arXiv subjects

Keshav Singh

Publications and source records attributed to Keshav Singh.

At least 19 recordsLinked to original sources

arXiv2026

Pinching Antenna Assisted Integrated Sensing and Communication Using Quadrature RSMA

This paper proposes a pinching antenna system (PASS)-assisted downlink quadrature rate-splitting multiple access (Q-RSMA)-based integrated sensing and communication (PASS-QRSMA-ISAC) framework for simultaneously supporting multi-user communication and target sensing. In the proposed framework, multiple pinching antennas (PAs) deployed along a dielectric waveguide serve communication users (CUs) distributed across a dual-room indoor environment, where line-of-sight (LoS) and non-line-of-sight (NLoS) propagation conditions coexist. By integrating PASS with Q-RSMA, the proposed framework improves interference management while reducing the dependence on successive interference cancellation. The communication and sensing signal models are developed, followed by the corresponding SNR/SINR expressions. To obtain tractable analytical insights, a two-user single-PA case under ideal waveguide conditions is first analysed, where the per-user communication outage probability (COP), system COP, sensing outage probability (SOP), and ergodic sum-rate are formulated using distance statistics, CDF/CCDF characterisations, and numerical integration methods. The analysis is then extended to the generalised multi-user multi-PA scenario by developing statistical characterisations for the LoS communication, NLoS communication, and sensing channels. Based on these characterisations, the per-user COP, system COP, SOP, and ergodic sum-rate are formulated and evaluated for the generalised framework. Monte Carlo simulations validate the analytical results and demonstrate the performance gains of the proposed PASS-QRSMA-ISAC framework over the considered benchmark schemes, including PASS-RSMA-ISAC, PASS-NOMA-ISAC, and PASS-SDMA-ISAC. The results further highlight the effects of various system parameters on the proposed framework.

eess.SP↗

arXiv2026

Distinguishing Mott and strain-induced anisotropic semimetals in magic-angle twisted bilayer graphene using Landau level spectroscopy

Recent quantum twisting microscopy experiments [1] have provided unprecedented momentum- and energy-resolved imaging of magic-angle twisted bilayer graphene, revealing a stark dichotomy between the heavy and light electronic characters of its narrow bands. In this work, we provide a sharp distinguishing criterion between the two most likely candidate states that exhibit spectroscopic features consistent with the experimental results. Focusing on charge neutrality, these states are 1) the 'Mott semimetal', governed by strong dynamical correlations, and 2) the 'strain-induced anisotropic semimetal', characterized by weak Hartree-Fock effects. We demonstrate that an out-of- plane magnetic field serves as a sharp discriminating probe based on the expected Landau level gap size hierarchy. We find that the strain-induced anisotropic semimetal has a robust gap hierarchy where the Chern C gaps, \Delta_C , decrease in order \Delta_{\pm 4} > \Delta_{\pm 8} > \Delta_{\pm 12}. In contrast, for the Mott semimetal, \Delta_{\pm 4} remains the largest gap, while the relative magnitude of the C = \pm 8, \pm 12 gaps depends sensitively on the applied magnetic field. We test the robustness of these results against strain orientation and lattice relaxation finding consistency between the gap hierarchy of the strained semimetal and previous incompressibility measurements at finite magnetic field [2].

cond-mat.str-el↗

arXiv2026

Joint Antenna Placement and Power Allocation for RSMA-Enabled Pinching Antenna Systems

This letter investigates a rate-splitting multiple access (RSMA)-enabled multi-user pinching antenna system (PASS). A fairness-aware sum-rate maximization problem is formulated to jointly optimize pinching antenna locations and common/private stream power allocation. The resulting mixed discrete-continuous non-convex problem is addressed using an alternating optimization framework that combines greedy antenna placement with successive convex approximation (SCA)-based power allocation. Numerical results demonstrate that the proposed RSMA-enabled PASS significantly improves achievable sum-rate, user fairness, and bit error rate (BER) performance compared with conventional non-RSMA PASS schemes.

eess.SP↗

arXiv2026

DL-Driven Optimization for ISAC System Equipped With Pinching and Movable Antennas

