arXiv subjects

Bulent Tavli

Publications and source records attributed to Bulent Tavli.

8 recordsLinked to original sources

arXiv2025

Energy-Aware Resource Allocation for Multi-Operator Cell-Free Massive MIMO in V-CRAN Architectures

Cell-free massive multiple-input multiple-output (MIMO) implemented in virtualized cloud radio access networks (V-CRAN) has emerged as a promising architecture to enhance spectral efficiency (SE), network flexibility, and energy efficiency (EE) in next-generation wireless systems. In this work, we develop a holistic optimization framework for the efficient deployment of cell-free massive MIMO in V-CRAN with multiple mobile network operators (MNOs). Specifically, we formulate a set of integer programming models to jointly optimize access point (AP) selection, user equipment (UE) association, cloud resource allocation, and MNO assignment while minimizing the maximum total power consumption (TPC) across MNOs. We consider two different scenarios based on whether UEs can be assigned to arbitrary MNOs or not. The numerical results demonstrate the impact of different deployment assumptions on power consumption, highlighting that flexible UE-MNO assignment significantly reduces TPC. The findings provide key insights into optimizing resource management in cell-free massive MIMO V-CRAN, paving the way for energy-efficient wireless network implementations.

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arXiv2021

Autonomous UAV Base Stations for Next Generation Wireless Networks: A Deep Learning Approach

To address the ever-growing connectivity demands of wireless communications, the adoption of ingenious solutions, such as Unmanned Aerial Vehicles (UAVs) as mobile Base Stations (BSs), is imperative. In general, the location of a UAV Base Station (UAV-BS) is determined by optimization algorithms, which have high computationally complexities and place heavy demands on UAV resources. In this paper, we show that a Convolutional Neural Network (CNN) model can be trained to infer the location of a UAV-BS in real time. In so doing, we create a framework to determine the UAV locations that considers the deployment of Mobile Users (MUs) to generate labels by using the data obtained from an optimization algorithm. Performance evaluations reveal that once the CNN model is trained with the given labels and locations of MUs, the proposed approach is capable of approximating the results given by the adopted optimization algorithm with high fidelity, outperforming Reinforcement Learning (RL)-based approaches. We also explore future research challenges and highlight key issues.

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arXiv2021

A Coverage-Aware Distributed k-Connectivity Maintenance Algorithm for Arbitrarily Large k in Mobile Sensor Networks

Mobile sensor networks (MSNs) have emerged from the interaction between mobile robotics and wireless sensor networks. MSNs can be deployed in harsh environments, where failures in some nodes can partition MSNs into disconnected network segments or reduce the coverage area. A k-connected network can tolerate at least k-1 arbitrary node failures without losing its connectivity. In this study, we present a coverage-aware distributed k-connectivity maintenance (restoration) algorithm that generates minimum-cost movements of active nodes after a node failure to preserve a persistent k value subject to a coverage conservation criterion. The algorithm accepts a coverage conservation ratio (as a trade-off parameter between coverage and movements) and facilitates coverage with the generated movements according to this value. Extensive simulations and testbed experiments reveal that the proposed algorithm restores k-connectivity more efficiently than the existing restoration algorithms. Furthermore, our algorithm can be utilized to maintain k-connectivity without sacrificing the coverage, significantly.

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arXiv2020

3-D Dynamic UAV Base Station Location Problem

We address a dynamic covering location problem of an Unmanned Aerial Vehicle Base Station (UAV-BS), where the location sequence of a single UAV-BS in a wireless communication network is determined to satisfy data demand arising from ground users. This problem is especially relevant in the context of smart grid and disaster relief. The vertical movement ability of the UAV-BS and non-convex covering functions in wireless communication restrict utilizing classical planar covering location approaches. Therefore, we develop new formulations to this emerging problem for a finite time horizon to maximize the total coverage. In particular, we develop a mixed-integer non-linear programming formulation which is non-convex in nature, and propose a Lagrangean Decomposition Algorithm (LDA) to solve this formulation. Due to high complexity of the problem, the LDA is still unable to find good local solutions to large-scale problems. Therefore, we develop a Continuum Approximation (CA) model and show that CA would be a promising approach in terms of both computational time and solution accuracy. Our numerical study also shows that the CA model can be a remedy to build efficient initial solutions for exact solution algorithms.

