pwc-archive/papers-with-abstracts huggingface dataset:
Movable-Antenna Array Empowered ISAC Systems for Low-Altitude Economy
Authors: Ziming Kuang, Wenchao Liu, Chunjie Wang, Zhenzhen Jin, Jinke Ren, Xuhui Zhang, Yanyan Shen
Date: 2024-06-11 00:00:00
Tasks: ISAC

This paper investigates a movable-antenna (MA) array empowered integrated sensing and communications (ISAC) over low-altitude platform (LAP) system to support low-altitude economy (LAE) applications. In the considered system, an unmanned aerial vehicle (UAV) is dispatched to hover in the air, working as the UAV-enabled LAP (ULAP) to provide information transmission and sensing simultaneously for LAE applications. To improve the throughput capacity and meet the requirement of the sensing beampattern threshold, we formulate a data rate maximization problem by jointly optimizing the transmit information and sensing beamforming, and the antenna positions of the MA array. Since the data rate maximization problem is non-convex with highly coupled variables, we propose an efficient alternation optimization based algorithm, which iteratively optimizes parts of the variables while fixing the others. Numerical results show the superiority of the proposed MA array-based scheme in terms of the achievable data rate and beamforming gain compared with two benchmark schemes.
ACCO: Automated Causal CNN Scheduling Optimizer for Real-Time Edge Accelerators
Authors: Jun Yin, Linyan Mei, Andre Guntoro, Marian Verhelst
Date: 2024-06-11 00:00:00
Tasks: Edge-computing, Scheduling

Spatio-Temporal Convolutional Neural Networks (ST-CNN) allow extending CNN capabilities from image processing to consecutive temporal-pattern recognition. Generally, state-of-the-art (SotA) ST-CNNs inflate the feature maps and weights from well-known CNN backbones to represent the additional time dimension. However, edge computing applications would suffer tremendously from such large computation or memory overhead. Fortunately, the overlapping nature of ST-CNN enables various optimizations, such as the dilated causal convolution structure and Depth-First (DF) layer fusion to reuse the computation between time steps and CNN sliding windows, respectively. Yet, no hardware-aware approach has been proposed that jointly explores the optimal strategy from a scheduling as well as a hardware point of view. To this end, we present ACCO, an automated optimizer that explores efficient Causal CNN transformation and DF scheduling for ST-CNNs on edge hardware accelerators. By cost-modeling the computation and data movement on the accelerator architecture, ACCO automatically selects the best scheduling strategy for the given hardware-algorithm target. Compared to the fixed dilated causal structure, ST-CNNs with ACCO reach an ~8.4x better Energy-Delay-Product. Meanwhile, ACCO improves ~20% in layer-fusion optimals compared to the SotA DF exploration toolchain. When jointly optimizing ST-CNN on the temporal and spatial dimension, ACCO's scheduling outcomes are on average 19x faster and 37x more energy-efficient than spatial DF schemes.
Movable Antenna Enhanced NOMA Short-Packet Transmission
Authors: Xinyuan He, Wen Chen, Qingqing Wu, Xusheng Zhu, Nan Cheng
Date: 2024-06-11 00:00:00
Tasks: Position

This letter investigates a short-packet downlink transmission system using non-orthogonal multiple access (NOMA) enhanced via movable antenna (MA). We focuses on maximizing the effective throughput for a core user while ensuring reliable communication for an edge user by optimizing the MAs' coordinates and the power and rate allocations from the access point (AP). The optimization challenge is approached by decomposing it into two subproblems, utilizing successive convex approximation (SCA) to handle the highly non-concave nature of channel gains. Numerical results confirm that the proposed solution offers substantial improvements in effective throughput compared to NOMA short-packet communication with fixed position antennas (FPAs).
Secrecy Energy Efficiency Maximization in RIS-Aided Wireless Networks
Authors: Robert Kuku Fotock, Alessio Zappone, Marco Di Renzo
Date: 2024-06-11 00:00:00

