metal dichalcogenides (TMDCs) in molten salts. By using this approach, various guest metal atoms (Mn, Fe, Co, Ni, Cu, and Ag) were intercalated into various TMDCs hosts (such as TiS2, NbS2, TaS2, TiSe2, NbSe2, TaSe2 and Ti0.5V0.5S2). The structure of the intercalated compound and intercalation mechanism was investigated. The results indicate that the vdW gap and valence state of TMDCs can be modified through metal intercalation, and the intercalation behavior is dictated by the electron work function. Such a chemical-scissor mediated intercalation provides an approach to tune the physical and chemical properties of TMDCs, which may open an avenue in functional application ranging from energy conversion to electronics.

cond-mat.mtrl-sci↗

arXiv2022

Chemical-scissor-mediated structural editing of layered transition metal carbides

Intercalation of non-van der Waals (vdW) layered materials can produce new 2D and 3D materials with unique properties, but it is difficult to achieve. Here, we describe a structural editing protocol for 3D non-vdW layered ternary carbides and nitrides (MAX phases) and their 2D vdW derivatives (MXenes). Gap-opening and species-intercalating stages were mediated by chemical scissors and guest intercalants, creating a large family of layered materials with unconventional elements and structures in MAX phases, as well as MXenes with versatile termination species. Removal of surface terminations by metal scissors and stitching of carbide layers by metal atoms leads to a reverse transformation from MXenes to MAX phases, and metal-intercalated 2D carbides. This scissor-mediated structural editing may enable structural and chemical tailoring of other layered ceramics.

cond-mat.mtrl-sci↗

arXiv2021

The Rendering from the Periodic System of the Elements on the stability, elastic, and electronic properties of M2AC phases

MAX phases are nanolaminated ternary materials that combine metallic and ceramic properties. Currently, the A-site elements replacement in traditional ones by later transition-metals opens a door to explore new types of MAX phases. In this work, we performed a systematic first-principle study to explore trends in stability, electronic structure and mechanical properties of 288 compositions of M2AX phases (M=Ti, V, Cr, Zr, Nb, Mo, Hf, Ta, W; A=Al, Si, P, S, Ga, Ge, As, Se, In, Sn, Sb, Te, Tl, Pb, Bi, Mn, Fe, Co, Ni, Cu, Zn, Tc, Ru, Rh, Pd, Ag, Cd, Os, Ir, Pt, Au, Hg; X=C). Such a dataset, combined with the rigid-band model been applied to most transition metal carbides, shows us the fundamental trends in bonding mechanisms and mechanical properties of MAX phases endowed with the periodic arrangements of M/A-site elements. It worth noting, in particular, the M-A d-d interactions of MAX phases uniquely contribute to the elastic constant C33.

cond-mat.mtrl-sci↗

arXiv2021

Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction

Diabetes prediction is an important data science application in the social healthcare domain. There exist two main challenges in the diabetes prediction task: data heterogeneity since demographic and metabolic data are of different types, data insufficiency since the number of diabetes cases in a single medical center is usually limited. To tackle the above challenges, we employ gradient boosting decision trees (GBDT) to handle data heterogeneity and introduce multi-task learning (MTL) to solve data insufficiency. To this end, Task-wise Split Gradient Boosting Trees (TSGB) is proposed for the multi-center diabetes prediction task. Specifically, we firstly introduce task gain to evaluate each task separately during tree construction, with a theoretical analysis of GBDT's learning objective. Secondly, we reveal a problem when directly applying GBDT in MTL, i.e., the negative task gain problem. Finally, we propose a novel split method for GBDT in MTL based on the task gain statistics, named task-wise split, as an alternative to standard feature-wise split to overcome the mentioned negative task gain problem. Extensive experiments on a large-scale real-world diabetes dataset and a commonly used benchmark dataset demonstrate TSGB achieves superior performance against several state-of-the-art methods. Detailed case studies further support our analysis of negative task gain problems and provide insightful findings. The proposed TSGB method has been deployed as an online diabetes risk assessment software for early diagnosis.

cs.LG↗

arXiv2021

Primary-Auxiliary Model Scheduling Based Estimation of the Vertical Wheel Force in a Full Vehicle System

In this work, we study estimation problems in nonlinear mechanical systems subject to non-stationary and unknown excitation, which are common and critical problems in design and health management of mechanical systems. A primary-auxiliary model scheduling procedure based on time-domain transmissibilities is proposed and performed under switching linear dynamics: In addition to constructing a primary transmissibility family from the pseudo-inputs to the output during the offline stage, an auxiliary transmissibility family is constructed by further decomposing the pseudo-input vector into two parts. The auxiliary family enables to determine the unknown working condition at which the system is currently running at, and then an appropriate transmissibility from the primary transmissibility family for estimating the unknown output can be selected during the online estimation stage. As a result, the proposed approach offers a generalizable and explainable solution to the signal estimation problems in nonlinear mechanical systems in the context of switching linear dynamics with unknown inputs. A real-world application to the estimation of the vertical wheel force in a full vehicle system are, respectively, conducted to demonstrate the effectiveness of the proposed method. During the vehicle design phase, the vertical wheel force is the most important one among Wheel Center Loads (WCLs), and it is often measured directly with expensive, intrusive, and hard-to-install measurement devices during full vehicle testing campaigns. Meanwhile, the estimation problem of the vertical wheel force has not been solved well and is still of great interest. The experimental results show good performances of the proposed method in the sense of estimation accuracy for estimating the vertical wheel force.

eess.SY↗

arXiv2021

Decentralized Linear MMSE Equalizer Under Colored Noise for Massive MIMO Systems

Conventional uplink equalization in massive MIMO systems relies on a centralized baseband processing architecture. However, as the number of base station antennas increases, centralized baseband processing architectures encounter two bottlenecks, i.e., the tremendous data interconnection and the high-dimensional computation. To tackle these obstacles, decentralized baseband processing was proposed for uplink equalization, but only applicable to the scenarios with unpractical white Gaussian noise assumption. This paper presents an uplink linear minimum mean-square error (L-MMSE) equalization method in the daisy chain decentralized baseband processing architecture under colored noise assumption. The optimized L-MMSE equalizer is derived by exploiting the block coordinate descent method, which shows near-optimal performance both in theoretical and simulation while significantly mitigating the bottlenecks.

eess.SP↗