IEEE journal on selected areas in communications : a publication of the IEEE Communications Society最新文献_第10页

Carrier-Assisted Entanglement Purification 载波辅助纠缠净化

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-01-01 Epub Date: 2026-07-06 DOI: 10.1109/JSAC.2026.3710152

Jaemin Kim;Karthik Mohan;Sung Won Yun;Joonwoo Bae

{"title":"Carrier-Assisted Entanglement Purification","authors":"Jaemin Kim;Karthik Mohan;Sung Won Yun;Joonwoo Bae","doi":"10.1109/JSAC.2026.3710152","DOIUrl":"10.1109/JSAC.2026.3710152","url":null,"abstract":"Entanglement distillation, a fundamental building block of quantum networks, enables the purification of noisy entangled states shared among distant nodes by local operations and classical communication. Its practical realization presents several technical challenges, including the storage of quantum states in quantum memory and the execution of coherent quantum operations on multiple copies of states within the quantum memory. In this work, we present an entanglement purification protocol via quantum communication, namely a carrier-assisted entanglement purification protocol, which utilizes two elements only: i) quantum memory for a single-copy entangled state shared by parties and ii) single qubits travelling between parties. We show that the protocol, when single-qubit transmission is noiseless, can purify a noisy entangled state shared by parties. When single-qubit transmission is noisy, the purification relies on types of noisy qubit channels; we characterize Pauli channels such that the protocol works for the purification. We address this limitation by using multiple carrier qubits, and show that for any depolarizing channel with channel fidelity greater than <inline-formula> <tex-math>$1/2$ </tex-math></inline-formula>, the protocol’s fixed-point fidelity approaches unity as the number of carriers increases. Our results significantly reduce the experimental overhead required for distilling entanglement: the practical advantage is demonstrated through parameters directly related to the capability of entanglement purification, such as noise in quantum memory, local measurements, channel use, and entanglement fidelity. We envisage that the protocol would make long-distance pure entanglement closer to a practical realization.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"5311-5326"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148401278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stochastic Quantum Spiking Neural Networks With Quantum Memory and Local Learning 具有量子记忆和局部学习的随机量子尖峰神经网络

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-01-01 Epub Date: 2026-07-16 DOI: 10.1109/JSAC.2026.3714333

Jiechen Chen;Bipin Rajendran;Osvaldo Simeone

{"title":"Stochastic Quantum Spiking Neural Networks With Quantum Memory and Local Learning","authors":"Jiechen Chen;Bipin Rajendran;Osvaldo Simeone","doi":"10.1109/JSAC.2026.3714333","DOIUrl":"10.1109/JSAC.2026.3714333","url":null,"abstract":"Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths. Neuromorphic systems built on spiking neurons excel at processing time series data efficiently through sparse, event-driven computation, consuming energy only upon input events. Quantum computing, on the other hand, operates on state spaces that grow exponentially in dimension with the number of qubits — as a consequence of tensor-product composition — with quantum states admitting superposition across basis states and entanglement between subsystems. Hybrid approaches combining these paradigms have begun to show potential, but existing quantum spiking models have important limitations. Notably, they implement classical memory mechanisms on single qubits, requiring repeated measurements to estimate firing probabilities, while relying on conventional backpropagation for training. In this paper, we propose a novel stochastic quantum spiking (SQS) neuron model that addresses these challenges. The SQS neuron uses multi-qubit quantum circuits to realize a spiking unit with internal quantum memory, enabling event-driven probabilistic spike generation in a single shot during inference. Furthermore, we study networks of SQS neurons, dubbed SQS neural networks (SQSNN), and demonstrate that they can be trained via a hardware-friendly local learning rule, eliminating the need for global classical backpropagation. The proposed SQSNN model is shown via experiments with both conventional and neuromorphic datasets to improve over previous quantum spiking neural networks, as well as over classical counterparts, when fixing the overall number of trainable parameters, highlighting its potential for event-driven applications such as neuromorphic integrated sensing and communications (N-ISAC).","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"5421-5436"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Design of Phase Shift and Transceiver Beamforming in RIS-Assisted Full-Duplex ISAC System RIS辅助全双工ISAC系统相移与收发机波束形成联合设计

