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As natural disasters become more frequent and severe, ensuring a resilient communications infrastructure is of paramount importance for effective disaster response and recovery. This disaster-resilient infrastructure should also respond to sustainability goals by providing an energy-efficient and economically feasible network that is accessible to everyone. This paper provides a comprehensive exploration of the technological solutions and strategies necessary to build and maintain resilient communications networks that can withstand and quickly recover from disaster scenarios. The paper starts with a survey of existing literature and related reviews to establish a solid foundation, followed by an overview of the global landscape of disaster communications and power supply management. We then introduce the key enablers of communications and energy resource technologies to support communications infrastructure, examining emerging trends that improve the resilience of these systems. Pre-disaster planning is emphasized as a critical phase where proactive communication and energy supply strategies can significantly mitigate the impact of disasters. We explore the essential technologies for disaster response, focusing on real-time communications and energy solutions that support rapid deployment and coordination in times of crisis. The paper presents post-disaster communication and energy management planning for effective rescue and evacuation operations. The main findings derived from the comprehensive survey are also summarized for each disaster phase. This is followed by an analysis of existing vendor products and services as well as standardization efforts and ongoing projects that contribute to the development of resilient infrastructures. A detailed case study of the Turkiye earthquakes is presented to illustrate the practical application of these technologies and strategies., Comment: 32 pages, submitted to IEEE

One of the most critical components of the Internet that an attacker could exploit is the DNS (Domain Name System) protocol and infrastructure. Researchers have been constantly developing methods to detect and defend against the attacks against DNS, specifically DNS flooding attacks. However, most solutions discard packets for defensive approaches, which can cause legitimate packets to be dropped, making them highly dependable on detection strategies. In this paper, we propose MTDNS, a resilient MTD-based approach that employs Moving Target Defense techniques through Software Defined Networking (SDN) switches to redirect traffic to alternate DNS servers that are dynamically created and run under the Network Function Virtualization (NFV) framework. The proposed approach is implemented in a testbed environment by running our DNS servers as separate Virtual Network Functions, NFV Manager, SDN switches, and an SDN Controller. The experimental result shows that the MTDNS approach achieves a much higher success rate in resolving DNS queries and significantly reduces average latency even if there is a DNS flooding attack., Comment: 6 pages, Accepted for publication at IEEE CCNC 2025

This paper deals with the problem of energy consumption minimization in Open RAN cell-free (CF) massive Multiple-Input Multiple-Output (mMIMO) systems under minimum per-user signal-to-noise-plus-interference ratio (SINR) constraints. Considering that several access points (APs) are deployed with multiple antennas, and they jointly serve multiple users on the same time-frequency resources, we design the precoding vectors that minimize the system power consumption, while preserving a minimum SINR for each user. We use a simple, yet representative, power consumption model, which consists of a fixed term that models the power consumption due to activation of the AP and a variable one that depends on the transmitted power. The mentioned problem boils down to a binary-constrained quadratic optimization problem, which is strongly non-convex. In order to solve this problem, we resort to a novel approach, which is based on the penalized convex-concave procedure. The proposed approach can be implemented in an O-RAN cell-free mMIMO system as an xApp in the near-real time RIC (RAN intelligent Controller). Numerical results show the potential of this approach for dealing with joint precoding optimization and AP selection.

The rapid development of quantum computing poses a significant threat to the security of current cryptographic systems, including those used in User Equipment (UE) for mobile communications. Conventional cryptographic algorithms such as Rivest-Shamir-Adleman (RSA) and Elliptic curve cryptography (ECC) are vulnerable to quantum computing attacks, which could jeopardize the confidentiality, integrity, and availability of sensitive data transmitted by UEs. This demo paper proposes the integration of Post-Quantum Cryptography (PQC) in TLS for UE Communication to mitigate the risks of quantum attacks. We present our setup and explain each of the components used. We also provide the entire workflow of the demo for other researchers to replicate the same setup. By addressing the implementation of PQC within a 5G network to secure UE-to-UE communication, this research aims to pave the way for developing quantum-resistant mobile devices and securing the future of wireless communications., Comment: 3 pages, 2 figures, Accepted for NoF 2024 Demo track

