Satellite communication (SatCom) systems play a vital role in providing global connectivity and enable a wide range of applications, including Internet of Things (IoT) connectivity for remote areas, such as forests and oceans. Two crucial resource allocation challenges in SatCom are beam placement (BP) and frequency assignment (FA) problems, which involve the clique covering (CC) and graph coloring (GC) problems, respectively. Conventional solutions for these problems incur excessive computational cost, which is intractable for classical computers. A promising approach is to formulate these problems using the Ising model, construct their Hamiltonians, and then solve them efficiently by a quantum computer. However, the current quantum computers have very limited hardware and can only handle rather small inputs. To overcome this limitation, we propose a hybrid-quantum-classical-computational pipeline where an efficient hamiltonian reduction method is the key for solving large CC/GC instances. Through experiments on real quantum computers, our reduction method outperforms commercial solutions, allowing quantum annealers to handle significantly larger BP/FA instances while maintaining high probability to achieve feasible solutions and near-optimal performance. Although the inherent hardness of the CC/GC problems cannot be overcome by quantum computing, our research contributes to the early exploration of quantum computing in the context of the complex optimization problems in SatCom systems, particularly in the realm of IoT connectivity for remote areas.

The amount of data generated by Earth observation satellites can be enormous, which poses a great challenge to the satellite-to-ground connections with limited rate. This paper considers problem of efficient downlink communication of multi-spectral satellite images for Earth observation using change detection. The proposed method for image processing consists of the joint design of cloud removal and change encoding, which can be seen as an instance of semantic communication, as it encodes important information, such as changed multi-spectral pixels (MPs), while aiming to minimize energy consumption. It comprises a three-stage end-to-end scoring mechanism that determines the importance of each MP before deciding its transmission. Specifically, the sensing image is (1) standardized and passed through a high-performance cloud filtering via the Cloud-Net model, (2) passed to the proposed scoring algorithm that uses Change-Net to identify MPs that have a high likelihood of being changed, compress them and forward the result to the ground station, and (3) reconstructed at ground gateway based on reference image and received data. The experimental results indicate that the proposed framework is effective in optimizing energy usage while preserving high-quality data transmission in satellite-based Earth observation applications.

Beam Placement (BP) is a well-known problem in Low-Earth Orbit (LEO) satellite communication (SatCom) systems, which can be modelled as an NP-hard clique cover problem. Recently, quantum computing has emerged as a novel technology which revolutionizes how to solve challenging optimization problems by formulating Quadratic Unconstrained Binary Optimization (QUBO), then preparing Hamiltonians as inputs for quantum computers. In this paper, we study how to use quantum computing to solve BP problems. However, due to limited hardware resources, existing quantum computers are unable to tackle large optimization spaces. Therefore, we propose an efficient Hamiltonian Reduction method that allows quantum processors to solve large BP instances encountered in LEO systems. We conduct our simulations on real quantum computers (D-Wave Advantage) using a real dataset of vessel locations in the US. Numerical results show that our algorithm outperforms commercialized solutions of D-Wave by allowing existing quantum annealers to solve 17.5 times larger BP instances while maintaining high solution quality. Although quantum computing cannot theoretically overcome the hardness of BP problems, this work contributes early efforts to applying quantum computing in satellite optimization problems, especially applications formulated as clique cover/graph coloring problems.

Most conventional Federated Learning (FL) models are using a star network topology where all users aggregate their local models at a single server (e.g., a cloud server). That causes significant overhead in terms of both communications and computing at the server, delaying the training process, especially for large scale FL systems with straggling nodes. This article proposes a novel edge network architecture that enables decentralizing the model aggregation process at the server, thereby significantly reducing the training delay for the whole FL network. Specifically, we design a highly-effective in-network computation framework (INC) consisting of a user scheduling mechanism, an in-network aggregation process (INA) which is designed for both primal- and primal-dual methods in distributed machine learning problems, and a network routing algorithm with theoretical performance bounds. The in-network aggregation process, which is implemented at edge nodes and cloud node, can adapt two typical methods to allow edge networks to effectively solve the distributed machine learning problems. Under the proposed INA, we then formulate a joint routing and resource optimization problem, aiming to minimize the aggregation latency. The problem turns out to be NP-hard, and thus we propose a polynomial time routing algorithm which can achieve near optimal performance with a theoretical bound. Simulation results showed that the proposed algorithm can achieve more than 99$\%$of the optimal solution and reduce the FL training latency, up to 5.6 times w.r.t other baselines. The proposed INC framework can not only help reduce the FL training latency but also significantly decrease cloud's traffic and computing overhead. By embedding the computing/aggregation tasks at the edge nodes and leveraging the multi-layer edge-network architecture, the INC framework can liberate FL from the star topology to enable large-scale FL.

Major bottlenecks of large-scale Federated Learning (FL) networks are the high costs for communication and computation. This is due to the fact that most of current FL frameworks only consider a star network topology where all local trained models are aggregated at a single server (e.g., a cloud server). This causes significant overhead at the server when the number of users are huge and local models' sizes are large. This paper proposes a novel edge network architecture which decentralizes the model aggregation process at the server, thereby significantly reducing the aggregation latency of the whole network. In this architecture, we propose a highly-effective in-network computation protocol consisting of two components. First, an in-network aggregation process is designed so that the majority of aggregation computations can be offloaded from cloud server to edge nodes. Second, a joint routing and resource allocation optimization problem is formulated to minimize the aggregation latency for the whole system at every learning round. The problem turns out to be NP-hard, and thus we propose a polynomial time routing algorithm which can achieve near optimal performance with a theoretical bound. Numerical results show that our proposed framework can dramatically reduce the network latency, up to 4.6 times. Furthermore, this framework can significantly decrease cloud's traffic and computing overhead by a factor of$K$/ M, where$K$is the number of users and$M$is the number of edge nodes, in comparison with conventional baselines.

