arXiv subjects

Anders E. Kalør

Publications and source records attributed to Anders E. Kalør.

At least 19 recordsLinked to original sources

arXiv2026

Conformal Decode-or-Erase: Certified Spiking Decoding for Short-Packet URLLC

Ultra-reliable low-latency communication (URLLC) must deliver short packets within a hard deadline at low error probability. A conventional receiver waits for the full packet before deciding, spending the full latency and energy even though many packets are resolvable well before the deadline. Committing early without a reliability guarantee, however, risks a silent wrong delivery, so the receiver is left choosing between wasted resources and uncontrolled errors. We propose Conformal Decode-or-Erase (CoDE), a spiking neural network (SNN) receiver that resolves this tension. The SNN reads one symbol per channel use and forms, at predetermined checkpoints, a set of candidate messages that provably contains the true one with a prescribed probability. CoDE commits once the set narrows to a singleton and otherwise declares an erasure that triggers hybrid automatic repeat request (HARQ) retransmission. A wrong commit means the true message fell outside that singleton. Hence, the prediction set provides an upper bound on the undetected error rate in a distribution-free manner and for any pretrained SNN and any calibration size. Simulations confirm reliability at roughly half a fixed-length decoder's latency and compute.

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arXiv2025

Data Sourcing Random Access using Semantic Queries for Massive IoT Scenarios

Efficiently retrieving relevant data from massive Internet of Things (IoT) networks is essential for downstream tasks such as machine learning. This paper addresses this challenge by proposing a novel data sourcing protocol that combines semantic queries and random access. The key idea is that the destination node broadcasts a semantic query describing the desired information, and the sensors that have data matching the query then respond by transmitting their observations over a shared random access channel, for example to perform joint inference at the destination. However, this approach introduces a tradeoff between maximizing the retrieval of relevant data and minimizing data loss due to collisions on the shared channel. We analyze this tradeoff under a tractable Gaussian mixture model and optimize the semantic matching threshold to maximize the number of relevant retrieved observations. The protocol and the analysis are then extended to handle a more realistic neural network-based model for complex sensing. Under both models, experimental results in classification scenarios demonstrate that the proposed protocol is superior to traditional random access, and achieves a near-optimal balance between inference accuracy and the probability of missed detection, highlighting its effectiveness for semantic query-based data sourcing in massive IoT networks.

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arXiv2025

Ultra-Low-Latency Edge Inference for Distributed Sensing

There is a broad consensus that artificial intelligence (AI) will be a defining component of the sixth-generation (6G) networks. As a specific instance, AI-empowered sensing will gather and process environmental perception data at the network edge, giving rise to integrated sensing and edge AI (ISEA). Many applications, such as autonomous driving and industrial manufacturing, are latency-sensitive and require end-to-end (E2E) performance guarantees under stringent deadlines. However, the 5G-style ultra-reliable and low-latency communication (URLLC) techniques designed with communication reliability and agnostic to the data may fall short in achieving the optimal E2E performance of perceptive wireless systems. In this work, we introduce an ultra-low-latency (ultra-LoLa) inference framework for perceptive networks that facilitates the analysis of the E2E sensing accuracy in distributed sensing by jointly considering communication reliability and inference accuracy. By characterizing the tradeoff between packet length and the number of sensing observations, we derive an efficient optimization procedure that closely approximates the optimal tradeoff. We validate the accuracy of the proposed method through experimental results, and show that the proposed ultra-Lola inference framework outperforms conventional reliability-oriented protocols with respect to sensing performance under a latency constraint.

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arXiv2025

Black-Box Edge AI Model Selection with Conformal Latency and Accuracy Guarantees

Edge artificial intelligence (AI) will be a central part of 6G, with powerful edge servers supporting devices in performing machine learning (ML) inference. However, it is challenging to deliver the latency and accuracy guarantees required by 6G applications, such as automated driving and robotics. This stems from the black-box nature of ML models, the complexities of the tasks, and the interplay between transmitted data quality, chosen inference model, and the random wireless channel. This paper proposes a novel black-box model selection framework for reliable real-time wireless edge AI designed to meet predefined requirements on both deadline violation probability and expected loss. Leveraging conformal risk control and non-parametric statistics, our framework intelligently selects the optimal model combination from a collection of black-box feature-extraction and inference models of varying complexities and computation times. We present both a fixed (relying on channel statistics) and a dynamic (channel-adaptive) model selection scheme. Numerical results validate the framework on a deadline-constrained image classification task while satisfying a maximum misclassification probability requirement. These results indicate that the proposed framework has the potential to provide reliable real-time edge AI services in 6G.

