Advanced Information and Communication
ISSN: 3106-1443 (Print)
ISSN: 3106-1451 (Online)
PAS-YOLO: an enhanced algorithm for high-precision non-cooperative UAV recognition
In light of the recent proliferation of unmanned aerial vehicles (UAVs) and the challenges posed by unauthorized flights interfering with air traffic, the precise detection and identification of UAV have become critical for ensuring security at low altitudes. This study introduces PAS-YOLO, an enhanced algorithm for detecting and recognizing UAV remote control signals, built upon the You Only Look Once version 12 (YOLOv12) framework, with the objective of improving UAV target identification capabilities. To augment the detection of small target signals and reduce the risk of remote control signal loss, a parallelized patch-aware attention (PPA) module is integrated into the backbone network. Addressing the limited feature representation capacity of YOLOv12, particularly the difficulty in distinguishing similar remote control signals through fine-grained features, the neck network is redesigned based on the Attentional Scale Sequence Fusion YOLO (ASF-YOLO) architecture. Furthermore, to broaden the receptive field and enhance the contextual extraction capability for signals with diverse time-frequency characteristics, the original area attention with C2f (A2C2f) module is refined by incorporating a switchable atrous convolution (SAConv) module. Experimental evaluations are performed using the publicly available Radio Frequency (RF) signal dataset DroneRFa, wherein the Short-Time Fourier Transform (STFT) is employed to generate a UAV time-frequency spectrum dataset. The results indicate that the proposed PAS-YOLO algorithm attains an average detection accuracy of 99.36% for mAP@50 and 75.38% for mAP@50:95 across 22 UAV remote control signal models. Compared to the baseline YOLOv12 model, these metrics represent improvements of 0.23% and 3.45%, respectively.
Channel parameter estimation and localization based on structured sparse bayesian CP decomposition in mmWave massive MIMO-OFDM systems
In this paper, we propose a structured sparse Bayesian CANDECOMP/PARAFAC (SSBCP) algorithm for channel parameter estimation and localization in millimeter-wave (mmWave) massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with receiver-side hardware impairments. Firstly, based on the physical mechanisms of receiver hardware impairments, the received signal containing both antenna-dependent sparse noise and additive Gaussian noise is constructed as a third-order parallel factor (PARAFAC) tensor. Secondly, by designing an equivalent hybrid precoding matrix, the original complex-valued tensor is transformed into a real-valued counterpart suitable for Bayesian processing. Thirdly, accurate estimation of the factor matrices is achieved through a structured sparse Bayesian tensor decomposition that incorporates binary latent variables to control the positions of sparse noise. Finally, the channel parameters are extracted from the estimated factor matrices and the localization is accomplished based on their geometric relationships. Simulation results show the proposed SSBCP algorithm outperforms existing algorithms across sparse noise ratios. Even under severe hardware distortion conditions, the proposed SSBCP algorithm maintains outstanding parameter estimation and localization performance in environments with multiple scattering paths.
Recent advances in near-field beam training and channel estimation for XL-MIMO systems
Extremely large-scale multiple-input multiple-output (XL-MIMO) is a key technology for next-generation wireless communication systems. By deploying significantly more antennas than conventional massive MIMO systems, XL-MIMO promises substantial improvements in spectral efficiency. However, due to the drastically increased array size, the conventional planar wave channel model is no longer accurate, necessitating a transition to a near-field spherical wave model. This shift challenges traditional beam training and channel estimation methods, which were designed for planar wave propagation. In this article, we present a comprehensive review of state-of-the-art beam training and channel estimation techniques for XL-MIMO systems. We analyze the fundamental principles, key methodologies, and recent advancements in this area, highlighting their respective strengths and limitations in addressing the challenges posed by the near-field propagation environment. Furthermore, we explore open research challenges that remain unresolved to provide valuable insights for researchers and engineers working toward the development of next-generation XL-MIMO communication systems.
