arXiv subjects

Halim Yanikomeroglu

Publications and source records attributed to Halim Yanikomeroglu.

At least 73 records · Page 4Linked to original sources

arXiv2025

Net-Zero 6G from Earth to Orbit: Sustainable Design of Integrated Terrestrial and Non-Terrestrial Networks

The integration of Terrestrial Networks (TN) and Non-Terrestrial Networks (NTN) plays a crucial role in bridging the digital divide and enabling Sixth Generation (6G) and beyond to achieve truly ubiquitous connectivity. However, combining TN and NTN introduces significant energy challenges due to the diverse characteristics and operational environments of these systems. In this paper, we present for the first time a comprehensive overview of the design challenges associated with achieving Net-Zero energy targets in integrated TN and NTN systems. We outline a set of key enabling technologies that can support the energy demands of such networks while aligning with Net-Zero objectives. To enhance the Energy Efficiency (EE) of integrated TN and NTN systems, we provide a use case analysis that leverages Artificial Intelligence (AI) to deliver adaptable solutions across diverse deployment scenarios. Finally, we highlight promising research directions that can guide the sustainable evolution of integrated TN and NTN.

cs.DC↗

arXiv2025

Green Traffic Engineering for Satellite Networks Using Segment Routing Flexible Algorithm

Large-scale low-Earth-orbit (LEO) constellations demand routing that simultaneously minimizes energy, guarantees delivery under congestion, and meets latency requirements for time-critical flows. We present a segment routing over IPv6 (SRv6) flexible algorithm (Flex-Algo) framework that consists of three logical slices: an energy-efficient slice (Algo 130), a high-reliability slice (Algo 129), and a latency-sensitive slice (Algo 128). The framework provides a unified mixed-integer linear program (MILP) that combines satellite CPU power, packet delivery rate (PDR), and end-to-end latency into a single objective, allowing a lightweight software-defined network (SDN) controller to steer traffic from the source node. Emulation of Telesat's Lightspeed constellation shows that, compared with different routing schemes, the proposed design reduces the average CPU usage by 73%, maintains a PDR above 91% during traffic bursts, and decreases urgent flow delay by 18 ms between Ottawa and Vancouver. The results confirm Flex-Algo's value as a slice-based traffic engineering (TE) tool for resource-constrained satellite networks.

cs.NI↗

arXiv2025

DSROQ: Dynamic Scheduling and Routing for QoE Management in LEO Satellite Networks

The modern Internet supports diverse applications with heterogeneous quality of service (QoS) requirements. Low Earth orbit (LEO) satellite constellations offer a promising solution to meet these needs, enhancing coverage in rural areas and complementing terrestrial networks in urban regions. Ensuring QoS in such networks requires joint optimization of routing, bandwidth allocation, and dynamic queue scheduling, as traffic handling is critical for maintaining service performance. This paper formulates a joint routing and bandwidth allocation problem where QoS requirements are treated as soft constraints, aiming to maximize user experience. An adaptive scheduling approach is introduced to prioritize flow-specific QoS needs. We propose a Monte Carlo tree search (MCTS)-inspired method to solve the NP-hard route and bandwidth allocation problem, with Lyapunov optimization-based scheduling applied during reward evaluation. Using the Starlink Phase 1 Version 2 constellation, we compare end-user experience and fairness between our proposed DSROQ algorithm and a benchmark scheme. Results show that DSROQ improves both performance metrics and demonstrates the advantage of joint routing and bandwidth decisions. Furthermore, we observe that the dominant performance factor shifts from scheduling to routing and bandwidth allocation as traffic sensitivity changes from latency-driven to bandwidth-driven.

cs.NI↗

arXiv2025

Capacity and IAPR Analysis for MIMO Faster-than-Nyquist Signaling with High Acceleration Rate

