pwc-archive/papers-with-abstracts huggingface dataset:
MASC: Integrated Sensing and Communications for the Martian Internet of Space
Authors: Haofan Dong, Ozgur B. Akan
Date: 2025-06-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

Mars exploration missions increasingly demand reliable communication systems, yet harsh environmental conditions -- particularly frequent dust storms, extreme Doppler effects, and stringent resource constraints -- pose unprecedented challenges to conventional communication approaches. This paper presents the Martian Adaptive Sensing and Communication (MASC) system specifically designed for the Martian environment. MASC establishes a physically interpretable channel model and develops three key components: environment-aware hybrid precoding, adaptive parameter mapping, and robust communication precoding. Simulation results demonstrate that MASC maintains 45 percent sensing coverage under severe dust conditions compared to only 5 percent with conventional methods, provides up to 2.5 dB signal-to-interference-plus-noise ratio (SINR) improvement at 50 percent channel state information (CSI) uncertainty, and yields 80 percent higher capacity in moderate dust storms. Using an epsilon-constraint multi-objective optimization approach, we enable mission planners to select operational modes ranging from communication-priority (0.33 bps/Hz capacity, 28 percent sensing coverage) to sensing-priority (90 percent coverage with minimal capacity), offering a versatile framework that balances environmental awareness with hyper-reliable data transmission. This work provides a validated blueprint for integrated sensing and communication (ISAC) in non-terrestrial networks (NTN), a key enabler for achieving ubiquitous connectivity in the 6G era.
DCFNet: Doppler Correction Filter Network for Integrated Sensing and Communication in Multi-User MIMO-OFDM Systems
Authors: Hyeonho Noh, Hyeonsu Lyu, Moe Z. Win, Hyun Jong Yang
Date: 2025-06-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is a headline feature for the forthcoming IMT-2030 and 6G releases, yet a concrete solution that fits within the established orthogonal frequency division multiplexing (OFDM) family remains open. Specifically, Doppler-induced inter-carrier interference (ICI) destroys sub-carrier orthogonality of OFDM sensing signals, blurring range-velocity maps and severely degrading sensing accuracy. Building on multi-user multi-input-multi-output (MIMO) OFDM systems, this paper proposes Doppler-Correction Filter Network (DCFNet), an AI-native ISAC model that delivers fine range-velocity resolution at minimal complexity without altering the legacy frame structure. A bank of DCFs first shifts dominant ICI energy away from critical Doppler bins; a compact deep learning network then suppresses the ICI. To further enhance the range and velocity resolutions, we propose DCFNet with local refinement (DCFNet-LR), which applies a generalized likelihood ratio test (GLRT) to refine target estimates of DCFNet to sub-cell accuracy. Simulation results show that DCFNet-LR runs $143\times$ faster than maximum likelihood search and achieves significantly superior performance, reducing the range RMSE by up to $2.7 \times 10^{-4}$ times and the velocity RMSE by $6.7 \times 10^{-4}$ times compared to conventional detection methods.
Multigroup Multicast Design for Pinching-Antenna Systems: Waveguide-Division or Waveguide-Multiplexing?
Authors: Shan Shan, Chongjun Ouyang, Yong Li, Yuanwei Liu
Date: 2025-06-19 00:00:00

This article addresses the design of multigroup multicast communications in the pinching-antenna system (PASS). A PASS-enabled multigroup transmission framework is proposed to maximize multicast rates under a couple of transmission architectures: waveguide-division (WD) and waveguide-multiplexing (WM). 1) For WD, an element-wise sequential optimization strategy is proposed for pinching beamforming, i.e., optimizing the activated positions of pinching antennas along dielectric waveguides. Meanwhile, a log-sum-exp projected gradient descent algorithm is proposed for transmit power allocation across waveguides. 2) For WM, a majorization-minimization (MM)-based framework is proposed to tackle the problem's non-smoothness and non-convexity. On this basis, a low-complexity element-wise sequential optimization method is developed for pinching beamforming using the MM surrogate objective. Furthermore, the optimal transmit beamformer structure is derived from the MM surrogate objective using the Lagrange duality, with an efficient transmit beamforming algorithm proposed using projected adaptive gradient descent. Numerical results demonstrate that: i) both WD and WM architectures in PASS achieve significant multicast rate improvements over conventional MIMO techniques, especially for systems with large service areas; ii) WM is more robust than WD in dense deployments, while WD excels when user groups are spatially separated.
Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons
Authors: Mathushaharan Rathakrishnan, Samiru Gayan, Rohit Singh, Amandeep Kaur, Hazer Inaltekin, Sampath Edirisinghe, H. Vincent Poor
Date: 2025-06-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