We consider a multi-user multi-server mobile edge computing (MEC) network with time-varying fading channels and formulate an offloading decision and resource allocation problem. To solve this mixed-integer non-convex problem, we propose two hybrid approaches that learn offloading strategy with DQN (opt-DQN) or Q-table (opt-QL) at each user equipment (UE). The communication resources are allocated with an optimization algorithm at each computational access point (CAP). We also propose a pure DQN method that learns both the offloading strategy and resource allocation via Q-learning (QL). We analyze the convergence behavior of the QL-based algorithms from a game-theoretical perspective and demonstrate the performance of the proposed hybrid approaches for different network sizes. The simulation results show that the hybrid approaches reach lower costs than other baseline algorithms and the pure-DQN approach. Moreover, the performance of the pure-DQN approach degrades severely as the network size increases, while opt-DQN still performs the best, followed by opt-QL. These observations demonstrate that the hybrid approach that combines the advantages of both QL and convex optimization is a promising design for a multi-user MEC network, wherein complicated offloading and resource allocation strategies need to be determined in a timely and accurate fashion.

By acquiring cloud-like capacities at the edge of a network, edge computing is expected to significantly improve user experience. In this paper, we formulate a hybrid edge-cloud computing system where an edge device with limited local resources can rent more from a cloud node and perform resource allocation to serve its users. The resource procurement and allocation decisions depend not only on the cloud's multiple rental options but also on the edge's local processing cost and capacity. We first propose an offline algorithm whose decisions are made with full information of future demand. Then, an online algorithm is proposed where the edge node makes irrevocable decisions in each timeslot without future information of demand. We show that both algorithms have constant performance bounds from the offline optimum. Numerical results acquired with Google cluster-usage traces indicate that the cost of the edge node can be substantially reduced by using the proposed algorithms, up to 80% in comparison with baseline algorithms. We also observe how the cloud's pricing structure and edge's local cost influence the procurement decisions.

Recently, Mobile-Edge Computing (MEC) has been considered as a powerful supplement to a wireless network by processing computationally intensive tasks for resource-limited mobile devices. However, despite saving computational energy at User Equipment (UE), there is additional transmission energy consumption. As a result, the joint offloading strategy should be carefully selected to save energy and computational time. In this work, we investigated a sum cost minimization problem in a multi-UE multi-computing access point (CAP) system with time-varying channels. Our approach combines the optimization-based resource allocation algorithm with a Q-learning-based strategy selection mechanism. Without the need for communication overhead for CSI and inter- neighborhood cost value exchange, our algorithm shows prominent performance over the benchmark schemes with moderate assumptions.

Mobile edge computing (MEC) is expected to provide cloud-like capacities for mobile users (MUs) at the edge of wireless networks. However, deploying MEC systems faces many challenges, one of which is to achieve an efficient distributed offloading mechanism for multiple users in time-varying wireless environments. In this paper, we study a multi-user multi-edge-node computation offloading problem. Since edge nodes' communication and computing capacities are limited which leads resource contention when many MUs offload to the same edge node at the same time, we formulate this problem as a non-cooperative exact potential game (EPG), where each MU, in each time slot, selfishly maximizes its number of processed central processor unit (CPU) cycles and reduces its energy consumption. Assuming that channel information is static and available to MUs, we show that MUs could achieve a Nash equilibrium via a best response-based offloading mechanism. Next, we extend the problem to a practical scenario, where the number of processed CPU cycles is time-varying and unknown to MUs because of the uncertain channel information. In this case, we adopt an unknown payoff game framework and prove that the EPG properties still hold. Then, we propose a model-free reinforcement learning offloading mechanism which helps MUs learn their long-term offloading strategies to maximize their long-term utilities. Numerical results illustrate that our proposed algorithm for unknown CSI outperforms other schemes, such as local processing and random assignment, and achieves up to 87.87% average long-term payoffs compared to the perfect CSI case.

The energy consumption and applications' execution latency of mobile devices (MDs) can be improved by migrating application tasks to a nearby edge device. In this paper, we propose an optimization framework to investigate the scenario when a MD can offload tasks to multiple access points (APs) and scale its central process unit (CPU) frequency. Firstly, the optimal solution is derived from an exhaustive search based approach; and then a semidefinite relaxation (SDR) based approach is proposed to efficiently solve the problem. The obtained results from our simulation indicate that the SDR-based algorithm is able to achieve close-to-optimal performance. We also show that our proposed scheme can reduce the MD's energy consumption and tasks' execution latency, by taking advantage of having multiple APs and flexible CPU frequency.

In this paper, we propose an optimization framework of offloading from a single mobile device (MD) to multiple edge devices. We aim to minimize both total tasks' execution latency and the MD's energy consumption by jointly optimizing the task allocation decision and the MD's central process unit (CPU) frequency. This paper considers two cases for the MD, i.e., fixed CPU frequency and elastic CPU frequency. Since these problems are NP-hard, we propose a linear relaxation-based approach and a semidefinite relaxation (SDR)-based approach for the fixed CPU frequency case, and an exhaustive search-based approach and an SDR-based approach for the elastic CPU frequency case. Our simulation results show that the SDR-based algorithms achieve near optimal performance. Performance improvement can be obtained with the proposed scheme in terms of energy consumption and tasks' execution latency when multiple edge devices and elastic CPU frequency are considered. Finally, we show that the MD's flexible CPU range can have an impact on the task allocation.