| 1. |
- Baltaci, Aygun, et al.
(författare)
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A Survey of Wireless Networks for Future Aerial Communications (FACOM)
- 2021
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Ingår i: IEEE Communications Surveys and Tutorials. - : Institute of Electrical and Electronics Engineers (IEEE). - 1553-877X. ; 23:4, s. 2833-2884
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Tidskriftsartikel (refereegranskat)abstract
- Electrification turned over a new leaf in aviation by introducing new types of aerial vehicles along with new means of transportation. Addressing a plethora of use cases, drones are gaining attention in the industry and increasingly appear in the sky. Emerging concepts of flying taxi enable passengers to be transported over several tens of kilometers. Therefore, unmanned traffic management systems are under development to cope with the complexity of future airspace, thereby resulting in unprecedented communication needs. Moreover, the long-term increase in the number of commercial airplanes pushes the limits of voice-oriented communications, and future options such as single-pilot operations demand robust connectivity. In this survey, we provide a comprehensive review and vision for enabling the connectivity applications of aerial vehicles utilizing current and future communication technologies. We begin by categorizing the connectivity use cases per aerial vehicle and analyzing their connectivity requirements. By reviewing more than 500 related studies, we aim for a comprehensive approach to cover wireless communication technologies, and provide an overview of recent findings from the literature toward the possibilities and challenges of employing the wireless communication standards. After analyzing the proposed network architectures, we list the open-source testbed platforms to facilitate future investigations by researchers. This study helped us observe that while numerous works focused on cellular technologies to enable connectivity for aerial platforms, a single wireless technology is not sufficient to meet the stringent connectivity demands of the aerial use cases, especially for the piloting operations. We identified the need of further investigations on multi-technology heterogeneous network architectures to enable robust and real-time connectivity in the sky. Future works should consider suitable technology combinations to develop unified aerial networks that can meet the diverse quality of service demands of the aerial use cases.
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| 2. |
- Bolakhrif, Amin, et al.
(författare)
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AI-Assisted Network Traffic Prediction Without Warm-Up Periods
- 2022
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Ingår i: 95th IEEE Vehicular Technology Conference - Spring, VTC 2022-SPRING. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Network traffic prediction in cellular networks improves reliability and efficiency of network resource use via proactive network management schemes. To this end, future traffic arrivals are anticipated via machine learning (ML)-based network traffic predictions based on historical network traffic data. Current literature on ML-based network traffic predictions employs warm-up periods, which are the required duration traffic flows are observed to make meaningful predictions. However, most flows are shorter than the warm-up period. This paper proposes a residual neural network (ResNet) architecture for individual network flow predictions, based on a deep-learning approach that removes the required warm-up period seen in other proposed methods. The ResNet architecture demonstrates the ability to accurately predict the magnitude of packet count, size, and duration of flows using only the information available at the arrival of the first packet such as IP addresses and utilized transport-layer protocols. The results indicate that the proposed method is able to predict the order of magnitude of individual flow characteristics with over 80% accuracy, outperforming traditional ML methods such as linear regression and decision trees.
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| 3. |
- Cetinkaya, Oktay, et al.
(författare)
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Coverage Performance of UAV-Powered Sensors for Energy-Neutral Networks with Recharging Stations
- 2023
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Ingår i: ICC 2023 - IEEE International Conference on Communications: Sustainable Communications for Renaissance. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2902-2908
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Konferensbidrag (refereegranskat)abstract
- The projected number of Internet of Things (IoT) sensors makes battery maintenance a challenging task. Although battery-less IoT is technologically viable, the sensors should be somehow energized, either locally or remotely. Unmanned aerial vehicles (UAVs) can respond to this quest via wireless power transfer (WPT). However, to achieve energy neutrality across the IoT networks and thus mitigate the maintenance issues, the UAVs providing energy and connectivity to IoT sensors must be supplied by recharging stations having multi-source energy harvesting (EH) capability. Yet, as these sensors rely solely on UAV-transferred power, the absence of UAVs causes sensor outages and hence loss of coverage when they visit recharging stations for battery replenishment. Hence, besides the UAV parameters (e.g., battery size and velocity), recharging duration and station density must be carefully determined to avoid these outages. To address that, this paper uses stochastic geometry to derive the coverage probability of UAV-powered sensors. Our analysis sheds light on the fundamental trade-offs and design guidelines for energy-neutral IoT networks with recharging stations in regard to the regulatory organization limitations, practical rectenna and UAV models, and the minimum power requirements of sensors.
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| 4. |
- Cha, Kyeong-Ju, et al.