A survey on insertion/deletion error detection and correction: progress and future directions
The detection and correction of insertion/deletion (indel) errors have become increasingly critical in domains such as traditional mobile communication systems, the Internet of Things (IoT), smart homes, smart healthcare, vehicular networks, and large-scale urban infrastructure, establishing it as a prominent research focus. As a typical form of synchronization error, the randomness and asymmetry of indel errors severely disrupt symbol alignment and induce significant synchronization drift, thereby imposing substantial challenges on reliable data transmission. This paper systematically reviews methodologies for detecting and correcting indel errors, tracing their evolution from model-driven to data-driven paradigms. First, we summarize the traditional technical framework, which includes synchronization markers, edit distance (ED) codes, sequence alignment, trellis/convolutional structures, and probabilistic models, with an analysis of their theoretical foundations, representative algorithms, and applicable scenarios. Next, we focus on recent advances in deep learning (DL)-based synchronization recovery methods and semantic communication-driven intelligent error correction frameworks, highlighting their distinct advantages over conventional approaches in handling complex channels and unstructured data. Finally, we outline the current research landscape and key challenges in this field and propose future directions for emerging scenarios such as 6th Generation (6G) ultra-reliable communication, satellite links, and ultra-high-density storage. This review aims to provide comprehensive insights and guidance for the design of synchronization and error correction mechanisms in next-generation communication systems.
Optical reconfigurable intelligent surfaces assisted visible light positioning and communications: performance analysis and design optimization
This paper investigates the integration of visible light positioning and communication (VLP&C) facilitated by optical reconfigurable intelligent surfaces (ORIS) to address line-of-sight (LoS) blockage challenges within indoor environments. In contrast to conventional VLP&C systems, which experience significant performance deterioration under LoS blockage, the proposed ORIS-assisted framework dynamically adjusts the reflection patterns to establish reliable non-LoS (NLoS) links. Initially, a comprehensive system model is formulated, encompassing the physical properties of ORIS, including an analysis of time delays and strategies for ORIS deployment. Subsequently, the Cramér-Rao lower bound (CRLB) for positioning accuracy is rigorously derived from the underlying signal models, thereby providing a realistic theoretical performance benchmark. Additionally, closed-form expression for the average mutual information (AMI) and bit error rate (BER) of the communication subsystem are developed, accounting for the finite-alphabet characteristics of on-off keying (OOK) modulation. The study further investigates the trade-offs between positioning accuracy and communication performance across various system parameters, such as the number of ORIS reflection units, half-power angle, and spatial distribution of users. Extensive simulation results demonstrate that the proposed ORIS-assisted system attains centimeter-level positioning accuracy alongside reliable communication performance, even in scenarios where LoS links are blocked. The theoretical findings are validated through Monte Carlo simulations, and the practical implementation challenges are discussed to inform future real-world deployments.
Energy efficiency optimization of STAR-RIS-aided ISAC systems based on user fairness
In this paper, the communication energy efficiency (EE) of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing and communications (ISAC) systems underlying a near-field scenario is investigated, where the dual-functional base station (DFBS) serves multiple users and senses multiple targets simultaneously. To ensure user fairness, we formulate an optimization problem that maximizes the minimum (max-min) communication EE while satisfying the minimum target illumination power requirement, the maximum transmission power budget, and the hardware constraints of STAR-RIS under its three operation modes. The formulated max-min optimization problem exhibits non-convexity due to the high coupling among the optimization variables. So as to resolve this issue, the fractional programming is first leveraged to transform the objective function into a more tractable structure. Then, the original max-min problem is transformed into an equivalent maximization problem via introducing the auxiliary variable. Next, we propose an alternating optimization framework to decouple the newly reformulated maximization problem into several sub-problems, which are optimized iteratively until convergence. Finally, the outcomes from the simulations are executed to confirm the advantages and effectiveness of the schemes we have introduced.
