The transition from monolithic quantum computing architectures to distributed quantum computing (DQC) is inevitable due to the limited number of qubits available in a single quantum processing unit (QPU) and the growing demand for utility-scale, fault-tolerant quantum systems. As a part of this transition, distributed quantum error correction (DQEC) emerges as a foundational component for addressing the error-prone nature of qubit storage and gate operations across multiple QPUs. In this paper, we provide a comprehensive review of recent advancements in the DQEC code families, including both topological and hypergraph product codes. We highlight how DQEC frameworks can enhance effective computational capacity and enable the realization of complex and scalable quantum codes on distributed quantum platforms. We also discuss the advantages and constraints associated with various stages of DQEC, including code design, encoding, syndrome extraction, and decoding. Furthermore, we outline key technical challenges and explore promising directions for future research to emphasize the pivotal role of DQC in advancing DQEC codes and shaping the development of future-generation quantum error correction codes.
The Version Age of Information (VAoI) quantifies information freshness by measuring the number of versions the receiver lags behind. This paper studies VAoI minimization in an M-user uplink non-orthogonal multiple access (NOMA) system where users maintain single-packet buffers and transmissions are constrained by average power and information-quality constraints, modeled by a general distortion function. A fundamental trade-off arises: transmitting more bits per update improves information quality but increases power consumption, reducing transmission opportunities and increasing VAoI, while transmitting fewer bits has the opposite effect. We formulate a weighted-sum VAoI minimization problem as a convex optimization problem. However, users' power allocations are coupled through multiple-access capacity constraints per channel state, leading to exponential complexity. To address this, we develop a VAoI-agnostic stationary randomized policy that jointly optimizes scheduling, bit allocation, and power control without tracking instantaneous VAoI, and achieves a provable 2-approximation to the globally optimal average VAoI. Leveraging Lagrangian dual decomposition, we derive closed-form expressions for the scheduling probabilities and power allocations, and efficiently determine the optimal successive interference cancellation decoding order, avoiding exhaustive search Numerical results show that NOMA significantly outperforms time-division multiple access (TDMA): at high power budgets, NOMA achieves near-zero VAoI, whereas TDMA saturates at a non-zero value, consistent with the analysis. The proposed general distortion framework accommodates diverse bit-priority structures by assigning unequal importance to different bits within an update.
Large language models (LLMs) have enabled a new class of agentic AI systems that reason, plan, and act by invoking external tools. However, most existing agentic architectures remain centralized and monolithic, limiting scalability, specialization, and interoperability. This paper proposes a framework for scalable agentic intelligence, termed the Internet of Agentic AI, in which autonomous, heterogeneous agents distributed across cloud and edge infrastructure dynamically form coalitions to execute task-driven workflows. We formalize a network-native model of agentic collaboration and introduce an incentive-compatible workflow-coalition feasibility framework that integrates capability coverage, network locality, and economic implementability. To enable scalable coordination, we formulate a minimum-effort coalition selection problem and propose a decentralized coalition formation algorithm. The proposed framework can operate as a coordination layer above the Model Context Protocol (MCP). A healthcare case study demonstrates how domain specialization, cloud-edge heterogeneity, and dynamic coalition formation enable scalable, resilient, and economically viable agentic workflows. This work lays the foundation for principled coordination and scalability in the emerging era of Internet of Agentic AI.
We consider a node-monitor pair, where the node's state varies with time. The monitor needs to track the node's state at all times; however, there is a fixed cost for each state query. So the monitor may instead predict the state using time-series forecasting methods, including time-series foundation models (TSFMs), and query only when prediction uncertainty is high. Since query decisions influence prediction accuracy, determining when to query is nontrivial. A natural approach is a greedy policy that predicts when the expected prediction loss is below the query cost and queries otherwise. We analyze this policy in a Markovian setting, where the optimal (OPT) strategy is a state-dependent threshold policy minimizing the time-averaged sum of query cost and prediction losses. We show that, in general, the greedy policy is suboptimal and can have an unbounded competitive ratio, but under common conditions such as identically distributed transition probabilities, it performs close to OPT. For the case of unknown transition probabilities, we further propose a projected stochastic gradient descent (PSGD)-based learning variant of the greedy policy, which achieves a favorable predict-query tradeoff with improved computational efficiency compared to OPT.
