Wireless energy transfer is an emerging technology that is used in networks of battery-powered devices in order to deliver energy and keep the network functional. Existing state-of-the-art studies have mainly focused on applying this technology on networks of relatively strong computational and communicational capabilities (wireless sensor networks, ad-hoc networks); also they assume energy transfer from special chargers to regular network nodes. Different from these works, we study how to efficiently transfer energy wirelessly in populations of battery-limited devices, towards prolonging their lifetime. In contrast to the state-of-the-art, we assume a much weaker population of distributed devices which are exchanging energy in a "peer to peer" manner with each other, without any special charger nodes. We address a quite general case of diverse energy levels and priorities in the network and study the problem of how the system can efficiently reach a weighted energy balance state distributively, under both loss-less and lossy power transfer assumptions. Three protocols are designed, analyzed and evaluated, achieving different performance trade-offs between energy balance quality, convergence time and energy efficiency.
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The increasing demand for video streaming services is the key driver of modern wireless and mobile communications. For robust and high-quality delivery of video content over wireless and mobile networks, the main challenge is sending image and video signals to single and multiple users over unstable and diverse channel environments. To this end, many studies have designed digital-based video delivery schemes, which mainly consist of a sequence of digital-based coding and transmission schemes. Although digital-based schemes perform well when the channel characteristics are known in advance, significant quality degradation, known as cliff and leveling effects, often occurs owing to the fluctuating channel characteristics. To prevent cliff and leveling effects irrespective of the channel characteristics of each user, a new paradigm for wireless and mobile video streaming has been proposed. Soft delivery schemes skip the digital operations of quantization and entropy and channel coding while directly mapping the power-assigned frequency--domain coefficients onto the transmission symbols. This modification is based on the fact that the pixel distortion due to communication noise is proportional to the magnitude of the noise, resulting in graceful quality improvement, wherein quality is improved gradually, according to the wireless channel quality without any cliff and leveling effects. Herein, we present a comprehensive summary of soft delivery schemes.
Deploying federated learning (FL) over wireless networks with resource-constrained devices requires balancing between accuracy, energy efficiency, and precision. Prior art on FL often requires devices to train deep neural networks (DNNs) using a 32-bit precision level for data representation to improve accuracy. However, such algorithms are impractical for resource-constrained devices since DNNs could require execution of millions of operations. Thus, training DNNs with a high precision level incurs a high energy cost for FL. In this paper, a quantized FL framework, that represents data with a finite level of precision in both local training and uplink transmission, is proposed. Here, the finite level of precision is captured through the use of quantized neural networks (QNNs) that quantize weights and activations in fixed-precision format. In the considered FL model, each device trains its QNN and transmits a quantized training result to the base station. Energy models for the local training and the transmission with the quantization are rigorously derived. An energy minimization problem is formulated with respect to the level of precision while ensuring convergence. To solve the problem, we first analytically derive the FL convergence rate and use a line search method. Simulation results show that our FL framework can reduce energy consumption by up to 53% compared to a standard FL model. The results also shed light on the tradeoff between precision, energy, and accuracy in FL over wireless networks.
Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. To address key challenges of enabling FL over a wireless fog-cloud system (e.g., non-i.i.d. data, users' heterogeneity), we first propose an efficient FL algorithm based on Federated Averaging (called FedFog) to perform the local aggregation of gradient parameters at fog servers and global training update at the cloud. Next, we employ FedFog in wireless fog-cloud systems by investigating a novel network-aware FL optimization problem that strikes the balance between the global loss and completion time. An iterative algorithm is then developed to obtain a precise measurement of the system performance, which helps design an efficient stopping criteria to output an appropriate number of global rounds. To mitigate the straggler effect, we propose a flexible user aggregation strategy that trains fast users first to obtain a certain level of accuracy before allowing slow users to join the global training updates. Extensive numerical results using several real-world FL tasks are provided to verify the theoretical convergence of FedFog. We also show that the proposed co-design of FL and communication is essential to substantially improve resource utilization while achieving comparable accuracy of the learning model.
The unprecedented surge of data volume in wireless networks empowered with artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by collecting datasets and training models centrally. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost due to increased data communication, (ii) threatened data privacy by allowing untrusted parties to utilise this information. Recently, in light of these limitations, a new emerging technique, coined as federated learning (FL), arose to bring ML to the edge of wireless networks. FL can extract the benefits of data silos by training a global model in a distributed manner, orchestrated by the FL server. FL exploits both decentralised datasets and computing resources of participating clients to develop a generalised ML model without compromising data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of FL. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth generation (B5G) and sixth generation (6G) communication systems. The purpose of this survey is to provide an overview of the state-of-the-art of FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.