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arXiv2025

Content-based Wake-up for Energy-efficient and Timely Top-k IoT Sensing Data Retrieval

Energy efficiency and information freshness are key requirements for sensor nodes serving Industrial Internet of Things (IIoT) applications, where a sink node collects informative and fresh data before a deadline, e.g., to control an external actuator. Content-based wake-up (CoWu) activates a subset of nodes that hold data relevant for the sink's goal, thereby offering an energy-efficient way to attain objectives related to information freshness. This paper focuses on a scenario where the sink collects fresh information on top-k values, defined as data from the nodes observing the k highest readings at the deadline. We introduce a new metric called top-k Query Age of Information (k-QAoI), which allows us to characterize the performance of CoWu by considering the characteristics of the physical process. Further, we show how to select the CoWu parameters, such as its timing and threshold, to attain both information freshness and energy efficiency. The numerical results reveal the effectiveness of the CoWu approach, which is able to collect top-k data with higher energy efficiency while reducing k-QAoI when compared to round-robin scheduling, especially when the number of nodes is large and the required size of k is small.

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arXiv2024

Wireless 6G Connectivity for Massive Number of Devices and Critical Services

Compared to the generations up to 4G, whose main focus was on broadband and coverage aspects, 5G has expanded the scope of wireless cellular systems towards embracing two new types of connectivity: massive machine-type communication (mMTC) and ultra-reliable low-latency communications (URLLC). This paper discusses the possible evolution of these two types of connectivity within the umbrella of 6G wireless systems. The paper consists of three parts. The first part deals with the connectivity for a massive number of devices. While mMTC research in 5G predominantly focuses on the problem of uncoordinated access in the uplink for a large number of devices, the traffic patterns in 6G may become more symmetric, leading to closed-loop massive connectivity. One of the drivers for this is distributed learning/inference. The second part of the paper discusses the evolution of wireless connectivity for critical services. While latency and reliability are tightly coupled in 5G, 6G will support a variety of safety critical control applications with different types of timing requirements, as evidenced by the emergence of metrics related to information freshness and information value. Additionally, ensuring ultra-high reliability for safety critical control applications requires modeling and estimation of the tail statistics of the wireless channel, queue length, and delay. The fulfillment of these stringent requirements calls for the development of novel AI-based techniques, incorporating optimization theory, explainable AI, generative AI and digital twins. The third part analyzes the coexistence of massive connectivity and critical services. We will consider scenarios in which a massive number of devices need to support traffic patterns of mixed criticality. This is followed by a discussion about the management of wireless resources shared by services with different criticality.

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arXiv2024

Unified Timing Analysis for Closed-Loop Goal-Oriented Wireless Communication

Goal-oriented communication has become one of the focal concepts in sixth-generation communication systems owing to its potential to provide intelligent, immersive, and real-time mobile services. The emerging paradigms of goal-oriented communication constitute closed loops integrating communication, computation, and sensing. However, challenges arise for closed-loop timing analysis due to multiple random factors that affect the communication/computation latency, as well as the heterogeneity of feedback mechanisms across multi-modal sensing data. To tackle these problems, we aim to provide a unified timing analysis framework for closed-loop goal-oriented communication (CGC) systems over fading channels. The proposed framework is unified as it considers computation, compression, and communication latency in the loop with different configurations. To capture the heterogeneity across multi-modal feedback, we categorize the sensory data into the periodic-feedback and event-triggered, respectively. We formulate timing constraints based on average and tail performance, covering timeliness, jitter, and reliability of CGC systems. A method based on saddlepoint approximation is proposed to obtain the distribution of closed-loop latency. The results show that the modified saddlepoint approximation is capable of accurately characterizing the latency distribution of the loop with analytically tractable expressions. This sets the basis for low-complexity co-design of communication and computation.

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arXiv2024

Prediction of Rare Channel Conditions using Bayesian Statistics and Extreme Value Theory

Estimating the probability of rare channel conditions is a central challenge in ultra-reliable wireless communication, where random events, such as deep fades, can cause sudden variations in the channel quality. This paper proposes a sample-efficient framework for predicting the statistics of such events by utilizing spatial dependency between channel measurements acquired from various locations. The proposed framework combines radio maps with non-parametric models and extreme value theory (EVT) to estimate rare-event channel statistics under a Bayesian formulation. The framework can be applied to a wide range of problems in wireless communication and is exemplified by rate selection in ultra-reliable communications. Notably, besides simulated data, the proposed framework is also validated with experimental measurements. The results in both cases show that the Bayesian formulation provides significantly better results in terms of throughput compared to baselines that do not leverage measurements from surrounding locations. It is also observed that the models based on EVT are generally more accurate in predicting rare-event statistics than non-parametric models, especially when only a limited number of channel samples are available. Overall, the proposed methods can significantly reduce the number of measurements required to predict rare channel conditions and guarantee reliability.

