arXiv subjects

Melike Erol-Kantarci

Publications and source records attributed to Melike Erol-Kantarci.

At least 55 records · Page 3Linked to original sources

arXiv2025

Generative AI-enabled Blockage Prediction for Robust Dual-Band mmWave Communication

In mmWave wireless networks, signal blockages present a significant challenge due to the susceptibility to environmental moving obstructions. Recently, the availability of visual data has been leveraged to enhance blockage prediction accuracy in mmWave networks. In this work, we propose a Vision Transformer (ViT)-based approach for visual-aided blockage prediction that intelligently switches between mmWave and Sub-6 GHz frequencies to maximize network throughput and maintain reliable connectivity. Given the computational demands of processing visual data, we implement our solution within a hierarchical fog-cloud computing architecture, where fog nodes collaborate with cloud servers to efficiently manage computational tasks. This structure incorporates a generative AI-based compression technique that significantly reduces the volume of visual data transmitted between fog nodes and cloud centers. Our proposed method is tested with the real-world DeepSense 6G dataset, and according to the simulation results, it achieves a blockage prediction accuracy of 92.78% while reducing bandwidth usage by 70.31%.

eess.SP↗

arXiv2024

LLM-Based Intent Processing and Network Optimization Using Attention-Based Hierarchical Reinforcement Learning

Intent-based network automation is a promising tool to enable easier network management however certain challenges need to be effectively addressed. These are: 1) processing intents, i.e., identification of logic and necessary parameters to fulfill an intent, 2) validating an intent to align it with current network status, and 3) satisfying intents via network optimizing functions like xApps and rApps in O-RAN. This paper addresses these points via a three-fold strategy to introduce intent-based automation for O-RAN. First, intents are processed via a lightweight Large Language Model (LLM). Secondly, once an intent is processed, it is validated against future incoming traffic volume profiles (high or low). Finally, a series of network optimization applications (rApps and xApps) have been developed. With their machine learning-based functionalities, they can improve certain key performance indicators such as throughput, delay, and energy efficiency. In this final stage, using an attention-based hierarchical reinforcement learning algorithm, these applications are optimally initiated to satisfy the intent of an operator. Our simulations show that the proposed method can achieve at least 12% increase in throughput, 17.1% increase in energy efficiency, and 26.5% decrease in network delay compared to the baseline algorithms.

cs.NI↗

arXiv2024

Explainable Multi-Agent Reinforcement Learning for Extended Reality Codec Adaptation

Extended Reality (XR) services are set to transform applications over 5th and 6th generation wireless networks, delivering immersive experiences. Concurrently, Artificial Intelligence (AI) advancements have expanded their role in wireless networks, however, trust and transparency in AI remain to be strengthened. Thus, providing explanations for AI-enabled systems can enhance trust. We introduce Value Function Factorization (VFF)-based Explainable (X) Multi-Agent Reinforcement Learning (MARL) algorithms, explaining reward design in XR codec adaptation through reward decomposition. We contribute four enhancements to XMARL algorithms. Firstly, we detail architectural modifications to enable reward decomposition in VFF-based MARL algorithms: Value Decomposition Networks (VDN), Mixture of Q-Values (QMIX), and Q-Transformation (Q-TRAN). Secondly, inspired by multi-task learning, we reduce the overhead of vanilla XMARL algorithms. Thirdly, we propose a new explainability metric, Reward Difference Fluctuation Explanation (RDFX), suitable for problems with adjustable parameters. Lastly, we propose adaptive XMARL, leveraging network gradients and reward decomposition for improved action selection. Simulation results indicate that, in XR codec adaptation, the Packet Delivery Ratio reward is the primary contributor to optimal performance compared to the initial composite reward, which included delay and Data Rate Ratio components. Modifications to VFF-based XMARL algorithms, incorporating multi-headed structures and adaptive loss functions, enable the best-performing algorithm, Multi-Headed Adaptive (MHA)-QMIX, to achieve significant average gains over the Adjust Packet Size baseline up to 10.7%, 41.4%, 33.3%, and 67.9% in XR index, jitter, delay, and Packet Loss Ratio (PLR), respectively.

cs.NI↗

arXiv2024

The impact of mobility, beam sweeping and smart jammers on security vulnerabilities of 5G cells

