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Channel Aging-Aware LSTM-Based Channel Prediction for Satellite Communications

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IEEE Networking Letters

IEEE Networking Letters 2024年第3期6卷 183-187页

作者： Abbasi, Omid Kaddoum, Georges Carleton University Non-Terrestrial Networks Lab Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Université du Québec École de Technologie Supérieure Department of Electrical Engineering MontrealQCH3C 1K3 Canada Lebanese American University Cyber Security Systems and Applied Ai Research Center Beirut2801 Lebanon

Satellite communication systems encounter channel aging issues due to the substantial distance that separates users and satellites. In such systems, the estimated channel state at a given time slot reflects the channel state from several time slots in the past. This letter proposes a long short-term memory (LSTM)-based architecture for channel prediction to mitigate the channel aging problem. The proposed scheme predicts the next time slot's channel based on a block of estimated channel state information (CSI) from previous time slots. We consider the effect of channel aging in the training phase so that channel prediction in the testing phase is performed based on availab.e data. We demonstrated through simulation experiments on new radio non-terrestrial network tapped delay line (NR NTN TDL) channel models, that our proposed scheme can effectively mitigate channel aging, and that it performs better than outdated channels. The proposed scheme improves the reliability and efficiency of satellite communication systems with long propagation delays. © 2019 IEEE.

关键词： Satellite communication systems

Exploring the 6G Potentials: Immersive, Hyper Reliable, and Low-Latency Communication

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arXiv 2024年

作者： Shamsabadi, Afsoon Alidadi Yadav, Animesh Gadallah, Yasser Yanikomeroglu, Halim Lab. The Department of SCE Carleton University Canada The School of EECS Ohio University AthensOH United States Egypt Systems and Computer Engineering in the Non-Terrestrial Networks Lab. Carleton University Canada

The transition towards the sixth-generation (6G) wireless telecommunications networks introduces significant challenges for researchers and industry stakeholders. The 6G technology aims to enhance existing usage scenarios through supporting innovative applications that require stringent key performance indicators (KPIs). In some critical use cases of 6G, multiple KPIs, including immersive throughput, with an envisioned peak data rate of 1 Tbps, hyper-reliability, in the range of 10−5 to 10−7, and hyper low-latency, between 0.1 and 1 ms, must be achieved simultaneously to deliver the expected service experience. However, this is challenging due to the conflicting nature of these KPIs. This article proposes a new service class of 6G as immersive, hyper reliable, and low-latency communication (IHRLLC), and introduces a potential network architecture to achieve the associated KPIs. Specifically, enhanced technologies, such as ultra-massive multiple-input multiple-output (umMIMO)-aided terahertz (THz) communications, reconfigurable intelligent surfaces (RIS), and non-terrestrial networks (NTN), are viewed as the key enablers for achieving immersive data rates and hyper reliability. Given the computational complexity involved in employing these technologies, we propose mathematical and computational enabling technologies, such as learn-to-optimize (L2O), generative-AI (GenAI), quantum computing, and network digital twin (NDT), to complement the proposed architecture and optimize the latency. © 2024, CC0.

关键词： Reconfigurable architectures

Integrated Robotic Aerial Base Stations Deployment and Backhaul Design in 6G Multihop networks

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Integrated Robotic Aerial Base Stations Deployment and Backh...

2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Shang, Wen Liao, Yuan Friderikos, Vasilis Yanikomeroglu, Halim Center of Telecommunication Research King's College London London United Kingdom Carleton University Non-Terrestrial Networks Lab Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350368369

To overcome the limited endurance of traditional unmanned aerial vehicles (UAVs), we propose a network of robotic aerial base stations (RABSs) that can energy-efficiently anchor into tall urban landforms, such as lampposts. This approach enables the creation of a hyper-flexible wireless multi-hop network, designed to support green, densified, and dynamic network requirements, thereby ensuring reliable long-term coverage for the whole observed region. The proposed network infrastructure can concurrently address the backhaul link capacity bottleneck and support access link traffic demand in the millimeter-wave (mmWave) frequency band. Specifically, the RABSs grasping locations, resource blocks (RBs) assignment, and route flow control are simultaneously optimized to maximize the served traffic demands. The group of RABSs capitalizes on the fact that traffic distribution varies considerably across both time and space within a given geographical area. Hence, they are able to relocate to suitable locations, i.e., 'follow' the traffic demand as it unfolds to increase the overall network efficiency. To tackle the curse of dimensionality of the proposed mixed-integer problem, we propose a greedy algorithm to obtain a competitive solution with low computational complexity. A wide set of numerical investigations reveals that RABSs could improve the served traffic demand. For instance, compared to networks with randomly deployed fixed small cells, the proposed mode serves at most 65% more traffic demand. © 2025 IEEE.