Recently, large language models (LLMs) have been gaining a lot of interest due to their adaptability and extensibility in emerging applications, including communication networks. It is anticipated that 6G mobile edge computing networks will be able to support LLMs as a service, as they provide ultra reliable low-latency communications and closed loop massive connectivity. However, LLMs are vulnerable to data and model privacy issues that affect the trustworthiness of LLMs to be deployed for user-based services. In this paper, we explore the security vulnerabilities associated with fine-tuning LLMs in 6G networks, in particular the membership inference attack. We define the characteristics of an attack network that can perform a membership inference attack if the attacker has access to the fine-tuned model for the downstream task. We show that the membership inference attacks are effective for any downstream task, which can lead to a personal data breach when using LLM as a service. The experimental results show that the attack success rate of maximum 92% can be achieved on named entity recognition task. Based on the experimental analysis, we discuss possible defense mechanisms and present possible research directions to make the LLMs more trustworthy in the context of 6G networks., Comment: 7 pages, 4 figures

In this paper, we present an innovative federated learning (FL) approach that utilizes Kolmogorov-Arnold Networks (KANs) for classification tasks. By utilizing the adaptive activation capabilities of KANs in a federated framework, we aim to improve classification capabilities while preserving privacy. The study evaluates the performance of federated KANs (F- KANs) compared to traditional Multi-Layer Perceptrons (MLPs) on classification task. The results show that the F-KANs model significantly outperforms the federated MLP model in terms of accuracy, precision, recall, F1 score and stability, and achieves better performance, paving the way for more efficient and privacy-preserving predictive analytics., Comment: This work has been submitted to IEEE for possible publication. Related Code: https://github.com/ezeydan/F-KANs.git

The transition to sustainable Open Radio Access Network (O-RAN) architectures brings new challenges for resource management, especially in predicting the utilization of Physical Resource Block (PRB)s. In this paper, we propose a novel approach to characterize the PRB load using probabilistic forecasting techniques. First, we provide background information on the O-RAN architecture and components and emphasize the importance of energy/power consumption models for sustainable implementations. The problem statement highlights the need for accurate PRB load prediction to optimize resource allocation and power efficiency. We then investigate probabilistic forecasting techniques, including Simple-Feed-Forward (SFF), DeepAR, and Transformers, and discuss their likelihood model assumptions. The simulation results show that DeepAR estimators predict the PRBs with less uncertainty and effectively capture the temporal dependencies in the dataset compared to SFF- and Transformer-based models, leading to power savings. Different percentile selections can also increase power savings, but at the cost of over-/under provisioning. At the same time, the performance of the Long-Short Term Memory (LSTM) is shown to be inferior to the probabilistic estimators with respect to all error metrics. Finally, we outline the importance of probabilistic, prediction-based characterization for sustainable O-RAN implementations and highlight avenues for future research.

The need for intelligent and efficient resource provisioning for the productive management of resources in real-world scenarios is growing with the evolution of telecommunications towards the 6G era. Technologies such as Open Radio Access Network (O-RAN) can help to build interoperable solutions for the management of complex systems. Probabilistic forecasting, in contrast to deterministic single-point estimators, can offer a different approach to resource allocation by quantifying the uncertainty of the generated predictions. This paper examines the cloud-native aspects of O-RAN together with the radio App (rApp) deployment options. The integration of probabilistic forecasting techniques as a rApp in O-RAN is also emphasized, along with case studies of real-world applications. Through a comparative analysis of forecasting models using the error metric, we show the advantages of Deep Autoregressive Recurrent network (DeepAR) over other deterministic probabilistic estimators. Furthermore, the simplicity of Simple-Feed-Forward (SFF) leads to a fast runtime but does not capture the temporal dependencies of the input data. Finally, we present some aspects related to the practical applicability of cloud-native O-RAN with probabilistic forecasting.