Faster-than-Nyquist (FTN) signaling is a non-orthogonal transmission technique offering a promising solution for future generations of communications. This paper studies the capacity of FTN signaling in multiple-input multiple-output (MIMO) channels for high acceleration factors. In our previous study [1], we found the capacity for MIMO FTN channels if the acceleration factor is larger than a certain threshold, which depends on the bandwidth of the pulse shape used. In this paper, we extend the capacity analysis to acceleration factors smaller than this mentioned threshold. In addition to capacity, we conduct peak-to-average power ratio (PAPR) analysis and simulation for MIMO FTN for varying acceleration factors for both Gaussian and QPSK symbol sets. Our analysis reveals important insights about transmission power and received signal-to-noise ratio (SNR) variation in FTN. As the acceleration factor approaches 0, if the transmission power is fixed, the received SNR diminishes, or if the received SNR is fixed, PAPR at the transmitter explodes.

cs.IT↗

arXiv2025

A Novel CNN Based Standalone Detector for Faster-than-Nyquist Signaling

This paper presents a novel convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling, introducing structured fixed kernel layers with domain-informed masking to effectively mitigate intersymbol interference (ISI). Unlike standard CNN architectures that rely on moving kernels, the proposed approach employs fixed convolutional kernels at predefined positions to explicitly learn ISI patterns at varying distances from the central symbol. To enhance feature extraction, a hierarchical filter allocation strategy is employed, assigning more filters to earlier layers for stronger ISI components and fewer to later layers for weaker components. This structured design improves feature representation, eliminates redundant computations, and enhances detection accuracy while maintaining computational efficiency. Simulation results demonstrate that the proposed detector achieves near-optimal bit error rate (BER) performance, comparable to the BCJR algorithm for the compression factor $τ\geq 0.7$, while offering up to $46\%$ and $84\%$ computational cost reduction over M-BCJR for BPSK and QPSK, respectively. Additional evaluations confirm the method's adaptability to high-order modulations (up to 64-QAM), resilience in quasi-static multipath Rayleigh fading channels, and effectiveness under LDPC-coded FTN transmission, highlighting its robustness and practicality.

eess.SP↗

arXiv2025

Integrating Terrestrial and Non-Terrestrial Networks for Sustainable 6G Operations: A Latency-Aware Multi-Tier Cell-Switching Approach

Sustainability is paramount in modern cellular networks, which face significant energy consumption challenges from rising mobile traffic and advancements in wireless technology. Cell-switching, well-established in literature as an effective solution, encounters limitations such as inadequate capacity and limited coverage when implemented through terrestrial networks (TN). This study enhances cell-switching by integrating non-terrestrial networks (NTN), including satellites (used for cell-switching for the first time), high altitude platform stations (HAPS), and uncrewed aerial vehicles (UAVs) into TN. This integration significantly boosts energy savings by expanding capacity, enhancing coverage, and increasing operational flexibility. We introduce a multi-tier cell-switching approach that dynamically offloads users across network layers to manage energy effectively and minimize delays, accommodating diverse user demands with a context aware strategy. Additionally, we explore the role of artificial intelligence (AI), particularly generative AI, in optimizing network efficiency through data compression, handover optimization between different network layers, and enhancing device compatibility, further improving the adaptability and energy efficiency of cell-switching operations. A case study confirms substantial improvements in network power consumption and user satisfaction, demonstrating the potential of our approach for future networks.

eess.SY↗

arXiv2025

DFT-s-OFDM with Chirp Modulation

In this paper, a new waveform called discrete Fourier transform spread orthogonal frequency division multiplexing with chirp modulation (DFT-s-OFDM-CM) is proposed for the next generation of wireless communications. The information bits are conveyed by not only Q-ary constellation symbols but also the starting frequency of chirp signal. It could maintain the benefits provided by the chirped discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM), e.g., low peak-to-average power ratio (PAPR), full frequency diversity exploitation, etc. Simulation results confirm that the proposed DFT-s-OFDM-CM could achieve higher spectral efficiency while keeping the similar bit error rate (BER) to that of chirped DFT-s-OFDM. In addition, when maintaining the same spectral efficiency, the proposed DFT-s-OFDM-CM with the splitting of information bits into two streams enables the use of lower-order constellation modulation and offers greater resilience to noise, resulting in a lower BER than the chirped DFT-s-OFDM.

