Federated Learning (FL) on low earth orbit (LEO) satellites represents a promising frontier for on-orbit edge intelligence. However, the inherent network dynamics and heterogeneity of datasets and resource across satellites pose challenges to efficient on-orbit FL. In this work, we model client selection and inter-satellite routing as a joint optimization problem. We derive and minimize an upper bound of the global empirical loss as the objective function, to enable fast convergence. We model the constraints of inter-satellite routing via time-varying graphs and network flow theory. We propose both exact and approximate solutions for the joint optimization problem. In addition, we formalize and prove the convergence property of our approach. Last, by simulation we demonstrate the efficiency and superiority of the proposed scheme for realistic satellite networking scenarios.

The integration of Artificial Intelligence (AI) and Internet of Things (IoT) devices has given rise to IoAT, promising transformative applications across various domains. Federated Learning (FL) and Split Learning (SL) are pivotal in harnessing the potential of IoAT, enabling decentralized model training while preserving data privacy. However, the heterogeneity and scalability challenges in IoAT environments necessitate advanced frameworks. In this paper, we introduce an integration of block-wise Neural Architecture Search (NAS) with a multi-partition SFL framework, called NASFLY. This approach uses only devices for actual model training and offers flexible scaling of model fragments to accommodate a wide range of device capabilities. In particular, NASFLY allows devices to utilize idle periods during the lengthy SFL forward and backward propagation phases. This is achieved by employing auxiliary model components dispatched from the server to conduct local supernet elastification using the device’s local dataset. Our method alternates between SFL for backbone network optimization and the local supernet elastification within NAS, where knowledge from the backbone network is transferred to the local supernet branches using distillation techniques. We also propose a device clustering algorithm to further improve training efficiency. Our experimental results demonstrate that this methodology significantly enhances device utilization and improves training efficiency compared with the conventional SFL.

Passively cooled base stations (PCBSs) offer low deployment cost and energy consumption for the next generation networks. By its nature, however, dealing with the thermal issue becomes crucial. For an outdoor PCBS, a major challenge is that the heat dissipation is uncertain over time. We address this online temperature scheduling problem with uncertain parameters via adjustable robust optimization (ARO) embedded into a re-optimization framework. In each optimization instance, temperature pre-scheduling is done to achieve solution robustness, looking ahead into forthcoming time slots. The solution is adaptive with respect to the gradually realized heat dissipation. Interestingly, we prove that the robust temperature pre-scheduling problem can be addressed via solving a compact linear program (LP), even though the number of possible realizations of heat dissipation is infinite. Simulation results show that our algorithm achieves robustness as well as very good average performance.

Passively cooled base stations (PCBSs) are highly relevant for achieving better efficiency in cost and energy. However, dealing with the thermal issue via load management, particularly for outdoor deployment of PCBS, becomes crucial. This is a challenge because the heat dissipation efficiency is subject to (uncertain) fluctuation over time. Moreover, load management is an online decision-making problem by its nature. In this paper, we demonstrate that a reinforcement learning (RL) approach, specifically Soft Actor-Critic (SAC), enables to make a PCBS stay cool. The proposed approach has the capability of adapting the PCBS load to the time-varying heat dissipation. In addition, we propose a denial and reward mechanism to mitigate the risk of overheating from the exploration such that the proposed RL approach can be implemented directly in a practical environment, i.e., online RL. Numerical results demonstrate that the learning approach can achieve as much as 88.6% of the global optimum. This is impressive, as our approach is used in an online fashion to perform decision-making without the knowledge of future heat dissipation efficiency, whereas the global optimum is computed assuming the presence of oracle that fully eliminates uncertainty. This paper pioneers the approach to the online PCBSs load management problem.

In multi-access edge computing (MEC) systems, there are multiple local cache servers caching contents to satisfy the users’ requests, instead of letting the users download via the remote cloud server. In this paper, a multi-cell content scheduling problem (MCSP) in MEC systems is considered. Taking into account jointly the freshness of the cached contents and the traffic data costs, we study how to schedule content updates along time in a multi-cell setting. Different from single-cell scenarios, a user may have multiple candidate local cache servers, and thus the caching decisions in all cells must be jointly optimized. We first prove that MCSP is NP-hard, then we formulate MCSP using integer linear programming, by which the optimal scheduling can be obtained for small-scale instances. For problem solving of large scenarios, via a mathematical reformulation, we derive a scalable optimization algorithm based on repeated column generation. Our performance evaluation shows the effectiveness of the proposed algorithm in comparison to an off-the-shelf commercial solver and a popularity-based caching.

