arXiv subjects

Tianyu Fang

Publications and source records attributed to Tianyu Fang.

At least 19 recordsLinked to original sources

arXiv2026

Performance Analysis and Joint Beamforming for Hybrid RIS-Aided Massive MIMO ISAC

In integrated sensing and communication (ISAC) systems, stringent sensing performance constraints can severely limit the power available for communication. Hybrid reconfigurable intelligent surfaces (HRISs) with capabilities of both passive reflection and active signal amplification can significantly improve communication performance in the power-limited regime. This motivates us to analyze and optimize the performance of an HRIS-aided multiple-input-multiple-output (mMIMO) ISAC system. We first estimate the effective uplink/downlink channels using the minimum mean square error method. We then derive closed-form expressions for the communication sum-rate and sensing Cram\'er-Rao lower bound (CRLB). It is shown that under the equal power allocation strategy, the CRLB remains independent of the HRIS coefficients. Then, we formulate a joint optimization problem of power allocation and HRIS beamforming to maximize the communication sum-rate while ensuring specified sensing CRLB constraints. To solve the formulated non-convex problem, we propose an alternating optimization algorithm based on fractional programming and successive convex approximation. Extensive simulations validate our analysis and proposed algorithm, showing significant improvements in both communication and sensing performances enabled by the HRIS. For example, an HRIS with only $4$ active elements offers $97.30\%$ improvement in the communication sum-rate, while ensuring a sensing CRLB constraint of $-30$ dB.

eess.SP↗

arXiv2026

Aromatic molecular emitters in a hexagonal boron nitride stack

Single polycyclic aromatic hydrocarbon molecules embedded in organic matrices have proven to be an excellent family of narrow-linewidth quantum emitters. Extending this host-guest setting to van der Waals materials offers the opportunity to combine the preeminent properties of molecular emitters with the access to the versatility of two-dimensional hetero-structures and devices. In this work, we incorporate perylene molecules into multi-layered hexagonal boron nitride stacks and observe gigahertz-narrow zero-phonon-line transitions at cryogenic temperatures. We unambiguously verify the origins of photon emission through vibronic spectra analysis. By combining hyperspectral localization measurements with quantum chemistry calculations, we examine the insertion mechanisms of perylene molecules in the hexagonal boron nitride stacks, and conclude that pristine hBN layers tend to expel molecules from the sandwich, while extended morphological defects, hydroxyl groups and unpassivated boron and nitrogen atoms assist to stabilize molecular bindings to hBN. Our work provides valuable insight for future work to deterministically integrate narrow-linewidth molecular emitters into van der Waals devices.

physics.optics↗

arXiv2026

Two-Layer Microwave Linear Analog Computer (MiLAC)-aided Multi-user MISO Networks

Microwave linear analog computer (MiLAC)-aided transmit beamforming, which processes transmitted symbols entirely in the analog domain, has recently emerged as a promising alternative to fully digital or hybrid beamforming architectures for single-user multi-antenna systems. However, recent studies have shown that deploying a single lossless and reciprocal MiLAC at the transmitter cannot achieve the same capacity as fully digital beamforming in multi-user scenarios. To address this limitation, we propose a novel two-layer MiLAC-aided beamforming architecture at the transmitter for a downlink multi-user multiple-input single-output (MISO) network. Leveraging microwave network theory, we first prove that lossless and reciprocal two-layer MiLAC-aided beamforming can achieve the same performance as digital beamforming, and we derive a closed-form mapping from digital beamforming to two-layer MiLAC analog beamforming. Furthermore, we formulate a sum-rate maximization problem and develop an efficient optimization framework to jointly optimize the power allocation and the scattering matrices for the proposed two-layer MiLAC architecture. Numerical results validate our theoretical findings and demonstrate that two-layer MiLAC achieves the same sum-rate performance as fully digital beamforming.

eess.SP↗

arXiv2026

Tri-Hybrid Beamforming Design for integrated Sensing and Communications

Tri-hybrid beamforming architectures have been proposed to enable energy-efficient communications systems in extra-largescale antenna arrays using low-cost programmable metasurface antennas. We study the tri-hybrid beamforming design for integrated sensing and communications (ISAC) to improve both communications and sensing performances. Specifically, we formulate a multi-objective optimization problem that balances communications signal-to-noise ratio (SNR) and the sensing power at a target direction, subject to constraints on the total power consumption and physical limitations inherent to the trihybrid beamforming architecture. We develop an efficient iterative algorithm in which the variables are updated in a closed form at each iteration, leading to a low-complexity and fast-execution design. Numerical results show that the tri-hybrid architecture improves spatial gain and energy efficiency, though with reduced beam alignment capability compared to conventional hybrid beamforming architectures.

