arXiv subjects

Anja Klein

Publications and source records attributed to Anja Klein.

17 recordsLinked to original sources

arXiv2026

Optimization and Deep Learning based Resource Allocation for UAV-Aided Wireless Communication with Rotatable Antenna Array

Multi-antenna unmanned aerial vehicle (UAV)-aided communication presents a promising solution to increase the system capacity and improve the quality of service (QoS) of the future wireless networks. In this paper, we equip a UAV platform with a rotatable antenna array (RAA), which can be rotated flexibly in three-dimensional (3D) space via an onboard gimbal, enabling additional spatial degrees of freedom (DoFs) for improving multiuser signal transmission and interference management. Compared with a conventional fixed antenna array (FAA), the RAA can proactively align users with the high-gain region of its antenna elements and reduce the spatial channel correlations among users. To demonstrate the advantages of RAA, we jointly design the RAA orientation and beamforming to maximize the sum-rate of multiple users subject to per-user QoS constraints. The formulated problem is highly nonconvex and exhibits strong coupling between the RAA orientation and beamforming variables. To solve this challenging problem, we propose first an optimization framework based on the penalty dual decomposition (PDD) method to iteratively optimize RAA orientation and beamforming. While the optimization framework yields high reliability in QoS satisfaction and favorable sum-rate performance, its iterative nature may hinder real-time deployment. To accelerate the joint design and preserve a high-quality solution, we further propose a deep learning (DL) framework based on graph neural networks (GNNs). Simulation results demonstrate that RAAs significantly outperform FAAs in UAV-aided communication. Additionally, the proposed optimization framework is capable of satisfying stringent QoS requirements with high reliability, while the proposed DL framework attains comparable sum-rate performance with substantially reduced computation time and exhibits robustness to user position information errors.

cs.IT↗

arXiv2026

Dynamic Hypergame for Task Assignment in Multi-platform Mobile Crowdsensing Under Incomplete Information

Mobile crowdsensing (MCS) is a promising distributed sensing paradigm for future wireless networks, where MCS platforms (MCSPs) recruit mobile units (MUs) through monetary incentives for sensing data collection. While most existing studies assume a single MCSP, practical deployments involve multiple competing MCSPs that simultaneously propose task offers to MUs, and MUs accept offers that maximize their revenue. This interaction gives rise to a two-sided matching game with contracts (MWC), decomposed into two components: (i) task proposal problem of the MCSPs and (ii) task acceptance problem of the MUs. To optimally solve (i), every MCSP requires information about other platforms' preferences and the qualities of the MUs in advance. Similarly, to solve (ii) optimally, the MUs require information about the task execution efforts of all tasks in advance. Such information is unavailable at the MCSPs and at the MUs. To address the challenge of unknown preferences of the other MCSPs, the MWC is posed as a dynamic hypergame, where every MCSP models the unknown preferences through perceptions and refines them over repeated interactions. To solve the dynamic hypergame under incomplete information, we propose PACMAB, a fully decentralized perception-aware two-sided learning framework where, (i) each MCSP learns an adaptive task proposal strategy under competition, and (ii) each MU learns task acceptance policy by estimating task execution efforts. Computational complexity of PACMAB shows that it scales favorably for the MCSPs as well as the MUs. Extensive simulations show that PACMAB consistently outperforms the benchmarks by completing at least 41% more tasks without assuming complete information.

cs.NI↗

arXiv2026

Federated Reinforcement Learning for Efficient Mobile Crowdsensing under Incomplete Information