It is envisioned that 6G networks will be supported by key architectural principles, including intelligence, decentralization, interoperability, and digitalization. With the advances in artificial intelligence (AI) and machine learning (ML), embedding intelligence into the foundation of wireless communication systems is recognized as essential for 6G and beyond. Existing radio access network (RAN) architectures struggle to meet the ever growing demands for flexibility, automation, and adaptability required to build self-evolving and autonomous wireless networks. In this context, this paper explores the transition towards AI-driven RAN (AI-RAN) by developing a novel AI-RAN framework whose performance is evaluated through a practical scenario focused on intelligent orchestration and resource optimization. Besides, the paper reviews the evolution of RAN architectures and sheds light on key enablers of AI-RAN including digital twins (DTs), intelligent reflecting surfaces (IRSs), large generative AI (GenAI) models, and blockchain (BC). Furthermore, it discusses the deployment challenges of AI-RAN, including technical and regulatory perspectives, and outlines future research directions incorporating technologies such as integrated sensing and communication (ISAC) and agentic AI.
A Scalable Factorization Approach for High-Order Structured Tensor Recovery
Authors: Zhen Qin, Michael B. Wakin, Zhihui Zhu
Date: 2025-06-19 00:00:00
Tasks: Tensor Decomposition

Tensor decompositions, which represent an $N$-order tensor using approximately $N$ factors of much smaller dimensions, can significantly reduce the number of parameters. This is particularly beneficial for high-order tensors, as the number of entries in a tensor grows exponentially with the order. Consequently, they are widely used in signal recovery and data analysis across domains such as signal processing, machine learning, and quantum physics. A computationally and memory-efficient approach to these problems is to optimize directly over the factors using local search algorithms such as gradient descent, a strategy known as the factorization approach in matrix and tensor optimization. However, the resulting optimization problems are highly nonconvex due to the multiplicative interactions between factors, posing significant challenges for convergence analysis and recovery guarantees. In this paper, we present a unified framework for the factorization approach to solving various tensor decomposition problems. Specifically, by leveraging the canonical form of tensor decompositions--where most factors are constrained to be orthonormal to mitigate scaling ambiguity--we apply Riemannian gradient descent (RGD) to optimize these orthonormal factors on the Stiefel manifold. Under a mild condition on the loss function, we establish a Riemannian regularity condition for the factorized objective and prove that RGD converges to the ground-truth tensor at a linear rate when properly initialized. Notably, both the initialization requirement and the convergence rate scale polynomially rather than exponentially with $N$, improving upon existing results for Tucker and tensor-train format tensors.
Multi-Domain Optimization Framework for ISAC: From Electromagnetic Shaping to Network Cooperation
Authors: Rang Liu, Ming Li, Mehdi Zafari, Bjorn Ottersten, A. Lee Swindlehurst
Date: 2025-06-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has emerged as a key feature for sixth-generation (6G) networks, providing an opportunity to meet the dual demands of communication and sensing. Existing ISAC research primarily focuses on baseband optimization at individual access points, with limited attention to the roles of electromagnetic (EM) shaping and network-wide coordination. The intricate interdependencies between these domains remain insufficiently explored, leaving their full potential for enhancing ISAC performance largely untapped. To bridge this gap, we consider multi-domain ISAC optimization integrating EM shaping, baseband processing, and network cooperation strategies that facilitate efficient resource management and system-level design. We analyze the fundamental trade-offs between these domains and offer insights into domain-specific and cross-domain strategies contributing to ISAC performance and efficiency. We then conduct a case study demonstrating the effectiveness of joint multi-domain optimization. Finally, we discuss key challenges and future research directions to connect theoretical advancements and practical ISAC deployments. This work paves the way for intelligent and scalable ISAC architectures, providing critical insights for their seamless integration into next-generation wireless networks.
Exploiting Both Pilots and Data Payloads for Integrated Sensing and Communications
Authors: Chen Xu, Xianghao Yu, Fan Liu, Shi Jin
Date: 2025-06-19 00:00:00
Tasks: ISAC

