Non-Terrestrial Networks (NTNs) play a critical role in ensuring uninterrupted communication services. Reliable communication between launch vehicles and ground stations is a cornerstone of mission-critical telemetry, tracking, and command (TT&C) systems. During high-dynamic phases such as ascent, main engine cutoff, serial staging, and re-entry burns, traditional ground-based antennas often encounter reduced signal quality due to rapidly changing elevation angles, Doppler effects, and terrain obstructions. These effects are exacerbated over oceanic re-entry zones, where terrestrial coverage is limited. To address this issue, this study proposes the integration of aerial vehicles (AVs) as the aerial segment, forming an Air-Ground Integrated Network (AGIN). AVs leverage their flexibility and ability to transmit via line-of-sight (LoS), act as wireless relay stations, effectively enhancing rocket signal transmission and improving Quality of Service (QoS), and agile relaying of communication signals. In this work, we explore a low-based airborne relay system capable of dynamically adjusting its position to maintain a reliable communication link with the rocket and forward the signal to a terrestrial base station (TBS). Next, we employ Deep Reinforcement Learning (DRL) techniques to optimize the UAV 3D position. Simulation results demonstrate that this approach significantly improves the network communication efficiency, providing essential theoretical foundations and technical references for achieving reliable and efficient SAGIN systems.

This paper proposes an integrated low Earth orbit (LEO) satellite and unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) framework that exploits the dual functionality of sensing and communication, enabling efficient detection of fish school zones and real-time data transmission to fishing vessel users in marine environments. This study focuses on two key components within the proposed framework: first, UAVs equipped with sensing radar perform fish school detection in marine environments by employing a region-based Vision Transformer (ViT) algorithm, using echo signals that are converted into range-doppler maps (RDMs). Second, to enhance system performance by addressing the challenges posed by UAV mobility and the need for adaptive beamforming, we formulate a joint optimization problem for UAV trajectory and beamforming. This problem is addressed using the proximal policy optimization (PPO) algorithm combined with the ViT model referred to as PPO-ViT, which incorporates the ViT detection results as part of the state input to guide optimal UAV trajectory planning and beamforming strategy. Simulation results demonstrate that the ViT-based detection achieves superior accuracy compared to other CNN models, while the PPO-ViT framework significantly improves average data rate and sensing precision over baseline methods.

With an aging rural population and declining birth rates posing challenges to agricultural productivity, we propose an AI Agriculture Automated Guided Vehicles system (A3GV). It leverages machine learning for real-time image recognition, enabling the vehicle to follow a target. ROS serves as the software framework for sensor coordination, offering features like Simultaneous Localization And Mapping (SLAM), obstacle avoidance, and autonomous navigation. This system alleviates the agricultural workload, and its adaptability is enhanced through a collaborative Unmanned Aerial Vehicle (UAV) setup. A dedicated mobile app enhances user experience by allowing remote vehicle control, mode switching, and autonomous navigation based on specific scenarios.

Recently, the development of Unmanned Aerial Vehicle (UAV) has been nearly matured and widely used in various fields. The combination of UAV and communication technologies, such as UAV Base Station (UAV-BS), can significantly increase the flexibility and scalability of the overall communication networks to provide more efficient communication services. While the UAV-BS improves the network service efficiency, the quality of services (QoS) in the air-to-ground communication link is highly affected unless the right users are unknown. In this paper, we propose the learning-based downlink user selection algorithm. The 3D downlink channel can be fast identified to judiciously select the users subset. In our proposed framework, we combine the k-means clustering and Convolutional Neural Network (CNN) that can increase the estimation accuracy of 3D wireless channels to enhance the communication service efficiency of the UAV-BS network. The field measurement results show that proposed method can achieve an average bit error rate (BER) of 3.56x10−7, which is better than the distance-based selection scheme that has an average of BER 2.88x10−3. The feasibility and effectiveness of the proposed method in real environment are proved, experimentally.

This paper applies Machine Learning (ML) to predict the quality of Air-to-Ground (A2G) links performance for Unmanned Aerial Vehicles Base Stations (UAV-BSs) services. UAV-BSs can instantly identify the status of the current 3D wireless channel in an unknown environment without relying on previous statistical channel modeling. The proposed method that employs the unsupervised learning clustering technology applying to A2G channel modeling in 3D wireless communication scenarios. As environment changing, the proposed method can derive the 3D temporary channel model based on collected RSS data and analyzing. To evaluate the proposed method, the simulation data and measurement data are used to co-verify the performance. As the results shown, the RMSE of conventional statistical channel model and proposed temporary channel model are very similar. The similarity achieves about 91.8% both of the simulation and experimental environments to verify the accuracy and feasibility of our proposed method, and that provides more fast and effective of 3D channel modeling approach.