Mobile crowdsensing (MCS) is a distributed sensing architecture that utilizes existing sensors on mobile units (MUs) to perform sensing tasks. A mobile crowdsensing platform (MCSP) publishes the sensing tasks and the MUs decide whether to participate in exchange for money. The MCS system is dynamic: the task requirements, the MUs' availability, and their available resources change over time. The MUs aim to find an efficient task participation strategy to maximize their income while the MCSP focuses on maximizing the number of completed tasks. As optimal strategies require perfect non-causal information about the MCS system, which is unavailable in realistic scenarios, the main challenge is to find an efficient task participation strategy for the MUs under incomplete information. To this end, a novel fully decentralized federated deep reinforcement learning algorithm, FDRL-PPO, is proposed. FDRL-PPO enables every MU to learn its own task participation strategy based on its experiences, available resources, and preferences, without relying on perfect non-causal information about the MCS system. To replenish their batteries, the MUs rely on energy harvesting. As a result, their available energy varies over time, leading to varying availability and fragmented learning experiences. To mitigate these challenges, the proposed approach leverages federated learning, enabling MUs to collaboratively improve their models without sharing private raw data like their own experiences. By exchanging only learned models, MUs collectively compensate for individual limitations, and find more scalable, robust, and efficient task participation strategies. Comprehensive evaluations on both synthetic and real-world datasets show that FDRL-PPO consistently outperforms benchmark algorithms in terms of task completion ratio, fairness in task completion, energy consumption, and number of conflicting proposals.

cs.LG↗

arXiv2026

Deep Sleep Scheduling for Satellite IoT via Simulation Based Optimization

The Satellite Internet of Things (S-IoT) enables global connectivity for remote sensing devices that must operate energy-efficiently over long time spans. We consider an S-IoT system consisting of a sender-receiver pair connected by a data channel and a feedback channel and capture its dynamics using a Markov Decision Process (MDP). To extend battery life, the sender has to decide on deep-sleep durations. Deep-sleep scheduling is the primary lever to reduce energy consumption, since sleeping devices consume only a fraction of their idle power. By choosing its deep-sleep duration online, the sender has to find a trade-off between energy consumption and data quality degradation at the receiver, captured by a weighted sum of costs. We quantify data quality degradation via the recently introduced Goal-Oriented Tensor (GoT) metric, which can take both age and content of delivered data into account. We assume a Markovian observed process and Markov channels with time-varying delay and erasure rates. The challenge is that content awareness of the GoT metric makes periodic transmissions inherently inefficient. Additionally, optimal sleep durations depends on the (unknown) future states of the observed process and the channels, both of which must be inferred online. We propose a novel algorithm using probabilistic simulation-based optimization (PSBO). With PSBO, the sensor forecasts future states based on estimated transition probabilities, and uses these forecasts to select the optimal deep-sleep duration. Extensive simulations and experiments with S-IoT hardware demonstrate superior performance of PSBO under diverse conditions.

cs.NI↗

arXiv2024

The Best Time for an Update: Risk-Sensitive Minimization of Age-Based Metrics

Popular methods to quantify transmitted data quality are the Age of Information (AoI), the Query Age of Information (QAoI), and the Age of Incorrect Information (AoII). We consider these metrics in a point-to-point wireless communication system, where the transmitter monitors a process and sends status updates to a receiver. The challenge is to decide on the best time for an update, balancing the transmission energy and the age-based metric at the receiver. Due to the inherent risk of high age-based metric values causing complications such as unstable system states, we introduce the new concept of risky states to denote states with high age-based metric. We use this new notion of risky states to quantify and minimize this risk of experiencing high age-based metrics by directly deriving the frequency of risky states as a novel risk-metric. Building on this foundation, we introduce two risk-sensitive strategies for AoI, QAoI and AoII. The first strategy uses system knowledge, i.e., channel quality and packet arrival probability, to find an optimal strategy that transmits when the age-based metric exceeds a tunable threshold. A lower threshold leads to higher risk-sensitivity. The second strategy uses an enhanced Q-learning approach and balances the age-based metric, the transmission energy and the frequency of risky states without requiring knowledge about the system. Numerical results affirm our risk-sensitive strategies' high effectiveness.