The vulnerability of 5G networks to jamming attacks has emerged as a significant concern. This paper contributes in two primary aspects. Firstly, it investigates the effect of a multi-jammer on 5G cell metrics, specifically throughput and goodput. The investigation is conducted within the context of a mobility model for user equipment (UE), with a focus on scenarios involving connected vehicles (CVs) engaged in a mission. Secondly, the vulnerability of synchronization signal block (SSB) components is examined concerning jamming power and beam sweeping. Notably, the study reveals that increasing jamming power beyond 40 dBm in our specific scenario configuration no longer decreases network throughput due to the re-transmission of packets through the hybrid automatic repeat request (HARQ) process. Furthermore, it is observed that under the same jamming power, the physical downlink shared channel (PDSCH) is more vulnerable than the primary synchronization signal (PSS) and secondary synchronization signal (SSS). However, a smart jammer can disrupt the cell search process by injecting less power and targeting PSS-SSS or physical broadcast channel (PBCH) data compared to a barrage jammer. On the other hand, beam sweeping proves effective in mitigating the impact of a smart jammer, reducing the error vector magnitude root mean square from 51.59% to 23.36% under the same jamming power.

cs.CR↗

arXiv2024

Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks

Federated learning (FL) is an innovative distributed artificial intelligence (AI) technique. It has been used for interdisciplinary studies in different fields such as healthcare, marketing and finance. However the application of FL in wireless networks is still in its infancy. In this work, we first overview benefits and concerns when applying FL to wireless networks. Next, we provide a new perspective on existing personalized FL frameworks by analyzing the relationship between cooperation and personalization in these frameworks. Additionally, we discuss the possibility of tuning the cooperation level with a choice-based approach. Our choice-based FL approach is a flexible and safe FL framework that allows participants to lower the level of cooperation when they feel unsafe or unable to benefit from the cooperation. In this way, the choice-based FL framework aims to address the safety and fairness concerns in FL and protect participants from malicious attacks.

cs.NI↗

arXiv2024

Multi-Modal Transformer and Reinforcement Learning-based Beam Management

Beam management is an important technique to improve signal strength and reduce interference in wireless communication systems. Recently, there has been increasing interest in using diverse sensing modalities for beam management. However, it remains a big challenge to process multi-modal data efficiently and extract useful information. On the other hand, the recently emerging multi-modal transformer (MMT) is a promising technique that can process multi-modal data by capturing long-range dependencies. While MMT is highly effective in handling multi-modal data and providing robust beam management, integrating reinforcement learning (RL) further enhances their adaptability in dynamic environments. In this work, we propose a two-step beam management method by combining MMT with RL for dynamic beam index prediction. In the first step, we divide available beam indices into several groups and leverage MMT to process diverse data modalities to predict the optimal beam group. In the second step, we employ RL for fast beam decision-making within each group, which in return maximizes throughput. Our proposed framework is tested on a 6G dataset. In this testing scenario, it achieves higher beam prediction accuracy and system throughput compared to both the MMT-only based method and the RL-only based method.

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arXiv2024

Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing

Network slicing is a pivotal paradigm in wireless networks enabling customized services to users and applications. Yet, intelligent jamming attacks threaten the performance of network slicing. In this paper, we focus on the security aspect of network slicing over a deep transfer reinforcement learning (DTRL) enabled scenario. We first demonstrate how a deep reinforcement learning (DRL)-enabled jamming attack exposes potential risks. In particular, the attacker can intelligently jam resource blocks (RBs) reserved for slices by monitoring transmission signals and perturbing the assigned resources. Then, we propose a DRL-driven mitigation model to mitigate the intelligent attacker. Specifically, the defense mechanism generates interference on unallocated RBs where another antenna is used for transmitting powerful signals. This causes the jammer to consider these RBs as allocated RBs and generate interference for those instead of the allocated RBs. The analysis revealed that the intelligent DRL-enabled jamming attack caused a significant 50% degradation in network throughput and 60% increase in latency in comparison with the no-attack scenario. However, with the implemented mitigation measures, we observed 80% improvement in network throughput and 70% reduction in latency in comparison to the under-attack scenario.

cs.NI↗

arXiv2024

Machine Learning-enabled Traffic Steering in O-RAN: A Case Study on Hierarchical Learning Approach