Integrated sensing and communications (ISAC) is one of the key enabling technologies in future sixth-generation (6G) networks. Current ISAC systems predominantly rely on deterministic pilot signals within the signal frame to accomplish sensing tasks. However, these pilot signals typically occupy only a small portion, e.g., 0.15% to 25%, of the time-frequency resources. To enhance the system utility, a promising solution is to repurpose the extensive random data payload signals for sensing tasks. In this paper, we analyze the ISAC performance of a multi-antenna system where both deterministic pilot and random data symbols are employed for sensing tasks. By capitalizing on random matrix theory (RMT), we first derive a semi-closed-form asymptotic expression of the ergodic linear minimum mean square error (ELMMSE). Then, we formulate an ISAC precoding optimization problem to minimize the ELMMSE, which is solved via a specifically tailored successive convex approximation (SAC) algorithm. To provide system insights, we further derive a closed-form expression for the asymptotic ELMMSE at high signal-to-noise ratios (SNRs). Our analysis reveals that, compared with conventional sensing implemented by deterministic signals, the sensing performance degradation induced by random signals is critically determined by the ratio of the transmit antenna size to the data symbol length. Based on this result, the ISAC precoding optimization problem at high SNRs is transformed into a convex optimization problem that can be efficiently solved. Simulation results validate the accuracy of the derived asymptotic expressions of ELMMSE and the performance of the proposed precoding schemes. Particularly, by leveraging data payload signals for sensing tasks, the sensing error is reduced by up to 5.6 dB compared to conventional pilot-based sensing.
Theoretical Analysis of Near-Field MIMO Channel Capacity and Mid-Band Experimental Validation
Authors: Haiyang Miao, Jianhua Zhang, Pan Tang, Heng Wang, Lei Tian, Guangyi Liu
Date: 2025-06-19 00:00:00

With the increase of multiple-input-multiple-output (MIMO) array size and carrier frequency, near-field MIMO communications will become crucial in 6G wireless networks. Due to the increase of MIMO near-field range, the research of near-field MIMO capacity has aroused wide interest. In this paper, we focus on the theoretical analysis and empirical study of near-field MIMO capacity. First, the near-field channel model is characterized from the electromagnetic information perspective. Second, with the uniform planar array (UPA), the channel capacity based on effective degree of freedom (EDoF) is analyzed theoretically, and the closed-form analytical expressions are derived in detail. Finally, based on the numerical verification of near-field channel measurement experiment at 13 GHz band, we reveal that the channel capacity of UPA-type MIMO systems decreases continuously with the communication distance increasing. It can be observed that the near-field channel capacity gain is relatively obvious when large-scale MIMO is adopted at both receiving and transmitter ends, but the near-field channel capacity gain may be limited in the actual communication system with the small antenna array at receiving end. This work will give some reference to the near-field communication systems.
Fractional Reasoning via Latent Steering Vectors Improves Inference Time Compute
Authors: Sheng Liu, Tianlang Chen, Pan Lu, Haotian Ye, Yizheng Chen, Lei Xing, James Zou
Date: 2025-06-18 00:00:00
Tasks: continuous-control, Continuous Control, GSM8K