Integrated sensing and communication (ISAC) is considered one of the key technologies for future wireless networks, due to its ability to provide accurate environment awareness and improve energy, spectral and hardware efficiency. Moreover, the introduction of recently developed pinching antennas (PAs) and movable antennas (MAs) has the potential to further improve the performance gains of ISAC. Therefore, our goal is to study the optimization of the sum-rate for an ISAC system equipped with PAs and MAs, capable of satisfying minimal sensing requirements. To achieve it, we derive a closed-form solution for the optimal sensing receive combiner, and show that it is determined by other optimization variables. For these other variables (i.e., the positions of the transmit PAs, the positions of the users' MAs, the communication precoding matrices, and the sensing transmit beamformer), we propose a deep learning (DL) network that finds their optimal values. To train the network in an unsupervised manner, we formulate a loss function consisting of the objective function, as well as the penalty terms related to the constraints for the PAs and MAs positions. Simulation results show that using PAs and MAs in ISAC systems provides a larger sum-rate compared to ISAC systems with only fixed antennas, and that this performance advantage is increased with the maximum transmit power. Furthermore, we demonstrate that the communication performance of the considered system is a bit more affected by the sensing signal-to-interference-plus-noise ratio (SINR) threshold compared to the sensing performance.

cs.IT↗

arXiv2026

Joint Trajectory and Resource Optimization for Dual-aerial ARIS-assisted NOMA-TNT Networks

Integrated terrestrial and non-terrestrial networks (ITNTNs) are envisioned as a key paradigm for sixth-generation (6G) wireless systems, enabling seamless global connectivity. In this paper, we investigate a dual-aerial active reconfigurable intelligent surface (ARIS)-assisted non-orthogonal multiple access (NOMA)-based ITNTN, where a terrestrial base station (TBS) and a satellite (SAT) simultaneously serve terrestrial and satellite users with the aid of a UAV-mounted ARIS and a HAP-mounted ARIS. Users are multiplexed via power-domain NOMA with a predefined SIC decoding order. We formulate an average sum-rate maximization problem by jointly optimizing transmit beamforming, ARIS coefficients, and the 3D trajectories of the UAV and HAP, subject to power, unit-modulus, ARIS power, and mobility constraints. The problem is highly non-convex due to coupled variables, nonlinear SINR expressions, ARIS amplification, and trajectory-dependent channels. To address this, a block coordinate descent (BCD)-based framework is proposed. Specifically, beamforming is optimized via WMMSE, ARIS phase shifts via a manifold-based RCG method, amplification factors via SCA, and trajectories via first-order approximations. The proposed algorithm is guaranteed to converge to a stationary point. Simulation results demonstrate that the proposed design achieves significant performance gains over benchmark schemes. In particular, it provides an average sum-rate improvement of approximately $8.44\%$ over passive RIS under given power constraints, highlighting the benefits of dual-aerial ARIS and joint communication-mobility optimization.

eess.SP↗

arXiv2026

Joint Trajectory and Resource Optimization for Aerial RIS-assisted Integrated TNT Networks

Integrated terrestrial and non-terrestrial networks (ITNTNs) are regarded as a key architectural paradigm for sixth-generation (6G) wireless systems. This paper investigates a dual-aerial reconfigurable intelligent surface (RIS)-assisted ITNTN, where a terrestrial base station (TBS) and a satellite (SAT) jointly serve terrestrial and satellite users with the aid of an unmanned aerial vehicle (UAV)-mounted RIS and a high-altitude platform (HAP)-mounted RIS. We formulate an average sum-rate maximization problem by jointly optimizing the TBS and SAT precoders, the RIS phase shift matrices, and the three-dimensional trajectories of the UAV and the HAP, subject to transmit power, unit-modulus, and mobility constraints. The resulting optimization problem is highly non-convex due to the strong coupling among the transmit precoders, RIS phase shifts, and aerial platform mobility. To efficiently address this challenge, we propose a block coordinate descent (BCD) framework that integrates weighted minimum mean square error (WMMSE) optimization for precoder design, a manifold-based Riemannian conjugate gradient (RCG) method for RIS phase-shift optimization, and successive convex approximation (SCA) for trajectory optimization. The proposed algorithm is shown to converge to a stationary point. The simulation results show that the proposed joint design achieves an approximately $7.05 \%$ higher average sum-rate compared to the random RIS scheme, highlighting the effectiveness of dual-aerial RIS deployment and joint communication-mobility optimization in ITNTNs.