The p-persistent CSMA protocol is central to random-access MAC analysis, but predicting saturation throughput in heterogeneous multi-hop wireless networks remains a hard problem. Simplified models that assume a single, shared interference domain can underestimate throughput by 48–62% in sparse topologies. Exact Markov-chain analyses are accurate but scale exponentially in computation time, making them impractical for large networks. These computational barriers motivate structural machine learning approaches like GNNs for scalable throughput prediction in general network topologies. Yet off-the-shelf GNNs struggle here: a standard GCN yields 63.94% normalized mean absolute error (NMAE) on heterogeneous networks because symmetric normalization conflates a node's direct interference with higher-order, cascading effects that pertain to how interference propagates over the network graph. Building on these insights, we propose the Decoupled Graph Convolutional Network (D-GCN), a novel architecture that explicitly separates processing of a node's own transmission probability from neighbor interference effects. D-GCN replaces mean aggregation with learnable attention, yielding interpretable, per-neighbor contribution weights while capturing complex multihop interference patterns. D-GCN attains 3.3% NMAE, outperforms strong baselines, remains tractable even when exact analytical methods become computationally infeasible, and enables gradient-based network optimization that achieves within 1% of theoretical optima.
With the dawn of AI factories ushering a new era of computing supremacy, development of strategies to effectively track and utilize the available computing resources is garnering utmost importance. These computing resources are often modeled as Markov sources, which oscillate between free and busy states, depending on their internal load and external utilization, and are commonly referred to as Markov machines (MMs). Most of the prior work solely focuses on the problem of tracking these MMs, while often assuming a rudimentary decision process that governs their utilization. Our key observation is that the ultimate goal of tracking a MM is to properly utilize it. In this work, we consider the problem of maximizing the utility of a MM, where the utility is defined as the average revenue generated by the MM. Assuming a Poisson job arrival process and a query-based sampling procedure to sample the state of the MM, we find the optimal times to submit the available jobs to the MM so as to maximize the average revenue generated per unit job. We show that, depending on the parameters of the MM, the optimal policy is in the form of either a threshold policy or a switching policy based on the age of our estimate of the state of the MM.
Terahertz (THz) inter-satellite links (ISLs) enable high-data-rate communication between satellites, addressing the need for faster communication in modern satellite systems. While more resilient than RF and optical systems, THz ISLs are still vulnerable to orbital perturbations, leading to severe beam misalignment and signal degradation. This paper presents the a first study evaluating composite directional THz links to mitigate the impact of these perturbations on THz ISLs. We leverage the angular dispersion property arising from wideband frequency diversity of THz channels and employ THz-scale Leaky Wave Antennas (LWAs) for passive beam steering, enhancing spatial coverage and adapting to satellite position changes. Through model-driven evaluations and over-the-air measurements, we quantify the perturbation tolerance of lab-scale LWA-based links across various geometries, highlighting bandwidth expansion limitations in mitigating angular instability and optimizing LWA geometry for reliable, high-performance THz ISLs.
We consider a novel resource allocation framework designed to achieve optimal resource management for edgeassisted inference tasks. This is obtained by a new optimization approach, addressed as conformal Lyapunov optimization, which integrates online conformal risk control (O-CRC) with conventional Lyapunov optimization (LO). Unlike traditional LO, this approach ensures compliance with deterministic long-term reliability constraints. Simulation results, based on an edgeassisted segmentation task, demonstrate the effectiveness of the proposed method in balancing energy consumption and inference performance, while maintaining strict control over deterministic long-term constraints, related to the false negative segmentation rate.