Traffic Steering is a crucial technology for wireless networks, and multiple efforts have been put into developing efficient Machine Learning (ML)-enabled traffic steering schemes for Open Radio Access Networks (O-RAN). Given the swift emergence of novel ML techniques, conducting a timely survey that comprehensively examines the ML-based traffic steering schemes in O-RAN is critical. In this article, we provide such a survey along with a case study of hierarchical learning-enabled traffic steering in O-RAN. In particular, we first introduce the background of traffic steering in O-RAN and overview relevant state-of-the-art ML techniques and their applications. Then, we analyze the compatibility of the hierarchical learning framework in O-RAN and further propose a Hierarchical Deep-Q-Learning (h-DQN) framework for traffic steering. Compared to existing works, which focus on single-layer architecture with standalone agents, h-DQN decomposes the traffic steering problem into a bi-level architecture with hierarchical intelligence. The meta-controller makes long-term and high-level policies, while the controller executes instant traffic steering actions under high-level policies. Finally, the case study shows that the hierarchical learning approach can provide significant performance improvements over the baseline algorithms.

cs.NI↗

arXiv2024

Self-Play Ensemble Q-learning enabled Resource Allocation for Network Slicing

In 5G networks, network slicing has emerged as a pivotal paradigm to address diverse user demands and service requirements. To meet the requirements, reinforcement learning (RL) algorithms have been utilized widely, but this method has the problem of overestimation and exploration-exploitation trade-offs. To tackle these problems, this paper explores the application of self-play ensemble Q-learning, an extended version of the RL-based technique. Self-play ensemble Q-learning utilizes multiple Q-tables with various exploration-exploitation rates leading to different observations for choosing the most suitable action for each state. Moreover, through self-play, each model endeavors to enhance its performance compared to its previous iterations, boosting system efficiency, and decreasing the effect of overestimation. For performance evaluation, we consider three RL-based algorithms; self-play ensemble Q-learning, double Q-learning, and Q-learning, and compare their performance under different network traffic. Through simulations, we demonstrate the effectiveness of self-play ensemble Q-learning in meeting the diverse demands within 21.92% in latency, 24.22% in throughput, and 23.63\% in packet drop rate in comparison with the baseline methods. Furthermore, we evaluate the robustness of self-play ensemble Q-learning and double Q-learning in situations where one of the Q-tables is affected by a malicious user. Our results depicted that the self-play ensemble Q-learning method is more robust against adversarial users and prevents a noticeable drop in system performance, mitigating the impact of users manipulating policies.

cs.NI↗

arXiv2024

Sustainable Task Offloading in Secure UAV-assisted Smart Farm Networks: A Multi-Agent DRL with Action Mask Approach

The integration of unmanned aerial vehicles (UAVs) with mobile edge computing (MEC) and Internet of Things (IoT) technology in smart farms is pivotal for efficient resource management and enhanced agricultural productivity sustainably. This paper addresses the critical need for optimizing task offloading in secure UAV-assisted smart farm networks, aiming to reduce total delay and energy consumption while maintaining robust security in data communications. We propose a multi-agent deep reinforcement learning (DRL)-based approach using a deep double Q-network (DDQN) with an action mask (AM), designed to manage task offloading dynamically and efficiently. The simulation results demonstrate the superior performance of our method in managing task offloading, highlighting significant improvements in operational efficiency by reducing delay and energy consumption. This aligns with the goal of developing sustainable and energy-efficient solutions for next-generation network infrastructures, making our approach an advanced solution for achieving both performance and sustainability in smart farming applications.

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arXiv2024

Cooperative Hierarchical Deep Reinforcement Learning based Joint Sleep and Power Control in RIS-aided Energy-Efficient RAN

Energy efficiency (EE) is one of the most important metrics for envisioned 6G networks, and sleep control, as a cost-efficient approach, can significantly lower power consumption by switching off network devices selectively. Meanwhile, the reconfigurable intelligent surface (RIS) has emerged as a promising technique to enhance the EE of future wireless networks. In this work, we jointly consider sleep and transmission power control for RIS-aided energy-efficient networks. In particular, considering the timescale difference between sleep control and power control, we introduce a cooperative hierarchical deep reinforcement learning (Co-HDRL) algorithm, enabling hierarchical and intelligent decision-making. Specifically, the meta-controller in Co-HDRL uses cross-entropy metrics to evaluate the policy stability of sub-controllers, and sub-controllers apply the correlated equilibrium to select optimal joint actions. Compared with conventional HDRL, Co-HDRL enables more stable high-level policy generations and low-level action selections. Then, we introduce a fractional programming method for RIS phase-shift control, maximizing the sum-rate under a given transmission power. In addition, we proposed a low-complexity surrogate optimization method as a baseline for RIS control. Finally, simulations show that the RIS-assisted sleep control can achieve more than 16\% lower energy consumption and 30\% higher EE than baseline algorithms.

