We leverage covert communication to enhance the security of a large-scale multi-tier Low Earth Orbit (LEO) satellite network against vigilant adversarial terrestrial Base Stations (BSs) aiming at detecting satellite transmissions. This approach involves deploying massive LEO satellites at different altitudes around Earth to form a multi-tier network serving as a backhaul for near-ground Unmanned Aerial Vehicles (UAVs) that provide network services to terrestrial mobile users. Meanwhile, terrestrial BSs attempt to detect satellite transmissions based on their own received signal powers. To evade detection, the LEO satellite network performs power control to obscure the satellite transmission within the co-channel interference among the LEO satellites. We formulate a two-stage Stackelberg game to model the conflict dynamics between the terrestrial BSs and the LEO satellite network. In this game, the terrestrial BSs act as non-cooperative followers at the lower stage aiming to minimize their detection errors. On the other hand, the LEO satellite network acts as the leader at the upper stage aiming to maximize its utility while ensuring communication covertness. In contrast to existing works that focus on a small set of network nodes, our study considers a large-scale multi-tier LEO satellite network and employs stochastic geometry to model the spatial distribution of network nodes. To achieve the Stackelberg equilibrium, we develop a bi-level algorithm based on Successive Convex Approximation (SCA) and golden-section search. Our numerical results provide practical insights, revealing a trade-off in leveraging co-channel interference (i.e., while it improves the communication covertness of satellite transmission, it simultaneously degrades the link reliability).

With the increasing deployment of IoT devices and applications, a large number of devices that can sense and monitor the environment in IoT network are needed. This trend also brings great challenges, such as data explosion and energy insufficiency. This paper proposes a system that integrates mobile edge computing (MEC) technology and simultaneous wireless information and power transfer (SWIPT) technology to improve the service supply capability of WSN-assisted IoT applications. A novel optimization problem is formulated to minimize the total system energy consumption under the constraints of data transmission rate and transmitting power requirements by jointly considering power allocation, CPU frequency, offloading weight factor and energy harvest weight factor. Since the problem is non-convex, we propose a novel alternate group iteration optimization (AGIO) algorithm, which decomposes the original problem into three subproblems, and alternately optimizes each subproblem using the group interior point iterative algorithm. Numerical simulations validate that the energy consumption of our proposed design is much lower than the two benchmark algorithms. The relationship between system variables and energy consumption of the system is also discussed.

A vital challenge in the deployment of federated learning (FL) over wireless networks is the high energy consumption incurred for the local computation and model update upload on energy-constrained devices such as IoT sensors. Equipping with energy harvesting (EH) modules is a promising solution that allows the devices to work in a self-sustainable manner. Moreover, quantizing the model updates can further improve the energy efficiency during the upload. In this paper, we propose an EH-enabled FL system with model quantization in which EH devices act as clients and client scheduling, model quantization, and transmit energy are jointly optimized to minimize the training loss while satisfying energy causality constraints and guaranteeing fairness in client selection. We formulate a non-convex mixed-integer nonlinear programming (MINLP) problem for the optimization. Then, by recasting the product of a continuous variable and a 0-1 variable in an equivalent linear form, we transform this non-convex MINLP problem into a convex problem and solve it. We present numerical evaluations on various datasets to show that our proposed system is stable and achieves high performance regardless of whether the loss function is convex or non-convex and whether the data distributions are independent and identically distributed (i.i.d.) or non-i.i.d.

This paper examines federated learning, a decentralized machine learning paradigm, focusing on privacy challenges. We introduce differential privacy mechanisms to protect privacy and quantify their impact on global model performance. Using evolutionary game theory, we establish a framework to analyze strategy dynamics and define utilities for different strategies based on Gaussian noise powers and training iterations. A differential privacy federated learning model (DPFLM) is analyzed within this framework. A key contribution is the thorough existence and stability analysis, identifying evolutionarily stable strategies (ESSs) and confirming their stability through simulations. This research provides theoretical insights for enhancing privacy protection in federated learning systems.

The analysis and optimization of energy-harvesting transmitters and receivers are different. This paper considers an end-to-end communication with an energy-harvesting receiver. The receiver has a time-switching architecture and can harvest energy from both a dedicated transmitter and other ambient radio-frequency (RF) sources. We first argue that the energy consumed for decoding (per channel use) can be expressed in terms of the gap to channel capacity and utilize this model to optimize two schemes for receiver operation. The two schemes are harvest-then-receive and harvest-when-receive, and they differ primarily in how and when they use the harvested energy for decoding. For transmission over a single block, we compare their performance from various aspects. Then we consider transmission over multiple blocks. When the energy harvested is all from the transmitter, we provide the solution for choosing the optimal code rate and fraction of channels used for energy harvesting for each block. When the energy harvested can also be from other RF sources, we provide a table-search algorithm to find a solution. Finally, we present some numerical examples to validate the accuracy of our analysis.

With the rapid development of Internet of Things (IOT) applications, the performance of existing wireless localization methods under non-line-of-sight (NLOS) transmission environments is seriously challenged. Therefore, this paper proposes a new definition of residual, i.e., the angle residual. The key target of angle residual is to detect line-of-sight (LOS) links from measured and calculated angle parameters. The target position is then estimated by two-step weighted least squares (TS-WLS) algorithm, where only positioning parameters of LOS link are included. Simulation shows that the proposed algorithm performs better than traditional algorithms, especially when the mobile node (MN) position and the NLOS link distribution are randomly generated. Therefore, the proposed method effectively improves the positioning performance and enhances the localization stability in real NLOS transmission environments.