This paper focuses on the problem of automatic link selection in multi-channel multiple access control using bandit feedback. In particular, a controller assigns multiple users to multiple channels in a time slotted system, where in each time slot at most one user can be assigned to a given channel and at most one channel can be assigned to a given user. Given that user i is assigned to channel j, the transmission fails with a fixed probability f(i,j). The failure probabilities are not known to the controller. The assignments are made dynamically using success/failure feedback. The goal is to maximize the time average utility, where we consider an arbitrary (possibly nonsmooth) concave and entrywise nondecreasing utility function. The problem of merely maximizing the total throughput has a solution of always assigning the same user-channel pairs and can be unfair to certain users, particularly when the number of channels is less than the number of users. Instead, our scheme allows various types of fairness, such as proportional fairness, maximizing the minimum, or combinations of these by defining the appropriate utility function. We propose an algorithm for this task that is adaptive and gets within O(log(T)/T-1/3) of optimality over any interval of T consecutive slots over which the success probabilities do not change. This performance is improved to O(1/root T) for single-channel problems with a minimum constraint on the rate of transmission attempts per user.
Cell-free massive MIMO (multiple-input multiple-output) is a promising infrastructure for 6G and beyond, offering significantly higher spectral efficiency than traditional cellular systems. In cell-free massive MIMO, a large number of low-cost access points (APs) are densely deployed, making hardware impairments inevitable due to cost-effective radio hardware. While the impact of quantization and other impairments has been extensively studied for narrowband channels, their effects in wideband scenarios remain relatively unexamined. This paper presents the first analysis of how low-resolution analog-to-digital converters (ADCs) affect the uplink performance of a cell-free massive MIMO system using an orthogonal frequency division multiplexing (OFDM) waveform. Both quantization-impaired channel estimation and data detection are considered, and the quantization-unaware and quantization-aware linear receivers are developed. To further mitigate the adverse effects of quantization at the bit level, an alternating direction method of multipliers (ADMM)-based receiver is proposed. Simulation results demonstrate that the ADMM-based receiver outperforms conventional linear receivers by orders of magnitude.
Indoor localization is a critical component of various applications, including assisted living, personnel monitoring, and asset tracking. Traditional localization methods relying on specialized sensors such as LiDAR, ultrasound, and 3D cameras offer high precision but suffer from high costs and limited interoperability. To address these challenges, this paper explores the Integrated Sensing and Communication (ISAC) paradigm, leveraging signal-based modalities focusing on received signal strength indication (RSSI). These measurements, inherently embedded in wireless communication packets, enable cost-effective and vendor-agnostic localization without the need for additional hardware. However, practical deployment remains challenging due to signal degradation from obstacles, multipath effects, and reflections. This paper presents an end-to-end localization framework utilizing COTS IoT devices and advanced RSSI processing techniques to enhance measurement reliability. By integrating filtering mechanisms and machine learning models, the proposed solution improves distance estimation, categorizes line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, and enhances localization accuracy. Additionally, an open and extendable edge-to-cloud infrastructure supports scalability and real-time processing. Experimental evaluation demonstrate the effectiveness of this approach in various aspects such as increase of RSSI reliability (increase of up to 82 % regarding standard deviation, drastic reduction of outliers' detection and fluctuation of more than 90 % to distances up to 2 m), accurate LoS-NLoS classification up to 99 % and overall localization increased accuracy more than 83 %.
Wireless interactive panoramic scene delivery imposes unique challenges compared to its wired or non-interactive counterparts. The wireless channel is throughput-constrained, which limits the ability to deliver large and high-quality panoramic images. On the other hand, the interactivity imposes a real-time constraint on the system and limits the use of a playback buffer. Also, wireless inputs are not delivered instantaneously like wired interrupt-based inputs, so the system must predict the user's head pose and the portion of the scene visible to them, called the viewport. This reveals a tradeoff: delivering a portion too small may not cover the viewport if the prediction error is too large, while delivering a portion too large may result in a failed wireless transmission. Likewise, delivering the portion at too high a quality may result in a failed transmission. Despite these challenges, we would like to guarantee an immersive experience for the user by delivering a high-quality and visually consistent panoramic scene. To that end, we aim to maximize the user's quality of experience, which we define as the combination of (1) cumulative quality, (2) long-term consistency, which quantifies the overall variance in perceived quality, and (3) short-term consistency, which quantifies abrupt quality changes. We formulate this problem as a risk-averse multi-armed bandit problem with reward-dependent switching costs, and develop a novel block-based UCB algorithm with opportunistic switching. We derive its theoretical regret upper bound, which matches results in prior work, and corroborate this result in trace-based simulations using a panoramic video streaming data trace.