Cloud RAN (C-RAN) where Base Band Units (BBUs) are collocated in a computing/processing center remotely away from their correspondent Remote Radio Heads (RRHs) for the efficient resource sharing is the prevailing RAN (Radio Access Network) design for next generation mobile networks. However, in C- RAN, the possible high latency from a RRH to the centralized Cloud BBU pool is not desirable for some latency critical applications. Thus, several local and smaller BBU pools are necessary to be deployed close to the RRHs to constrain the latency. This RAN architecture is so-call Fog RAN (F-RAN). A Mobile Edge Computing (MEC) center can be deployed beside or nearby a F-RAN BBU pool for timely processing. In this paper, we tackle the BBU resource allocation problem (a modified bin packing problem) between the set of RRHs and the set of BBU pools in F-RAN so that only a minimal number of BBU pools, i.e., the bins in a bin packing problem, will be turned on to serve all RRHs and so to save the energy consumption. Also, for the stability and fault tolerance, in addition to saving energy, the proposed algorithms also perform load balancing amid serving BBU pools. Our extensive simulation results show that the proposed scheme for energy efficient BBU resource allocation is able to achieve the goal of saving energy consumption and load balancing.

To meet the soaring demand of high data rate from today's applications of mobile Internet, small cells would be widely deployed to achieve higher data rate and better spectral efficiency in 4G and B4G/5G cellular networks. However, when the network traffic load is low, a large number of small-cell base stations could incur a tremendous energy waste. To overcome this pitfall, in this paper, we propose a traffic-aware power saving scheme (TAPS) by which a small-cell base station can adequately switch its operation mode - namely, normal, sniff and low-duty modes - according to its traffic load to reduce its power consumption. Simulation results show that TAPS can save a large amount of energy but only slightly hamper system performance compared with the case without power-saving scheme.

Motivated by applications to modern networking technologies, there has been interest in designing efficient gossip-based protocols for computing aggregate functions. While gossip-based protocols provide robustness due to their randomized nature, reducing the message and time complexity of these protocols is also of paramount importance in the context of resource-constrained networks such as sensor and peer-to-peer networks. We present provably time-optimal efficient gossip-based algorithms for aggregate computation with almost optimal message complexity. Given an n-node network, our algorithms guarantee that all the nodes can compute the common aggregates (such as Max, Min, Average, Sum, and Count) of their values in optimal $O(\log n)$ time and using $O(n \log \log n)$ messages. Our result improves on the algorithm of Kempe, Dobra, and Gehrke [Proceedings of the IEEE Annual Symposium on Foundations of Computer Science, 2003, pp. 482–491] that is time-optimal but uses $O(n \log n)$ messages, as well as on the algorithm of Kashyap et al. [Proceedings of Symposium on Principles of Database Systems, 2006, pp. 308–317] that uses $O(n \log \log n)$ messages but is not time-optimal (takes $O(\log n \log \log n)$ time). Furthermore, we show that our algorithms can be used to improve gossip-based aggregate computation in sparse communication networks, such as in peer-to-peer networks. The main technical ingredient of our algorithm is a technique called distributed random ranking (DRR) that can be useful in other applications as well. DRR gives an efficient distributed procedure to partition the network into a forest of (disjoint) trees of small size. Since the size of each tree is small, aggregates within each tree can be efficiently obtained at their respective roots. All the roots then perform a uniform gossip algorithm on their local aggregates to reach a distributed consensus on the global aggregates. Our algorithms are non-address-oblivious. In contrast, we show a lower bound of $\Omega(n\log n)$ on the message complexity of any address-oblivious algorithm for computing aggregates. This shows that non-address-oblivious algorithms are needed to obtain significantly better message complexity. Our lower bound holds regardless of the number of rounds taken or the size of the messages used. Our lower bound is the first nontrivial lower bound for gossip-based aggregate computation and also gives the first formal proof that computing aggregates is strictly harder than rumor spreading in the address-oblivious model.