This work proposes a provably convergent and low complexity optimization algorithm for the maximization of the secrecy energy efficiency in the uplink of a wireless network aided by a Reconfigurable Intelligent Surface (RIS), in the presence of an eavesdropper. The mobil users' transmit powers and the RIS reflection coefficients are optimized. Numerical results show the performance of the proposed methods and compare the use of active and nearly-passive RISs from an energy-efficient perspective.
Revolutionizing Wireless Networks with Self-Supervised Learning: A Pathway to Intelligent Communications
Authors: Zhixiang Yang, Hongyang Du, Dusit Niyato, Xudong Wang, Yu Zhou, Lei Feng, Fanqin Zhou, Wenjing Li, Xuesong Qiu
Date: 2024-06-11 00:00:00
Tasks: Self-Supervised Learning, Semantic Communication

With the rapid proliferation of mobile devices and data, next-generation wireless communication systems face stringent requirements for ultra-low latency, ultra-high reliability, and massive connectivity. Traditional AI-driven wireless network designs, while promising, often suffer from limitations such as dependency on labeled data and poor generalization. To address these challenges, we present an integration of self-supervised learning (SSL) into wireless networks. SSL leverages large volumes of unlabeled data to train models, enhancing scalability, adaptability, and generalization. This paper offers a comprehensive overview of SSL, categorizing its application scenarios in wireless network optimization and presenting a case study on its impact on semantic communication. Our findings highlight the potentials of SSL to significantly improve wireless network performance without extensive labeled data, paving the way for more intelligent and efficient communication systems.
Tackling Delayed CSI in a Distributed Multi-Satellite MIMO Communication System
Authors: Yasaman Omid, Sangarapillai Lambotharan, Mahsa Derakhshani
Date: 2024-06-10 00:00:00

In this study, we explore the integration of satellites with ground-based communication networks. Specifically, we analyze downlink data transmission from a constellation of satellites to terrestrial users and address the issue of delayed channel state information (CSI). The satellites cooperate in data transmission within a cluster to create a unified, distributed massive multiple input, multiple output (MIMO) system. The CSI used for this process is inherently outdated, particularly due to the delay from the most distant satellite in the cluster. Therefore, in this paper, we develop a precoding strategy that leverages the long-term characteristics of CSI uncertainty to compensate for the undesirable impact of these unavoidable delays. Our proposed method is computationally efficient and particularly effective in lower frequency bands. As such, it holds significant promise for facilitating the integration of satellite and terrestrial communication, especially within frequency bands of up to 1 GHz.
Feature Characterization for Profile Surface Texture
Authors: Alexander Müller, Matthias Eifler, Arsalan Jawaid, Jörg Seewig
Date: 2024-06-10 00:00:00
Tasks: Segmentation

Conventional field parameters for surface measurement use all data points, while feature characterization focuses on subsets extracted by watershed segmentation. This approach enables the extraction of specific features that are potentially responsible for the function of the surface or are a direct reflection of the manufacturing process, allowing for a more accurate assessment of both aspects. Feature characterization with the underlying watershed segmentation for areal surface topographies has been standardized for over a decade and is well established in industry and research. In contrast, feature characterization for surface profiles has been standardized recently, and the corresponding standard for watershed segmentation is planned to be published in the near future. Since the standards do not provide guidelines for implementation, this paper presents an unambiguous algorithm of the watershed segmentation and the feature characterization for surface profiles. This framework provides the basis for future work, mainly investigating the relationship between feature parameters based on feature characterization and the function of the surface or manufacturing process. For this purpose, recommendations for the configuration and extensions of the toolbox can also be developed, which could find their way into the ISO standards.
Machine Unlearning for Uplink Interference Cancellation
Authors: Eray Guven, Gunes Karabulut Kurt
Date: 2024-06-10 00:00:00
Tasks: Inference Attack, Machine Unlearning, Membership Inference Attack