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arXiv2024

Experimental Study of Spatial Statistics for Ultra-Reliable Communications

This paper presents an experimental validation for prediction of rare fading events using channel distribution information (CDI) maps that predict channel statistics from measurements acquired at surrounding locations using spatial interpolation. Using experimental channel measurements from 127 locations, we demonstrate the use case of providing statistical guarantees for rate selection in ultra-reliable low-latency communication (URLLC) using CDI maps. By using only the user location and the estimated map, we are able to meet the desired outage probability with a probability between 93.6-95.6% targeting 95%. On the other hand, a model-based baseline scheme that assumes Rayleigh fading meets the target outage requirement with a probability of 77.2%. The results demonstrate the practical relevance of CDI maps for resource allocation in URLLC.

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arXiv2023

Timely and Massive Communication in 6G: Pragmatics, Learning, and Inference

5G has expanded the traditional focus of wireless systems to embrace two new connectivity types: ultra-reliable low latency and massive communication. The technology context at the dawn of 6G is different from the past one for 5G, primarily due to the growing intelligence at the communicating nodes. This has driven the set of relevant communication problems beyond reliable transmission towards semantic and pragmatic communication. This paper puts the evolution of low-latency and massive communication towards 6G in the perspective of these new developments. At first, semantic/pragmatic communication problems are presented by drawing parallels to linguistics. We elaborate upon the relation of semantic communication to the information-theoretic problems of source/channel coding, while generalized real-time communication is put in the context of cyber-physical systems and real-time inference. The evolution of massive access towards massive closed-loop communication is elaborated upon, enabling interactive communication, learning, and cooperation among wireless sensors and actuators.

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arXiv2023

Goal-Oriented Scheduling in Sensor Networks with Application Timing Awareness

Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest in pragmatic , or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multiple clients with different, and potentially conflicting, objectives. We capture the goal-oriented aspect through the metric of Value of Information (VoI), which considers the estimation of the remote process as well as the timing constraints. However, the most common definition of VoI is simply the Mean Square Error (MSE) of the whole system state, regardless of the relevance for a specific client. Our work aims to overcome this limitation by including different summary statistics, i.e., value functions of the state, for separate clients, and a diversified query process on the client side, expressed through the fact that different applications may request different functions of the process state at different times. A query-aware Deep Reinforcement Learning (DRL) solution based on statically defined VoI can outperform naive approaches by 15-20%.

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arXiv2023

Over-the-Air Multi-View Pooling for Distributed Sensing

Sensing is envisioned as a key network function of the 6G mobile networks. Artificial intelligence (AI)-empowered sensing fuses features of multiple sensing views from devices distributed in edge networks for the edge server to perform accurate inference. This process, known as multi-view pooling, creates a communication bottleneck due to multi-access by many devices. To alleviate this issue, we propose a task-oriented simultaneous access scheme for distributed sensing called Over-the-Air Pooling (AirPooling). The existing Over-the-Air Computing (AirComp) technique can be directly applied to enable Average-AirPooling by exploiting the waveform superposition property of a multi-access channel. However, despite being most popular in practice, the over-the-air maximization, called Max-AirPooling, is not AirComp realizable as AirComp addresses a limited subset of functions. We tackle the challenge by proposing the novel generalized AirPooling framework that can be configured to support both Max- and Average-AirPooling by controlling a configuration parameter. The former is realized by adding to AirComp the designed pre-processing at devices and post-processing at the server. To characterize the end-to-end sensing performance, the theory of classification margin is applied to relate the classification accuracy and the AirPooling error. Furthermore, the analysis reveals an inherent tradeoff of Max-AirPooling between the accuracy of the pooling-function approximation and the effectiveness of noise suppression. Using the tradeoff, we optimize the configuration parameter of Max-AirPooling, yielding a sub-optimal closed-form method of adaptive parametric control. Experimental results obtained on real-world datasets show that AirPooling provides sensing accuracies close to those achievable by the traditional digital air interface but dramatically reduces the communication latency.

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arXiv2023

A Decentralized Policy for Minimization of Age of Incorrect Information in Slotted ALOHA Systems

The Age of Incorrect Information (AoII) is a metric that can combine the freshness of the information available to a gateway in an Internet of Things (IoT) network with the accuracy of that information. As such, minimizing the AoII can allow the operators of IoT systems to have a more precise and up-to-date picture of the environment in which the sensors are deployed. However, most IoT systems do not allow for centralized scheduling or explicit coordination, as sensors need to be extremely simple and consume as little power as possible. Finding a decentralized policy to minimize the AoII can be extremely challenging in this setting. This paper presents a heuristic to optimize AoII for a slotted ALOHA system, starting from a threshold-based policy and using dual methods to converge to a better solution. This method can significantly outperform state-independent policies, finding an efficient balance between frequent updates and a low number of packet collisions.