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arXiv2020

Privacy Protection via Joint Real and Reactive Load Shaping in Smart Grids

Frequent metering of electricity consumption is crucial for demand side management in smart grids. However, metered data can be processed fairly easily by employing well-established nonintrusive appliance load monitoring techniques to infer appliance usage, which reveals information about consumers' private lives. Existing load shaping techniques for privacy primarily focus only on altering metered real power, whereas smart meters collect reactive power consumption data as well for various purposes. This study addresses consumer privacy preservation via load shaping in a demand response scheme, considering both real and reactive power. We build a multi-objective optimization framework that enables us to characterize the interplay between privacy maximization, user cost minimization, and user discomfort minimization objectives. Our results reveal that minimizing information leakage due to a single component, e.g., real power, would suffer from overlooking information leakage due to the other component, e.g., reactive power, causing sub-optimal decisions. In fact, joint shaping of real and reactive power components results in the best possible privacy preservation performance, which leads to more than a twofold increase in privacy in terms of mutual information.

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arXiv2020

Holistic Privacy for Electricity, Water, and Natural Gas Metering in Next Generation Smart Homes

In smart electricity grids, high time granularity (HTG) power consumption data can be decomposed into individual appliance load signatures via Nonintrusive Appliance Load Monitoring techniques to expose appliance usage profiles. Various methods ranging from load shaping to noise addition and data aggregation have been proposed to mitigate this problem. However, with the growing scarcity of natural resources, utilities other than electricity (such as water and natural gas) have also begun to be subject to HTG metering, which creates privacy issues similar to that of electricity. Therefore, employing privacy protection countermeasures for only electricity usage is ineffective for appliances that utilize additional/other metered resources. As such, existing privacy countermeasures and metrics need to be reevaluated to address not only electricity, but also any other resource that is metered. Furthermore, a holistic privacy protection approach for all metered resources must be adopted as the information leak from any of the resources has a potential to render the privacy preserving countermeasures for all the other resources futile. This paper introduces the privacy preservation problem for multiple HTG metered resources and explores potential solutions for its mitigation.

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arXiv2018

UAV Base Station Location Optimization for Next Generation Wireless Networks: Overview and Future Research Directions

Unmanned aerial vehicles mounted base stations (UAV-BSs) are expected to become one of the significant components of the Next Generation Wireless Networks (NGWNs). Rapid deployment, mobility, higher chances of unobstructed propagation path, and flexibility features of UAV-BSs have attracted significant attention. Despite, potentially, high gains brought by UAV-BSs in NGWNs, many challenges are also introduced by them. Optimal location assignment to UAV-BSs, arguably, is the most widely investigated problem in the literature on UAV-BSs in NGWNs. This paper presents a comprehensive survey of the literature on the location optimization of UAV-BSs in NGWNs. A generic optimization framework through a universal Mixed Integer Non-Linear Programming (MINLP) formulation is constructed and the specifications of its constituents are elaborated. The generic problem is classified into a novel taxonomy. Due to the highly challenging nature of the optimization problem a range of solutions are adopted in the literature which are also covered under the aforementioned classification. Furthermore, future research directions on UAV-BS location optimization in 5G and beyond non-terrestrial aerial communication systems are discussed.

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arXiv2018

Backhaul-Aware Optimization of UAV Base Station Location and Bandwidth Allocation for Profit Maximization

Unmanned Aerial Vehicle Base Stations (UAVBSs) are envisioned to be an integral component of the next generation Wireless Communications Networks (WCNs) by dynamically moving the supply towards the demand. A significant drawback of the state-of-the-art have been designing a WCN in which the service-oriented performance measures (e.g., throughput) are optimized without considering different relevant decisions such as determining the location and allocating the resources, jointly. In this study, we address the UAVBS location and bandwidth allocation problems together to optimize the total network profit. In particular, a Mixed-Integer Non-Linear Programming (MINLP) formulation is developed, in which the location of a single UAVBS and bandwidth allocations to users are jointly determined. The objective is to maximize the total profit without exceeding the backhaul and access capacities. The profit gained from a specific user is assumed to be a piecewise-linear function of the provided data rate level, where higher data rate levels would yield higher profit. Due to high complexity of the MINLP, we propose an efficient heuristic algorithm with lower computational complexity. We show that, when the UAVBS location is determined, the resource allocation problem can be reduced to a Multidimensional Binary Knapsack Problem (MBKP), which can be solved in pseudo-polynomial time. To exploit this structure, the optimal bandwidth allocations are determined by solving several MBKPs in a search algorithm. We test the performance of our algorithm with two heuristics and with the MINLP model solved by a commercial solver. Our numerical results show that the proposed algorithm outperforms the alternative solution approaches and would be a promising tool to improve the total network profit.

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