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Elif Tugce Ceran

Publications and source records attributed to Elif Tugce Ceran.

9 recordsLinked to original sources

Self-Adaptive Threshold ALOHA

We propose Self-Adaptive Threshold ALOHA (SATA), a distributed random-access policy to minimize the Age of Information (AoI). SATA uses 1-bit broadcast feedback and requires no explicit coordination or message passing among nodes. {We show that this minimal feedback is sufficient for each node to perfectly track the number of active users and adjust its access probabilities accordingly.} For any initial network state, SATA converges to a collision-free TDMA steady state in finite time whenever the age threshold satisfies $Γ\geq n$, where $n$ is the network size. We derive closed-form expressions for the long-term average AoI and throughput in steady state, and establish that, for $Γ=n$, the expected transient duration scales as $O(n\log n)$. Simulation results confirm that SATA closely approaches TDMA performance across a wide range of network sizes, significantly outperforming Slotted ALOHA, Threshold ALOHA, and 1-persistent Threshold Slotted ALOHA. Notably, the performance gap between SATA and the benchmark random-access policies becomes more pronounced as network size grows.

cs.NI

MASK: Multi-Agent Semantic K-Scheduling for Risk-Sensitive 6G Robotics

Realizing the vision of 6G connected robotics requires reconciling high-performance collaborative control with the rigid spectral limitations of physical wireless channels. In realistic collaborative sensing scenarios, spectral resources are quantized into finite physical resource blocks or orthogonal subcarriers, rendering simultaneous transmission by all agents infeasible. To address this, we propose Multi-Agent Semantic K-Scheduling (MASK), a control architecture designed to sustain robust, risk-aware coordination under strict instantaneous bandwidth caps. We introduce Arbiter-Assisted Semantic Information Gating (A-SIG), a lightweight coordination mechanism that enforces hard access constraints by scheduling only the top-K agents based on locally computed semantic importance scores. By aggregating these prioritized observations into a compact latent state, a self-supervised global encoder enables a distributional policy to mitigate tail risks despite data sparsity. We evaluate MASK across diverse benchmarks, demonstrating that it matches the performance of communication-unconstrained baselines even when channel access is restricted to a small fraction of the swarm size. Furthermore, the framework exhibits inherent resilience to packet erasures, validating semantic scheduling as a critical enabler for resource-constrained 6G systems.

cs.RO

Age of Entanglement in Satellite Repeater Chains with Intermittent Availability

Timely availability of high-fidelity entanglement is essential for emerging quantum networks. This paper introduces the Age of Entanglement (AoE) as a novel performance metric that captures the freshness of bipartite entanglement under continuous distribution in quantum repeater chains. AoE extends classical Age of Information (AoI)-based metrics to quantum networking by capturing storage, decoherence, and probabilistic entanglement generation and swapping. We study a satellite-assisted quantum repeater network in which entangled pairs are generated probabilistically, stored in quantum memories that suffer from decoherence, and combined to form end-to-end entangled links. Satellite-ground connectivity is intermittent and modeled as a two-state Markov chain. The resulting AoE minimization problem is formulated as an infinite-horizon Markov decision process (MDP), where control actions determine when to generate, store, or swap entangled pairs under stochastic link availability and memory degradation. Using relative value iteration, we characterize AoE-optimal policies and evaluate their performance numerically. Our results highlight the impact of decoherence, imperfect operations, and visibility dynamics, and show that the proposed dynamic policies significantly outperform swap-as-soon-as-possible and greedy entanglement generation strategies. Our results provide practical design and control guidelines for satellite-enabled quantum repeater chains supporting continuous entanglement distribution.

cs.NI

Deep Reinforcement Learning Enhanced Rate-Splitting Multiple Access for Interference Mitigation

This study explores the application of the rate-splitting multiple access (RSMA) technique, vital for interference mitigation in modern communication systems. It investigates the use of precoding methods in RSMA, especially in complex multiple-antenna interference channels, employing deep reinforcement learning. The aim is to optimize precoders and power allocation for common and private data streams involving multiple decision-makers. A multi-agent deep deterministic policy gradient (MADDPG) framework is employed to address this complexity, where decentralized agents collectively learn to optimize actions in a continuous policy space. We also explore the challenges posed by imperfect channel side information at the transmitter. Additionally, decoding order estimation is addressed to determine the optimal decoding sequence for common and private data sequences. Simulation results demonstrate the effectiveness of the proposed RSMA method based on MADDPG, achieving the upper bound in single-antenna scenarios and closely approaching theoretical limits in multi-antenna scenarios. Comparative analysis shows superiority over other techniques such as MADDPG without rate-splitting, maximal ratio transmission (MRT), zero-forcing (ZF), and leakage-based precoding methods. These findings highlight the potential of deep reinforcement learning-driven RSMA in reducing interference and enhancing system performance in communication systems.

