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Low-Complexity Multi-Target Detection in ELAA ISAC
IF 3.7 3区 计算机科学
IEEE Communications Letters Pub Date : 2025-01-30 DOI: 10.1109/LCOMM.2025.3537457
Diluka Galappaththige;Shayan Zargari;Chintha Tellambura;Geoffrey Ye Li
{"title":"Low-Complexity Multi-Target Detection in ELAA ISAC","authors":"Diluka Galappaththige;Shayan Zargari;Chintha Tellambura;Geoffrey Ye Li","doi":"10.1109/LCOMM.2025.3537457","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3537457","url":null,"abstract":"Multi-target detection and communication with extremely large-scale antenna arrays (ELAAs) operating at high frequencies necessitate generating multiple beams. However, conventional algorithms are slow and computationally intensive. For instance, they can simulate a 200-antenna system over two weeks, and the time complexity grows exponentially with the number of antennas. Thus, this letter explores an ultra-low-complex solution for a multi-user, multi-target integrated sensing and communication (ISAC) system equipped with an ELAA base station (BS). It maximizes the communication sum rate while meeting sensing beampattern gain targets and transmit power constraints. As this problem is non-convex, a Riemannian stochastic gradient descent-based augmented Lagrangian manifold optimization (SGALM) algorithm is developed, which searches on a manifold to ensure constraint compliance. The algorithm achieves ultra-low complexity and superior runtime performance compared to conventional algorithms. For example, it is 56 times faster than the standard benchmark for 257 BS antennas.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"620-624"},"PeriodicalIF":3.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Selective Experience Sharing in Reinforcement Learning Enhances Interference Management
IF 3.7 3区 计算机科学
IEEE Communications Letters Pub Date : 2025-01-29 DOI: 10.1109/LCOMM.2025.3535898
Madan Dahal;Mojtaba Vaezi
{"title":"Selective Experience Sharing in Reinforcement Learning Enhances Interference Management","authors":"Madan Dahal;Mojtaba Vaezi","doi":"10.1109/LCOMM.2025.3535898","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3535898","url":null,"abstract":"We propose a novel multi-agent reinforcement learning (RL) approach for inter-cell interference mitigation, in which agents selectively share their experiences with other agents. Each base station is equipped with an agent, which receives signal-to-interference-plus-noise ratio from its own associated users. This information is used to evaluate and selectively share experiences with neighboring agents. The idea is that even a few pertinent experiences from other agents can lead to effective learning. This approach enables fully decentralized training and execution, minimizes information sharing between agents and significantly reduces communication overhead, which is typically the burden of interference management. The proposed method outperforms state-of-the-art multi-agent RL techniques where training is done in a decentralized manner. Furthermore, with a 75% reduction in experience sharing, the proposed algorithm achieves 98% of the spectral efficiency obtained by algorithms sharing all experiences.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"615-619"},"PeriodicalIF":3.7,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Consistency-Guided Robust Learning for Content-Agnostic Radio Frequency Fingerprinting
IF 3.7 3区 计算机科学
IEEE Communications Letters Pub Date : 2025-01-29 DOI: 10.1109/LCOMM.2025.3535879
Yu Wang;Guan Gui
{"title":"Consistency-Guided Robust Learning for Content-Agnostic Radio Frequency Fingerprinting","authors":"Yu Wang;Guan Gui","doi":"10.1109/LCOMM.2025.3535879","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3535879","url":null,"abstract":"Radio Frequency Fingerprinting (RFF) is viewed as a potential strategy to enhance wireless security by utilizing inherent hardware characteristics of transmitters. Recently, Deep Learning (DL)-based RFF methods have been extensively studied and significantly improved identification performance. However, new challenges are introduced, particularly content dependency. This dependency emerges when signals contain unique transmitter identifiers (IDs), such as the ICAO addresses in Automatic Dependent Surveillance-Broadcast (ADS-B) system. In such cases, DL models may prioritize these IDs over the intrinsic hardware fingerprint information, resulting in inflated accuracy. Moreover, as these IDs are vulnerable to tampering, their reliability and robustness are substantially compromised. To overcome this, we propose a novel content-agnostic RFF method that incorporates a consistency-guided robust learning framework. The proposed method employs a masking mechanism to zero out signal segments associated with transmitter IDs and processes both original and masked signals through a shared feature embedding, ensuring minimal content dependency while thoroughly extracting fingerprint information across the entire signal. To enhance its effectiveness, we introduce semantic consistency regularization to align the feature semantics of original and masked signals. Additionally, attention consistency regularization, leveraging class activation mapping, is employed to constrain the attention distribution across the two signal variants. These complementary strategies effectively mitigate the risk of over-reliance on transmitter IDs, ensuring comprehensive extraction of fingerprint information. Simulation results demonstrate robust identification despite transmitter ID tampering, and highlight its content independence. The codes can be downloaded at <uri>https://github.com/BeechburgPieStar/ CGRL-for-Content-Agnostic-RFF</uri>.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"610-614"},"PeriodicalIF":3.7,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep Reinforcement Learning for Joint Time and Power Management in SWIPT-EH CIoT
