pwc-archive/papers-with-abstracts huggingface dataset:
Optimum Power Allocation for Low Rank Wi-Fi Channels: A Comparison with Deep RL Framework
Authors: Muhammad Ahmed Mohsin, Sagnik Bhattacharya, Kamyar Rajabalifardi, Rohan Pote, John M. Cioffi
Date: 2025-01-20 00:00:00
Tasks: Deep Reinforcement Learning

Upcoming Augmented Reality (AR) and Virtual Reality (VR) systems require high data rates ($\geq$ 500 Mbps) and low power consumption for seamless experience. With an increasing number of subscribing users, the total number of antennas across all transmitting users far exceeds the number of antennas at the access point (AP). This results in a low rank wireless channel, presenting a bottleneck for uplink communication systems. The current uplink systems that use orthogonal multiple access (OMA) and the proposed non-orthogonal multiple access (NOMA), fail to achieve the required data rates / power consumption under predominantly low rank channel scenarios. This paper introduces an optimal power sub carrier allocation algorithm for multi-carrier NOMA, named minPMAC, and an associated time-sharing algorithm that adaptively changes successive interference cancellation decoding orders to maximize sum data rates in these low rank channels. This Lagrangian based optimization technique, although globally optimum, is prohibitive in terms of runtime, proving inefficient for real-time scenarios. Hence, we propose a novel near-optimal deep reinforcement learning-based energy sum optimization (DRL-minPMAC) which achieves real-time efficiency. Extensive experimental evaluations show that minPMAC achieves 28\% and 39\% higher data rates than NOMA and OMA baselines. Furthermore, the proposed DRL-minPMAC runs ~5 times faster than minPMAC and achieves 83\% of the global optimum data rates in real time
The Expected Peak-to-Average Power Ratio of White Gaussian Noise in Sampled I/Q Data
Authors: Adam Wunderlich, Aric Sanders
Date: 2025-01-20 00:00:00

One of the fundamental endeavors in radio frequency (RF) metrology is to measure the power of signals, where a common aim is to estimate the peak-to-average power ratio (PAPR), which quantifies the ratio of the maximum (peak) to the mean value. For a finite number of discrete-time samples of baseband in-phase and quadrature (I/Q) white Gaussian noise (WGN) that are independent and identically distributed with zero mean, we derive a closed-form, exact formula for mean PAPR that is well-approximated by the natural logarithm of the number of samples plus Euler's constant. Additionally, we give related theoretical results for the mean crest factor (CF). After comparing our main result to previously published approximate formulas, we examine how violations of the WGN assumptions in sampled I/Q data result in deviations from the expected value of PAPR. Finally, utilizing a measured RF I/Q acquisition, we illustrate how our formula for mean PAPR can be applied to spectral analysis with spectrograms to verify when measured RF emissions are WGN in a given frequency band.
Energy Consumption Reduction for UAV Trajectory Training : A Transfer Learning Approach
Authors: Chenrui Sun, Swarna Bindu Chetty, Gianluca Fontanesi, Jie Zhang, Amirhossein Mohajerzadeh, David Grace, Hamed Ahmadi
Date: 2025-01-20 00:00:00
Tasks: Transfer Learning

The advent of 6G technology demands flexible, scalable wireless architectures to support ultra-low latency, high connectivity, and high device density. The Open Radio Access Network (O-RAN) framework, with its open interfaces and virtualized functions, provides a promising foundation for such architectures. However, traditional fixed base stations alone are not sufficient to fully capitalize on the benefits of O-RAN due to their limited flexibility in responding to dynamic network demands. The integration of Unmanned Aerial Vehicles (UAVs) as mobile RUs within the O-RAN architecture offers a solution by leveraging the flexibility of drones to dynamically extend coverage. However, UAV operating in diverse environments requires frequent retraining, leading to significant energy waste. We proposed transfer learning based on Dueling Double Deep Q network (DDQN) with multi-step learning, which significantly reduces the training time and energy consumption required for UAVs to adapt to new environments. We designed simulation environments and conducted ray tracing experiments using Wireless InSite with real-world map data. In the two simulated environments, training energy consumption was reduced by 30.52% and 58.51%, respectively. Furthermore, tests on real-world maps of Ottawa and Rosslyn showed energy reductions of 44.85% and 36.97%, respectively.
Channel Sounding Using Multiplicative Arrays Based on Successive Interference Cancellation Principle
Authors: Zhangzhang Jiang, Zhiqiang Yuan, Chunhui Li, Le Yu, Wei Fan
Date: 2025-01-19 00:00:00
Tasks: parameter estimation