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-01-01 Epub Date: 2025-09-17 DOI: 10.1109/JSAC.2025.3610802

Zijun Wu;Haijun Zhang;Yuzheng Ren;Qifu Tyler Sun;Yao Zou;Linlin Li;Tianyao Huang

{"title":"Joint Design of Phase Shift and Transceiver Beamforming in RIS-Assisted Full-Duplex ISAC System","authors":"Zijun Wu;Haijun Zhang;Yuzheng Ren;Qifu Tyler Sun;Yao Zou;Linlin Li;Tianyao Huang","doi":"10.1109/JSAC.2025.3610802","DOIUrl":"10.1109/JSAC.2025.3610802","url":null,"abstract":"Integrated Sensing and Communication (ISAC) is becoming increasingly important in next-generation wireless networks. This paper focuses on an ISAC system supported by a reconfigurable intelligent surface (RIS), where a full-duplex base station (BS) simultaneously performs uplink multi-user communication, downlink multi-user communication, and radar sensing tasks with the assistance of the RIS. To maximize the sum rate of all downlink and uplink users, an optimization problem is formulated, subject to multiple constraints, including target detection signal-to-interference-plus-noise ratio, self-interference, BS transmission power, user transmission power, and unit-modulus constraints of RIS reflection coefficients. To address the complex non-convex optimization problems, efficient solving algorithms are proposed, and their performance is validated through simulations. The results demonstrate that the RIS-assisted full-duplex ISAC (RAFD-ISAC) system significantly enhances both communication and sensing performance. The proposed joint beamforming and reflection design offers a novel solution for the deep integration of sensing and communication in next-generation networks.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"468-479"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145077701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From Cramér–Rao To Barankin: Fundamental Trade-Off in OFDM Integrated Sensing and Communication Systems 从cram<s:1> - rao到Barankin: OFDM集成传感与通信系统的基本权衡

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-01-01 Epub Date: 2025-09-23 DOI: 10.1109/JSAC.2025.3612931

Zhe Huang;Yubo Wan;An Liu;Yunlong Cai

{"title":"From Cramér–Rao To Barankin: Fundamental Trade-Off in OFDM Integrated Sensing and Communication Systems","authors":"Zhe Huang;Yubo Wan;An Liu;Yunlong Cai","doi":"10.1109/JSAC.2025.3612931","DOIUrl":"10.1109/JSAC.2025.3612931","url":null,"abstract":"Integrated sensing and communication (ISAC) has emerged as a key technology for future communication systems. In this paper, we provide a general framework to reveal the fundamental trade-off between sensing and communication in OFDM systems, where a unified ISAC signal is exploited to perform both tasks. To evaluate the sensing performance, we introduce two representative performance metrics: The Cramér-Rao Bound (CRB) and the Barakin Bound (BRB). For the asymptotic case when the number of subcarriers is large, we show that the asymptotically optimal input distribution that achieves the Pareto boundary point of the Capacity-CRB<inline-formula> <tex-math>$backslash $ </tex-math></inline-formula>BRB region is Gaussian and the entire Pareto boundary can be obtained by solving a power allocation problem. We prove that the power allocation problem for obtaining the Capacity-CRB region can be calculated by solving a convex optimization problem. However, the Capacity-BRB region is more difficult to be characterized due to the non-convexity of the optimization problem. Therefore, we propose an iterative algorithm to obtain an inner bound of the Capacity-BRB region. Moreover, we derive the sufficient conditions under which the Gaussian distribution remains asymptotically optimal when the number of subcarriers approaches infinity and the delay gap between two targets approaches zero simultaneously. For the non-asymptotic case, an optimization problem is formulated for sensing-optimal input distribution and the capacity-achieving distribution is derived under the sensing-optimal constraints. Finally, numerical simulations are conducted to verify the theoretical analysis and provide useful insights.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"62-75"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks 通信网络中人工智能生命周期的能源成本

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-01-01 Epub Date: 2025-12-11 DOI: 10.1109/JSAC.2025.3642835