Machine unlearning (MUL) is introduced as a means to achieve interference cancellation within artificial intelligence (AI)-enabled wireless systems. It is observed that interference cancellation with MUL demonstrates $30\%$ improvement in a classification task accuracy in the presence of a corrupted AI model. Accordingly, the necessity for instantaneous channel state information for existing interference source is eliminated and a corrupted latent space with interference noise is cleansed with MUL algorithm, achieving this without the necessity for either retraining or dataset cleansing. A Membership Inference Attack (MIA) served as a benchmark for assessing the efficacy of MUL in mitigating interference within a neural network model. The advantage of the MUL algorithm was determined by evaluating both the probability of interference and the quantity of samples requiring retraining. In a simple signal-to-noise ratio classification task, the comprehensive improvement across various test cases in terms of accuracy demonstrates that MUL exhibits extensive capabilities and limitations, particularly in native AI applications.
Imageless Contraband Detection Using a Millimeter-Wave Dynamic Antenna Array via Spatial Fourier Domain Sampling
Authors: Daniel Chen, Anton Schlegel, Jeffrey A. Nanzer
Date: 2024-06-09 00:00:00

We demonstrate an imageless method of concealed contraband detection using a real-time 75 GHz rotationally dynamic antenna array. The array measures information in the two-dimensional Fourier domain and captures a set of samples that is sufficient for detecting concealed objects yet insufficient for generating full image, thereby preserving the privacy of screened subjects. The small set of Fourier samples contains sharp spatial frequency features in the Fourier domain which correspond to sharp edges of man-made objects such as handguns. We evaluate a set of classification methods: threshold-based, K-nearest neighbor, and support vector machine using radial basis function; all operating on arithmetic features directly extracted from the sampled Fourier-domain responses measured by a dynamically rotating millimeter-wave active interferometer. Noise transmitters are used to produce thermal-like radiation from scenes, enabling direct Fourier-domain sampling, while the rotational dynamics circularly sample the two-dimensional Fourier domain, capturing the sharp-edge induced responses. We experimentally demonstrate the detection of concealed metallic gun-shape object beneath clothing on a real person in a laboratory environment and achieved an accuracy and F1-score both at 0.986. The presented technique not only prevents image formation due to efficient Fourier-domain space sub-sampling but also requires only 211 ms from measurement to decision.
Spatial Correlation Modeling and RS-LS Estimation of Near-Field Channels with Uniform Planar Arrays
Authors: Özlem Tuğfe Demir, Alva Kosasih, Emil Björnson
Date: 2024-06-09 00:00:00

Extremely large aperture arrays (ELAAs) can offer massive spatial multiplexing gains in the radiative near-field region in beyond 5G systems. While near-field channel modeling for uniform linear arrays has been extensively explored in the literature, uniform planar arrays-despite their advantageous form factor-have been somewhat neglected due to their more complex nature. Spatial correlation is crucial for non-line-of-sight channel modeling. Unlike far-field scenarios, the spatial correlation properties of near-field channels have not been thoroughly investigated. In this paper, we start from the fundamentals and develop a near-field spatial correlation model for arbitrary spatial scattering functions. Furthermore, we derive the lower dimensional subspace where the channel vectors can exist. It is based on prior knowledge of the three-dimensional coverage region where scattering clusters exists and we derive a tractable one-dimensional integral expression. This subspace is subsequently employed in a reduced-subspace least squares (RS-LS) estimation method for near-field channels, thereby enhancing performance over the traditional least squares estimator without the need for having full spatial correlation matrix knowledge.
Double-RIS-Assisted Orbital Angular Momentum Near-Field Secure Communications
Authors: Liping Liang, Minmin Wang, Wenchi Cheng, Wei zhang
Date: 2024-06-09 00:00:00

