This paper investigates the maximization of the achievable rate for users served by a high-altitude platform (HAP) acting as a flying base station in the downlink of rate-splitting multiple access (RSMA)-based terahertz (THz) communication systems. Considering the dynamic and uncertain environment caused by user mobility and molecular absorption effects, we propose a generative diffusion model (DM)-based deep reinforcement learning approach to address this challenge. The problem is formulated as a Markov decision process, aiming to maximize the long-term achievable rate for all users by jointly optimizing power allocation and the common rate splitting ratio. Moreover, the generative DM significantly improves the decision-making capabilities of a deep reinforcement learning algorithm, namely the deep deterministic policy gradient (DDPG). Experimental simulations demonstrate the effectiveness of the proposed DM-DDPG algorithm compared to alternative schemes.

In this paper, we propose a Sustainable Sensing Federated Learning (S2FL) system where Internet-of-Things (IoT) devices and mobile users harvest energy wirelessly to perform data sensing, local Federated Learning (FL) training, and model update transmissions. We formulate a joint optimization problem that considers transmission power, CPU frequency, bandwidth allocation, and time allocation to maximize long-term learning efficiency. To solve this complex and dynamic problem, we develop an algorithm that integrates Deep Reinforcement Learning (DRL) with optimization techniques, leveraging the Deep Deterministic Policy Gradient (DDPG) algorithm for time allocation and a Lagrangian-Based Block Coordinate Descent (BCD) Method for per-slot resource optimization. Simulation results demonstrate that our proposed DDPG-based DRL-S2FL algorithm significantly outperforms benchmark schemes, achieving up to 15 % higher average reward compared to Deep Q-Networks (DQN) and 40 % greater performance than Random strategies. This work highlights the effectiveness of combining advanced optimization techniques with deep reinforcement learning to enhance federated learning performance in dynamic wireless environments.

Federated learning (FL) has found many successes in wireless communications; however, the implementation of FL has been hindered by the energy limitation of mobile devices (MDs) and the availability of training data at MDs. Wireless power transfer (WPT) and mobile crowdsensing (MCS) are promising technologies that can be leveraged to power energy-limited MDs and acquire data for learning tasks. How to integrate WPT and MCS towards sustainable FL solutions is a research topic entirely missing from the open literature. This work for the first time investigates a resource allocation problem in collaborative sensing-assisted sustainable FL (S2FL) networks with the goal of minimizing the total completion time. In particular, we investigate a practical harvesting-sensing-training-transmitting protocol in which energy-limited MDs first harvest energy from RF signals, use it to gain a reward for user participation, sense the training data from the environment, train the local models at MDs, and transmit the model updates to the edge server. The total completion time minimization problem of jointly optimizing power transfer, transmit power allocation, data sensing, bandwidth allocation, local model training, and data transmission is complicated due to the non-convex objective function, highly non-convex constraints, and strongly coupled variables. In order to solve that problem, we apply the decomposition technique and develop a computationally-efficient path-following algorithm to obtain the solution. In particular, inner convex approximations are developed for the resource allocation subproblem, and the subproblems are performed alternatively in an iterative fashion. Simulation results are provided to evaluate the effectiveness of the proposed S2FL algorithm in reducing the completion time up to 21.45% in comparison with other benchmark schemes. Further, we investigate an extension of our work from frequency division multiple access (FDMA) to non-orthogonal multiple access (NOMA) and show that NOMA can speed up the total completion time 8.36% on average of the considered FL system.

Federated learning (FL) has found numerous applications in wireless and mobile networks thanks to its distinctive features. However, efficient FL networks require to address the energy limitation of FL users. Exploited the flexible deployment and agile mobility of unmanned aerial vehicles (UAVs), this work proposes to dispatch the UAV with edge computing capabilities as an aerial energy source to wirelessly power FL users and as an aerial server for model aggregation. In this regard, we investigate a resource allocation problem that minimizes the energy consumption of FL users and the aerial server. To resolve the nonconvexity of the formulated problem, we propose to decompose the entire set of variables into three blocks, and then develop an iterative algorithm. Simulations results are presented to show that our proposed algorithm significantly outperforms several benchmarks.

In existing computing systems, such as edge computing and cloud computing, several emerging applications and practical scenarios are mostly unavailable or only partially implemented. To overcome the limitations that restrict such applications, the development of a comprehensive computing paradigm has garnered attention in both academia and industry. However, a gap exists in the literature, owing to the scarce research, and a comprehensive computing paradigm is yet to be systematically designed and reviewed. This study introduces a novel concept, calledaerial computing, via the amalgamation of aerial radio access networks and edge computing, which attempts to bridge the gap. Specifically, first, we propose a novel comprehensive computing architecture that is composed of low-altitude computing (LAC), high-altitude computing (HAC), and satellite computing platforms, along with conventional computing systems. We determine that aerial computing offers several desirable attributes: global computing service, better mobility, higher scalability and availability, and simultaneity. Second, we comprehensively discuss key technologies that facilitate aerial computing, including energy refilling, edge computing, network softwarization, frequency spectrum, multiaccess techniques, artificial intelligence, and big data. In addition, we discuss vertical domain applications (e.g., smart cities, smart vehicles, smart factories, and smart grids) supported by aerial computing. Finally, we highlight several challenges that need to be addressed and their possible solutions.

Abstract Large service companies run complex customer service processes to provide communication services to their customers. The flawless execution of these processes is essential because customer service is an important differentiator. They must also be able to predict if processes will complete successfully or run into exceptions in order to intervene at the right time, preempt problems and maintain customer service. Business process data are sequential in nature and can be very diverse. Thus, there is a need for an efficient sequential forecasting methodology that can cope with this diversity. This paper proposes two approaches, a sequential k nearest neighbour and an extension of Markov models both with an added component based on sequence alignment. The proposed approaches exploit temporal categorical features of the data to predict the process next steps using higher order Markov models and the process outcomes using sequence alignment technique. The diversity aspect of the data is also added by considering subsets of similar process sequences based on k nearest neighbours. We have shown, via a set of experiments, that our sequential k nearest neighbour offers better results when compared with the original ones; our extension Markov model outperforms random guess, Markov models and hidden Markov models.