Shih-Kai Chou;Jernej Hribar;Vid Hanžel;Mihael Mohorčič;Carolina Fortuna

{"title":"The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks","authors":"Shih-Kai Chou;Jernej Hribar;Vid Hanžel;Mihael Mohorčič;Carolina Fortuna","doi":"10.1109/JSAC.2025.3642835","DOIUrl":"10.1109/JSAC.2025.3642835","url":null,"abstract":"Artificial Intelligence (AI) is being incorporated in several optimization, scheduling, orchestration as well as in native communication network functions. This paradigm shift results in increased energy consumption, however, quantifying the end-to-end energy consumption of adding intelligence to communication systems remains an open challenge since conventional energy consumption metrics focus on either communication, computation infrastructure, or model development. To address this, we propose a new metric, the Energy Cost of AI Lifecycle (eCAL) of an AI model in a system. eCAL captures the energy consumption throughout the development, deployment and utilization of an AI-model providing intelligence in a communication network by (i) analyzing the complexity of data collection and manipulation in individual components and (ii) deriving overall and per-bit energy consumption. We show that as a trained AI model is used more frequently for inference, its energy cost per inference decreases, since the fixed training energy is amortized over a growing number of inferences. For a simple case study we show that eCAL for 100 inferences is 2.73 times higher than for 1000 inferences. Additionally, we have developed a modular and extendable open-source simulation tool to enable researchers, practitioners, and engineers to calculate the end-to-end energy cost with various configurations and across various systems, ensuring adaptability to diverse use cases.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"2427-2443"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11298182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145728951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image Semantic Communication With Quadtree Partition-Based Coding 基于四叉树分割编码的图像语义通信

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-01-01 Epub Date: 2025-12-16 DOI: 10.1109/JSAC.2025.3644827

Yinhuan Huang;Zhijin Qin

{"title":"Image Semantic Communication With Quadtree Partition-Based Coding","authors":"Yinhuan Huang;Zhijin Qin","doi":"10.1109/JSAC.2025.3644827","DOIUrl":"10.1109/JSAC.2025.3644827","url":null,"abstract":"Deep learning based semantic communication (DeepSC) system has emerged as a promising paradigm for efficient wireless transmission. However, existing image DeepSC methods, frequently encounter challenges in balancing rate-distortion performance and computational complexity, and often exhibit inferior performance compared to traditional schemes, especially on high-resolution datasets. To address these limitations, we propose a novel image DeepSC system, using quadtree partition-based joint semantic-channel coding, named Quad-DeepSC, which maintains low complexity while achieving state-of-the-art transmission performance. Based on maturing learned image compression technologies, we establish a unified DeepSC system design and training pipeline. The proposed Quad-DeepSC integrates quadtree partition-based entropy estimation and feature coding modules with lightweight feature extraction and reconstruction networks to form an end-to-end architecture. During training, all components except the feature coding modules are jointly optimized as a compact learned image codec, Quad-LIC, for source compression tasks. The pretrained Quad-LIC is then embedded into Quad-DeepSC and fine-tuned end-to-end over wireless channels. Extensive experimental results demonstrate that Quad-DeepSC is the first DeepSC system to surpass conventional communication systems, which employ VTM for source coding and adopt the optimal MCS index under 3GPP standards for channel coding and digital modulation, in performance across datasets of varying resolutions. Notably, both Quad-DeepSC and Quad-LIC exhibit minimal latency, rendering them well-suited for deployment in real-time wireless communication systems.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"2765-2778"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11301684","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Live High-Fidelity Semantic Communication via Cross-Modal Fusion for Volumetric Video 基于跨模态融合的体积视频实时高保真语义通信

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-01-01 Epub Date: 2025-12-17 DOI: 10.1109/JSAC.2025.3644826