Ultra-massive multiple-input and multiple-output (MIMO) systems have been seen as the key radio technology for the advancement of wireless communication systems, due to its capability to better utilize the spatial dimension of the propagation channels. Channel sounding is essential for developing accurate and realistic channel models for the massive MIMO systems. However, channel sounding with large-scale antenna systems has faced significant challenges in practice. The real antenna array based (RAA) sounder suffers from high complexity and cost, while virtual antenna array (VAA) solutions are known for its long measurement time. Notably, these issues will become more pronounced as the antenna array configuration gets larger for future radio systems. In this paper, we propose the concept of multiplicative array (MA) for channel sounding applications to achieve large antenna aperture size with reduced number of required antenna elements. The unique characteristics of the MA are exploited for wideband spatial channel sounding purposes, supported by both one-path and multi-path numerical simulations. To address the fake paths and distortion in the angle delay profile issues inherent for MA in multipath channel sounding, a novel channel parameter estimation algorithm for MA based on successive interference cancellation (SIC) principle is proposed. Both numerical simulations and experimental validation results are provided to demonstrate the effectiveness and robustness of the proposed SIC algorithm for the MA. This research contributes significantly to the channel sounding and characterization of massive MIMO systems for future applications.
Design and Prototyping of Filtering Active STAR-RIS with Adjustable Power Splitting
Authors: Rongguang Song, Haifan Yin, Xilong Pei, Lin Cao, Taorui Yang, Xue Ren, Yuanwei Liu
Date: 2025-01-19 00:00:00

Reconfigurable Intelligent Surfaces (RISs) have emerged as a transformative technology for next-generation wireless communication systems, offering unprecedented control over electromagnetic wave propagation. In particular, Simultaneously Transmitting and Reflecting RISs (STAR-RISs) have garnered significant attention due to their full-space coverage. This paper presents an active STAR-RIS, which enables independent control of both transmission and reflection phases and features out-of-band harmonic suppression. Unlike the traditional passive RIS, the proposed design integrates active amplification to overcome the inherent passive losses, significantly enhancing signal strength and system performance. Additionally, the system supports dynamic power allocation between transmission and reflection modes, providing greater flexibility to meet diverse communication demands in complex propagation environments. The versatility of the design is further validated by extending the Radar Cross Section (RCS)-based path loss model to the STAR-RIS. This design improves efficiency, flexibility, and adaptability, offering a promising solution for future wireless communication systems, particularly in scenarios requiring simultaneous control of transmission and reflection signals.
Ambient Backscatter Communication in LTE Uplink Sounding Reference Signal
Authors: Jingyi Liao, Tianshu Zhang, Kalle Ruttik, Riku Jäntti, Dinh-Thuy Phan-Huy
Date: 2025-01-19 00:00:00

Ambient Internet of Things (AIoT), recently standardized by the 3rd Generation Partnership Project (3GPP), demands a low-power wide-area communication solution that operates several orders of magnitude below the power requirements of existing 3GPP specifications. Ambient backscatter communication (AmBC) is considered as a competitive potential technique by harvesting energy from the ambient RF signal. This paper considers a symbiotic AmBC into Long Term Evolution (LTE) cellular system uplink. Leveraging by LTE uplink channel estimation ability, AIoT conveys its own message to Base Station (BS) by modulating backscatter path. We explore the detector design, analyze the error performance of the proposed scheme, provide exact expression and its Guassian approximation for the error probability. We corroborate the receiver error performance by Monte Carlo simulation. Analysis of communication range reveals AmBC achieves a reasonable BER of order of magnitude $10^{-2}$ within four times wavelength reading distance. In addition, a AmBC prototype in LTE uplink confirms the its feasibility. The over-the-air experiment results validate theoretical analysis. Hence, the proposed AmBC approach enables AIoT deployment with minimal changes to the LTE system.
Synesthesia of Machines (SoM)-Aided FDD Precoding with Sensing Heterogeneity: A Vertical Federated Learning Approach
Authors: Haotian Zhang, Shijian Gao, Weibo Wen, Xiang Cheng
Date: 2025-01-19 00:00:00
Tasks: Federated Learning, Vertical Federated Learning