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arXiv2022

Query Timing Analysis for Content-based Wake-up Realizing Informative IoT Data Collection

Information freshness and high energy-efficiency are key requirements for sensor nodes serving Industrial Internet of Things (IIoT) applications, where a sink node must collect informative data before a deadline to control an external element. Pull-based communication is an interesting approach for optimizing information freshness and saving wasteful energy. To this end, we apply Content-based Wake-up (CoWu), in which the sink can activate a subset of nodes observing informative data at the time that wake-up signal is received. In this case, the timing of the wake-up signal plays an important role: early transmission leads to high reliability in data collection, but the received data may become obsolete by the deadline, while later transmission ensures a higher timeliness of the sensed data, but some nodes might not manage to communicate their data before the deadline. This letter investigates the timing for data collection using CoWu and characterizes the gain of CoWu. The obtained numerical results show that CoWu improves accuracy, while reducing energy consumption by about 75% with respect to round-robin scheduling.

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arXiv2022

Delivering Ultra-Reliable Low-Latency Communications via Statistical Radio Maps

High reliability guarantees for Ultra-Reliable Low-Latency Communications (URLLC) require accurate knowledge of channel statistics, used as an input for rate selection. Exploiting the spatial consistency of channel statistics arises as a promising solution, allowing a base station to predict the propagation conditions and select the communication parameters for a new user from samples collected from previous users of the network. Based on this idea, this article provides a timely framework to exploit long-range channel spatial correlation through so-called statistical radio maps, enabling URLLC communications with given statistical guarantees. The framework is exemplified by predicting the channel capacity distribution both in a location-based radio map and in a latent space rendered by a channel chart, the latter being a localization-free approach based on channel state information (CSI). It is also shown how to use the maps to select the transmission rate in a new location that achieves a target level of reliability. Finally, several future directions and research challenges are also discussed.

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arXiv2022

Timely Monitoring of Dynamic Sources with Observations from Multiple Wireless Sensors

Age of Information (AoI) has recently received much attention due to its relevance in IoT sensing and monitoring applications. In this paper, we consider the problem of minimizing the AoI in a system in which a set of sources are observed by multiple sensors in a many-to-many relationship, and the probability that a sensor observes a source depends on the state of the source. This model represents many practical scenarios, such as the ones in which multiple cameras or microphones are deployed to monitor objects moving in certain areas. We formulate the scheduling problem as a Markov Decision Process, and show how the age-optimal scheduling policy can be obtained. We further consider partially observable variants of the problem, and devise approximate policies for large state spaces. Our evaluations show that the approximate policies work well in the considered scenarios, and that the fact that sensors can observe multiple sources is beneficial, especially when there is high uncertainty of the source states.

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arXiv2022

Predictive Rate Selection for Ultra-Reliable Communication using Statistical Radio Maps

This paper proposes exploiting the spatial correlation of wireless channel statistics beyond the conventional received signal strength maps by constructing statistical radio maps to predict any relevant channel statistics to assist communications. Specifically, from stored channel samples acquired by previous users in the network, we use Gaussian processes (GPs) to estimate quantiles of the channel distribution at a new position using a non-parametric model. This prior information is then used to select the transmission rate for some target level of reliability. The approach is tested with synthetic data, simulated from urban micro-cell environments, highlighting how the proposed solution helps to reduce the training estimation phase, which is especially attractive for the tight latency constraints inherent to ultra-reliable low-latency (URLLC) deployments.

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arXiv2022

Common Message Acknowledgments: Massive ARQ Protocols for Wireless Access

Massive random access plays a central role in supporting the Internet of Things (IoT), where a subset of a large population of users simultaneously transmit small packets to a central base station. While there has been much research on the design of protocols for massive access in the uplink, the problem of providing message acknowledgments back to the users has been somewhat neglected. Reliable communication needs to rely on two-way communication for acknowledgement and retransmission. Nevertheless, because of the many possible subsets of active users, providing acknowledgments requires a significant amount of bits. Motivated by this, we define the problem of massive ARQ (Automatic Retransmission reQuest) protocol and introduce efficient methods for joint encoding of multiple acknowledgements in the downlink. The key idea towards reducing the number of bits used for massive acknowledgements is to allow for a small fraction of false positive acknowledgments. We analyze the implications of this approach and the impact of acknowledgment errors in scenarios with massive random access. Finally, we show that these savings can lead to a significant increase in the reliability when retransmissions are allowed since it allows the acknowledgment message to be transmitted more reliably using a much lower rate.

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