Tianyi Gong;Zijian Cao;Zhicheng Liang;Dayou Zhang;Fangxin Wang;Shuguang Cui

{"title":"Live High-Fidelity Semantic Communication via Cross-Modal Fusion for Volumetric Video","authors":"Tianyi Gong;Zijian Cao;Zhicheng Liang;Dayou Zhang;Fangxin Wang;Shuguang Cui","doi":"10.1109/JSAC.2025.3644826","DOIUrl":"10.1109/JSAC.2025.3644826","url":null,"abstract":"Semantic communication (SC) emerges as a breakthrough paradigm for efficient data transmission in next-generation communication networks. However, SC is still in the infant stage with quite a few limitations, such as insufficient semantic representation capacity, high communication latency, and the susceptibility to channel noise. In this paper, we propose LiFiSC, a cross-modal fusion based generative semantic communication framework with strong semantic compression capacity and high-fidelity semantic restoration. We then extend it to LiFiSCvv, specifically designed to achieve 3D volumetric video transmission and reconstruction with photorealistic visual quality and pixel-level visual consistency, providing end-to-end live watching experience with acceptable latency. We innovatively incorporate unified vision-language encoding into semantic communication, achieving superior semantic understanding and compression. LiFiSCvv comprises three key components: 1) Information redundancy reduction through lightweight video analysis and structure-from-motion techniques, decreasing reconstruction cost; 2) Cross-modal fusion learning driven codec mechanisms that enable efficient semantic representation and robust transmission against channel impairments; and 3) Feed-forward vision transformer for rapid volumetric video reconstruction and rendering. Comprehensive evaluation results demonstrate that LiFiSCvv achieves a live high-fidelity and visually consistent video watching experience, with only seconds of end-to-end latency and around <inline-formula> <tex-math>$42times $ </tex-math></inline-formula> semantic compression, significantly outperforming SOTA methods in general image and volumetric video transmission. The project page is available here: <uri>https://inml-tygong.github.io/LiFiSCvv/</uri>","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"2750-2764"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal and Suboptimal Decoders Under Finite-Alphabet Interference: A Mismatched Decoding Perspective 有限字母干扰下的最优和次优解码器：一个不匹配的解码视角

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-01-01 Epub Date: 2025-12-16 DOI: 10.1109/JSAC.2025.3644829

Sibo Zhang;Bruno Clerckx

{"title":"Optimal and Suboptimal Decoders Under Finite-Alphabet Interference: A Mismatched Decoding Perspective","authors":"Sibo Zhang;Bruno Clerckx","doi":"10.1109/JSAC.2025.3644829","DOIUrl":"10.1109/JSAC.2025.3644829","url":null,"abstract":"Interference widely exists in communication systems and is often not optimally treated at the receivers due to limited knowledge and/or computational burden. Evolutions of receivers have been proposed to balance complexity and spectral efficiency, for example, for 6G, while commonly used performance metrics, such as capacity and mutual information (MI), fail to capture the suboptimal treatment of interference, leading to potentially inaccurate performance evaluations. Mismatched decoding is an information-theoretic tool for analyzing communications with suboptimal decoders. In this work, we use mismatched decoding to analyze communications with decoders that treat interference suboptimally, aiming at more accurate performance metrics. Specifically, we consider a finite-alphabet input Gaussian channel under interference, representative of modern systems, where the decoder can be matched (optimal) or mismatched (suboptimal) to the channel. The matched capacity is derived using MI, while a lower bound on the mismatched capacity under various decoding metrics is derived using generalized mutual information (GMI). We show that the decoding metric in the proposed channel model is closely related to the behavior of the demodulator in bit-interleaved coded modulation (BICM) systems. Simulations illustrate that GMI/MI accurately predicts the throughput of BICM-type systems with various demodulators. Finally, we extend the channel model and the GMI to multiple antenna cases, with an example of multi-user multiple-input-single-output (MU-MISO) precoder optimization problem considering GMI under different decoding strategies. In short, this work discovers new insights about the impact of interference, proposes novel receivers, and introduces a new design and performance evaluation framework that more accurately captures the effect of interference.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"2779-2793"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy-Enhanced Grey Wolf Optimizer for Hybrid Beamforming in mmWave Vehicular Networks 毫米波车载网络混合波束形成的模糊增强灰狼优化算法

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-01-01 Epub Date: 2026-01-15 DOI: 10.1109/JSAC.2026.3654881