Unlike other single-point Artificial Intelligence (AI)-based prediction techniques, such as Long-Short Term Memory (LSTM), probabilistic forecasting techniques (e.g., DeepAR and Transformer) provide a range of possible outcomes and associated probabilities that enable decision makers to make more informed and robust decisions. At the same time, the architecture of Open RAN has emerged as a revolutionary approach for mobile networks, aiming at openness, interoperability and innovation in the ecosystem of RAN. In this paper, we propose the use of probabilistic forecasting techniques as a radio App (rApp) within the Open RAN architecture. We investigate and compare different probabilistic and single-point forecasting methods and algorithms to estimate the utilization and resource demands of Physical Resource Blocks (PRBs) of cellular base stations. Through our evaluations, we demonstrate the numerical advantages of probabilistic forecasting techniques over traditional single-point forecasting methods and show that they are capable of providing more accurate and reliable estimates. In particular, DeepAR clearly outperforms single-point forecasting techniques such as LSTM and Seasonal-Naive (SN) baselines and other probabilistic forecasting techniques such as Simple-Feed-Forward (SFF) and Transformer neural networks.

Natural disasters can have catastrophic consequences, a poignant example is the series of $7.7$ and $7.6$ magnitude earthquakes that devastated T\"urkiye on February 6, 2023. To limit the damage, it is essential to maintain the communications infrastructure to ensure individuals impacted by the disaster can receive critical information. The disastrous earthquakes in T\"urkiye have revealed the importance of considering communications and energy solutions together to build resilient and sustainable infrastructure. Thus, this paper proposes an integrated space-air-ground-sea network architecture that utilizes various communications and energy-enabling technologies. This study aims to contribute to the development of robust and sustainable disaster-response frameworks. In light of the T\"urkiye earthquakes, two methods for network management are proposed: the first aims to ensure sustainability in the pre-disaster phase and the second aims to maintain communications during the in-disaster phase. In these frameworks, communications technologies such as High Altitude Platform Station(s)(HAPS), which are among the key enablers to unlock the potential of 6G networks, and energy technologies such as Renewable Energy Sources (RES), Battery Energy Storage Systems (BESSs), and Electric Vehicles (EVs) have been used as the prominent technologies. By simulating a case study, we demonstrate the performance of a proposed framework for providing network resiliency. The paper concludes with potential challenges and future directions to achieve a disaster-resilient network architecture solution., Comment: accepted in IEEE COMMAG

The proliferation of mobile networks and their increasing importance to modern life, combined with the emerging threat of quantum computing, present new challenges and opportunities for cybersecurity. This paper addresses the complexity of protecting these critical infrastructures against future quantum attacks while considering operational sustainability. We begin with an overview of the current landscape, identify the main vulnerabilities in mobile networks, and evaluate existing security solutions with new post-quantum cryptography (PQC) methods. We then present a quantum-secure architecture with PQC and Quantum Key Distribution (QKD) tailored explicitly for sustainable mobile networks and illustrate its applicability with several use cases that emphasize the need for advanced protection measures in this new era. In addition, a comprehensive analysis of PQC algorithm families is presented, focusing on their suitability for integration in mobile environments, with particular attention to the trade-offs between energy consumption and security improvements. Finally, recommendations for strengthening mobile networks against quantum threats are provided through a detailed examination of current challenges and opportunities., Comment: This paper has been accepted for publication by the 33rd International Symposium on High-Performance Parallel and Distributed Computing (HPDC) 2024. The copyright is with the ACM and the final version will be published by the ACM

The rapid development of artificial intelligence (AI) techniques has triggered a revolution in beyond fifth-generation (B5G) and upcoming sixth-generation (6G) mobile networks. Despite these advances, efficient resource allocation in dynamic and complex networks remains a major challenge. This paper presents an experimental implementation of deep reinforcement learning (DRL) enhanced with graph neural networks (GNNs) on a real 5G testbed. The method addresses the explainability of GNNs by evaluating the importance of each edge in determining the model's output. The custom sampling functions feed the data into the proposed GNN-driven Monte Carlo policy gradient (REINFORCE) agent to optimize the gNodeB (gNB) radio resources according to the specific traffic demands. The demo demonstrates real-time visualization of network parameters and superior performance compared to benchmarks., Comment: 3 pages, 8 figures

