arXiv subjects

Jacek Kibiłda

Publications and source records attributed to Jacek Kibiłda.

16 recordsLinked to original sources

arXiv2026

Resilience under Uncertainty: Securing 6G through Stochastic Reinstantiation of RAN Functions

The disaggregation of base stations into discrete RAN functions introduces new threats to mobile networks, as failures in one RAN function can trigger cascading failures and interrupt entire function chains, with potential to degrade network performance and disrupt service. In this paper, we propose the first resilience mechanism for disaggregated mobile networks that leverages the adaptive reinstantiation of RAN functions under uncertainty to mitigate disruptions and maintain service continuity in the presence of compromised infrastructure. Our mechanism reacts to cascading failures that disrupt Radio Units (RUs) by reinstantiating Central Units (CUs) and Distributed Units (DUs) in alternative cloud locations, restoring their function chains while accounting for uncertainty in users' locations and wireless channel conditions during the in-failure state. We formulate this recovery process as a two-stage stochastic optimization problem, where reinstantiation and routing decisions are made under uncertainty, and bandwidth allocation decisions are performed after uncertainty is resolved. We solve the problem using a Sample Average Approximation (SAA)-based solution as a tractable, deterministic equivalent problem. We numerically evaluate our approach on a real-world disaggregated mobile network topology across multiple failure scenarios and traffic demand conditions, and our results demonstrate that our solution can achieve up to 80% higher recovery performance compared to conventional resilience mechanisms.

cs.NI↗

arXiv2026

Conflict Detection in AI-RAN: Efficient Interaction Learning and Autonomous Graph Reconstruction

Artificial Intelligence (AI)-native mobile networks represent a fundamental step toward 6G, where learning, inference, and decision making are embedded into the Radio Access Network (RAN) itself. In such networks, multiple AI agents optimize the network to achieve distinct and often competing objectives. As such, conflicts become inevitable and have the potential to degrade performance, cause instability, and disrupt service. Current approaches for conflict detection rely on conflict graphs created from relationships between AI agents, parameters, and Key Performance Indicators (KPIs). Existing works often rely on complex and computationally expensive Graph Neural Networks (GNNs) and depend on manually chosen thresholds to create conflict graphs. In this work, we present the first systematic framework for conflict detection in AI-native mobile networks, propose an efficient two-tower encoder architecture for learning interactions based on data from the RAN, and introduce a data-driven sparsity-based mechanism for autonomously reconstructing conflict graphs without manual fine-tuning.

cs.NI↗

arXiv2025

Adaptive Reallocation of RAN Functions for Resilient 6G Networks

The disaggregation of base stations into discrete RAN functions introduces new threats to mobile networks, as failures in one RAN function can trigger cascading failures and disrupt the entire functional chain, impacting network performance and leading to outages. In this paper, we propose the first resilience mechanism leveraging the adaptive placement of RAN functions to mitigate disruptions and recover service continuity in the presence of compromised infrastructure. Our model detects disrupted RUs due to cascading failures, reacts by re-instantiating CU and DU in alternative cloud locations, and recovers service continuity by reestablishing functional chains. We formulate this recovery process as an optimization problem that maximizes post-failure network performance while considering computational and communication constraints of the infrastructure. We numerically evaluated our approach on a real-world mobile network topology under multiple failure scenarios, and demonstrated that our solution recovers up to 70% higher throughput compared to conventional resilience mechanisms.

cs.NI↗

arXiv2025

Dimensioning and Optimization of Reliability Coverage in Local 6G Networks

Enabling vertical use cases for the sixth generation (6G) wireless networks, such as automated manufacturing, immersive extended reality (XR), and self-driving fleets, will require network designs that meet reliability and latency targets in well-defined service areas. In order to establish a quantifiable design objective, we introduce the novel concept of reliability coverage, defined as the percentage area covered by communication services operating under well-defined reliability and performance targets. Reliability coverage allows us to unify the different network design tasks occurring at different time scales, namely resource orchestration and allocation, resulting in a single framework for dimensioning and optimization in local 6G networks. The two time scales, when considered together, yield remarkably consistent results and allow us to observe how stringent reliability/latency requirements translate into the increased wireless network resource demands.