IF 3.7 3区 计算机科学
IEEE Communications Letters Pub Date : 2025-01-29 DOI: 10.1109/LCOMM.2025.3536182
Nadia Abdolkhani;Nada Abdel Khalek;Walaa Hamouda;Iyad Dayoub
{"title":"Deep Reinforcement Learning for Joint Time and Power Management in SWIPT-EH CIoT","authors":"Nadia Abdolkhani;Nada Abdel Khalek;Walaa Hamouda;Iyad Dayoub","doi":"10.1109/LCOMM.2025.3536182","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3536182","url":null,"abstract":"This letter presents a novel deep reinforcement learning (DRL) approach for joint time allocation and power control in a cognitive Internet of Things (CIoT) system with simultaneous wireless information and power transfer (SWIPT). The CIoT transmitter autonomously manages energy harvesting (EH) and transmissions using a learnable time switching factor while optimizing power to enhance throughput and lifetime. The joint optimization is modeled as a Markov decision process under small-scale fading, realistic EH, and interference constraints. We develop a double deep Q-network (DDQN) enhanced with an upper confidence bound. Simulations benchmark our approach, showing superior performance over existing DRL methods.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 4","pages":"660-664"},"PeriodicalIF":3.7,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Improved Frameless ALOHA Scheme With Feedback Strategy
IF 3.7 3区 计算机科学
IEEE Communications Letters Pub Date : 2025-01-28 DOI: 10.1109/LCOMM.2025.3535759
Yin Zhang;Liqun Zhao;Yuli Zhao;Francis C. M. Lau;Hai Yu;Zhiliang Zhu;Bin Zhang
{"title":"An Improved Frameless ALOHA Scheme With Feedback Strategy","authors":"Yin Zhang;Liqun Zhao;Yuli Zhao;Francis C. M. Lau;Hai Yu;Zhiliang Zhu;Bin Zhang","doi":"10.1109/LCOMM.2025.3535759","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3535759","url":null,"abstract":"This letter presents a novel frameless ALOHA random access scheme based on the online fountain codes. In this work, we incorporate feedback into the conventional frameless ALOHA scheme and utilize the uni-partite graph to depict the user-slot relationship. We further employ successive interference cancellation (SIC) for user resolution. We derive a formula for computing the optimal access probability, which is combined with the online fountain codes. To evaluate the throughput of our proposed scheme, we analyze it using the relevant theory of random graph. Our theoretical and simulation results are consistent, and demonstrate that our proposed scheme outperforms the traditional frameless ALOHA scheme in terms of the throughput and the packet error rate.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"605-609"},"PeriodicalIF":3.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fluid Antenna for Importance-Based Ranking (FAIR) Semantic Communications
IF 3.7 3区 计算机科学
IEEE Communications Letters Pub Date : 2025-01-27 DOI: 10.1109/LCOMM.2025.3532638
Shih Yu Chang;Hsiao-Hwa Chen
{"title":"Fluid Antenna for Importance-Based Ranking (FAIR) Semantic Communications","authors":"Shih Yu Chang;Hsiao-Hwa Chen","doi":"10.1109/LCOMM.2025.3532638","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3532638","url":null,"abstract":"This letter explores the applications of fluid antenna in wireless semantic communications, aiming to fill up a gap in existing research. In particular, this letter applies Fluid Antenna for Importance-based Ranking (FAIR) systems, which can reconfigure communication encoders and fluid antenna parameters dynamically based on the significance of individual words in a sentence. A reconfiguration and power optimization algorithm is proposed to enhance transmission efficiency. The system’s performance is evaluated using Word Error Rate (WER) and ROUGE metrics, demonstrating its potential to improve the performance of semantic communications, particularly in dynamic and resource-constrained environments.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"567-571"},"PeriodicalIF":3.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Doppler Power Spectrum in Channels With von Mises-Fisher Distribution of Scatterers
IF 3.7 3区 计算机科学
IEEE Communications Letters Pub Date : 2025-01-27 DOI: 10.1109/LCOMM.2025.3534520
Kenan Turbic;Martin Kasparick;Sławomir Stańczak