(författare)
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When Wireless Localization Meets Artificial Intelligence : Basics, Challenges, Synergies, and Prospects
- 2023
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Ingår i: Applied Sciences. - : MDPI AG. - 2076-3417. ; 13:23
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Forskningsöversikt (refereegranskat)abstract
- The rapid development of information communication and artificial intelligence (AI) technology is driving innovation in various new application fields such as autonomous driving, augmented reality, and the metaverse. In particular, the advancement of wireless localization technology plays a great role in these cutting-edge technologies. However, traditional wireless localization systems rely on the global navigation satellite system (GNSS), which is ineffective in indoor or underground environments. To overcome this issue, indoor positioning systems (IPS) have gained attention, and various localization techniques utilizing wireless communication were studied. Subsequently, AI technologies are improving the performance of wireless localization and addressing problems that were previously difficult to solve. In this paper, we summarize wireless localization techniques and define the factors that impede their performance. Furthermore, we categorize AI algorithms and present examples of how they can be used to address these hindering factors. Finally, we propose open research directions and prospects for AI-assisted wireless localization.
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| 5. |
- Deng, Yuhang, et al.
(författare)
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D3QN-Based Trajectory and Handover Management for UAVs Co-existing with Terrestrial Users
- 2023
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Ingår i: 2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 103-110
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Konferensbidrag (refereegranskat)abstract
- The ubiquitous cellular network is a strong candidate for providing UAVs’ wireless connectivity. Due to the maneuverability advantage and higher altitude, UAVs could have line-of-sight (LoS) connectivity with more base station (BS) candidates than terrestrial users. However, the LoS connectivity could also enhance the propagation of up-link interference caused by UAVs over co-existing terrestrial users. In addition, UAVs would perform more handovers than terrestrial users when moving due to the extensive overlap in the coverage areas of many BS candidates. The solution is to bypass the overlapping coverage areas by designing the UAVs’ trajectory and to reduce interference by optimizing radio resource allocation through handover management. This paper studies the joint optimization of a UAV’s trajectory design and handover management to minimize the weighted sum of three key performance indicators (KPIs): delay, up-link interference, and handover numbers. A dueling double deep Q-network (D3QN) based reinforcement learning algorithm is proposed to solve the optimization problem. Results show that the proposed approach can reduce the handover numbers by 90% and the interference by 18% at the cost of a small increment in transmission delay when compared with the benchmark scheme, which controls the UAV to move along the shortest path and perform handover based on received signal strength. Finally, we verify the advantage of introducing trajectory design, which can reduce the interference by 29% and eliminate the handover numbers by 33% when compared to the D3QN-based policy without trajectory design.
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| 6. |
- Hao, Kun, et al.
(författare)
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Counterfactual and Causal Analysis for AI-Based Modulation and Coding Scheme Selection
- 2023
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Ingår i: 2023 IEEE Globecom Workshops, GC Wkshps 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 32-37
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Konferensbidrag (refereegranskat)abstract
- In recent years, Artificial Intelligence (AI) solutions for Modulation and Coding Scheme (MCS) selection have been predominantly characterized as black-box models, which suffer from limited interpretability and consequently hinder trust in these algorithms. Moreover, the majority of existing eXplainable AI (XAI) research primarily emphasizes enhancing explainability without concurrently improving the model's performance which makes performance and interpretability a tradeoff. This paper aims to address these issues by employing counterfactual and causal analysis to increase the interpretability and trustworthi-ness of black-box models. In particular, we propose CounterFac-tual Retrain (CF-Retrain), the first algorithm that utilizes coun-terfactual explanations to improve model performance and make the process of performance enhancement more interpretable. Additionally, we conduct a causal analysis and compare the results with those obtained from an analysis based on the SHapley Additive exPlanations (SHAP) value feature importance. This comparison leads to the proposal of novel hypotheses and insights for model optimization in future research.
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| 7. |
- Hofmann, Sandra, et al.
(författare)
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Combined Optimal Topology Formation and Rate Allocation for Aircraft to Aircraft Communications
- 2019
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Ingår i: IEEE International Conference on Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538680889
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Konferensbidrag (refereegranskat)abstract
- Providing broadband in-flight Internet connectivity to aircraft is challenging. Today's options include satellite communications (SC) and direct air-to-ground communication (DA2GC). To overcome data rate, delay and cost limitations of SC and coverage limitations of DA2GC, one can extend DA2GC with air-to-air communication (A2AC) by enabling multi-hop communication. To investigate the A2AC performance, we construct a mixed integer linear programming (MILP) problem of DA2GC and A2AC, jointly considering interference in topology formation and flow assignment. Our objective is to maximize the number of aircraft that can be connected with a given specific minimum data rate threshold. The evaluation is performed for low aircraft density scenarios over the North Atlantic. We show that in the investigated scenarios, over 90 % of aircraft can have at least 50 Mbps, some being up to 1600 kilometers away from the closest base station (BS). Furthermore, we identify antenna capabilities as an important factor for A2AC performance.