cs.NI↗

arXiv2025

Learning and Reconstructing Conflicts in O-RAN: A Graph Neural Network Approach

The Open Radio Access Network (O-RAN) architecture enables the deployment of third-party applications on the RAN Intelligent Controllers (RICs). However, the operation of third-party applications in the Near Real-Time RIC (Near-RT RIC), known as xApps, may result in conflicting interactions. Each xApp can independently modify the same control parameters to achieve distinct outcomes, which has the potential to cause performance degradation and network instability. The current conflict detection and mitigation solutions in the literature assume that all conflicts are known a priori, which does not always hold due to complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we introduce the first data-driven method for reconstructing and labeling conflict graphs in O-RAN. Specifically, we leverage GraphSAGE, an inductive learning framework, to dynamically learn the hidden relationships between xApps, parameters, and KPIs. Our numerical results, based on a conflict model used in the O-RAN conflict management literature, demonstrate that our proposed method can effectively reconstruct conflict graphs and identify the conflicts defined by the O-RAN Alliance.

cs.NI↗

arXiv2020

Performance Evaluation of Scheduling in 5G-mmWave Networks under Human Blockage

The millimetre-wave spectrum provisions enormous enhancement to the achievable data rate of 5G networks. However, human blockages affecting the millimetre-wave signal can severely degrade the performance if proper resource allocation is not considered. In this paper, we assess how conventional schedulers, such as the Proportional Fair scheduler, react to the presence of blockage. Our results show that the resource allocation may disfavour users suffering from blockage, leading to low data rate for those users. To circumvent this problem, we show that the data rate of those users can be improved by using a scheduler adapted to react to upcoming blockage events. The adapted scheduler aims at proactively allocating the resources before a blockage happens, mitigating losses. Such adaptation is motivated by recent progress in blockage prediction for millimetre-wave signals in a dynamic human blockage scenario. Our extensive simulations indicate gains in the 1st percentile rate and fairness with respect to Proportional Fair scheduler when blockage conditions are severe.

cs.IT↗

arXiv2020

Performance Analysis and Optimization of Cache-Assisted CoMP for Clustered D2D Networks

Caching at mobile devices and leveraging cooperative device-to-device (D2D) communications are two promising approaches to support massive content delivery over wireless networks while mitigating the effects of interference. To show the impact of cooperative communication on the performance of cache-enabled D2D networks, the notion of device clustering must be factored in to convey a realistic description of the network performance. In this regard, this paper develops a novel mathematical model, based on stochastic geometry and an optimization framework for cache-assisted coordinated multi-point (CoMP) transmissions with clustered devices. Devices are spatially distributed into disjoint clusters and are assumed to have a surplus memory to cache files from a known library, following a random probabilistic caching scheme. Desired contents that are not self-cached can be obtained via D2D CoMP transmissions from neighboring devices or, as a last resort, from the network. For this model, we analytically characterize the offloading gain and rate coverage probability as functions of the system parameters. An optimal caching strategy is then defined as the content placement scheme that maximizes the offloading gain. For a tractable optimization framework, we pursue two separate approaches to obtain a lower bound and a provably accurate approximation of the offloading gain, which allows us to obtain optimized caching strategies.

cs.IT↗

arXiv2020

Indoor Millimeter-Wave Systems: Design and Performance Evaluation

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. While we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. The paper introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts, and then moves on to experimentally-validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.

cs.NI↗

arXiv2018

Cooperative Transmission and Probabilistic Caching for Clustered D2D Networks

In this paper, we aim at maximizing the cache offloading gain for a clustered \ac{D2D} caching network by exploiting probabilistic caching and cooperative transmission among the cluster devices. Devices with surplus memory probabilistically cache a content from a known library. A requested content is either brought from the device's local cache, cooperatively transmitted from catering devices, or downloaded from the macro base station as a last resort. Using stochastic geometry, we derive a closed-form expression for the offloading gain and formulate the offloading maximization problem. In order to simplify the objective function and obtain analytically tractable expressions, we derive a lower bound on the offloading gain, for which a suboptimal solution is obtained when considering a special case. Results reveal that the obtained suboptimal solution can achieve up to 12% increase in the offloading gain compared to the Zipf's caching technique. Besides, we show that the spatial scaling parameters of the network, e.g., the density of clusters and distance between devices in the same cluster, play a crucial role in identifying the tradeoff between the content diversity gain and the cooperative transmission gain.