High complexity in precoding design for frequency division duplex systems necessitates streamlined solutions. Guided by Synesthesia of Machines (SoM), this paper introduces a heterogeneous multi-vehicle, multi-modal sensing aided precoding scheme within a vertical federated learning (VFL) framework, which significantly minimizes pilot sequence length while optimizing the system's sum rate. We address the challenges posed by local data heterogeneity due to varying on-board sensor configurations through a meticulously designed VFL training procedure. To extract valuable channel features from multi-modal sensing, we employ three distinct data preprocessing methods that convert raw data into informative representations relevant for precoding. Additionally, we propose an online training strategy based on VFL framework, enabling the scheme to adapt dynamically to fluctuations in user numbers. Numerical results indicate that our approach, utilizing short pilot sequences, closely approximates the performance of traditional optimization methods with perfect channel state information.
A Note on the Conversion of Nonnegative Integers to the Canonical Signed-digit Representation
Authors: R. J. Cintra
Date: 2025-01-19 00:00:00

This note addresses the signed-digit representation of nonnegative binary integers. Popular literature methods for the conversion into the canonical signed-digit representation are reviewed and revisited. A method based on string substitution is discussed.
A Novel Hybrid Precoder With Low-Resolution Phase Shifters and Fronthaul Capacity Limitation
Authors: Parisa Ramezani, Alva Kosasih, Emil Björnson
Date: 2025-01-18 00:00:00
Tasks: Quantization

In massive MIMO systems, fully digital precoding offers high performance but has significant implementation complexity and energy consumption, particularly at millimeter frequencies and beyond. Hybrid analog-digital architectures provide a practical alternative by reducing the number of radio frequency (RF) chains while retaining performance in spatially sparse multipath scenarios. However, most hybrid precoder designs assume ideal, infinite-resolution analog phase shifters, which are impractical in real-world scenarios. Another practical constraint is the limited fronthaul capacity between the baseband processor and array, implying that each entry of the digital precoder must be picked from a finite set of quantization labels. To minimize the sum rate degradation caused by quantized analog and digital precoders, we propose novel designs inspired by the sphere decoding (SD) algorithm. We demonstrate numerically that our proposed designs outperform traditional methods, ensuring minimal sum rate loss in hybrid precoding systems with low-resolution phase shifters and limited fronthaul capacity.
Generalized Spatial Modulation Aided Affine Frequency Division Multiplexing
Authors: Zeping Sui, Zilong Liu, Leila Musavian, Lie-Liang Yang, Lajos Hanzo
Date: 2025-01-18 00:00:00

Generalized spatial modulation-aided affine frequency division multiplexing (GSM-AFDM) is conceived for reliable multiple-input multiple-output (MIMO) communications over doubly selective channels. We commence by proposing several low-complexity detectors for large-scale GSM-AFDM systems. Specifically, we introduce the linear minimum mean square error (LMMSE) equalizer-based maximum likelihood detector (LMMSE-MLD). By exploiting the GSM properties, we then derive the LMMSE-based transmit-antenna activation pattern (TAP) check-based log-likelihood ratio detector (LMMSE-TC-LLRD). In addition, we propose a pair of new detectors, namely the greedy residual check detector (GRCD) and the reduced space check detector (RSCD). We also derive a bit error rate (BER) upper-bound by considering the MLD. Our simulation results demonstrate that 1) the BER upper bound derived is tight for moderate to high signal-to-noise ratios (SNRs), 2) the proposed GSM-AFDM achieves lower BER than its conventional counterparts, and 3) the conceived detectors strike a compelling trade-off between the BER and complexity.
Mathematical model of parameters relevance in adaptive level-crossing sampling for electrocardiogram signals
Authors: Silvio Zanoli, Giovanni Ansaloni, Tomás Teijeiro, David Atiena
Date: 2025-01-18 00:00:00
Tasks: Bayesian Optimization, Data Compression