eess.SP↗

arXiv2025

Data-Driven Spectrum Demand Prediction: A Spatio-Temporal Framework with Transfer Learning

Accurate spectrum demand prediction is crucial for informed spectrum allocation, effective regulatory planning, and fostering sustainable growth in modern wireless communication networks. It supports governmental efforts, particularly those led by the international telecommunication union (ITU), to establish fair spectrum allocation policies, improve auction mechanisms, and meet the requirements of emerging technologies such as advanced 5G, forthcoming 6G, and the internet of things (IoT). This paper presents an effective spatio-temporal prediction framework that leverages crowdsourced user-side key performance indicators (KPIs) and regulatory datasets to model and forecast spectrum demand. The proposed methodology achieves superior prediction accuracy and cross-regional generalizability by incorporating advanced feature engineering, comprehensive correlation analysis, and transfer learning techniques. Unlike traditional ITU models, which are often constrained by arbitrary inputs and unrealistic assumptions, this approach exploits granular, data-driven insights to account for spatial and temporal variations in spectrum utilization. Comparative evaluations against ITU estimates, as the benchmark, underscore our framework's capability to deliver more realistic and actionable predictions. Experimental results validate the efficacy of our methodology, highlighting its potential as a robust approach for policymakers and regulatory bodies to enhance spectrum management and planning.

cs.LG↗

arXiv2025

Max-Min Fairness-Oriented Beamforming Design in HAPS-Enabled ISAC for 6G Networks

This paper presents a high-altitude platform station (HAPS)-enabled integrated sensing and communication (ISAC) system designed for sixth-generation (6G) networks. Positioned in the stratosphere, HAPS serves as a super-macro base station, leveraging advanced beamforming techniques to enable communication and sensing simultaneously. This research addresses the need for equitable service distribution in 6G networks by focusing on fairness within the HAPS-ISAC system. It tackles a non-convex optimization problem that balances sensing beampattern gain and signal-to-interference-plus-noise ratio (SINR) requirements among communication users (CUs) using a max-min fairness approach while adhering to power constraints. The proposed HAPS-ISAC framework ensures efficient resource allocation, reliable coverage, and improved sensing accuracy. Simulation results validate the potential of HAPS-ISAC as a pivotal enabler for 6G networks and integrated communication-sensing systems.

eess.SP↗

arXiv2025

Dynamic Activation and Assignment of SDN Controllers in LEO Satellite Constellations

Software-defined networking (SDN) has emerged as a promising approach for managing traditional satellite communication. This enhances opportunities for future services, including integrating satellite and terrestrial networks. In this paper, we have developed an SDN-enabled framework for Low Earth Orbit (LEO) satellite networks, incorporating the OpenFlow protocol, all within an OMNeT++ simulation environment. Dynamic controller assignment is one of the most significant challenges for large LEO constellations. Due to the movement of LEO satellites, satellite-controller assignments must be updated frequently to maintain low propagation delays. To address this issue, we present a dynamic satellite-to-controller assignment (DSCA) optimization problem that continuously adjusts these assignments. Our optimal DSCA (Opt-DSCA) approach minimizes propagation delay and optimizes the number of active controllers. Our preliminary results demonstrate that the DSCA approach significantly outperforms the static satellite-to-controller assignment (SSCA) approach. While SSCA may perform better with more controllers, this scheme fails to adapt to satellite movements. Our DSCA approach consistently improves network efficiency by dynamically reassigning satellites based on propagation delays. Further, we found diminishing returns when the number of controllers is increased beyond a certain point, suggesting optimal performance with a limited number of controllers. Opt-DSCA lowers propagation delays and improves network performance by optimizing satellite assignments and reducing active controllers.

eess.SY↗

arXiv2025

A Novel Domain-Aware CNN Architecture for Faster-than-Nyquist Signaling Detection

