Federated Learning (FL) has emerged as a promising framework for distributed learning, but its growing complexity has led to significant energy consumption, particularly from computations on the client side. This challenge is especially critical in energy-harvesting FL (EHFL) systems, where device availability fluctuates due to limited and time-varying energy resources. We propose FedBacys, a battery-aware FL framework that introduces cyclic client participation based on users' battery levels to cope with these issues. FedBacys enables clients to save energy and strategically perform local training just before their designated transmission time by clustering clients and scheduling their involvement sequentially. This design minimizes redundant computation, reduces system-wide energy usage, and improves learning stability. Our experiments demonstrate that FedBacys outperforms existing approaches in terms of energy efficiency and performance consistency, exhibiting robustness even under non-i.i.d. training data distributions and with very infrequent battery charging. This work presents the first comprehensive evaluation of cyclic client participation in EHFL, incorporating both communication and computation costs into a unified, resource-aware scheduling strategy.

In this paper, we analyze the performance of a wireless caching system with heterogeneous traffic and relaying capabilities satisfying secrecy constraints for one of two receiving users. In this setup, the second user has no secrecy requirements and receives cacheable content either from the relay helper or the core network through a wireless base station. The wireless relay helper can assist both users since it is equipped with finite storage that is split into cacheable and non-cacheable storage. Concurrently, a passive eavesdropper tries to overhear transmissions to the user with secrecy requirements. Consequently, we examine how this relay’s storage split and the eavesdropper affect the performance of the average throughput and delay of the system as the transmission powers, the relay’s transmission probability, and the relay’s cache size vary. © 2023 IEEE.

This paper investigates goal-oriented communication for remote estimation of multiple Markov sources in resource-constrained networks. An agent decides the updating times of the sources and transmits the packet to a remote destination over an unreliable channel with delay. The destination is tasked with source reconstruction for actuation. We utilize the metric cost of actuation error (CAE) to capture the state-dependent actuation costs. We aim for a sampling policy that minimizes the long-term average CAE subject to an average resource constraint. We formulate this problem as an average-cost constrained Markov Decision Process (CMDP) and relax it into an unconstrained problem by utilizing Lyapunov drift techniques. Then, we propose a low-complexity drift-plus-penalty (DPP) policy for systems with known source/channel statistics and a Lyapunov optimization-based deep reinforcement learning (LO-DRL) policy for unknown environments. Our policies significantly reduce the number of uninformative transmissions by exploiting the timing of the important information.

Reward machines inform reinforcement learning agents about the reward structure of the environment and often drastically speed up the learning process. However, reward machines only accept Boolean features such as robot-reached-gold. Consequently, many inherently numeric tasks cannot profit from the guidance offered by reward machines. To address this gap, we aim to extend reward machines with numeric features such as distance-to-gold. For this, we present two types of reward machines: numeric-Boolean and numeric. In a numeric-Boolean reward machine, distance-to-gold is emulated by two Boolean features distance-to-gold-decreased and robot-reached-gold. In a numeric reward machine, distance-to-gold is used directly alongside the Boolean feature robot-reached-gold. We compare our new approaches to a baseline reward machine in the Craft domain, where the numeric feature is the agent-to-target distance. We use cross-product Q-learning, Q-learning with counter-factual experiences, and the options framework for learning. Our experimental results show that our new approaches significantly outperform the baseline approach. Extending reward machines with numeric features opens up new possibilities of using reward machines in inherently numeric tasks.

At the forefront of cutting-edge technologies, this text provides a comprehensive treatment of a crucial network performance metric, ushering in new opportunities for rethinking the whole design of communication systems. Detailed exposition of the communication and network theoretic foundations of Age of Information (AoI) gives the reader a solid background, and discussion of the implications on signal processing and control theory shed light on the important potential of recent research. The text includes extensive real-world applications of this vital metric, including caching, the Internet of Things (IoT), and energy harvesting networks. The far-reaching applications of AoI include networked monitoring systems, cyber-physical systems such as the IoT, and information-oriented systems and data analytics applications ranging from the stock market to social networks. The future of this exciting subject in 5G communication systems and beyond make this a vital resource for graduate students, researchers and professionals.