Mandavi Dubey;Vijay Dubey;Pi-Chung Wang

{"title":"Fuzzy-Enhanced Grey Wolf Optimizer for Hybrid Beamforming in mmWave Vehicular Networks","authors":"Mandavi Dubey;Vijay Dubey;Pi-Chung Wang","doi":"10.1109/JSAC.2026.3654881","DOIUrl":"10.1109/JSAC.2026.3654881","url":null,"abstract":"Millimeter wave (mmWave) communication has emerged as a key enabler of high-throughput, low-latency wireless connectivity for next-generation intelligent transportation systems. However, its deployment in vehicular environments presents significant challenges due to high mobility, rapidly changing topologies, and frequent transitions between line-of-sight and non-line-of-sight conditions. To address these challenges, this paper proposes an adaptive beamforming framework that integrates fuzzy logic with the grey wolf optimizer (GWO), termed fuzzy-enhanced GWO (FE-GWO), to jointly optimize beam steering angles, power allocation, and beamforming weights in real time. The proposed framework utilizes a 3D hybrid beamforming model with a uniform planar array, constructed from two orthogonal uniform linear arrays, to enable independent control over azimuth and elevation angles. To enhance beam quality, a Hamming window is applied to the array factor to suppress sidelobes and minimize inter-beam interference. Fuzzy logic is embedded in the optimization process to improve the algorithm’s ability to handle uncertainties arising from dynamic channel conditions, including signal-to-interference-plus-noise ratio, path loss, and vehicular mobility. Simulation results demonstrate that the proposed FE-GWO algorithm outperforms traditional and baseline optimization algorithms. Our algorithm achieves notable improvements in SINR, throughput, spectral efficiency, and packet delivery ratio, while effectively reducing path loss, energy consumption, and beam switching latency.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3317-3331"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Meta-Learning-Based Handover Management in NextG O-RAN 基于元学习的nexto - ran切换管理

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-01-01 Epub Date: 2026-01-12 DOI: 10.1109/JSAC.2026.3652037

Michail Kalntis;George Iosifidis;José Suárez-Varela;Andra Lutu;Fernando A. Kuipers

{"title":"Meta-Learning-Based Handover Management in NextG O-RAN","authors":"Michail Kalntis;George Iosifidis;José Suárez-Varela;Andra Lutu;Fernando A. Kuipers","doi":"10.1109/JSAC.2026.3652037","DOIUrl":"10.1109/JSAC.2026.3652037","url":null,"abstract":"While traditional handovers (THOs) have served as a backbone for mobile connectivity, they increasingly suffer from failures and delays, especially in dense deployments and high-frequency bands. To address these limitations, 3GPP introduced Conditional Handovers (CHOs) that enable proactive cell reservations and user-driven execution. However, both handover (HO) types present intricate trade-offs in signaling, resource usage, and reliability. This paper presents unique, countrywide mobility management datasets from a top-tier mobile network operator (MNO) that offer fresh insights into these issues and call for adaptive and robust HO control in next-generation networks. Motivated by these findings, we propose <monospace>CONTRA</monospace>, a framework that, for the first time, jointly optimizes THOs and CHOs within the O-RAN architecture. We study two variants of <monospace>CONTRA</monospace>: one where users are a priori assigned to one of the HO types, reflecting distinct service or user-specific requirements, as well as a more dynamic formulation where the controller decides on-the-fly the HO type, based on system conditions and needs. To this end, it relies on a practical meta-learning algorithm that adapts to runtime observations and guarantees performance comparable to an oracle with perfect future information (universal no-regret). <monospace>CONTRA</monospace>is specifically designed for near-real-time deployment as an O-RAN xApp and aligns with the 6G goals of flexible and intelligent control. Extensive evaluations leveraging crowdsourced datasets show that <monospace>CONTRA</monospace>improves user throughput and reduces both THO and CHO switching costs, outperforming 3GPP-compliant and Reinforcement Learning (RL) baselines in dynamic and real-world scenarios.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3251-3267"},"PeriodicalIF":17.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0