Test-time compute has emerged as a powerful paradigm for improving the performance of large language models (LLMs), where generating multiple outputs or refining individual chains can significantly boost answer accuracy. However, existing methods like Best-of-N, majority voting, and self-reflection typically apply reasoning in a uniform way across inputs, overlooking the fact that different problems may require different levels of reasoning depth. In this work, we propose Fractional Reasoning, a training-free and model-agnostic framework that enables continuous control over reasoning intensity at inference time, going beyond the limitations of fixed instructional prompts. Our method operates by extracting the latent steering vector associated with deeper reasoning and reapplying it with a tunable scaling factor, allowing the model to tailor its reasoning process to the complexity of each input. This supports two key modes of test-time scaling: (1) improving output quality in breadth-based strategies (e.g., Best-of-N, majority voting), and (2) enhancing the correctness of individual reasoning chains in depth-based strategies (e.g., self-reflection). Experiments on GSM8K, MATH500, and GPQA demonstrate that Fractional Reasoning consistently improves performance across diverse reasoning tasks and models.
Near-Field SWIPT with gMIMO in the Upper Mid-Band: Opportunities, Challenges, and the Way Forward
Authors: Özlem Tugfe Demir, Mustafa Ozger, Ferdi Kara, Woong-Hee Lee, Emil Björnson
Date: 2025-06-18 00:00:00

This paper explores the integration of simultaneous wireless information and power transfer (SWIPT) with gigantic multiple-input multiple-output (gMIMO) technology operating in the upper mid-band frequency range (7-24 GHz). The near-field propagation achieved by gMIMO introduces unique opportunities for energy-efficient, high-capacity communication systems that cater to the demands of 6G wireless networks. Exploiting spherical wave propagation, near-field SWIPT with gMIMO enables precise energy and data delivery, enhancing spectral efficiency through beamfocusing and massive spatial multiplexing. This paper discusses theoretical principles, design challenges, and enabling solutions, including advanced channel estimation techniques, precoding strategies, and dynamic array configurations such as sparse and modular arrays. Through analytical insights and a case study, this paper demonstrates the feasibility of achieving optimized energy harvesting and data throughput in dense and dynamic environments. These findings contribute to advancing energy-autonomous Internet-of-Everything (IoE) deployments, smart factory networks, and other energy-autonomous applications aligned with the goals of next-generation wireless technologies.
Analyzing URA Geometry for Enhanced Spatial Multiplexing and Extended Near-Field Coverage
Authors: Ahmed Hussain, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil
Date: 2025-06-18 00:00:00

With the deployment of large antenna arrays at high frequency bands, future wireless communication systems are likely to operate in the radiative near-field. Unlike far-field beam steering, near-field beams can be focused within a spatial region of finite depth, enabling spatial multiplexing in both the angular and range dimensions. This paper derives the beamdepth for a generalized uniform rectangular array (URA) and investigates how array geometry influences the near-field beamdepth and the limits where near-field beamfocusing is achievable. To characterize the near-field boundary in terms of beamfocusing and spatial multiplexing gains, we define the effective beamfocusing Rayleigh distance (EBRD) for a generalized URA. Our analysis reveals that while a square URA achieves the narrowest beamdepth, the EBRD is maximized for a wide or tall URA. However, despite its narrow beamdepth, a square URA may experience a reduction in multiuser sum rate due to its severely constrained EBRD. Simulation results confirm that a wide or tall URA achieves a sum rate of 3.5 X more than that of a square URA, benefiting from the extended EBRD and improved spatial multiplexing capabilities.
Effect of Signal Quantization on Performance Measures of a 1st Order One Dimensional Differential Microphone Array
Authors: Shweta Pal, Arun Kumar, Monika Agrawal
Date: 2025-06-18 00:00:00
Tasks: Quantization