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arXiv2024

Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networks

In recent years, machine learning (ML) techniques have created numerous opportunities for intelligent mobile networks and have accelerated the automation of network operations. However, complex network tasks may involve variables and considerations even beyond the capacity of traditional ML algorithms. On the other hand, large language models (LLMs) have recently emerged, demonstrating near-human-level performance in cognitive tasks across various fields. However, they remain prone to hallucinations and often lack common sense in basic tasks. Therefore, they are regarded as assistive tools for humans. In this work, we propose the concept of "generative AI-in-the-loop" and utilize the semantic understanding, context awareness, and reasoning abilities of LLMs to assist humans in handling complex or unforeseen situations in mobile communication networks. We believe that combining LLMs and ML models allows both to leverage their respective capabilities and achieve better results than either model alone. To support this idea, we begin by analyzing the capabilities of LLMs and compare them with traditional ML algorithms. We then explore potential LLM-based applications in line with the requirements of next-generation networks. We further examine the integration of ML and LLMs, discussing how they can be used together in mobile networks. Unlike existing studies, our research emphasizes the fusion of LLMs with traditional ML-driven next-generation networks and serves as a comprehensive refinement of existing surveys. Finally, we provide a case study to enhance ML-based network intrusion detection with synthesized data generated by LLMs. Our case study further demonstrates the advantages of our proposed idea.

cs.LG↗

arXiv2024

Extended Reality (XR) Codec Adaptation in 5G using Multi-Agent Reinforcement Learning with Attention Action Selection

Extended Reality (XR) services will revolutionize applications over 5th and 6th generation wireless networks by providing seamless virtual and augmented reality experiences. These applications impose significant challenges on network infrastructure, which can be addressed by machine learning algorithms due to their adaptability. This paper presents a Multi- Agent Reinforcement Learning (MARL) solution for optimizing codec parameters of XR traffic, comparing it to the Adjust Packet Size (APS) algorithm. Our cooperative multi-agent system uses an Optimistic Mixture of Q-Values (oQMIX) approach for handling Cloud Gaming (CG), Augmented Reality (AR), and Virtual Reality (VR) traffic. Enhancements include an attention mechanism and slate-Markov Decision Process (MDP) for improved action selection. Simulations show our solution outperforms APS with average gains of 30.1%, 15.6%, 16.5% 50.3% in XR index, jitter, delay, and Packet Loss Ratio (PLR), respectively. APS tends to increase throughput but also packet losses, whereas oQMIX reduces PLR, delay, and jitter while maintaining goodput.

cs.NI↗

arXiv2024

Generative AI Empowered LiDAR Point Cloud Generation with Multimodal Transformer

Integrated sensing and communications is a key enabler for the 6G wireless communication systems. The multiple sensing modalities will allow the base station to have a more accurate representation of the environment, leading to context-aware communications. Some widely equipped sensors such as cameras and RADAR sensors can provide some environmental perceptions. However, they are not enough to generate precise environmental representations, especially in adverse weather conditions. On the other hand, the LiDAR sensors provide more accurate representations, however, their widespread adoption is hindered by their high cost. This paper proposes a novel approach to enhance the wireless communication systems by synthesizing LiDAR point clouds from images and RADAR data. Specifically, it uses a multimodal transformer architecture and pre-trained encoding models to enable an accurate LiDAR generation. The proposed framework is evaluated on the DeepSense 6G dataset, which is a real-world dataset curated for context-aware wireless applications. Our results demonstrate the efficacy of the proposed approach in accurately generating LiDAR point clouds. We achieve a modified mean squared error of 10.3931. Visual examination of the images indicates that our model can successfully capture the majority of structures present in the LiDAR point cloud for diverse environments. This will enable the base stations to achieve more precise environmental sensing. By integrating LiDAR synthesis with existing sensing modalities, our method can enhance the performance of various wireless applications, including beam and blockage prediction.