To satisfy the various demands of growing devices and services, emerging high-frequency-based technologies promote near-field wireless communications. Therefore, near-field physical layer security has attracted much attention to facilitate the wireless information security against illegitimate eavesdropping. However, highly correlated channels between legitimate transceivers and eavesdroppers of existing multiple-input multiple-output (MIMO) based near-field secure technologies along with the low degrees of freedom significantly limit the enhancement of security results in wireless communications. To significantly increase the secrecy rates of near-field wireless communications, in this paper we propose the double-reconfigurable-intelligent-surface (RIS) assisted orbital angular momentum (OAM) secure scheme, where RISs with few reflecting elements are easily deployed to reconstruct the direct links blocked by obstacles between the legitimate transceivers, mitigate the inter-mode interference caused by the misalignment of legitimate transceivers, and adjust the OAM beams direction to interfere with eavesdroppers. Meanwhile, due to the unique orthogonality among OAM modes, the OAM-based joint index modulation and artificial noise scheme is proposed to weaken the information acquisition by eavesdroppers while increasing the achievable rate with the low cost of legitimate communications. To maximize the secrecy rate of our proposed scheme, we develop the Riemannian manifold conjugate gradient (RMCG)-based alternative optimization (AO) algorithm to jointly optimize the transmit power allocation of OAM modes and phase shifts of double RISs. Numerical results show that our proposed double-RIS-assisted OAM near-field secure scheme outperforms the existing works in terms of the secrecy rate and the eavesdropper's bit error rate.
Integrated Sensing and Communication for Anti-Jamming with OAM
Authors: Liping Liang, Wenchi Cheng, Wei zhang, Zhuohui Yao
Date: 2024-06-09 00:00:00
Tasks: Integrated sensing and communication, ISAC, Position

The spectrum share and open nature of wireless channels enable integrated sensing and communication (ISAC) susceptible to hostile jamming attacks. Due to the intrinsic orthogonality and rich azimuth angle information of orbital angular momentum (OAM), vortex electromagnetic waves with helical phase fronts have shown great potential to achieve high-resolution imaging and strong anti-jamming capability of wireless communication. Focusing on significantly enhancing the anti-jamming results of ISAC systems with limited bandwidth under hostile jamming, in this paper we propose a novel ISAC for anti-jamming with OAM scheme, where the OAM legitimate transmitter can simultaneously sense the position of jammers with dynamic behavior and send data to multiple OAM legitimate users. Specifically, the OAM modes for sensing and communications are respectively hopped according to pre-set index modulation information to suppress jamming. To acquire the position of the jammer, we develop the enhanced multiple-signal-classification-based three-dimension position estimation scheme with continuous sensing in both frequency and angular domains, where the OAM transmitter is designed with the concentric uniform-circular-array mono-static method, to significantly increase the azimuthal resolution. Then, based on the acquired jamming channel state information, we develop the joint transmit-receive beamforming and power allocation scheme, where the transmit and receive beamforming matrices are dynamically adjusted to mitigate the mixed interference containing inter-mode interference, inter-user interference, and jamming, thus maximizing the achievable sum rates (ASRs) of all users. Numerical results demonstrate that our proposed scheme can significantly increase the ASR under broadband jamming attacks and achieve high detection accuracy of targets .
Two-Stage Resource Allocation in Reconfigurable Intelligent Surface Assisted Hybrid Networks via Multi-Player Bandits
Authors: Jingwen Tong, Hongliang Zhang, Liqun Fu, Amir Leshem, Zhu Han
Date: 2024-06-09 00:00:00
Tasks: Thompson Sampling

This paper considers a resource allocation problem where several Internet-of-Things (IoT) devices send data to a base station (BS) with or without the help of the reconfigurable intelligent surface (RIS) assisted cellular network. The objective is to maximize the sum rate of all IoT devices by finding the optimal RIS and spreading factor (SF) for each device. Since these IoT devices lack prior information on the RISs or the channel state information (CSI), a distributed resource allocation framework with low complexity and learning features is required to achieve this goal. Therefore, we model this problem as a two-stage multi-player multi-armed bandit (MPMAB) framework to learn the optimal RIS and SF sequentially. Then, we put forth an exploration and exploitation boosting (E2Boost) algorithm to solve this two-stage MPMAB problem by combining the $\epsilon$-greedy algorithm, Thompson sampling (TS) algorithm, and non-cooperation game method. We derive an upper regret bound for the proposed algorithm, i.e., $\mathcal{O}(\log^{1+\delta}_2 T)$, increasing logarithmically with the time horizon $T$. Numerical results show that the E2Boost algorithm has the best performance among the existing methods and exhibits a fast convergence rate. More importantly, the proposed algorithm is not sensitive to the number of combinations of the RISs and SFs thanks to the two-stage allocation mechanism, which can benefit high-density networks.
Two Power Allocation and Beamforming Strategies for Active IRS-aided Wireless Network via Machine Learning
Authors: Qiankun Cheng, Jiatong Bai, Baihua Shi, Wei Gao, Feng Shu
Date: 2024-06-09 00:00:00