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| 8. |
- Jeong, Jin-Young, et al.
(författare)
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Compressed Sensing Verses Auto-Encoder : On the Perspective of Signal Compression and Restoration
- 2024
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 12, s. 41967-41979
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a comparison between compressed sensing (CS) and auto-encoder (AE) for compression and restoration of signals. The study used K-sparse vectors and generated an under-determined system, which is a system of linear equations with fewer equations than unknowns. By using CS and AE under various specific conditions, the accuracy of the signal restoration is compared with mean squared error (MSE). The experimental methodology includes comparing and analyzing the signal recovery performance by altering the algorithm and various parameters. The result represents the performance and accuracy of signal compression and restoration obtained using both techniques. It also provides a comprehensive analysis of CS and AE methods. The importance of this research and the possibility of practical application in various fields are discussed. Overall, this study provides insights into the comparison of CS and AE techniques in the field of sparse signal compression and restoration.
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| 9. |
- Kim, Jung-Hwan, et al.
(författare)
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An NN-Aided Near-and-Far-Field Classifier via Channel Hankelization in XL-MIMO Systems
- 2024
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 12, s. 41934-41941
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Tidskriftsartikel (refereegranskat)abstract
- Compared to classical communication systems, sixth-generation (6G) communication requires higher data rates, lower latency, improved energy efficiency, and more diverse users. To satisfy these many requirements, the extremely large-scale massive multiple-input multiple-output (XL-MIMO) system is attracting attention as a promising technology in 6G communication. Depending on the distance between a transmitter and a receiver, the electromagnetic radiation channels in XL-MIMO systems are divided into two models: near-field and far-field channels. The main difference between far-field and near-field is the phase-linearity, resulting in a need for a differentiated system design such as beam management. As a consequence, it is essential to classify near-field and far-field. This paper presents a new neural network (NN)-aided framework for classifying near-field and far-field using the partially captured channel in downlink scenarios in XL-MIMO systems. It is based on the mathematical reasoning that an effective latent space can be constructed with a small amount of data by using the singular values of the channel Hankelization. Briefly, it is to determine the one-hot encoding vector corresponding to each field and learn the singular values of the Hankelized channel matrix. It is noteworthy that this framework operates using the short length of input vectors and the small size of the training dataset. Simulation results show that the proposed method shows the detection rate of about 90% in almost all scenarios. Interestingly, the proposed method shows almost 100% of detection ratio in high SNR environments. It is believed that the proposed method shows superior performance than naive approaches in various environments, discovering the suitable domain to classify near-field and far-field channels.
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| 10. |
- Manzoor, Aunas, et al.
(författare)
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Combined Airspace and Non-Terrestrial 6G Networks for Advanced Air Mobility
- 2024
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Ingår i: 20th International Conference on the Design of Reliable Communication Networks, DRCN 2024. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 47-53
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Konferensbidrag (refereegranskat)abstract
- Advanced Air Mobility (AAM) is defined as the transportation of goods and humans via aerial vehicles (AVs) for both urban and rural air mobility. For a safe and seamless operation of AVs, an ultra-reliable and low-latency communication (URLLC) service for their command and control link is desired. Combined Airspace and Non-Terrestrial Networks (ASN) have the potential to enable remote piloting for AAM to meet the stringent URLLC reliability and latency requirements of an AV. We propose to utilize multi-connectivity paths through direct-Air-To-ground communication (DA2GC), relaying AV s via air-To-Air communication (A2A), high altitude platform (HAP), and LEO satellites. We formulate an optimization problem for multi-connectivity path selection with their respective resource block (RB) allocation under limited resource constraints. The problem is solved by decomposing it into two sub-problems of multi-connectivity path selection and RB allocation. An efficient RB allocation scheme is proposed to allocate the limited RB resources of each path to the best AV s using the Knapsack al-gorithm. Next, we perform multi-connectivity selection to reduce the total cost by preferring the available low-cost paths while meeting the reliability requirements. Simulation results reveal that the proposed scheme can reduce up to 28 % of total cost by considering the differentiated cost for path selection as compared to the cost-Agnostic resource allocation.
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