Optical wireless communication (OWC) is a promising technology that has the potential to provide Tb/s aggregate rates. In this paper, interference management is studied in a Laser-based optical wireless network where vertical-cavity surface-emitting (VCSEL) lasers are used for data transmission. In particular, rate splitting (RS) and hierarchical rate splitting (HRS) are proposed to align multi-user interference, while maximizing the multiplexing gain of the network. Basically, RS serves multiple users simultaneously by splitting a message of a user into common and private messages, each message with a certain level of power, while on the other side users decode their messages following a specific methodology. The performance of the conventional RS scheme is limited in high density wireless networks. Therefore, the HRS scheme is developed aiming to achieve high rates where users are divided into multiple groups, and a new message called outer common message is used for managing inter-group interference. We formulate an optimization problem that addresses power allocation among the messages of the HRS scheme to further enhance the performance of the network. The results show that the proposed approach provides high achievable rates compared with the conventional RS and HRS schemes in different scenarios.
Graph neural network (GNN) is an efficient neural network model for graph data and is widely used in different fields, including wireless communications. Different from other neural network models, GNN can be implemented in a decentralized manner with information exchanges among neighbors, making it a potentially powerful tool for decentralized control in wireless communication systems. The main bottleneck, however, is wireless channel impairments that deteriorate the prediction robustness of GNN. To overcome this obstacle, we analyze and enhance the robustness of the decentralized GNN in different wireless communication systems in this paper. Specifically, using a GNN binary classifier as an example, we first develop a methodology to verify whether the predictions are robust. Then, we analyze the performance of the decentralized GNN binary classifier in both uncoded and coded wireless communication systems. To remedy imperfect wireless transmission and enhance the prediction robustness, we further propose novel retransmission mechanisms for the above two communication systems, respectively. Through simulations on the synthetic graph data, we validate our analysis, verify the effectiveness of the proposed retransmission mechanisms, and provide some insights for practical implementation.
The communication system is a critical part of the system design for the autonomous UAV. It has to address different considerations, including efficiency, reliability and mobility of the UAV. In addition, a multi-UAV system requires a communication system to assist information sharing, task allocation and collaboration in a team of UAVs. In this paper, we review communication solutions for supporting a team of UAVs while considering an application in the power line inspection industry. We provide a review of candidate wireless communication technologies {for supporting communication in UAV applications. Performance measurements and UAV-related channel modeling of those candidate technologies are reviewed. A discussion of current technologies for building UAV mesh networks is presented. We then analyze the structure, interface and performance of robotic communication middleware, ROS and ROS2. Based on our review, the features and dependencies of candidate solutions in each layer of the communication system are presented.
Queue length violation probability, i.e., the tail distribution of the queue length, is a widely used statistical quality-of-service (QoS) metric in wireless communications. Many previous works conducted tail distribution analysis on the control policies with the assumption that the condition of the large deviations theory (LDT) is satisfied. LDT indicates that the tail distribution of the queue length has a linear-decay-rate exponent. However, there are many control policies which do not meet that assumption, while the optimal control policy may be included in these policies. In this paper, we put our focus on the analysis of the tail distribution of the queue length from the perspective of cross-layer design in wireless link transmission. Specifically, we divide the wireless link transmission systems into three scenarios according to the decay rate of the queue-length tail distribution with the finite average power consumption. A heuristic policy is conceived to prove that the arbitrary-decay-rate tail distribution with the finite average power consumption exists in Rayleigh fading channels. Based on this heuristic policy, we generalize the analysis to Nakagami-m fading channels. Numerical results with approximation validate our analysis.
We present a flexible public transit network design model which optimizes a social access objective while guaranteeing that the system's costs and transit times remain within a preset margin of their current levels. The purpose of the model is to find a set of minor, immediate modifications to an existing bus network that can give more communities access to the chosen services while having a minimal impact on the current network's operator costs and user costs. Design decisions consist of reallocation of existing resources in order to adjust line frequencies and capacities. We present a hybrid tabu search/simulated annealing algorithm for the solution of this optimization-based model. As a case study we apply the model to the problem of improving equity of access to primary health care facilities in the Chicago metropolitan area. The results of the model suggest that it is possible to achieve better primary care access equity through reassignment of existing buses and implementation of express runs, while leaving overall service levels relatively unaffected.
We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual keyboard for smartphones. Server-based training using stochastic gradient descent is compared with training on client devices using the Federated Averaging algorithm. The federated algorithm, which enables training on a higher-quality dataset for this use case, is shown to achieve better prediction recall. This work demonstrates the feasibility and benefit of training language models on client devices without exporting sensitive user data to servers. The federated learning environment gives users greater control over their data and simplifies the task of incorporating privacy by default with distributed training and aggregation across a population of client devices.
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