This paper models an active intelligent reflecting surface (IRS) -assisted wireless communication network, which has the ability to adjust power between BS and IRS. We aim to maximize the signal-to-noise ratio of user by jointly designing power allocation (PA) factor, active IRS phase shift matrix, and beamforming vector of BS, subject to a total power constraint. To tackle this non-convex problem, we solve this problem by alternately optimizing these variables. Firstly, the PA factor is designed via polynomial regression method. Next, BS beamforming vector and IRS phase shift matrix are obtained by Dinkelbach's transform and successive convex approximation methods. To reduce the high computational complexity of the above proposed algorithm, we maximize achievable rate (AR) and use closed-form fractional programming method to transform the original problem into an equivalent form. Then, we address this problem by iteratively optimizing auxiliary variables, BS and IRS beamformings. Simulation results show that the proposed algorithms can effectively improve the AR performance compared to fixed PA strategies, aided by passive IRS, and without IRS.
Efficient Beamforming Feedback Information-Based Wi-Fi Sensing by Feature Selection
Authors: Xin Li, Jingzhi Hu, Jun Luo
Date: 2024-06-09 00:00:00
Tasks: feature selection

Wi-Fi sensing leveraging plain-text beamforming feedback information (BFI) in multiple-input-multiple-output (MIMO) systems attracts increasing attention. However, due to the implicit relationship between BFI and the channel state information (CSI), quantifying the sensing capability of BFI poses a challenge in building efficient BFI-based sensing algorithms. In this letter, we first derive a mathematical model of BFI, characterizing its relationship with CSI explicitly, and then develop a closed-form expression of BFI for 2x2 MIMO systems. To enhance the efficiency of BFI-based sensing by selecting only the most informative features, we quantify the sensing capacity of BFI using the Cramer-Rao bound (CRB) and then propose an efficient CRB-based BFI feature selection algorithm. Simulation results verify that BFI and CSI exhibit comparable sensing capabilities and that the proposed algorithm halves the number of features, reducing 20% more parameters than baseline methods, at the cost of only slightly increasing positioning errors.
Distributed Combinatorial Optimization of Downlink User Assignment in mmWave Cell-free Massive MIMO Using Graph Neural Networks
Authors: Bile Peng, Bihan Guo, Karl-Ludwig Besser, Luca Kunz, Ramprasad Raghunath, Anke Schmeink, Eduard A Jorswieck, Giuseppe Caire, H. Vincent Poor
Date: 2024-06-09 00:00:00
Tasks: Combinatorial Optimization, Graph Neural Network

Millimeter wave (mmWave) cell-free massive MIMO (CF mMIMO) is a promising solution for future wireless communications. However, its optimization is non-trivial due to the challenging channel characteristics. We show that mmWave CF mMIMO optimization is largely an assignment problem between access points (APs) and users due to the high path loss of mmWave channels, the limited output power of the amplifier, and the almost orthogonal channels between users given a large number of AP antennas. The combinatorial nature of the assignment problem, the requirement for scalability, and the distributed implementation of CF mMIMO make this problem difficult. In this work, we propose an unsupervised machine learning (ML) enabled solution. In particular, a graph neural network (GNN) customized for scalability and distributed implementation is introduced. Moreover, the customized GNN architecture is hierarchically permutation-equivariant (HPE), i.e., if the APs or users of an AP are permuted, the output assignment is automatically permuted in the same way. To address the combinatorial problem, we relax it to a continuous problem, and introduce an information entropy-inspired penalty term. The training objective is then formulated using the augmented Lagrangian method (ALM). The test results show that the realized sum-rate outperforms that of the generalized serial dictatorship (GSD) algorithm and is very close to the upper bound in a small network scenario, while the upper bound is impossible to obtain in a large network scenario.