In this paper, we study asynchronous federated learning (FL) in a wireless distributed learning network (WDLN). To allow each edge device to use its local data more efficiently via asynchronous FL, transmission scheduling in the WDLN for asynchronous FL should be carefully determined considering system uncertainties, such as time-varying channel and stochastic data arrivals, and the scarce radio resources in the WDLN. To address this, we propose a metric, called an effectivity score, which represents the amount of learning from asynchronous FL. We then formulate an Asynchronous Learning-aware transmission Scheduling (ALS) problem to maximize the effectivity score and develop three ALS algorithms, called ALSA-PI, BALSA, and BALSA-PO, to solve it. If the statistical information about the uncertainties is known, the problem can be optimally and efficiently solved by ALSA-PI. Even if not, it can be still optimally solved by BALSA that learns the uncertainties based on a Bayesian approach using the state information reported from devices. BALSA-PO suboptimally solves the problem, but it addresses a more restrictive WDLN in practice, where the AP can observe a limited state information compared with the information used in BALSA. We show via simulations that the models trained by our ALS algorithms achieve performances close to that by an ideal benchmark and outperform those by other state-of-the-art baseline scheduling algorithms in terms of model accuracy, training loss, learning speed, and robustness of learning. These results demonstrate that the adaptive scheduling strategy in our ALS algorithms is effective to asynchronous FL.

In this paper, we consider a heterogeneous mobile cloud computing (HMCC) system that consists of remote cloud servers, local cloudlets, task offloading mobile devices (TMDs), non-task offloading MDs (NTMDs), and radio access networks such as cellular networks and WLANs. TMDs have the capability of task offloading to remote cloud servers or cloudlets, whereas NTMDs are conventional cellular users that do not have such capability. By using stochastic geometry, we analyze the outage probability of task offloading in the MCC system with only remote cloud servers and that in the HMCC with both remote cloud servers and cloudlets. The analysis provides useful information, i.e., how the varying system parameters affect the outage probability. From the analysis, we show that there is an intrinsic limitation in reducing the outage probability in the MCC system due to the outage when accessing remote cloud servers. In addition, we show that the use of cloudlets is a promising solution to overcome this limitation. However, a tradeoff exists in using cloudlets due to their deployment and operation costs. Thus, to address this tradeoff, we also study the optimal cloudlet deployment to maximize the cloud service provider's profit while guaranteeing maximum outage probability requirements.

In this paper, we study a random access (RA) procedure to support the massive connectivity of the Internet of Things (IoT) devices, also known as the IoT connectivity. Compared with the previous RA procedures that have limitations to support the IoT connectivity due to the exponentially increased access delay, we develop an RA procedure by applying the sparse code multiple access (SCMA) to reduce the access delay and increase the ratio of the IoT devices that successfully complete their RA procedures. We provide the theoretical performance analysis of the proposed RA procedure with the performance metrics such as the RA success probability, the average access delay, the RA throughput, and the average number of preamble transmissions. Then, we provide the numerical results to evaluate the performance of the proposed RA procedure based on our analysis and the ns-3 simulator. Numerical results show that our proposed RA procedure is able to support the massive connectivity requirement with improved RA performance metrics compared with the conventional RA procedures.

In this paper, we study radio and energy resource management in renewable energy-powered wireless networks, where base stations (BSs) are powered by both on-grid and renewable energy sources and can share their harvested energy with each other. To efficiently manage those resources, we propose a hierarchical and distributed resource management framework based on deep reinforcement learning. The proposed framework minimizes the on-grid energy consumption while satisfying the data rate requirement of each user. It is composed of three different policies in a distributed and hierarchical way. An intercell interference coordination policy constrains the transmission power at each BS to coordinate the intercell interference among the BSs. Under the power constraints, a distributed radio resource allocation policy of each BS determines its own user scheduling and power control. Lastly, an energy sharing policy manages the energy resources of the BSs by sharing the harvested energy via power lines between them. Through the simulation, we demonstrate that the proposed framework can effectively reduce the on-grid energy consumption while satisfying the data rate requirements.

In this paper, we consider IoT systems that can be applied to various applications with low-mobility or static IoT devices such as wireless sensor networks and charging systems for low-power devices with communication. The IoT systems consist of IoT devices and a hybrid access point (H-AP) powered by both on-grid and renewable energy sources. The IoT devices have a capability to harvest energy from the H-AP’s RF signal, and they perform their tasks by using only their harvested energy. We consider the tasks do not have a real-time requirement which can be stored in the task queues of the IoT devices and performed later. We study resource and task scheduling for the IoT systems which aims at minimizing the on-grid energy consumption at the H-AP while guaranteeing the minimum average data rate and minimum task performing rates of IoT devices. To achieve the goal, we first propose a centralized resource and task scheduling algorithm. However, its computational complexity and signaling overhead are too large due to the task scheduling for each IoT device. Thus, to resolve these issues, we propose a hybrid resource and task scheduling algorithm in which each IoT device determines its own task scheduling in a distributed manner and the H-AP determines the resource scheduling. We then provide performance analyses showing that our proposed algorithms are asymptotically optimal and well satisfy the QoS requirements of IoT devices even with distributed task scheduling. Through the simulation results, we verify the analyses and show the performance of our algorithms.

In this letter, we study an energy management system (EMS) with an inconsistent energy supply that aims to minimize energy costs while avoiding failing to satisfy energy demands. To this end, we propose a robust EMS algorithm based on safe reinforcement learning which can effectively exploit short-horizon forecasts on system uncertainties. We show via experimental results using real datasets that our robust EMS algorithm outperforms other state-of-the-art algorithms in terms of both robustness and cost-efficiency thanks to its capability of utilizing short-horizon forecasts.