cs.IT↗

arXiv2026

On the Performance of Lossless Reciprocal MiLAC Architectures in Multi-User Networks

Microwave linear analog computer (MiLAC)-aided beamforming, which processes the transmitted symbols fully in the analog domain, has recently emerged as a promising alternative to fully digital and hybrid beamforming architectures for multiple-input multiple-output (MIMO) systems. While prior studies have shown that lossless and reciprocal MiLAC can achieve the same capacity as digital beamforming in a single-user MIMO network, its performance in multi-user scenarios remains unknown. To answer this question, in this work, we establish a downlink multi-user multiple-input single-output (MU-MISO) network with a MiLAC-aided transmitter, and investigate its sum-rate performance. Based on the microwave network theory, we first prove that lossless and reciprocal MiLAC cannot achieve the same performance as digital beamforming in a general MU-MISO network. Then, we formulate a sum-rate maximization problem and develop an efficient optimization framework to jointly optimize the power allocation and the scattering matrix for MiLAC. Numerical results validate our theoretical analysis and demonstrate that MiLAC is a promising architecture for future extremely large-scale MIMO systems.

eess.SP↗

arXiv2025

Generalized Beyond-Diagonal RIS Architectures: Theory and Design via Structure-oriented Symmetric Unitary Projection

Beyond-diagonal reconfigurable intelligent surface (BD-RIS), which enables advanced wave control through interconnection of RIS elements, are gaining growing recognition as a promising technology for 6G and beyond. However, the enhanced flexibility of BD-RIS in controlling the phase and amplitude of reflected signals comes at the cost of high circuit complexity. In this paper, we propose two novel BD-RIS architectures, namely, the stem-connected RIS and cluster-connected RIS, to explore trade-off between circuit complexity and performance. Specifically, the proposed stem-connected RIS is capable of achieving the same performance as fully-connected RIS while significantly reducing circuit complexity. The proposed cluster-connected RIS offers a unified framework that generalizes existing BD-RIS architectures--including single-connected, fully-connected, group-connected, tree-connected (arrowhead), and forest-connected (arrowhead) RISs--as special cases. This framework enables a much more flexible trade-offs between circuit complexity and system performance than existing ones. Based on the proposed BD-RIS architectures, we introduce a novel and generalized structure-oriented symmetric unitary projection method for designing the scattering matrix across all BD-RIS configurations. This method is effectively applied to solve the sum channel gain maximization problem and other utility-based optimization problems. Numerical results demonstrate that the proposed stem-connected RIS is the simplest architecture that achieves optimal BD-RIS performance, while the cluster-connected RIS further enlarges the performance-complexity trade-off range. Furthermore, the proposed projection-based algorithms demonstrate high efficiency.

eess.SP↗

arXiv2025

BuildingBRep-11K: Precise Multi-Storey B-Rep Building Solids with Rich Layout Metadata

With the rise of artificial intelligence, the automatic generation of building-scale 3-D objects has become an active research topic, yet training such models still demands large, clean and richly annotated datasets. We introduce BuildingBRep-11K, a collection of 11 978 multi-storey (2-10 floors) buildings (about 10 GB) produced by a shape-grammar-driven pipeline that encodes established building-design principles. Every sample consists of a geometrically exact B-rep solid-covering floors, walls, slabs and rule-based openings-together with a fast-loading .npy metadata file that records detailed per-floor parameters. The generator incorporates constraints on spatial scale, daylight optimisation and interior layout, and the resulting objects pass multi-stage filters that remove Boolean failures, undersized rooms and extreme aspect ratios, ensuring compliance with architectural standards. To verify the dataset's learnability we trained two lightweight PointNet baselines. (i) Multi-attribute regression. A single encoder predicts storey count, total rooms, per-storey vector and mean room area from a 4 000-point cloud. On 100 unseen buildings it attains 0.37-storey MAE (87 \% within $\pm1$), 5.7-room MAE, and 3.2 m$^2$ MAE on mean area. (ii) Defect detection. With the same backbone we classify GOOD versus DEFECT; on a balanced 100-model set the network reaches 54 \% accuracy, recalling 82 \% of true defects at 53 \% precision (41 TP, 9 FN, 37 FP, 13 TN). These pilots show that BuildingBRep-11K is learnable yet non-trivial for both geometric regression and topological quality assessment

cs.LG↗

arXiv2025

Optimal ISAC Beamforming Structure and Efficient Algorithms for Sum Rate and CRLB Balancing