eess.SP↗

arXiv2026

Secure and Robust Beamforming Design for STAR-RIS-aided MU-MIMO ISAC Systems

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) offer a transformative approach for integrated sensing and communication (ISAC) systems, particularly for enhancing physical layer security (PLS). This paper investigates a robust, secure downlink transmission framework for a STAR-RIS empowered multi-user (MU) multiple-input multiple-output (MU-MIMO) system, where a multi-antenna dual-function radar and communication base station (DFRC-BS) that simultaneously transmits confidential messages to multiple intended users (IUs) and performs target sensing in the presence of malicious eavesdroppers. To optimize system security, we formulate a worst-case robust beamforming problem to maximize the secrecy rate. This formulation jointly designs the active transmit beamforming at the BS and the passive reflection, transmission coefficients at the STAR-RIS, adheres to transmit power budgets, user quality-of-service (QoS) thresholds, sensing signal-to-interference-plus-noise ratio (SINR) requirements, maximum tolerable eavesdropping leakage, and practical phase shifts constraints. To efficiently tackle the formulated problem, we develop an alternating optimization (AO) algorithm. Specifically, the S-procedure is employed to solve semi-infinite channel uncertainty constraints, while semidefinite relaxation (SDR) and penalty convex-concave programming (CCP) are applied to obtain tractable suboptimal solutions. Extensive simulation results validate the efficacy of the proposed framework and demonstrate significant improvement in spectral efficiency compared to conventional reflecting-only RIS (R-RIS) systems under stringent sensing conditions.

eess.SP↗

arXiv2025

Optimal Beamforming Design for Multi-user MIMO Near-Field ISAC Systems with Movable Antennas

Integrated sensing and communication (ISAC) has been recognized as one of the key technologies capable of simultaneously improving communication and sensing services in future wireless networks. Moreover, the introduction of recently developed movable antennas (MAs) has the potential to further increase the performance gains of ISAC systems. Although the gains of MA-enabled ISAC systems are relatively well studied in the far field, they remain almost unexplored in near-field scenarios. Motivated by this, in this paper we maximize the weighted sum rate (WSR) for communication users while maintaining a minimum sensing requirement in an MA-enabled near-field ISAC system. To achieve this goal, we propose algorithms that optimize the communication precoding matrices, the sensing transmit beamformer, the sensing receive combiner, the positions of the users' MAs and the positions of the base station (BS) transmit MAs in an alternating manner for the considered ISAC system, for the cases where linear procoding and zero-forcing (ZF) precoding are employed at the BS. Simulation results show that using MAs in near-field ISAC systems provides a substantial performance advantage compared to near-field ISAC systems equipped with fixed antennas only. We show that the scheme with linear precoding achieves larger WSR for unequal users' weight rates, while the scheme with ZF precoding maintains an approximately constant WSR for all users' weight rates. Additionally, we demonstrate that the WSRs of the proposed schemes are highly dependent on the inter-antenna interference between different user's MAs, and that the sensing performance is significantly more affected by the minimum sensing signal-to-interference-plus-noise ratio (SINR) threshold compared to the communication performance.

cs.IT↗

arXiv2025

Weighted Sum Rate Optimization for Movable Antenna Enabled Near-Field ISAC

Integrated sensing and communication (ISAC) has been recognized as one of the key technologies capable of simultaneously improving communication and sensing services in future wireless networks. Moreover, the introduction of recently developed movable antennas (MAs) has the potential to further increase the performance gains of ISAC systems. Achieving these gains can pose a significant challenge for MA-enabled ISAC systems operating in the near-field due to the corresponding spherical wave propagation. Motivated by this, in this paper we maximize the weighted sum rate (WSR) for communication users while maintaining a minimal sensing requirement in an MA-enabled near-field ISAC system. To achieve this goal, we propose an algorithm that optimizes the sensing receive combiner, the communication precoding matrices, the sensing transmit beamformer and the positions of the users' MAs in an alternating manner. Simulation results show that using MAs in near-field ISAC systems provides a substantial performance advantage compared to near-field ISAC systems with only fixed antennas. Additionally, we demonstrate that the highest WSR is obtained when larger weights are allocated to the users placed closer to the BS, and that the sensing performance is significantly more affected by the minimum sensing signal-to-interference-plus-noise ratio (SINR) threshold compared to the communication performance.

cs.IT↗

arXiv2025

Advanced Architectures Integrated with Agentic AI for Next-Generation Wireless Networks