关键词： Drones

Integrated Robotic Aerial Base Stations Deployment and Backhaul Design in 6G Multihop networks

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Integrated Robotic Aerial Base Stations Deployment and Backh...

IEEE Conference on Wireless Communications and Networking

作者： Wen Shang Yuan Liao Vasilis Friderikos Halim Yanikomeroglu Center of Telecommunication Research King's College London London U.K. Department of Systems and Computer Engineering Non-Terrestrial Networks Lab Carleton University Ottawa Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

To overcome the limited endurance of traditional unmanned aerial vehicles (UAVs), we propose a network of robotic aerial base stations (RABSs) that can energy-efficiently anchor into tall urban landforms, such as lampposts. This approach enables the creation of a hyper-flexible wireless multi-hop network, designed to support green, densified, and dynamic network requirements, thereby ensuring reliable long-term coverage for the whole observed region. The proposed network infrastructure can concurrently address the backhaul link capacity bottleneck and support access link traffic demand in the millimeter-wave (mmWave) frequency band. Specifically, the RABSs grasping locations, resource blocks (RBs) assignment, and route flow control are simultaneously optimized to maximize the served traffic demands. The group of RABSs capitalizes on the fact that traffic distribution varies considerably across both time and space within a given geographical area. Hence, they are able to relocate to suitable locations, i.e., ‘follow’ the traffic demand as it unfolds to increase the overall network efficiency. To tackle the curse of dimensionality of the proposed mixed-integer problem, we propose a greedy algorithm to obtain a competitive solution with low computational complexity. A wide set of numerical investigations reveals that RABSs could improve the served traffic demand. For instance, compared to networks with randomly deployed fixed small cells, the proposed mode serves at most 65% more traffic demand.

关键词： 6G mobile communication Base stations Wireless networks Spread spectrum communication Pressing Reliability engineering Resource management Vehicle dynamics Robots Backhaul networks

Joint Robotic Aerial Base Station Deployment and Wireless Backhauling in 6G Multi-hop networks

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arXiv 2024年

作者： Shang, Wen Liao, Yuan Friderikos, Vasilis Yanikomeroglu, Halim Center of Telecommunication Research King’s College London London United Kingdom Non-Terrestrial Networks Lab Department of Systems and Computer Engineering Carleton University Ottawa Canada

Due to their ability to anchor into tall urban landforms, such as lampposts or street lights, robotic aerial base stations (RABSs) can create a hyper-flexible wireless multi-hop heterogeneous network to meet the forthcoming green, densified, and dynamic network deployment to support, inter alia, high data rates. In this work, we propose a network infrastructure that can concurrently support the wireless backhaul link capacity and access link traffic demand in the millimeter-wave (mmWave) frequency band. The RABSs grasping locations, resource blocks (RBs) assignment, and route flow control are simultaneously optimized to maximize the served traffic demands. Robotic base stations capitalize on the fact that traffic distribution varies considerably across both time and space within a given geographical area. Hence, they are able to relocate to suitable locations, i.e.,’follow’ the traffic demand as it unfolds to increase the overall network efficiency. To tackle the curse of dimensionality of the proposed mixed-integer linear problem, we propose a greedy algorithm to obtain a competitive solution with low computational complexity. Compared to baseline models, which are heterogeneous networks with randomly deployed fixed small cells and pre-allocated RBs for wireless access and backhaul links, a wide set of numerical investigations reveals that robotic base stations could improve the served traffic demand. Specifically, the proposed mode serves at most 65% more traffic demand compared to an equal number of deployed fixed small cells. © 2024, CC BY.

关键词： Antennas

Strategic Demand-Planning in Wireless networks: Can Generative-AI Save Spectrum and Energy?

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arXiv 2024年

作者： Çiloğlu, Berk Koç, Görkem Berkay Shamsabadi, Afsoon Alidadi Ozturk, Metin Yanikomeroglu, Halim Electrical and Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Turkey Non-Terrestrial Networks Lab Systems and Computer Engineering Carleton University Ottawa Canada

Generative-AI (GenAI), a novel technology capable of producing various types of outputs, including text, images, and videos, offers significant potential for wireless communications. This article introduces the concept of strategic demand-planning through demand-lab.ling, demand-shaping, and demand-rescheduling. Accordingly, GenAI is proposed as a powerful tool to facilitate demand-shaping in wireless networks. More specifically, GenAI is used to compress and convert the content of various types (e.g., from a higher bandwidth mode to a lower one, such as from a video to text), which subsequently enhances performance of wireless networks in various usage scenarios, such as cell-switching, user association and load balancing, interference management, as well as disasters and unusual gatherings. Therefore, GenAI can serve a function in saving energy and spectrum in wireless networks. With recent advancements in AI, including sophisticated algorithms like large language models and the development of more powerful hardware built exclusively for AI tasks, such as AI accelerators, the concept of demand-planning, particularly demand-shaping through GenAI, becomes increasingly relevant. Furthermore, recent efforts to make GenAI accessible on devices, such as user terminals, make the implementation of this concept even more straightforward and feasible. Copyright © 2024, The Authors. All rights reserved.