{"title":"Doppler Power Spectrum in Channels With von Mises-Fisher Distribution of Scatterers","authors":"Kenan Turbic;Martin Kasparick;Sławomir Stańczak","doi":"10.1109/LCOMM.2025.3534520","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3534520","url":null,"abstract":"This letter presents an analytical analysis of the Doppler spectrum in von Mises-Fisher (vMF) scattering channels. A simple closed-form expression for the Doppler spectrum is derived and used to investigate the impact of the vMF scattering parameters, i.e., the mean direction and the degree of concentration of scatterers. The spectrum is observed to exhibit exponential behavior for mobile antenna motion parallel to the mean direction of scatterers, while conforming to a Gaussian-like shape for the perpendicular motion. The validity of the obtained results is verified by comparison against the results of Monte Carlo simulations, where an exact match is observed.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"596-599"},"PeriodicalIF":3.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Power Control for Edge ML Inference With Hypernetwork Meta-Parameters
IF 3.7 3区 计算机科学
IEEE Communications Letters Pub Date : 2025-01-27 DOI: 10.1109/LCOMM.2025.3534320
Jiaying Zhang;Qiushuo Hou;Guanding Yu
{"title":"Power Control for Edge ML Inference With Hypernetwork Meta-Parameters","authors":"Jiaying Zhang;Qiushuo Hou;Guanding Yu","doi":"10.1109/LCOMM.2025.3534320","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3534320","url":null,"abstract":"In recent years, machine learning (ML) tasks have been widely deployed at the edge of wireless networks, e.g., autonomous cars and tactile robots. However, the impairments of wireless channels between devices, such as fading and noise, deteriorate the effectiveness of ML inference tasks. In this work, we propose an efficient framework with an adaptive power control mechanism, which considers the constraint of the limited energy budget of edge devices. To guarantee the inference performance of ML tasks that are transmitted through wireless channels, we design hypernetworks with meta-parameters. The hypernetwork takes the context, such as the network condition, as the input and outputs the parameters of the power control network and artificial intelligence (AI) model. The training loss is designed by minimizing the trade-off between inference performance and energy consumption. Simulation results verify the effectiveness of the proposed adaptive inference framework on energy saving while ensuring the accuracy of inferring ML tasks.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"591-595"},"PeriodicalIF":3.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Downlink OTFS-RSMA Cross-Domain Transmission Scheme and Sum-Rate Maximization
IF 3.7 3区 计算机科学
IEEE Communications Letters Pub Date : 2025-01-27 DOI: 10.1109/LCOMM.2025.3534858
Qiwei Huai;Weina Yuan;Yue Wu;Pingzhi Fan
{"title":"Downlink OTFS-RSMA Cross-Domain Transmission Scheme and Sum-Rate Maximization","authors":"Qiwei Huai;Weina Yuan;Yue Wu;Pingzhi Fan","doi":"10.1109/LCOMM.2025.3534858","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3534858","url":null,"abstract":"Rate splitting multiple access (RSMA) is an emerging multiple access scheme that plays a significant role in improving user sum-rate. This letter proposes a downlink orthogonal time frequency space-RSMA (OTFS-RSMA) cross-domain transmission scheme based on heterogeneous mobile user grouping and multicast technology, where high-speed users are served in the delay-Doppler (DD) domain and low-speed users are served in the time-frequency (TF) domain. Specifically, the OTFS-RSMA scheme categorizes users into high-speed users group and low-speed users group based on their mobility and then uses multicast to send cross-domain signals to the respective user groups, with RSMA used for transmission within the groups. By jointly optimizing the rate allocation and power control, this letter proposes a two-stage algorithm based on the coati optimization algorithm (COA) to maximize user sum-rate under the constraints of base station transmission power and QoS rate requirements. Our simulation results show that the proposed OTFS-RSMA scheme significantly outperforms the OTFS-NOMA scheme in terms of improving sum-rate.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 3","pages":"600-604"},"PeriodicalIF":3.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Collision-Aware Intel Rate Adaptation Algorithm for IEEE 802.11 WLANs 面向 IEEE 802.11 WLAN 的碰撞感知英特尔速率自适应算法
IF 3.7 3区 计算机科学
IEEE Communications Letters Pub Date : 2025-01-27 DOI: 10.1109/LCOMM.2025.3534250
Luhan Wang;Mark Davis
{"title":"Collision-Aware Intel Rate Adaptation Algorithm for IEEE 802.11 WLANs","authors":"Luhan Wang;Mark Davis","doi":"10.1109/LCOMM.2025.3534250","DOIUrl":"https://doi.org/10.1109/LCOMM.2025.3534250","url":null,"abstract":"Collision-Aware-Intel (CA-Intel) which is a modified version of the widely used Rate Adaptation Algorithm (RAA), Iwl-Mvm-Rs, is proposed to enhance performance by incorporating collision awareness. CA-Intel builds upon Iwl-Mvm-Rs by introducing an error condition estimation mechanism and an adaptive Request-To-Send/Clear-To-Send (RTS/CTS) mechanism. The presence of collisions is detected by analyzing the difference in Frame Error Rates (FERs) between transmissions protected by RTS/CTS and those that are not. Then it will adaptively enable the RTS/CTS mechanism based on current error conditions to avoid collisions. CA-Intel has been implemented in the ns-3 simulator, demonstrating that it effectively reduces FER and improves throughput.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 4","pages":"655-659"},"PeriodicalIF":3.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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