This paper investigates a range of cutting-edge technologies and architectural innovations aimed at simplifying network operations, reducing operational expenditure (OpEx), and enabling the deployment of new service models. The focus is on (i) Proposing novel, more efficient 6G architectures, with both Control and User planes enabling the seamless expansion of services, while addressing long-term 6G network evolution. (ii) Exploring advanced techniques for constrained artificial intelligence (AI) operations, particularly the design of AI agents for real-time learning, optimizing energy consumption, and the allocation of computational resources. (iii) Identifying technologies and architectures that support the orchestration of backend services using serverless computing models across multiple domains, particularly for vertical industries. (iv) Introducing optically-based, ultra-high-speed, low-latency network architectures, with fast optical switching and real-time control, replacing conventional electronic switching to reduce power consumption by an order of magnitude.

cs.NI↗

arXiv2024

CmWave and Sub-THz: Key Radio Enablers and Complementary Spectrum for 6G

Sixth-generation (6G) networks are poised to revolutionize communication by exploring alternative spectrum options, aiming to capitalize on strengths while mitigating limitations in current fifth-generation (5G) spectrum. This paper explores the potential opportunities and emerging trends for cmWave and sub-THz spectra as key radio enablers. This paper poses and answers three key questions regarding motivation of additional spectrum to explore the strategic implementation and benefits of cmWave and sub-THz spectra. Also, we show using case studies how these complementary spectrum bands will enable new applications in 6G, such as integrated sensing and communication (ISAC), re-configurable intelligent surfaces (RIS) and non-terrestrial networks (NTN). Numerical simulations reveal that the ISAC performance of cmWave and sub-THz spectra outperforms that of existing 5G spectrum, including sub-6 GHz and mmWave. Additionally, we illustrate the effective interplay between RIS and NTN to counteract the effects of high attenuation at sub-THz frequencies. Finally, ongoing standardization endeavors, challenges and promising directions are elucidated for these complementary spectrum bands.

eess.SP↗

arXiv2024

An Overview of Intelligent Meta-surfaces for 6G and Beyond: Opportunities, Trends, and Challenges

With the impending arrival of the sixth generation (6G) of wireless communication technology, the telecommunications landscape is poised for another revolutionary transformation. At the forefront of this evolution are intelligent meta-surfaces (IS), emerging as a disruptive physical layer technology with the potential to redefine the capabilities and performance metrics of future wireless networks. As 6G evolves from concept to reality, industry stakeholders, standards organizations, and regulatory bodies are collaborating to define the specifications, protocols, and interoperability standards governing IS deployment. Against this background, this article delves into the ongoing standardization efforts, emerging trends, potential opportunities, and prevailing challenges surrounding the integration of IS into the framework of 6G and beyond networks. Specifically, it provides a tutorial-style overview of recent advancements in IS and explores their potential applications within future networks beyond 6G. Additionally, the article identifies key challenges in the design and implementation of various types of intelligent surfaces, along with considerations for their practical standardization. Finally, it highlights potential future prospects in this evolving field.

cs.NI↗

arXiv2023

Topological heavy fermions in magnetic field

The recently introduced topological heavy fermion model (THFM) provides a means for interpreting the low-energy electronic degrees of freedom of the magic angle twisted bilayer graphene as hybridization amidst highly dispersing topological conduction and weakly dispersing localized heavy fermions. In order to understand the Landau quantization of the ensuing electronic spectrum, a generalization of THFM to include the magnetic field B is desired, but currently missing. Here we provide a systematic derivation of the THFM in B and solve the resulting model to obtain the interacting Hofstadter spectra for single particle charged excitations. While naive minimal substitution within THFM fails to correctly account for the total number of magnetic subbands within the narrow band i.e. its total Chern number, our method -- based on projecting the light and heavy fermions onto the irreducible representations of the magnetic translation group -- reproduces the correct total Chern number. Analytical results presented here offer an intuitive understanding of the nature of the (strongly interacting) Hofstadter bands.

cond-mat.str-el↗

arXiv2022

TYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation

Providing feedback on the argumentation of the learner is essential for developing critical thinking skills, however, it requires a lot of time and effort. To mitigate the overload on teachers, we aim to automate a process of providing feedback, especially giving diagnostic comments which point out the weaknesses inherent in the argumentation. It is recommended to give specific diagnostic comments so that learners can recognize the diagnosis without misinterpretation. However, it is not obvious how the task of providing specific diagnostic comments should be formulated. We present a formulation of the task as template selection and slot filling to make an automatic evaluation easier and the behavior of the model more tractable. The key to the formulation is the possibility of creating a template set that is sufficient for practical use. In this paper, we define three criteria that a template set should satisfy: expressiveness, informativeness, and uniqueness, and verify the feasibility of creating a template set that satisfies these criteria as a first trial. We will show that it is feasible through an annotation study that converts diagnostic comments given in a text to a template format. The corpus used in the annotation study is publicly available.