Artificial intelligence (AI) and Machine Learning (ML) are considered as key enablers for realizing the full potential of fifth-generation (5G) and beyond mobile networks, particularly in the context of resource management and orchestration. In this demonstration, we consider a fully-fledged 5G mobile network and develop a multi-agent deep reinforcement learning (DRL) framework for RAN resource allocation. By leveraging local monitoring information generated by a shared gNodeB instance (gNB), each DRL agent aims to optimally allocate radio resources concerning service-specific traffic demands belonging to heterogeneous running services. We perform experiments on the deployed testbed in real-time, showing that DRL-based agents can allocate radio resources fairly while improving the overall efficiency of resource utilization and minimizing the risk of over provisioning., Comment: 2 pages, 3 figures

Along with the potential emergence of quantum computing, safety and security of new and complex communication services such as automated driving need to be redefined in the post-quantum era. To ensure reliable, continuous and secure operation of these scenarios, quantum resistant security algorithms (QRSAs) that enable secure connectivity must be integrated into the network management and orchestration systems of mobile networks. This paper explores a roadmap study of post-quantum era convergence with cellular connectivity using the Service & Computation Orchestrator (SCO) framework for enhanced data security in radio access and backhaul transmission with a particular focus on Vehicle-to-Everything (V2X) services. Using NTRU as a QSRA, we have shown that the parallelization performance of Toom-Cook and Karatsuba computation methods can vary based on different CPU load conditions through extensive simulations and that the SCO framework can facilitate the selection of the most efficient computation for a given QRSA. Finally, we discuss the evaluation results, identify the current standardization efforts, and possible directions for the coexistence of post-quantum and mobile network connectivity through a SCO framework that leverages parallel computing., Comment: accepted to IEEE Communications Standards Magazine

Softwarization and virtualization are key concepts for emerging industries that require ultra-low latency. This is only possible if computing resources, traditionally centralized at the core of communication networks, are moved closer to the user, to the network edge. However, the realization of Edge Computing (EC) in the sixth generation (6G) of mobile networks requires efficient resource allocation mechanisms for the placement of the Virtual Network Functions (VNFs). Machine learning (ML) methods, and more specifically, Reinforcement Learning (RL), are a promising approach to solve this problem. The main contributions of this work are twofold: first, we obtain the theoretical performance bound for VNF placement in EC-enabled6G networks by formulating the problem mathematically as a finite Markov Decision Process (MDP) and solving it using a dynamic programming method called Policy Iteration (PI). Second, we develop a practical solution to the problem using RL, where the problem is treated with Q-Learning that considers both computational and communication resources when placing VNFs in the network. The simulation results under different settings of the system parameters show that the performance of the Q-Learning approach is close to the optimal PI algorithm (without having its restrictive assumptions on service statistics). This is particularly interesting when the EC resources are scarce and efficient management of these resources is required., Comment: This version of the paper is submitted to ICIN 2022

Future telecommunication systems are expected to co-exist with different backhauling nodes such as terrestrial or satellite systems. Satellite connectivity can add flexibility to backhauling networks and provide an alternative route for transmission. This paper presents experimental comparisons of satellite and terrestrial cellular networks and evaluates their performances in terms of different Key Performance Indicators (KPIs) including Channel Quality Index (CQI), Modulation Coding Scheme (MCS) index, Downlink throughput, Frame Utilization (FU) and number of Resource Block (RB) utilization ratios. Our experimental satellite network system uses a real satellite backhaul deployment and works in Ka band (with two specific sub-bands on 19 Ghz in downlink and 29 Ghz in uplink). As a benchmark, we compare our system with live terrestrial network in which backhaul connection is cellular backhaul. Our experiments reveal three main observations: First observation is that there exists FU and number of RB utilization problems in the satellite link even though there exists a single test user equipment (UE) with high CQI and MCS index values. Second observation is that in satellite link relatively low number of Protocol Data Units (PDUs) are generated at Radio Link Controller (RLC) layer compared to the Packet Data Convergence Control (PDCP) layer. Finally, our third observation concludes that the excessive existence of PDCP PDUs can be due to existence of General Packet Radio Service (GPRS) Tunneling Protocol-User Plane (GTP-U) accelerator where an optimal balance between the caching size and the number of UEs using satellite eNodeB is needed. For this reason, our experimental results reveal the existence of a trade-off between the supported number of users on satellite link and the GTP-U acceleration rate.