Motivated by applications to modern networking technologies, there has been interest in designing efficient gossip-based protocols for computing aggregate functions. While gossip-based protocols provide robustness due to their randomized nature, reducing the message and time complexity of these protocols is also of paramount importance in the context of resource-constrained networks such as sensor and peer-to-peer networks. We present the first provably almost-optimal gossip-based algorithms for aggregate computation that are both time optimal and message-optimal. Given a n-node network, our algorithms guarantee that all the nodes can compute the common aggregates (such as Min, Max, Count, Sum, Average, Rank etc.) of their values in optimal O(log n) time and using O(n log log n) messages. Our result improves on the algorithm of Kempe et al. [9] that is time-optimal, but uses O(n log n) messages as well as on the algorithm of Kashyap et al. [8] that uses O(n log log n) messages, but is not time-optimal (takes O(log n log log n) time). Furthermore, we show that our algorithms can be used to improve gossip-based aggregate computation in sparse communication networks, such as in peer-to-peer networks. The main technical ingredient of our algorithm is a technique called distributed random ranking

We present DRR-gossip, an energy-efficient and robust aggregate computation algorithm in sensor networks. We prove that the DRR-gossip algorithm requires O(n) messages and O(n3/2/log1/2 n) one-hop wireless transmissions to obtain aggregates on a random geometric graph. This reduces the energy consumption by at least a factor of 1/log n over the standard uniform gossip algorithm. Experiments validate the theoretical results and show that DRR-gossip needs much less transmissions than other gossip-based schemes.

Dynamical connection graph changes are inherent in networks such as peer-to-peer networks, wireless ad hoc networks, and wireless sensor networks. Considering the influence of the frequent graph changes is thus essential for precisely assessing the performance of applications and algorithms on such networks. In this paper, using stochastic hybrid systems (SHSs), we model the dynamics and analyze the performance of an epidemic-like algorithm, distributed random grouping (DRG), for average aggregate computation on a wireless sensor network with dynamical graph changes. Particularly, we derive the convergence criteria and the upper bounds on the running time of the DRG algorithm for a set of graphs that are individually disconnected but jointly connected in time. An effective technique for the computation of a key parameter in the derived bounds is also developed. Numerical results and an application extended from our analytical results to control the graph sequences are presented to exemplify our analysis.

A wireless sensor network consists of a large number of small, resource-constrained devices and usually operates in hostile environments that are prone to link and node failures. Computing aggregates such as average, minimum, maximum and sum is fundamental to various primitive functions of a sensor network, such as system monitoring, data querying, and collaborative information processing. In this paper, we present and analyze a suite of randomized distributed algorithms to efficiently and robustly compute aggregates. Our distributed random grouping (DRG) algorithm is simple and natural and uses probabilistic grouping to progressively converge to the aggregate value. DRG is local and randomized and is naturally robust against dynamic topology changes from link/node failures. Although our algorithm is natural and simple, it is nontrivial to show that it converges to the correct aggregate value and to bound the time needed for convergence. Our analysis uses the eigenstructure of the underlying graph in a novel way to show convergence and to bound the running time of our algorithms. We also present simulation results of our algorithm and compare its performance to various other known distributed algorithms. Simulations show that DRG needs far fewer transmissions than other distributed localized schemes

A wireless sensor network consists of a large number of small, resource-constrained devices and usually operates in hostile environments that are prone to link and node failures. Computing aggregates such as average, minimum, maximum and sum is fundamental to various primitive functions of a sensor network like system monitoring, data querying, and collaborative information processing. In this paper we present and analyze a suite of randomized distributed algorithms to efficiently and robustly compute aggregates. Our distributed random grouping (DRG) algorithm is simple and natural and uses probabilistic grouping to progressively converge to the aggregate value. DRG is local and randomized and is naturally robust against dynamic topology changes from link/node failures. Although our algorithm is natural and simple, it is nontrivial to show that it converges to the correct aggregate value and to bound the time needed for convergence. Our analysis uses the eigen-structure of the underlying graph in a novel way to show convergence and to bound the running time of our algorithms. We also present simulation results of our algorithm and compare its performance to various other known distributed algorithms. Simulations show that DRG needs much less transmissions than other distributed localized schemes, namely gossip and broadcast flooding.

The code-division multiple-access (CDMA) scheme has been considered as one possible choice of the future standards for cellular networks because of its various advantages. Since there can be only one carrier frequency being used in CDMA systems, a handoff scheme with diversity, a so-called "soft handoff", was proposed for higher communication quality and capacity. A mathematical model is developed to analyze the soft handoff process. Markov's concept is applied to describe the system's steady state statistical behavior. System performance such as blocking probability, handoff refused probability, and channel efficiency are also determined. It is concluded that the larger the area the soft handoff region is, the better users in the cellular network will feel.

In this paper a tractable model is developed to analyze the soft handoff process. A Markovian conception is applied to describe the system's statistic behavior in steady state. Theoretical performances such as channel efficiency, blocking probability and handoff refused probability are also determined.