In practical systems, recorded analog signals must be digitized for processing, introducing quantization as a critical aspect of data acquisition. While prior studies have examined quantization effects in various signal processing contexts, its impact on differential microphone arrays (DMAs), particularly in one-dimensional (1D) first-order configurations, remains unexplored. This paper investigates the influence of signal quantization on performance of first-order 1D DMAs across various beampatterns. An analytical expression for quantized beamformed output for a first-order 1D DMA has been formulated. The effect of signal quantization has been studied on array performance measures such as the Beampattern, Directivity Factor (DF), Front-to-Back Ratio (FBR), and null depth (ND). Simulation results reveal that beampattern shape remains structurally invariant across quantization bit depths, with quantization primarily affecting ND. DF and FBR remain constant with the varying number of quantization bits. Additionally, ND is shown to be frequency-independent; however, it increases with increasing quantization bit depths, enhancing interference suppression. The study also examines the effect of steering nulls across the azimuthal range, showing that ND degrades as the null moves closer to the source look direction, indicating reduced interference suppression.
Urban RIS-Assisted HAP Networks: Performance Analysis Using Stochastic Geometry
Authors: Islam M. Tanash, Ayush Kumar Dwivedi, Taneli Riihonen
Date: 2025-06-18 00:00:00
Tasks: Point Processes

This paper studies a high-altitude platform (HAP) network supported by reconfigurable intelligent surfaces (RISs). The practical irregular placement of HAPs and RISs is modeled using homogeneous Poisson point processes, while buildings that cause blockages in urban areas are modeled as a Boolean scheme of rectangles. We introduce a novel approach to characterize the statistical channel based on generalized Beta prime distribution. Analytical expressions for coverage probability and ergodic capacity in an interference-limited system are derived and validated through Monte Carlo simulations. The findings show notable performance improvements and reveal the impact of various system parameters, including blockages effect which contribute in mitigating interference from the other visible HAPs. This proposed system could enhance connectivity and enable effective data offloading in urban environments.
A Smooshed BMOCZ Zero Constellation for CFO Estimation Without Channel Coding
Authors: Anthony Joseph Perre, Parker Huggins, Alphan Sahin
Date: 2025-06-14 00:00:00

In this study, we propose a new binary modulation on conjugate-reciprocal zeros (BMOCZ) zero constellation, which we call smooshed binary modulation on conjugate-reciprocal zeros (SBMOCZ), to address carrier frequency offset (CFO)-induced zero rotation without depending on channel coding. In our approach, we modify the phase mapping of Huffman BMOCZ by shrinking the angle between adjacent zeros, except for the first and last, to introduce a gap in the zero constellation. By discerning the gap location in the received polynomial, the receiver can estimate and correct the phase rotation. We demonstrate the error rate performance of SBMOCZ relative to Huffman BMOCZ, showing that SBMOCZ addresses a CFO-induced rotation at the cost of a modest performance reduction compared to Huffman BMOCZ in the absence of a CFO. Finally, we compare SBMOCZ to Huffman BMOCZ using a cyclically permutable code (CPC), showing a 4 dB bit error rate (BER) improvement in a fading channel, while demonstrating comparable performance across other simulations.
Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access
Authors: Anup Mishra, Čedomir Stefanović, Xiuqiang Xu, Petar Popovski, Israel Leyva-Mayorga
Date: 2025-06-18 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Flexible and efficient wireless resource sharing across heterogeneous services is a key objective for future wireless networks. In this context, we investigate the performance of a system where latency-constrained internet-of-things (IoT) devices coexist with a broadband user. The base station adopts a grant-free access framework to manage resource allocation, either through orthogonal radio access network (RAN) slicing or by allowing shared access between services. For the IoT users, we propose a reinforcement learning (RL) approach based on double Q-Learning (QL) to optimise their repetition-based transmission strategy, allowing them to adapt to varying levels of interference and meet a predefined latency target. We evaluate the system's performance in terms of the cumulative distribution function of IoT users' latency, as well as the broadband user's throughput and energy efficiency (EE). Our results show that the proposed RL-based access policies significantly enhance the latency performance of IoT users in both RAN Slicing and RAN Sharing scenarios, while preserving desirable broadband throughput and EE. Furthermore, the proposed policies enable RAN Sharing to be energy-efficient at low IoT traffic levels, and RAN Slicing to be favourable under high IoT traffic.