cs.IT↗

arXiv2023

Decentralized Online Learning in Task Assignment Games for Mobile Crowdsensing

The problem of coordinated data collection is studied for a mobile crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially publishes sensing tasks to the available mobile units (MUs) that signal their willingness to participate in a task by sending sensing offers back to the MCSP. From the received offers, the MCSP decides the task assignment. A stable task assignment must address two challenges: the MCSP's and MUs' conflicting goals, and the uncertainty about the MUs' required efforts and preferences. To overcome these challenges a novel decentralized approach combining matching theory and online learning, called collision-avoidance multi-armed bandit with strategic free sensing (CA-MAB-SFS), is proposed. The task assignment problem is modeled as a matching game considering the MCSP's and MUs' individual goals while the MUs learn their efforts online. Our innovative "free-sensing" mechanism significantly improves the MU's learning process while reducing collisions during task allocation. The stable regret of CA-MAB-SFS, i.e., the loss of learning, is analytically shown to be bounded by a sublinear function, ensuring the convergence to a stable optimal solution. Simulation results show that CA-MAB-SFS increases the MUs' and the MCSP's satisfaction compared to state-of-the-art methods while reducing the average task completion time by at least 16%.

cs.SI↗

arXiv2023

Matching Game for Optimized Association in Quantum Communication Networks

Enabling quantum switches (QSs) to serve requests submitted by quantum end nodes in quantum communication networks (QCNs) is a challenging problem due to the heterogeneous fidelity requirements of the submitted requests and the limited resources of the QCN. Effectively determining which requests are served by a given QS is fundamental to foster developments in practical QCN applications, like quantum data centers. However, the state-of-the-art on QS operation has overlooked this association problem, and it mainly focused on QCNs with a single QS. In this paper, the request-QS association problem in QCNs is formulated as a matching game that captures the limited QCN resources, heterogeneous application-specific fidelity requirements, and scheduling of the different QS operations. To solve this game, a swap-stable request-QS association (RQSA) algorithm is proposed while considering partial QCN information availability. Extensive simulations are conducted to validate the effectiveness of the proposed RQSA algorithm. Simulation results show that the proposed RQSA algorithm achieves a near-optimal (within 5%) performance in terms of the percentage of served requests and overall achieved fidelity, while outperforming benchmark greedy solutions by over 13%. Moreover, the proposed RQSA algorithm is shown to be scalable and maintain its near-optimal performance even when the size of the QCN increases.

cs.NI↗

arXiv2023

Safehaul: Risk-Averse Learning for Reliable mmWave Self-Backhauling in 6G Networks

Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today's mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing a Key Performance Indicator (KPI) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing average performance, Safehaul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks but also exhibits significantly more reliable performance (e.g., 71.4% less variance in achieved latency).

cs.NI↗

arXiv2022

Optimal Offloading Strategies for Edge-Computing via Mean-Field Games and Control

The optimal offloading of tasks in heterogeneous edge-computing scenarios is of great practical interest, both in the selfish and fully cooperative setting. In practice, such systems are typically very large, rendering exact solutions in terms of cooperative optima or Nash equilibria intractable. For this purpose, we adopt a general mean-field formulation in order to solve the competitive and cooperative offloading problems in the limit of infinitely large systems. We give theoretical guarantees for the approximation properties of the limiting solution and solve the resulting mean-field problems numerically. Furthermore, we verify our solutions numerically and find that our approximations are accurate for systems with dozens of edge devices. As a result, we obtain a tractable approach to the design of offloading strategies in large edge-computing scenarios with many users.

cs.DC↗

arXiv2019

Energy and Social Cost Minimization for Data Dissemination in Wireless Networks: Centralized and Decentralized Approaches

We study multi-hop data-dissemination in a wireless network from one source to multiple nodes where some of the nodes of the network act as re-transmitting nodes and help the source in data dissemination. In this network, we study two scenarios; i) the transmitting nodes do not need an incentive for transmission and ii) they do need an incentive and are paid by their corresponding receiving nodes by virtual tokens. We investigate two problems; P1) network power minimization for the first scenario and P2) social cost minimization for the second scenario, defined as the total cost paid by the nodes of the network for receiving data. In this paper, to address P1 and P2, we propose centralized and decentralized approaches that determine which of the nodes of the network should act as transmitting nodes, find their transmit powers and their corresponding receiving nodes. For the sake of energy efficiency, in our model, we employ maximal-ratio combining (MRC) at the receivers so that a receiver can be served by multiple transmitters. The proposed decentralized approach is based on a non-cooperative cost-sharing game (CSG). In our proposed game, every receiving node chooses its respective transmitting nodes and consequently, a cost is assigned to it according to the power imposed on its chosen transmitting nodes. We discuss how the network is formed in a decentralized way, find the action of the nodes in the game and show that, despite being decentralized, the proposed game converges to a stable solution. To find the centralized global optimum, which is a benchmark to our decentralized approach, we use a mixed-integer-liner-program (MILP). Simulation results show that our proposed decentralized approach outperforms the conventional algorithms in terms of energy efficiency and social cost while it can address the need for an incentive for collaboration.