This paper proposes a convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling that employs structured fixed kernel layers with domain-informed masking to mitigate intersymbol interference (ISI). Unlike standard CNNs with sliding kernels, the proposed method utilizes fixed-position kernels to directly capture ISI effects at varying distances from the central symbol. A hierarchical filter allocation strategy is also introduced, assigning more filters to earlier layers for strong ISI patterns and fewer to later layers for weaker ones. This design improves detection accuracy while reducing redundant operations. Simulation results show that the detector achieves near-optimal bit error rate (BER) performance for $τ\geq 0.7$, closely matching the BCJR algorithm, and offers computational gains of up to $46\%$ and $84\%$ over M-BCJR for BPSK and QPSK, respectively. Comparative analysis with other methods further highlights the efficiency and effectiveness of the proposed approach. To the best of our knowledge, this is the first application of a fixed-kernel CNN architecture tailored for FTN detection in the literature.

eess.SP↗

arXiv2025

AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks

Sixth-generation (6G) networks are designed to meet the hyper-reliable and low-latency communication (HRLLC) requirements of safety-critical applications such as autonomous driving. Integrating non-terrestrial networks (NTN) into the 6G infrastructure brings redundancy to the network, ensuring continuity of communications even under extreme conditions. In particular, high-altitude platform stations (HAPS) stand out for their wide coverage and low latency advantages, supporting communication reliability and enhancing information freshness, especially in rural areas and regions with infrastructure constraints. In this paper, we present reinforcement learning-based approaches using deep deterministic policy gradient (DDPG) to dynamically optimize the age-of-information (AoI) in HAPS-enabled vehicle-to-everything (V2X) networks. The proposed method improves information freshness and overall network reliability by enabling independent learning without centralized coordination. The findings reveal the potential of HAPS-supported solutions, combined with DDPG-based learning, for efficient AoI-aware resource allocation in platoon-based autonomous vehicle systems.

cs.NI↗

arXiv2025

Reciprocity-Aware Convolutional Neural Networks for Map-Based Path Loss Prediction

Path loss modeling is a widely used technique for estimating point-to-point losses along a communications link from transmitter (Tx) to receiver (Rx). Accurate path loss predictions can optimize use of the radio frequency spectrum and minimize unwanted interference. Modern path loss modeling often leverages data-driven approaches, using machine learning to train models on drive test measurement datasets. Drive tests primarily represent downlink scenarios, where the Tx is located on a building and the Rx is located on a moving vehicle. Consequently, trained models are frequently reserved for downlink coverage estimation, lacking representation of uplink scenarios. In this paper, we demonstrate that data augmentation can be used to train a path loss model that is generalized to uplink, downlink, and backhaul scenarios, training using only downlink drive test measurements. By adding a small number of synthetic samples representing uplink scenarios to the training set, root mean squared error is reduced by > 8 dB on uplink examples in the test set.

cs.LG↗

arXiv2025

PAPR Analysis for MIMO FTN Signaling with Gaussian Symbols

Faster-than-Nyquist signaling serves as a promising solution for improving spectral efficiency in future generations of communications. However, its nature of fast acceleration brings highly overlapped pulses that lead to worse peak-to-average power ratio (PAPR) performance. In this paper, we investigate the PAPR behavior of MIMO FTN using Gaussian symbols under optimal power allocation for two power constraints: fixed transmit power and fixed received signal-to-noise-ratio (SNR). Our findings reveal that PAPR is mainly determined by the acceleration factor and the power constraint, but power allocation optimization does not change the PAPR behavior for Gaussian signaling.

eess.SP↗

arXiv2025

Enhancing Sustainability in HAPS-Assisted 6G Networks: Load Estimation Aware Cell Switching