Caching popular contents at cell edge has been recognized as a promising way to facilitate rapid content delivery and alleviate backhaul burden. The content popularity is greatly influenced by recommendations by content providers. In this paper, we leverage this fact to jointly optimize caching and recommendation towards higher caching efficiency. We focus on both personalized and incumbent-aware recommendation. The incumbent content refers to the content that a user is currently browsing, resulted by the user's short-term interest. We model and formulate the resulting cache efficiency maximization problem subject to user satisfaction requirements. We prove the NP-hardness of the problem, and reformulate it using integer linear programming, enabling to solve optimally small-scale instances. Based on problem analysis with a graph representation, we derive three polynomial-time algorithms, where the recommendation sub-problem is solved to global optimum. Among these algorithms, the first two are based on sub-modularity, with$1-e^{-1}$approximation guarantee under mild conditions, while the last one is an alternation-based algorithm with fast convergence. Numerical results show the close-to-optimal performance of the proposed algorithms.

Performance degradation due to target deviation by, for example, drift or jitter, presents a significant issue to inter-satellite laser communications. In particular, with periodic acquisition for positioning the satellite receiver, deviation may arise in the time period between two consecutive acquisition operations. We propose a robust optimization approach to the problem. To solve the robust optimization problem, we deploy a process of alternately solving a decision maker’s problem and an adversarial problem. The former optimizes the divergence angle for a subset of the uncertainty set, whereas the latter is used to explore if the subset needs to be augmented. Simulation results show the approach leads to significantly more robust performance than using the divergence angle as if there is no deviation, or other ad-hoc schemes.

In multi-access edge computing (MEC) systems, there are several local cache servers caching contents to satisfy the users’ requests, instead of letting the users download via the remote cloud server. In this paper, a content scheduling problem (CSP) in MEC systems is considered. Taking into account jointly the freshness of the cached contents and the traffic data costs, we study how to schedule content updates along time in a multi-cell setting. Different from single-cell scenarios, a user may have multiple candidate cache servers, and thus all cells and their caching decisions must be jointly taken. We first prove that CSP is $\mathcal{N}\mathcal{P}$-hard, then we formulate CSP using integer linear programming. For problem solving, via a mathematical reformulation, we derive a column generation algorithm embedded into a rounding scheme. Our performance evaluation demonstrates that the solutions obtained are within 0.8% from global optimality.

Deploying Intelligent reflecting surfaces (IRS) to enhance wireless communications is a promising technique. In this paper, we consider resource minimization in a multi-IRS-assisted multi-cell system, subject to finite user data demand. In our problem, the interference generated by a cell is not known a priori, as it depends on the resource consumption level of the cell. Therefore, the cells are highly coupled in interference, and the overall problem is non-convex. To tackle it, we first solve the single-cell problem by an algorithm based on the Majorization-Minimization method. Then, we embed this algorithm into an algorithmic framework to obtain a locally optimal solution to the multi-cell problem. Simulation results demonstrate the benefit of optimal IRS configuration in time-frequency resource utilization in the multi-cell system.

So far, the research of wireless powered communication networks (WPCNs) mainly considers the scenarios with a single radio‐frequency (RF) energy transmitter (ET) and a single sink. However, in practice, there are many applications where multiple ETs and sinks need to be deployed. This study focuses on large‐scale WPCNs having multiple RF ETs and sinks. Specifically, the authors aim to minimise the total energy provision by optimising ETs' transmit powers with the node‐throughput demand and sum‐throughput demand, respectively. For the node‐throughput demand case, they firstly formulate it to be a convex optimisation problem, then transform it to be a linear programming (LP) problem, and finally present a distributed algorithm to obtain the optimal solution. For the sum‐throughput demand case, they firstly formulate it to be a non‐linear optimisation problem, then prove its convexity and finally propose an efficient dual subgradient algorithm to obtain the optimal solution. Simulation results demonstrate that compared to the sum‐throughput demand, imposing the node‐throughput demand can effectively alleviate the throughput unfairness at the cost of increased energy provision; the proposed optimal algorithms can substantially decrease the total energy provision of ETs; the energy provision reduction percentage achieved by their schemes increases as the number of ETs increases.

Radio‐frequency (RF) energy harvesting is one promising technology to power the nodes in wireless networks. This study focuses on large‐scale wireless powered communication networks having multiple RF energy transmitters (ETs) and sinks, which almost have not been investigated previously. The authors aim to optimise the throughput via optimizing the transmit power allocation of ETs subject to a total power budget. Specifically, for the sum‐throughput maximisation (STM) problem, they firstly formulate it to be a non‐linear optimisation problem, then prove its convexity and finally propose an efficient dual sub‐gradient algorithm to solve it. Owing to the throughput unfairness among nodes of the STM approach, they further consider the common‐throughput maximisation (CTM; i.e. the worst node's throughput) and propose a very efficient algorithm for it. This algorithm divides the CTM problem into a master problem and a subproblem. The subproblem of determining the feasibility of a given common‐throughput is solved by transforming it to a linear problem whose optimal solution indicates the feasibility. The master problem of determining the maximal common‐throughput is solved by using the bisection search method. Simulation results demonstrate the effectiveness of the CTM approach to mitigate the throughput unfairness problem at the cost of decreased sum‐throughput.