Digital acquisition of bio-signals has been mostly dominated by uniform time sampling following the Nyquist theorem. However, in recent years, new approaches have emerged, focused on sampling a signal only when certain events happen. Currently, the most prominent of these approaches is Level Crossing (LC) sampling. Conventional level crossing analog-to-digital converters (LC-ADC) are often designed to make use of statically defined and uniformly spaced levels. However, a different positioning of the levels, optimized for bio-signals monitoring, can potentially lead to better performing solutions. In this work, we compare multiple LC-level definitions, including statically defined (uniform and logarithmic) configurations and optimization-driven designs (randomized and Bayesian optimization), assessing their ability to maintain signal fidelity while minimizing the sampling rate. In this paper, we analyze the performance of these different methodologies, which is evaluated using the root mean square error (RMSE), the sampling reduction factor (SRF) -- a metric evaluating the sampling compression ratio -- , and error per event metrics to gauge the trade-offs between signal fidelity and data compression. Our findings reveal that optimization-driven LC-sampling, particularly those using Bayesian methods, achieve a lower RMSE without substantially impacting the error per event compared to static configurations, but at the cost of an increase in the sampling rate.
Learning to reconstruct signals with inexact sensing operator via knowledge distillation
Authors: Roman Jacome, Leon Suarez, Romario Gualdrón-Hurtado, Luis Gonzalez, Henry Arguello
Date: 2025-01-18 00:00:00
Tasks: Knowledge Distillation

In computational optical imaging and wireless communications, signals are acquired through linear coded and noisy projections, which are recovered through computational algorithms. Deep model-based approaches, i.e., neural networks incorporating the sensing operators, are the state-of-the-art for signal recovery. However, these methods require exact knowledge of the sensing operator, which is often unavailable in practice, leading to performance degradation. Consequently, we propose a new recovery paradigm based on knowledge distillation. A teacher model, trained with full or almost exact knowledge of a synthetic sensing operator, guides a student model with an inexact real sensing operator. The teacher is interpreted as a relaxation of the student since it solves a problem with fewer constraints, which can guide the student to achieve higher performance. We demonstrate the improvement of signal reconstruction in computational optical imaging for single-pixel imaging with miscalibrated coded apertures systems and multiple-input multiple-output symbols detection with inexact channel matrix.
Low-Complexity Iterative Precoding Design for Near-field Multiuser Systems With Spatial Non-Stationarity
Authors: Mengyu Liu, Cunhua Pan, Kangda Zhi, Hong Ren, Cheng-Xiang Wang, Jiangzhou Wang, Yonina C. Eldar
Date: 2025-01-18 00:00:00

Extremely large antenna arrays (ELAA) are regarded as a promising technology for supporting sixth-generation (6G) networks. However, the large number of antennas significantly increases the computational complexity in precoding design, even for linearly regularized zero-forcing (RZF) precoding. To address this issue, a series of low-complexity iterative precoding are investigated. The main idea of these methods is to avoid matrix inversion of RZF precoding. Specifically, RZF precoding is equivalent to a system of linear equations that can be solved by fast iterative algorithms, such as random Kaczmarz (RK) algorithm. Yet, the performance of RK-based precoding algorithm is limited by the energy distributions of multiple users, which restricts its application in ELAA-assisted systems. To accelerate the RK-based precoding, we introduce the greedy random Kaczmarz (GRK)-based precoding by using the greedy criterion-based selection strategy. To further reduce the complexity of the GRK-based precoding, we propose a visibility region (VR)-based orthogonal GRK (VR-OGRK) precoding that leverages near-field spatial non-stationarity, which is characterized by the concept of VR. Next, by utilizing the information from multiple hyperplanes in each iteration, we extend the GRK-based precoding to the aggregation hyperplane Kaczmarz (AHK)-based pecoding algorithm, which further enhances the convergence rate. Building upon the AHK algorithm, we propose a VR-based orthogonal AHK (VR-OAHK) precoding to further reduce the computational complexity. Furthermore, the proposed iterative precoding algorithms are proven to converge to RZF globally at an exponential rate. Simulation results show that the proposed algorithms achieve faster convergence and lower computational complexity than benchmark algorithms, and yield very similar performance to the RZF precoding.
Predictive Target-to-User Association in Complex Scenarios via Hybrid-Field ISAC Signaling
Authors: Yifeng Yuan, Miaowen Wen, Xinhu Zheng, Shuoyao Wang, Shijian Gao
Date: 2025-01-18 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper presents a novel and robust target-to-user (T2U) association framework to support reliable vehicle-to-infrastructure (V2I) networks that potentially operate within the hybrid field (near-field and far-field). To address the challenges posed by complex vehicle maneuvers and user association ambiguity, an interacting multiple-model filtering scheme is developed, which combines coordinated turn and constant velocity models for predictive beamforming. Building upon this foundation, a lightweight association scheme leverages user-specific integrated sensing and communication (ISAC) signaling while employing probabilistic data association to manage clutter measurements in dense traffic. Numerical results validate that the proposed framework significantly outperforms conventional methods in terms of both tracking accuracy and association reliability.
Channel Estimation and Beamforming Design for MF-RIS-Aided Communication Systems
Authors: Zaihao Pan, Wen Wang, Gaofeng Nie, Ailing Zheng, Wanli Ni
Date: 2025-01-18 00:00:00