关键词： Demand response

A Lightweight Machine Learning Approach for Delay-Aware Cell-Switching in 6G HAPS networks

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A Lightweight Machine Learning Approach for Delay-Aware Cell...

IEEE International Conference on Communications Workshops, ICC

作者： Görkem Berkay Koç Berk Çiloğlu Metin Ozturk Halim Yanikomeroglu Electrical and Electronics Engineering Ankara Yıldırım Beyazit University Ankara Türkiye Non-Terrestrial Networks (NTN) Lab Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

This study investigates the integration of a high altitude platform station (HAPS), a non-terrestrial network (NTN) node, into the cell-switching paradigm for energy savings. By doing so, the sustainability and ubiquitous connectivity targets of the sixth generation of communication systems (6G) can be achieved simultaneously. Additionally, a delay-aware approach is also adopted, where the delay profiles of users are respected with a best-effort strategy. To this end, a novel, simple, and lightweight $Q$ -learning algorithm is designed to address the cell-switching optimization problem. Various interference scenarios and delay situations between base stations are examined in terms of energy consumption and quality-of-service (QoS), and the results confirm the efficacy of the proposed algorithm.

关键词： 6G mobile communication Machine learning algorithms Conferences Noise Quality of service Interference Switches

Multi-Tier non-terrestrial Networking for Disaster Communications: A Layered Clustering Approach

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Multi-Tier Non-Terrestrial Networking for Disaster Communica...

IEEE Signal Processing and Communications Applications (SIU)

作者： Metin Ozturk Berk Çiloğlu Görkem Berkay Koç Halim Yanikomeroglu Electrical and Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Türkiye Non-Terrestrial Networks (NTN) Lab Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

It is crucial to deploy temporary non-terrestrial networks (NTN) in disaster situations where terrestrial networks are no longer operable. Deploying uncrewed aerial vehicle base stations (UAV-BSs) can provide a radio access network (RAN); however, the backhaul link may also be damaged and unserviceable in such disaster conditions. In this regard, high-altitude platform stations (HAPS) spark attention as they can be deployed as super macro base stations (SMBS) and data centers. Therefore, in this study, we investigate a three-layer heterogeneous network with different topologies to prolong the lifespan of the temporary network by using UAV-BSs for RAN services and HAPS-SMBS as a backhaul. Furthermore, a two-layer clustering algorithm is proposed to handle the UAV-BS ad-hoc networking effectively.

关键词： Base stations Network topology Disasters Signal processing algorithms Clustering algorithms Signal processing Topology

Novel Methods for Load Estimation in Cell Switching in HAPS-Assisted Sustainable 6G networks

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arXiv 2024年

作者： Salamatmoghadasi, Maryam Ozturk, Metin Yanikomeroglu, Halim Non-Terrestrial Networks Lab Department of Systems and Computer Engineering Carleton University OttawaONK1S5B6 Canada Ankara Yildirim Beyazit University Ankara06010 Turkey

In the evolving landscape of vertical heterogeneous networks (vHetNets), the practice of cell switching—particularly for small base stations (SBSs)—faces a significant challenge due to the lack of accurate data on the traffic load of sleeping SBSs. This information gap is crucial as it hinders the feasibility and applicability of existing power consumption optimization methods;however, the studies in the literature predominantly assume perfect knowledge about the traffic load of sleeping SBSs. Addressing this critical issue, our study introduces innovative methodologies for estimating the traffic load of sleeping SBSs in a vHetNet including the integration of a high altitude platform (HAPS) as a super macro base station (SMBS) into the terrestrial network. We propose three distinct spatial interpolation-based estimation schemes: clustering-based, distance-based, and random neighboring selection. Employing a real data set for empirical validations, we compare the estimation performance of the developed traffic load estimation schemes and assess the impact of estimation errors. Our findings demonstrate that accurate estimation of sleeping SBSs’ traffic loads is essential for making network power consumption optimization methods both feasible and applicable in vHetNets. © 2024, CC BY.

关键词： Electric power utilization