cs.CV↗

arXiv2024

Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection

Large language models (LLMs), especially generative pre-trained transformers (GPTs), have recently demonstrated outstanding ability in information comprehension and problem-solving. This has motivated many studies in applying LLMs to wireless communication networks. In this paper, we propose a pre-trained LLM-empowered framework to perform fully automatic network intrusion detection. Three in-context learning methods are designed and compared to enhance the performance of LLMs. With experiments on a real network intrusion detection dataset, in-context learning proves to be highly beneficial in improving the task processing performance in a way that no further training or fine-tuning of LLMs is required. We show that for GPT-4, testing accuracy and F1-Score can be improved by 90%. Moreover, pre-trained LLMs demonstrate big potential in performing wireless communication-related tasks. Specifically, the proposed framework can reach an accuracy and F1-Score of over 95% on different types of attacks with GPT-4 using only 10 in-context learning examples.

cs.LG↗

arXiv2024

Transformer-Based Wireless Traffic Prediction and Network Optimization in O-RAN

This paper introduces an innovative method for predicting wireless network traffic in concise temporal intervals for Open Radio Access Networks (O-RAN) using a transformer architecture, which is the machine learning model behind generative AI tools. Depending on the anticipated traffic, the system either launches a reinforcement learning-based traffic steering xApp or a cell sleeping rApp to enhance performance metrics like throughput or energy efficiency. Our simulation results demonstrate that the proposed traffic prediction-based network optimization mechanism matches the performance of standalone RAN applications (rApps/ xApps) that are always on during the whole simulation time while offering on-demand activation. This feature is particularly advantageous during instances of abrupt fluctuations in traffic volume. Rather than persistently operating specific applications irrespective of the actual incoming traffic conditions, the proposed prediction-based method increases the average energy efficiency by 39.7% compared to the "Always on Traffic Steering xApp" and achieves 10.1% increase in throughput compared to the "Always on Cell Sleeping rApp". The simulation has been conducted over 24 hours, emulating a whole day traffic pattern for a dense urban area.

cs.NI↗

arXiv2024

Bypassing a Reactive Jammer via NOMA-Based Transmissions in Critical Missions

Wireless networks can be vulnerable to radio jamming attacks. The quality of service under a jamming attack is not guaranteed and the service requirements such as reliability, latency, and effective rate, specifically in mission-critical military applications, can be deeply affected by the jammer's actions. This paper analyzes the effect of a reactive jammer. Particularly, reliability, average transmission delay, and the effective sum rate (ESR) for a NOMA-based scheme with finite blocklength transmissions are mathematically derived taking the detection probability of the jammer into account. Furthermore, the effect of UEs' allocated power and blocklength on the network metrics is explored. Contrary to the existing literature, results show that gNB can mitigate the impact of reactive jamming by decreasing transmit power, making the transmissions covert at the jammer side. Finally, an optimization problem is formulated to maximize the ESR under reliability, delay, and transmit power constraints. It is shown that by adjusting the allocated transmit power to UEs by gNB, the gNB can bypass the jammer effect to fulfill the 0.99999 reliability and the latency of 5ms without the need for packet re-transmission.

cs.CR↗

arXiv2024

Federated Learning with Dual Attention for Robust Modulation Classification under Attacks

Federated learning (FL) allows distributed participants to train machine learning models in a decentralized manner. It can be used for radio signal classification with multiple receivers due to its benefits in terms of privacy and scalability. However, the existing FL algorithms usually suffer from slow and unstable convergence and are vulnerable to poisoning attacks from malicious participants. In this work, we aim to design a versatile FL framework that simultaneously promotes the performance of the model both in a secure system and under attack. To this end, we leverage attention mechanisms as a defense against attacks in FL and propose a robust FL algorithm by integrating the attention mechanisms into the global model aggregation step. To be more specific, two attention models are combined to calculate the amount of attention cast on each participant. It will then be used to determine the weights of local models during the global aggregation. The proposed algorithm is verified on a real-world dataset and it outperforms existing algorithms, both in secure systems and in systems under data poisoning attacks.

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