cs.NI↗

arXiv2019

Joint Relaying and Spatial Sharing Multicast Scheduling for mmWave Networks

Millimeter-wave (mmWave) communication plays a vital role to efficiently disseminate large volumes of data in beyond-5G networks. Unfortunately, the directionality of mmWave communication significantly complicates efficient data dissemination, particularly in multicasting, which is gaining more and more importance in emerging applications (e.g., V2X, public safety). While multicasting for systems operating at lower frequencies (i.e., sub-6GHz) has been extensively studied, they are sub-optimal for mmWave systems as mmWave has significantly different propagation characteristics, i.e., using the directional transmission to compensate for the high path loss and thus promoting spectrum sharing. In this paper, we propose novel multicast scheduling algorithms by jointly exploiting relaying and spatial sharing gains while aiming to minimize the multicast completion time. We first characterize the min-time mmWave multicasting problem with a comprehensive model and formulate it with an integer linear program (ILP). We further design a practical and scalable distributed algorithm named mmDiMu, based on gradually maximizing the transmission throughput over time. Finally, we carry out validation through extensive simulations in different scales and the results show that mmDiMu significantly outperforms conventional algorithms with around 95% reduction on multicast completion time.

cs.NI↗

arXiv2018

Little Boxes: A Dynamic Optimization Approach for Enhanced Cloud Infrastructures

The increasing demand for diverse, mobile applications with various degrees of Quality of Service requirements meets the increasing elasticity of on-demand resource provisioning in virtualized cloud computing infrastructures. This paper provides a dynamic optimization approach for enhanced cloud infrastructures, based on the concept of cloudlets, which are located at hotspot areas throughout a metropolitan area. In conjunction, we consider classical remote data centers that are rigid with respect to QoS but provide nearly abundant computation resources. Given fluctuating user demands, we optimize the cloudlet placement over a finite time horizon from a cloud infrastructure provider's perspective. By the means of a custom tailed heuristic approach, we are able to reduce the computational effort compared to the exact approach by at least three orders of magnitude, while maintaining a high solution quality with a moderate cost increase of 5.8% or less.

cs.NI↗

arXiv2018

Cost Sharing Games for Energy-Efficient Multi-Hop Broadcast in Wireless Networks

We study multi-hop broadcast in wireless networks with one source node and multiple receiving nodes. The message flow from the source to the receivers can be modeled as a tree-graph, called broadcast-tree. The problem of finding the minimum-power broadcast-tree (MPBT) is NP-complete. Unlike most of the existing centralized approaches, we propose a decentralized algorithm, based on a non-cooperative cost-sharing game. In this game, every receiving node, as a player, chooses another node of the network as its respective transmitting node for receiving the message. Consequently, a cost is assigned to the receiving node based on the power imposed on its chosen transmitting node. In our model, the total required power at a transmitting node consists of (i) the transmit power and (ii) the circuitry power needed for communication hardware modules. We develop our algorithm using the marginal contribution (MC) cost-sharing scheme and show that the optimum broadcast-tree is always a Nash equilibrium (NE) of the game. Simulation results demonstrate that our proposed algorithm outperforms conventional algorithms for the MPBT problem. Besides, we show that the circuitry power, which is usually ignored by existing algorithms, significantly impacts the energy-efficiency of the network.