cs.CL↗

arXiv2022

LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments

In argumentative discourse, persuasion is often achieved by refuting or attacking others arguments. Attacking is not always straightforward and often comprise complex rhetorical moves such that arguers might agree with a logic of an argument while attacking another logic. Moreover, arguer might neither deny nor agree with any logics of an argument, instead ignore them and attack the main stance of the argument by providing new logics and presupposing that the new logics have more value or importance than the logics present in the attacked argument. However, no existing studies in the computational argumentation capture such complex rhetorical moves in attacks or the presuppositions or value judgements in them. In order to address this gap, we introduce LPAttack, a novel annotation scheme that captures the common modes and complex rhetorical moves in attacks along with the implicit presuppositions and value judgements in them. Our annotation study shows moderate inter-annotator agreement, indicating that human annotation for the proposed scheme is feasible. We publicly release our annotated corpus and the annotation guidelines.

cs.CL↗

arXiv2021

Annotating Implicit Reasoning in Arguments with Causal Links

Most of the existing work that focus on the identification of implicit knowledge in arguments generally represent implicit knowledge in the form of commonsense or factual knowledge. However, such knowledge is not sufficient to understand the implicit reasoning link between individual argumentative components (i.e., claim and premise). In this work, we focus on identifying the implicit knowledge in the form of argumentation knowledge which can help in understanding the reasoning link in arguments. Being inspired by the Argument from Consequences scheme, we propose a semi-structured template to represent such argumentation knowledge that explicates the implicit reasoning in arguments via causality. We create a novel two-phase annotation process with simplified guidelines and show how to collect and filter high-quality implicit reasonings via crowdsourcing. We find substantial inter-annotator agreement for quality evaluation between experts, but find evidence that casts a few questions on the feasibility of collecting high-quality semi-structured implicit reasoning through our crowdsourcing process. We release our materials(i.e., crowdsourcing guidelines and collected implicit reasonings) to facilitate further research towards the structured representation of argumentation knowledge.

cs.CL↗

arXiv2021

An Analysis on Rate-Splitting Multiple Access for IRS Aided 6G Communication

Integrating intelligent reflecting surface (IRS) and Rate-Splitting Multiple Access (RSMA) is an effective solution to improve the spectral/energy efficiency in next-generation (beyond 5G (B5G) and 6G) wireless networks. In this paper, we investigate a rate-splitting (RS)-based transmission technique for an IRS-aided communication network involving both near and cell-edge users. In particular, we derive a new architecture called IRS-RS that leverages the interplay between RS and IRS, with an aim to maximize the weighted sum-rate (WSR) of users by selecting the reflecting coefficients at the IRS and designing beamformers at the BS under the constraints of power at the base station (BS), quality of service (QoS) at each user and finite resolution at the IRS. To solve the non-convex WSR maximization problem, we propose an alternating algorithm and compare its performance with baseline non-orthogonal multiple access (NOMA) based transmission for an IRS-aided communication network for both perfect and imperfect CSIT cases. Through numerical results, it is shown that the proposed IRS-RS architecture yields better QoS with respect to the cell-edge users when compared to IRS-NOMA transmission scheme.

eess.SP↗

arXiv2021

Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation

Generalized Spatial Modulation (GSM) is being considered for high capacity and energy-efficient networks of the future. However, signal detection due to inter-channel interference among the active antennas is a challenge in GSM systems and is the focus of this letter. Specifically, we explore the feasibility of using deep neural networks (DNN) for signal detection in GSM. In particular, we propose a block DNN (B-DNN) based architecture, where the active antennas and their transmitted constellation symbols are detected by smaller sub-DNNs. After N-ordinary DNN detection, the Euclidean distance-based soft constellation algorithm is implemented. The proposed B-DNN detector achieves a BER performance that is superior to traditional block zero-forcing (B-ZF) and block minimum mean-squared error (B-MMSE) detection schemes and similar to that of classical maximum likelihood (ML) detector. Further, the proposed method requires less computation time and is more accurate than alternative conventional numerical methods.

eess.SP↗