Large-scale wireless networks pose significant challenges in resource scheduling, where the solution space grows exponentially with network size. While network decomposition offers a promising solution by breaking networks into manageable subnetworks, existing approaches, including spectral clustering, fail to effectively capture the complex service relationships between base stations (BSs) and users, particularly in networks with massive user populations. This paper presents BSCCD (Bidirectional Spectral Co-Clustering Based Decomposition), a new decomposition scheme that addresses these challenges through two key innovations: (i) a two-round spectral co-clustering framework that captures bidirectional BS-user relationships, and (ii) a user node merging strategy that handles massive user populations. Extensive experiments on real-world datasets from multiple Chinese cities demonstrate that BSCCD reduces computation latency by up to 61.91% compared to global optimization, while achieving more than 10% improvement in solution quality over traditional clustering approaches. The advantage is particularly pronounced in medium-scale networks, where BSCCD outperforms traditional methods by 28.76%. Our results demonstrate BSCCD's practical viability for resource scheduling in contemporary wireless networks, especially in scenarios with complex BS-user interactions and large user populations.
Internet of Things applications require timely access to information collected from sensors deployed over large geographic areas. However, such applications often experience highly-varying network conditions that prevent the timely delivery of information updates related to source data from sensors. Moreover, IoT applications have different metrics of interest and patterns to request source data. This article explicitly addresses the timely delivery of information updates in heterogeneous IoT scenarios with different application-specific goals. For this purpose, it introduces new metrics based on age of information (AoI) to accurately describe timeliness of updates in such a context. Moreover, it analytically derives optimal update generation policies for different request patterns to minimize the overall update age in an IoT system and maximize fairness of updates. Finally, it carries out a thorough performance evaluation of the proposed policies for representative request patterns with a real-world dataset of Internet connectivity. The obtained results demonstrate that the proposed policies are competitive with those in the state of the art, with a two order of magnitude reduction in energy consumption and up to a 19.9% higher fairness.
This paper introduces a novel optimization framework for Network Functions Virtualization (NFV) that addresses the efficient implementation of end-to-end service requests in physical networks. Our approach characterizes each server node by a reliability function reflecting its computational load, which aids in balancing workloads and mitigating congestion. By optimizing the reliability metrics along the route, our approach ensures robust end-to-end service quality. We formulate the NFV deployment problem as a non-convex mixed-integer non-linear programming (MINLP) model aimed at minimizing both deployment and operational costs while maximizing resource utilization. Given the NP-hard nature of the problem, we develop efficient linearization techniques and bounding schemes, using also dynamic programming, to convert the formulation into a tractable mixed-integer linear programming (MILP) model. Additionally, a cutting-plane-based heuristic with a warm-start strategy is proposed to further accelerate convergence. Experimental evaluations on real-world network topologies demonstrate that our framework offers scalable and cost-effective solutions compared to existing approaches.
The stability of aircraft remains vulnerable to sudden external disturbances and unpredictable vortices. The aircraft's attitude angles undergo rapid changes due to random turbulence. Consequently, to ensure safety, it is essential to control the aircraft's control surfaces, i.e., ailerons, elevators, and rudder angles, to maintain its static stability. Although classical closedloop control methods have been widely adopted, their limited adaptability to changing dynamics calls for more robust solutions. Reinforcement learning (RL) offers adaptive capabilities but often demands a large number of training parameters and substantial computational resources, which may be impractical for real-time lightweight aircraft applications. To overcome these limitations, this paper introduces a quantum aircraft with the quantum actorcritic networks-based aircraft control (QACN-AC) algorithm. By utilizing quantum neural networks (QNN), QACN-AC significantly reduces the number of parameters required for training, thus mitigating computational overhead while preserving robust control performance. The QACN-AC's effectiveness is validated through realistic simulations leveraging Boeing's B777 specifications. The results highlight QACN-AC's superiority over conventional RL, evidenced by a 1.25x higher control performance and a 760x reduction in the number of required parameters.