In this letter, we study the beamforming design for channel estimation of multi-functional reconfigurable intelligent surface (MF-RIS)-aided multi-user communications that supports simultaneous signal reflection, refraction, and amplification. A least square (LS) based channel estimator is proposed for MF-RIS by considering both the coupled MF-RIS beams and the introduced thermal noise. With the discrete fourier transform (DFT)-matrix, the MF-RIS beamforming design problem is simplified under the proposed LS channel estimator. The optimal MF-RIS beamforming design that achieves the Cram\'er-Rao lower bound (CRLB) of channel estimator is obtained with the proposed alternating optimization algorithm. Simulation results demonstrate the effectiveness of the proposed beamforming design in reducing the impact of thermal noise.
Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications
Authors: Yueling Zhou, Achintha Wijesinghe, Yue Wang, Songyang Zhang, Zhipeng Cai
Date: 2025-01-18 00:00:00
Tasks: Edge-computing, Federated Learning, Novel Concepts, Semantic Communication, Semantic Compression

Enriching information of spectrum coverage, radiomap plays an important role in many wireless communication applications, such as resource allocation and network optimization. To enable real-time, distributed spectrum management, particularly in the scenarios with unstable and dynamic environments, the efficient transmission of spectrum coverage information for radiomaps from edge devices to the central server emerges as a critical problem. In this work, we propose an innovative physics-enhanced semantic communication framework tailored for efficient radiomap transmission based on generative learning models. Specifically, instead of bit-wise message passing, we only transmit the key "semantics" in radiomaps characterized by the radio propagation behavior and surrounding environments, where semantic compression schemes are utilized to reduce the communication overhead. Incorporating the novel concepts of Radio Depth Maps, the radiomaps are reconstructed from the delivered semantic information backboned on the conditional generative adversarial networks. Our framework is further extended to facilitate its implementation in the scenarios of multi-user edge computing, by integrating with federated learning for collaborative model training while preserving the data privacy. Experimental results show that our approach achieves high accuracy in radio coverage information recovery at ultra-high bandwidth efficiency, which has great potentials in many wireless-generated data transmission applications.
Perception-Guided EEG Analysis: A Deep Learning Approach Inspired by Level of Detail (LOD) Theory
Authors: BG Tong
Date: 2025-01-11 00:00:00
Tasks: EEG, Rhythm, Unity

Objective: This study explores a novel deep learning approach for EEG analysis and perceptual state guidance, inspired by Level of Detail (LOD) theory. The goal is to improve perceptual state identification accuracy and advance personalized psychological therapy. Methods: Portable EEG devices and music rhythm signals were used for data collection. LOD theory was applied to dynamically adjust EEG signal processing, extracting core perceptual features. A Unity-based software system integrated EEG data with audio materials. The deep learning model combined a CNN for feature extraction and classification, and a DQN for reinforcement learning to optimize rhythm adjustments. Results: The CNN achieved 94.05% accuracy in perceptual state classification. The DQN guided subjects to target states with a 92.45% success rate, averaging 13.2 rhythm cycles. However, only 50% of users reported psychological alignment with the target state, indicating room for improvement. Discussion: The results validate the potential of LOD-based EEG biofeedback. Limitations include dataset source, label subjectivity, and reward function optimization. Future work will expand to diverse subjects, incorporate varied musical elements, and refine reward functions for better generalization and personalization.