Mobile Network Operators (MNOs) are evolving towards becoming data-driven, while delivering capacity to collect and analyze data. This can help in enhancing user experiences while empowering the operation workforce and building new business models. Mobile traffic demands of users can give insights to MNOs to plan, decide and act depending on network conditions. In this paper, we investigate the behaviour of Istanbul residents using the cellular network traffic activity over spatial and temporal dimensions via exploratory factor analysis (EFA) using a major MNO's cellular network traffic data in Turkey. Our results reveal various time and spatial patterns for Istanbul residents such as morning and evening commuting factors, business and residential factors as well as nightlife and weekend afternoon factors as the most prominent cultural behaviour. The analysis results also demonstrate interesting findings such as tunnels and transportation paths selected by Istanbul residents may differ during morning rush work hour compared to evening rush after-work hour., Comment: accepted to IEEE VTC 2018

Software-Defined Networking (SDN) paradigm provides many features including hardware abstraction, programmable networking and centralized policy control. One of the main benefits used along with these features is core/backhaul network virtualization which ensures sharing of mobile core and backhaul networks among Mobile Operators (MOs). In this paper, we propose a virtualized SDN-based Evolved Packet System (EPS) cellular network architecture including design of network virtualization controller. After virtualization of core/backhaul network elements, eNodeBs associated with MOs become a part of resource allocation problem for Backhaul Transport Providers (BTPs). We investigate the performance of our proposed architecture where eNodeBs are assigned to the MOs using quality-of-service (QoS)-aware and QoS-unaware scheduling algorithms under the consideration of time-varying numbers and locations of user equipments (UEs) through Monte-Carlo simulations. The performances are compared with traditional EPS in Long Term Evolution (LTE) architecture and the results reveal that our proposed architecture outperforms the traditional cellular network architecture., Comment: accepted for publication in Computers & Electrical Engineering Journal (Elsevier) Special Issue on Software-Defined Control for Next Generation Wireless Systems

In order to cope with the relentless data tsunami in $5G$ wireless networks, current approaches such as acquiring new spectrum, deploying more base stations (BSs) and increasing nodes in mobile packet core networks are becoming ineffective in terms of scalability, cost and flexibility. In this regard, context-aware $5$G networks with edge/cloud computing and exploitation of \emph{big data} analytics can yield significant gains to mobile operators. In this article, proactive content caching in $5$G wireless networks is investigated in which a big data-enabled architecture is proposed. In this practical architecture, vast amount of data is harnessed for content popularity estimation and strategic contents are cached at the BSs to achieve higher users' satisfaction and backhaul offloading. To validate the proposed solution, we consider a real-world case study where several hours of mobile data traffic is collected from a major telecom operator in Turkey and a big data-enabled analysis is carried out leveraging tools from machine learning. Based on the available information and storage capacity, numerical studies show that several gains are achieved both in terms of users' satisfaction and backhaul offloading. For example, in the case of $16$ BSs with $30\%$ of content ratings and $13$ Gbyte of storage size ($78\%$ of total library size), proactive caching yields $100\%$ of users' satisfaction and offloads $98\%$ of the backhaul., Comment: accepted for publication in IEEE Communications Magazine, Special Issue on Communications, Caching, and Computing for Content-Centric Mobile Networks

Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: velocity, voracity, volume and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul offloadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platform and the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4 Gbyte of storage size (87% of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations., Comment: 8 pages, 5 figures