One of the fundamental challenges in 5G and beyond technologies is to support short packet transmissions while ensuring ultra-reliable communication. Due to the distributed nature of the networks, such as machine-to-machine (M2M) communications, interference is unavoidable. The impact of interference on the system’s performance must be better understood when users are constrained to transmit short packets. In addition, users’ traffic is bursty. Thus, they may not always have data to send. This work considers a two-user Z-interference channel (Z-IC) under Rayleigh fading. The work characterizes the stability region corresponding to prominent interference mitigation schemes such as treating interference as noise, successive interference cancellation, and joint decoding schemes using the finite block-length information theory framework. The developed results consider the packet length, rate, and underlying channel model. Evaluating stability region involves determining the probability of successful decoding for the various interference mitigation techniques. The different probabilities of successful decoding are characterized for various interference mitigation techniques. These results are not explored in the existing literature in the context of Z-IC. The developed results also help to explore the impact of interference on average delay and the average age of information for various interference mitigation techniques.

With the advent of 5G technology, the notion of latency got a prominent role in wireless connectivity, serving as a proxy term for addressing the requirements for real-time communication. As wireless systems evolve toward 6G, the ambition to immerse the digital into physical reality will increase. Besides making the real-time requirements more stringent, this immersion will bring the notions of time, simultaneity, presence, and causality to a new level of complexity A growing body of research points out that latency is insufficient to parameterize all real-time requirements. Notably, one such requirement that received significant attention is information freshness, defined through the Age of Information (AoI) and its derivatives. In general, the metrics derived from a conventional black-box approach to communication network design are not representative of new distributed paradigms, such as sensing, learning, or distributed consensus. The objective of this article is to investigate the general notion of timing in wireless communication systems and networks, and its relation to effective information generation, processing, transmission, and reconstruction at the senders and receivers. We establish a general statistical framework of timing requirements in wireless communication systems, which subsumes both latency and AoI. The framework is made by associating a timing component with the two basic statistical operations: decision and estimation. We first use the framework to present a representative sample of the existing works that deal with timing in wireless communication. Next, it is shown how the framework can be used with different communication models of increasing complexity, starting from the basic Shannon one-way communication model and arriving at communication models for consensus, distributed learning, and inference. Overall, this article fills an important gap in the literature by providing a systematic treatment of various timing measures in wireless communication and sets the basis for design and optimization for the next-generation real-time systems.

Future wireless networks will be characterized by heterogeneous traffic requirements. Examples can be low-latency or minimum-througput requirements. Therefore, the network has to adjust to different needs. Usually, users with low-latency requirements have to deliver their demand within a specific time frame, i.e., before a deadline, and they coexist with throughput oriented users. In addition, mobile devices have a limited-power budget and therefore, a power-efficient scheduling scheme is required by the network. In this work, we cast a stochastic network optimization problem for minimizing the packet drop rate while guaranteeing a minimum throughput and taking into account the limited-power capabilities of the users. We apply tools from Lyapunov optimization theory in order to provide an algorithm, named Dynamic Power Control (DPC) algorithm, that solves the formulated problem in real time. It is proved that the DPC algorithm gives a solution arbitrarily close to the optimal one. Simulation results show that our algorithm outperforms the baseline Largest-Debt-First (LDF) algorithm for short deadlines and multiple users.

In this work, we study age-optimal scheduling with stability constraints in a multiple access channel with two heterogeneous source nodes transmitting to a common destination. The first node is connected to a power grid and it has randomly arriving data packets. Another energy harvesting (EH) sensor monitors a stochastic process and sends status updates to the destination. We formulate an optimization problem that aims at minimizing the average age of information (AoI) of the EH node subject to the queue stability condition of the grid-connected node. First, we consider a Probabilistic Random Access (PRA) policy where both nodes make independent transmission decisions based on some fixed probability distributions. We show that with this policy, the average AoI is equal to the average peak AoI, if the EH node only sends freshly generated samples. In addition, we derive the optimal solution in closed form, which reveals some interesting properties of the considered system. Furthermore, we consider a Drift-Plus-Penalty (DPP) policy and develop AoI-optimal and peak-AoI-optimal scheduling algorithms using the Lyapunov optimization theory. Simulation results show that the DPP policy outperforms the PRA policy in various scenarios, especially when the destination node has low multi-packet reception capabilities.