Integrated sensing and communications (ISAC) has emerged as a promising paradigm to unify wireless communications and radar sensing, enabling efficient spectrum and hardware utilization. A core challenge with realizing the gains of ISAC stems from the unique challenges of dual purpose beamforming design due to the highly non-convex nature of key performance metrics such as sum rate for communications and the Cramer-Rao lower bound (CRLB) for sensing. In this paper, we propose a low-complexity structured approach to ISAC beamforming optimization to simultaneously enhance spectral efficiency and estimation accuracy. Specifically, we develop a successive convex approximation (SCA) based algorithm which transforms the original non-convex problem into a sequence of convex subproblems ensuring convergence to a locally optimal solution. Furthermore, leveraging the proposed SCA framework and the Lagrange duality, we derive the optimal beamforming structure for CRLB optimization in ISAC systems. Our findings characterize the reduction in radar streams one can employ without affecting performance. This enables a dimensionality reduction that enhances computational efficiency. Numerical simulations validate that our approach achieves comparable or superior performance to the considered benchmarks while requiring much lower computational costs.

cs.IT↗

arXiv2025

Low-Complexity Cramér-Rao Lower Bound and Sum Rate Optimization in ISAC Systems

While Cramér-Rao lower bound is an important metric in sensing functions in integrated sensing and communications (ISAC) designs, its optimization usually involves a computationally expensive solution such as semidefinite relaxation. In this paper, we aim to develop a low-complexity yet efficient algorithm for CRLB optimization. We focus on a beamforming design that maximizes the weighted sum between the communications sum rate and the sensing CRLB, subject to a transmit power constraint. Given the non-convexity of this problem, we propose a novel method that combines successive convex approximation (SCA) with a shifted generalized power iteration (SGPI) approach, termed SCA-SGPI. The SCA technique is utilized to approximate the non-convex objective function with convex surrogates, while the SGPI efficiently solves the resulting quadratic subproblems. Simulation results demonstrate that the proposed SCA-SGPI algorithm not only achieves superior tradeoff performance compared to existing method but also significantly reduces computational time, making it a promising solution for practical ISAC applications.

cs.IT↗

arXiv2025

Joint Active and Passive Beamforming Optimization for Beyond Diagonal RIS-aided Multi-User Communications

Benefiting from its capability to generalize existing reconfigurable intelligent surface (RIS) architectures and provide additional design flexibility via interactions between RIS elements, beyond-diagonal RIS (BD-RIS) has attracted considerable research interests recently. However, due to the symmetric and unitary passive beamforming constraint imposed on BD-RIS, existing joint active and passive beamforming optimization algorithms for BD-RIS either exhibit high computational complexity to achieve near optimal solutions or rely on heuristic algorithms with substantial performance loss. In this paper, we address this issue by proposing an efficient optimization framework for BD-RIS assisted multi-user multi-antenna communication networks. Specifically, we solve the weighted sum rate maximization problem by introducing a novel beamforming optimization algorithm that alternately optimizes active and passive beamforming matrices using iterative closed-form solutions. Numerical results demonstrate that our algorithm significantly reduces computational complexity while ensuring a sub-optimal solution.

eess.SP↗

arXiv2024

A Novel Q-stem Connected Architecture for Beyond-Diagonal Reconfigurable Intelligent Surfaces

Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has garnered significant research interest recently due to its ability to generalize existing reconfigurable intelligent surface (RIS) architectures and provide enhanced performance through flexible inter-connection among RIS elements. However, current BD-RIS designs often face challenges related to high circuit complexity and computational complexity, and there is limited study on the trade-off between system performance and circuit complexity. To address these issues, in this work, we propose a novel BD-RIS architecture named Q-stem connected RIS that integrates the characteristics of existing single connected, tree connected, and fully connected BD-RIS, facilitating an effective trade-off between system performance and circuit complexity. Additionally, we propose two algorithms to design the RIS scattering matrix for a Q-stem connected RIS aided multi-user broadcast channels, namely, a low-complexity least squares (LS) algorithm and a suboptimal LS-based quasi-Newton algorithm. Simulations show that the proposed architecture is capable of attaining the sum channel gain achieved by fully connected RIS while reducing the circuit complexity. Moreover, the proposed LS-based quasi-Newton algorithm significantly outperforms the baselines, while the LS algorithm provides comparable performance with a substantial reduction in computational complexity.

cs.IT↗

arXiv2024

Model-Based Machine Learning for Max-Min Fairness Beamforming Design in JCAS Systems

Joint communications and sensing (JCAS) is expected to be a crucial technology for future wireless systems. This paper investigates beamforming design for a multi-user multi-target JCAS system to ensure fairness and balance between communications and sensing performance. We jointly optimize the transmit and receive beamformers to maximize the weighted sum of the minimum communications rate and sensing mutual information. The formulated problem is highly challenging due to its non-smooth and non-convex nature. To overcome the challenges, we reformulate the problem into an equivalent but more tractable form. We first solve this problem by alternating optimization (AO) and then propose a machine learning algorithm based on the AO approach. Numerical results show that our algorithm scales effectively with the number of the communications users and provides better performance with shorter run time compared to conventional optimization approaches.

eess.SP↗

arXiv2024

Multi-Static Cell-Free Massive MIMO ISAC: Performance Analysis and Power Allocation

In this work, we consider a cell-free massive multiple-input multiple-output (MIMO) integarted sensing and communications (ISAC) system with maximum-ratio transmission schemes combined with multistatic radar-type sensing. Our focus lies on deriving closed-form expressions for the achievable communications rate and the Cramér-Rao lower bound (CRLB), which serve as performance metrics for communications and sensing operations, respectively. The expressions enable us to investigate important operational characteristics of multistatic cell-free massive MIMO-ISAC, including the mutual effects of communications and sensing as well as the advantages stemming from using numerous distributed antenna arrays for each functionality. Furthermore, we optimize the power allocation among the access points to maximize the communications rate while guaranteeing the CRLB constraints and total transmit power budget. Extensive numerical results are presented to validate our theoretical findings and demonstrate the efficiency of the proposed power allocation approach.

eess.SP↗

arXiv2024

Beamforming Design for Max-Min Fairness Performance Balancing in ISAC Systems

Integrated sensing and communications (ISAC) is envisioned as a key technology for future wireless communications. In this paper, we consider a downlink monostatic ISAC system wherein the base station serves multiple communications users and sensing targets at the same time in the presence of clutter. We aim at both guaranteeing fairness among the communications users while simultaneously balancing the performances of communications and sensing functionalities. Therefore, we optimize the transmit and receive beamformers to maximize the weighted minimum signal-to-interference and clutter-plus-noise ratios. The design problem is highly challenging due to the non-smooth and non-convex objective function and strongly coupled variables. We propose two efficient methods to solve the problem. First, we rely on fractional programming and transform the original problem into convex sub-problems, which can be solved with standard convex optimization tools. To further reduce the complexity and dependence on numerical tools, we develop a novel approach to address the inherent non-smoothness of the formulated problem. Finally, the efficiencies of the proposed designs are demonstrated by numerical results.

cs.IT↗

arXiv2024

Rate Splitting Multiple Access: Optimal Beamforming Structure and Efficient Optimization Algorithms