Over-the-air federated learning (OTA FL) provides a joint computation and communication approach to design FL systems with improved efficiency. By leveraging the superposition property of wireless channels, OTA FL enables the automatic aggregation of intermediate parameters-such as gradients-across a large number of clients, significantly reducing communication overhead while concurrently enhancing transmission privacy. However, gradient aggregation requires all clients to use identical model architectures, a condition often impractical in real-world scenarios due to variations in client hardware and computational capabilities. This mismatch can lead to scalability issues and system incompatibilities. To address this challenge, we propose a model-agnostic method based on model prototypes that enables collaborative training across clients with heterogeneous models. The proposed method bypasses the requirements of the conventional model weight/gradient updates with prototype vector aggregation, without requiring the model structures of all clients to be identical. To the best of our knowledge, the proposed method is the first to explore the prototypical model-heterogeneous OTA FL with desirable training performance and extremely low communication cost. We conducted extensive experiments to verify the efficacy of the proposed method. The results show that our approach not only significantly reduces communication overhead but also exploits the superior capabilities of large models to enhance the performance of smaller models.
Full-duplex (FD) communication improves spectral efficiency by allowing simultaneous transmission and reception on the same frequency band. This paper analyzes a two-tier network consisting of macrocells and picocells, where base stations (BSs) operate in FD mode while users operate in half-duplex (HD) mode. However, interference remains a major challenge in these systems, significantly impacting coverage probability. To enhance network performance, we incorporate user offloading and resource partitioning. Offloading redistributes users from macrocells to picocells to balance the network load. However, offloaded users often experience lower SINR compared to those connected to macrocells. Resource partitioning is applied to mitigate this SINR degradation and reduce interference. This method allocates a fraction of time or frequency resources where macrocells remain inactive, allowing picocells to serve users with less interference. We derive analytical expressions for the downlink coverage probability, considering the effects of offloading, resource partitioning, and key network parameters. Our results indicate that load balancing by itself is insufficient; however, when resource partitioning is combined with offloading, DL coverage probability is significantly improved.
The Kelly or proportional allocation mechanism is a simple and efficient auction-based decentralized resource allocation scheme that distributes an infinitely divisible resource proportionally to the agents' bids. When agents are aware of the allocation mechanism, their interactions form a game. The properties of its Nash equilibria are well understood under the simplifying assumption of unbounded budgets. In this paper, we analyze the game in a more realistic budget-constrained setting, motivated by its optimality in terms of the liquid price of anarchy (LPoA). Specifically, we establish a sufficient condition for the uniqueness of the Nash equilibrium and design a distributed sequential learning procedure that provably converges to the equilibrium. In particular, our sufficient condition holds when the payoff functions of the agents are of the proportional fair type in the allocated fraction. Finally, extensive numerical experiments shed light on the interplay between the heterogeneity of the payoff functions and the agents' budgets.
We investigate the role of channel access schemes in enhancing the timeliness of status updates in sensor networks. Specifically, we model the large-scale sensor network as a Poisson cellular network and derive the network average Age of Information (AoI) under two channel access schemes: random and scheduled access. Our findings reveal that the e!ectiveness of these schemes is influenced by the signal-to-interference ratio (SIR) decoding threshold, which often reflects the length of communication data. For short-packet communications, performance di!erences among various channel access strategies are minimal, and the gains from scheduling are limited; in fact, some simple scheduling strategies may not outperform basic random strategies. Conversely, scheduled access schemes demonstrate a distinct performance advantage for long-packet communications. The round robin scheme consistently yields the best performance among the four protocols we examined-slotted ALOHA, frame slotted ALOHA, random scheduling, and round robin scheduling. This is due to its ability to mitigate intra-cell interference and regularize both status updates and channel access periods for each sensor, which is particularly beneficial in reducing AoI.