eess.SP↗

arXiv2018

On Minimizing Energy Consumption for D2D Clustered Caching Networks

We formulate and solve the energy minimization problem for a clustered device-to-device (D2D) network with cache-enabled mobile devices. Devices are distributed according to a Poisson cluster process (PCP) and are assumed to have a surplus memory which is exploited to proactively cache files from a library. Devices can retrieve the requested files from their caches, from neighboring devices in their proximity (cluster), or from the base station as a last resort. We minimize the energy consumption of the proposed network under a random prob- abilistic caching scheme, where files are independently cached according to a specific probability distribution. A closed-form expression for the D2D coverage probability is obtained. The energy consumption problem is then formulated as a function of the caching distribution, and the optimal probabilistic caching distribution is obtained. Results reveal that the proposed caching distribution reduces energy consumption up to 33% as compared to caching popular files scheme.

cs.DC↗

arXiv2018

Impact of UAV Antenna Configuration on Wireless Connectivity in Urban Environments

The growing presence of UAVs has led operators to explore the issue of how to provide wireless service to UAVs. The achieveable service quality will be affected by a number of factors, including the type of antenna that the UAVs are equipped with and whether to use existing cellular infrastructure or deploy dedicated ground stations to service these new user devices. In this paper we employ stochastic geometry to carry out an analysis of the UAV coverage probability that can be achieved with a network of dedicated ground stations using a sub-6GHz technology such as LTE under different deployment conditions, given different types of UAV antennas. We provide analytical expressions for the coverage probability; we also evaluate the performance of the GS network against the case where the UAVs are served by a typical terrestrial BS network.

cs.NI↗

arXiv2018

A Stochastic Model for UAV Networks Positioned Above Demand Hotspots in Urban Environments

cs.IT↗

arXiv2017

Backhaul For Low-Altitude UAVs in Urban Environments

Unmanned Aerial Vehicles (UAVs) acting as access points in cellular networks require wireless backhauls to the core network. In this paper we employ stochastic geometry to carry out an analysis of the UAV backhaul performance that can be achieved with a network of dedicated ground stations. We provide analytical expressions for the probability of successfully establishing a backhaul and the expected data rate over the backhaul link, given either an LTE or a millimeter-wave backhaul. We demonstrate that increasing the density of the ground station network gives diminishing returns on the performance of the UAV backhaul, and that for an LTE backhaul the ground stations can benefit from being colocated with an existing base station network.

cs.NI↗

arXiv2017

A Stochastic Geometry Model of Backhaul and User Coverage in Urban UAV Networks

Wireless access points on unmanned aerial vehicles (UAVs) are being considered for mobile service provisioning in commercial networks. To be able to efficiently use these devices in cellular networks it is necessary to first have a qualitative and quantitative understanding of how their design parameters reflect on the service quality experienced by the end user. In this paper we model a network of UAVs operating at a certain height above ground to provide wireless service within coverage areas shaped by their directional antennas, with the UAVs using the existing terrestrial base station network for wireless backhaul. We provide an analytical expression for the coverage probability experienced by a typical user as a function of the UAV parameters. Using our derivations we demonstrate the existence of an optimum UAV height which maximises the end user coverage probability. We then explore a scenario where the UAVs adjust their individual heights to meet their backhaul requirements while at the same time attempting to maximise the coverage probability of the end user on the ground.

cs.NI↗

arXiv2017

Coverage Analysis for Low-Altitude UAV Networks in Urban Environments

cs.NI↗

arXiv2017

Radio Access Network and Spectrum Sharing in Mobile Networks: A Stochastic Geometry Perspective

Next generation mobile networks will rely ever more heavily on resource sharing. In this article we study the sharing of radio access network and spectrum among mobile operators. We assess the impact of sharing these two types of resources on the performance of spatially distributed mobile networks. We apply stochastic geometry to observe the combined effect of, for example, the level of spatial clustering among the deployed base stations, the shared network size, or the coordination in shared spectrum use on network coverage and expected user data rate. We uncover some complex effects of mobile network resource sharing, which involve non-linearly scaling gains and performance trade-offs related to the sharing scenario or the spatial clustering level.

cs.NI↗