cs.IT

Optimization of AoII and QAoII in Multi-User Links

We consider a network with multiple sources and a base station that send time-sensitive information to remote clients. The Age of Incorrect Information (AoII) captures the freshness of the informative pieces of status update packets at the destinations. We derive the closed-form Whittle Index formulation for a push-based multi-user network over unreliable channels with AoII-dependent cost functions. We also propose a new semantic performance metric for pull-based systems, named the Age of Incorrect Information at Query (QAoII), that quantifies AoII at particular instants when clients generate queries. Simulation results demonstrate that the proposed Whittle Index-based scheduling policies for both AoII and QAoII-dependent cost functions are superior to benchmark policies, and adopting query-aware scheduling can significantly improve the timeliness for scenarios where a single user or multiple users are scheduled at a time.

cs.IT

Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting

The time average expected age of information (AoI) is studied for status updates sent over an error-prone channel from an energy-harvesting transmitter with a finite-capacity battery. Energy cost of sensing new status updates is taken into account as well as the transmission energy cost better capturing practical systems. The optimal scheduling policy is first studied under the hybrid automatic repeat request (HARQ) protocol when the channel and energy harvesting statistics are known, and the existence of a threshold-based optimal policy is shown. For the case of unknown environments, average-cost reinforcement-learning algorithms are proposed that learn the system parameters and the status update policy in real-time. The effectiveness of the proposed methods is demonstrated through numerical results.

cs.IT

A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ

Scheduling the transmission of time-sensitive information from a source node to multiple users over error-prone communication channels is studied with the goal of minimizing the long-term average age of information (AoI) at the users. A long-term average resource constraint is imposed on the source, which limits the average number of transmissions. The source can transmit only to a single user at each time slot, and after each transmission, it receives an instantaneous ACK/NACK feedback from the intended receiver, and decides when and to which user to transmit the next update. Assuming the channel statistics are known, the optimal scheduling policy is studied for both the standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols. Then, a reinforcement learning(RL) approach is introduced to find a near-optimal policy, which does not assume any a priori information on the random processes governing the channel states. Different RL methods including average-cost SARSAwith linear function approximation (LFA), upper confidence reinforcement learning (UCRL2), and deep Q-network (DQN) are applied and compared through numerical simulations

cs.IT

Average Age of Information with Hybrid ARQ under a Resource Constraint

Scheduling of the transmission of status updates over an error-prone communication channel is studied in order to minimize the long-term average age of information (AoI) at the destination, under an average resource constraint at the source node, which limits the average number of transmissions. After each transmission, the source receives an instantaneous ACK/NACK feedback, and decides on the next update, without a priori knowledge on the success of the future transmissions. The optimal scheduling policy is studied under different feedback mechanisms; in particular, standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols are considered. Average-cost reinforcement learning algorithms are proposed when the error probabilities for the HARQ system are unknown.

cs.IT

Optimal Energy Allocation Policies for a High Altitude Flying Wireless Access Point

Inspired by recent industrial efforts toward high altitude flying wireless access points powered by renewable energy, an online resource allocation problem for a mobile access point (AP) travelling at high altitude is formulated. The AP allocates its resources (available energy) to maximize the total utility (reward) provided to a sequentially observed set of users demanding service. The problem is formulated as a 0/1 dynamic knapsack problem with incremental capacity over a finite time horizon, the solution of which is quite open in the literature. We address the problem through deterministic and stochastic formulations. For the deterministic problem, several online approximations are proposed based on an instantaneous threshold that can adapt to short-time-scale dynamics. For the stochastic model, after showing the optimality of a threshold based solution on a dynamic programming (DP) formulation, an approximate threshold based policy is obtained. The performances of proposed policies are compared with that of the optimal solution obtained through DP.

cs.NI