cs.GT↗

arXiv2017

Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing

In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance rate and quality. Task assignment is challenging, since a worker's performance (i) may fluctuate, depending on both the worker's current personal context and the task context, (ii) is not known a priori, but has to be learned over time. Moreover, learning context-specific worker performance requires access to context information, which may not be available at a central entity due to communication overhead or privacy concerns. Additionally, evaluating worker performance might require costly quality assessments. In this paper, we propose a context-aware hierarchical online learning algorithm addressing the problem of performance maximization in MCS. In our algorithm, a local controller (LC) in the mobile device of a worker regularly observes the worker's context, her/his decisions to accept or decline tasks and the quality in completing tasks. Based on these observations, the LC regularly estimates the worker's context-specific performance. The mobile crowdsourcing platform (MCSP) then selects workers based on performance estimates received from the LCs. This hierarchical approach enables the LCs to learn context-specific worker performance and it enables the MCSP to select suitable workers. In addition, our algorithm preserves worker context locally, and it keeps the number of required quality assessments low. We prove that our algorithm converges to the optimal task assignment strategy. Moreover, the algorithm outperforms simpler task assignment strategies in experiments based on synthetic and real data.

cs.LG↗

arXiv2017

Multi-Agent Reinforcement Learning for Energy Harvesting Two-Hop Communications with a Partially Observable State

We consider an energy harvesting (EH) transmitter communicating with a receiver through an EH relay. The harvested energy is used for data transmission, including the circuit energy consumption. As in practical scenarios, the system state, comprised by the harvested energy, battery levels, data buffer levels, and channel gains, is only partially observable by the EH nodes. Moreover, the EH nodes have only outdated knowledge regarding the channel gains for their own transmit channels. Our goal is to find distributed transmission policies aiming at maximizing the throughput. A channel predictor based on a Kalman filter is implemented in each EH node to estimate the current channel gain for its own channel. Furthermore, to overcome the partial observability of the system state, the EH nodes cooperate with each other to obtain information about their parameters during a signaling phase. We model the problem as a Markov game and propose a multi-agent reinforcement learning algorithm to find the transmission policies. We show the trade-off between the achievable throughput and the signaling required, and provide convergence guarantees for the proposed algorithm. Results show that even when the signaling overhead is taken into account, the proposed algorithm outperforms other approaches that do not consider cooperation.

cs.IT↗

arXiv2016

Context-Aware Proactive Content Caching with Service Differentiation in Wireless Networks

Content caching in small base stations or wireless infostations is considered to be a suitable approach to improve the efficiency in wireless content delivery. Placing the optimal content into local caches is crucial due to storage limitations, but it requires knowledge about the content popularity distribution, which is often not available in advance. Moreover, local content popularity is subject to fluctuations since mobile users with different interests connect to the caching entity over time. Which content a user prefers may depend on the user's context. In this paper, we propose a novel algorithm for context-aware proactive caching. The algorithm learns context-specific content popularity online by regularly observing context information of connected users, updating the cache content and observing cache hits subsequently. We derive a sublinear regret bound, which characterizes the learning speed and proves that our algorithm converges to the optimal cache content placement strategy in terms of maximizing the number of cache hits. Furthermore, our algorithm supports service differentiation by allowing operators of caching entities to prioritize customer groups. Our numerical results confirm that our algorithm outperforms state-of-the-art algorithms in a real world data set, with an increase in the number of cache hits of at least 14%.

cs.NI↗

arXiv2015

Maximizing the Sum Rate in Cellular Networks Using Multi-Convex Optimization

In this paper, we propose a novel algorithm to maximize the sum rate in interference-limited scenarios where each user decodes its own message with the presence of unknown interferences and noise considering the signal-to-interference-plus-noise-ratio. It is known that the problem of adapting the transmit and receive filters of the users to maximize the sum rate with a sum transmit power constraint is non-convex. Our novel approach is to formulate the sum rate maximization problem as an equivalent multi-convex optimization problem by adding two sets of auxiliary variables. An iterative algorithm which alternatingly adjusts the system variables and the auxiliary variables is proposed to solve the multi-convex optimization problem. The proposed algorithm is applied to a downlink cellular scenario consisting of several cells each of which contains a base station serving several mobile stations. We examine the two cases, with or without several half-duplex amplify-and-forward relays assisting the transmission. A sum power constraint at the base stations and a sum power constraint at the relays are assumed. Finally, we show that the proposed multi-convex formulation of the sum rate maximization problem is applicable to many other wireless systems in which the estimated data symbols are multi-affine functions of the system variables.

cs.IT↗