Joint optimization for common rate allocation and beamforming design have been widely studied in rate splitting multiple access (RSMA) empowered multiuser multi-antenna transmission networks. Due to the highly coupled optimization variables and non-convexity of the joint optimization problems, emerging algorithms such as weighted minimum mean square error (WMMSE) and successive convex approximation (SCA) have been applied to RSMA which typically approximate the original problem with a sequence of disciplined convex subproblems and solve each subproblem by an optimization toolbox. While these approaches are capable of finding a viable solution, they are unable to offer a comprehensive understanding of the solution structure and are burdened by high computational complexity. In this work, for the first time, we identify the optimal beamforming structure and common rate allocation for the weighted sum-rate (WSR) maximization problem of RSMA. We then propose a computationally efficient optimization algorithm that jointly optimizes the beamforming and common rate allocation without relying on any toolbox. Specifically, we first approximate the original WSR maximization problem with a sequence of convex subproblems based on fractional programming (FP). Numerical results show that the proposed algorithm achieves the same performance but takes only 0.5\% or less simulation time compared with the state-of-the-art WMMSE, SCA, and FP algorithms. Experimental results show that the proposed two methods have similar mean square error (MSE) performance compared with traditional method using CVX optimization tool, however, computational complexities are greatly reduced. The proposed algorithms pave the way for the practical and efficient optimization algorithm design for RSMA and its applications in 6G.

cs.IT↗

arXiv2023

Optimal Beamforming Structure and Efficient Optimization Algorithms for Generalized Multi-Group Multicast Beamforming Optimization

In this work, we focus on solving non-smooth non-convex maximization problems in multi-group multicast transmission. Leveraging Karush-Kuhn-Tucker (KKT) optimality conditions and successive incumbent transcending (SIT) duality, we thoroughly analyze the optimal beamforming structure for a set of optimization problems characterized by a general utility-based objective function. By exploiting the identified optimal structure, we further unveil inherent low-dimensional beamforming structures within the problems, which are asymptotically optimal in various regimes of transmit signal-to-noise ratios (SNRs) or the number of transmit antennas. Building upon the discovered optimal and low-dimensional beamforming structures, we then propose highly efficient and toolbox-free optimization algorithms to solve a specific multi-group multicast optimization problem based on the weighted sum rate (WSR) utility function. The proposed algorithms first use the cyclic maximization (CM) framework to decompose the problem into multiple subproblems, each has an optimal or low-dimensional closed-form beamforming solution structure. Then, we propose the projected adaptive gradient descent (PAGD) algorithm to compute the optimal Lagrangian dual variables for each subproblem. Numerical results show that the proposed algorithms maintain comparable or improved WSR performance compared to baseline algorithms, while dramatically reducing the computational complexity. Notably, the proposed ultra-low-complexity algorithms based on low-dimensional beamforming structures achieve near optimal WSR performance with extremely low computational complexity. This complexity remains independent of the number of transmit antennas, making them promising and practical for extremely large multiple-input multiple-output (XL-MIMO) applications in 6G.

cs.IT↗

arXiv2023

A Low-Complexity Beamforming Design for Beyond-Diagonal RIS aided Multi-User Networks

Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has been proposed recently as a novel and generalized RIS architecture that offers enhanced wave manipulation flexibility and large coverage expansion. However, the beyond-diagonal mathematical model in BD-RIS inevitably introduces additional optimization challenges in beamforming design. In this letter, we derive a closed-form solution for the BD-RIS passive beamforming matrix that maximizes the sum of the effective channel gains among users. We further propose a computationally efficient two-stage beamforming framework to jointly design the active beamforming at the base station and passive beamforming at the BD-RIS to enhance the sum-rate for a BD-RIS aided multi-user multi-antenna network.Numerical results show that our proposed algorithm achieves a higher sum-rate while requiring less computation time compared to state-of-the-art algorithms. The proposed algorithm paves the way for practical beamforming design in BD-RIS aided wireless networks.

cs.IT↗

arXiv2023

Optimal Beamforming Structure for Rate Splitting Multiple Access

In this paper, we aim at maximizing the weighted sum-rate (WSR) of rate splitting multiple access (RSMA) in multi-user multi-antenna transmission networks through the joint optimization of rate allocation and beamforming. Unlike conventional methods like weighted minimum mean square error (WMMSE) and standard fractional programming (FP), which tackle the non-convex WSR problem iteratively using disciplined convex subproblems and optimization toolboxes, our work pioneers a novel toolbox-free approach. For the first time, we identify the optimal beamforming structure and common rate allocation for WSR maximization in RSMA by leveraging FP and Lagrangian duality. Then we propose an algorithm based on FP and fixed point iteration to optimize the beamforming and common rate allocation without the need for optimization toolboxes. Our numerical results demonstrate that the proposed algorithm attains the same performance as standard FP and classical WMMSE methods while significantly reducing computational time.

cs.IT↗