This study introduces and addresses the critical challenge of traffic load estimation in cell switching within vertical heterogeneous networks. The effectiveness of cell switching is significantly limited by the lack of accurate traffic load data for small base stations (SBSs) in sleep mode, making many load-dependent energy-saving approaches impractical, as they assume perfect knowledge of traffic loads, an assumption that is unrealistic when SBSs are inactive. In other words, when SBSs are in sleep mode, their traffic loads cannot be directly known and can only be estimated, inevitably with corresponding errors. Rather than proposing a new switching algorithm, we focus on eliminating this foundational barrier by exploring effective prediction techniques. A novel vertical heterogeneous network model is considered, integrating a high-altitude platform station (HAPS) as a super macro base station (SMBS). We investigate both spatial and temporal load estimation approaches, including three spatial interpolation schemes, random neighboring selection, distance based selection, and multi level clustering (MLC), alongside a temporal deep learning method based on long short-term memory (LSTM) networks. Using a real world dataset for empirical validation, our results show that both spatial and temporal methods significantly improve estimation accuracy, with the MLC and LSTM approaches demonstrating particularly strong performance.

eess.SY↗

arXiv2025

Optimizing Network Performance and Resource Allocation in HAPS-UAV Integrated Sensing and Communication Systems for 6G

This paper proposes an innovative approach by leveraging uncrewed aerial vehicles (UAVs) as base stations (BSs) and a high-altitude platform station (HAPS) as the central processing unit (CPU) in an integrated sensing and communication (ISAC) system for 6G networks. We explore the challenges, applications, and advantages of ISAC systems in next-generation networks, highlighting the significance of optimizing position and power control. Our approach integrates HAPS and UAVs to enhance wireless coverage, particularly in remote areas. UAVs function as dual-purpose access points (APs), using their maneuverability and line-of-sight (LoS) aerial-to-ground (A2G) links to transmit combined communication and sensing signals. The scheme operates in two time slots: in the first slot, UAVs transmit dedicated signals to communication users (CUs) and potential targets. UAVs detect targets in specific ground locations and, after signal transmission, receive reflected signals from targets. In the second slot, UAVs relay these signals to HAPS, which performs beamforming to align signals for each CU from various UAVs. UAVs decode information from HAPS and adjust transmissions to maximize the beam pattern efficiency toward the desired targets. We formulate a multi-objective optimization problem to maximize both the minimum signal-to-interference-plus-noise ratio (SINR) for CUs and the echo signal power from the targets. This is achieved by finding the optimal power allocation for CUs in each UAV, subject to constraints on the maximum total power in each UAV and the transmitted beam pattern gain. Simulation results demonstrate the effectiveness of this approach in enhancing network performance, resource allocation, fairness, and system optimization. Using HAPS as the CPU, computational tasks are offloaded from UAVs, which conserves energy and improves network performance.

eess.SP↗

arXiv2025

On-Demand HAPS-Assisted Communication System for Public Safety in Emergency and Disaster Response

Natural disasters often disrupt communication networks and severely hamper emergency response and disaster management. Existing solutions, such as portable communication units and cloud-based network architectures, have improved disaster resilience but fall short if both the Radio Access Network (RAN) and backhaul infrastructure become inoperable. To address these challenges, we propose a demand-driven communication system supported by High Altitude Platform Stations (HAPS) to restore communication in an affected area and enable effective disaster relief. The proposed emergency response network is a promising solution as it provides a rapidly deployable, resilient communications infrastructure. The proposed HAPS-based communication can play a crucial role not only in ensuring connectivity for mobile users but also in restoring backhaul connections when terrestrial networks fail. As a bridge between the disaster management center and the affected areas, it can facilitate the exchange of information in real time, collect data from the affected regions, and relay crucial updates to emergency responders. Enhancing situational awareness, coordination between relief agencies, and ensuring efficient resource allocation can significantly strengthen disaster response capabilities. In this paper, simulations show that HAPS with hybrid optical/THz links boosts backhaul capacity and resilience, even in harsh conditions. HAPS-enabled RAN in S- and Ka-bands ensures reliable communication for first responders and disaster-affected populations. This paper also explores the integration of HAPS into emergency communication frameworks and standards, as it has the potential to improve network resilience and support effective disaster management.

cs.NI↗

arXiv2025

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

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.

cs.NI↗