Terrestrial Cellular Networks Research Articles

Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System.

This paper presents an innovative approach towards space-ground integrated communication systems by combining terrestrial cellular networks, UAV networks, and satellite networks, leveraging advanced slicing technology. The proposed architecture addresses the challenges posed by future user surges and aims to reduce network overhead effectively. Central to our approach is the introduction of a marginal mobile station (MS)-assisted network resource allocation decision architecture. Building upon this foundation, we introduce the DP-DQN model, an enhanced decision-making algorithm tailored for MSs in dynamic network environments. Furthermore, this study introduces a feedback mechanism to ensure the accuracy and adaptability of the marginalization model over time. Through extensive simulations and experimental validations, our DP-DQN-based edge decision method demonstrates substantial potential in alleviating core network overhead while improving success access ratios compared to conventional methods.

AHA-BV: Access and handover authentication protocol with batch verification for satellite–terrestrial integrated networks

At present, the rapid development of satellite capabilities has prompted the proposal of satellite–terrestrial integrated networks (STIN), which solves the problem of limited signal coverage of terrestrial cellular networks, further promotes the globalization process, and realizes global data sharing and on-demand use. However, due to the high openness of satellite-to-ground links in STIN, users are vulnerable to attacks such as eavesdropping, replay, tampering, and impersonation when requesting access to satellite nodes and obtaining subscription services. To ensure the security and reliability, many authentication protocols have been proposed, but there are still some shortcomings, such as high authentication overhead, vulnerability to certain attacks. In addition, for inter-satellite handovers caused by the highly dynamic topology of satellites, the computational overhead of existing handover authentication mechanisms is too high to be applied to frequent inter-satellite handover scenarios in STIN. To address the above issues, in this paper, we propose a new access and handover authentication protocol with batch verification for STIN, namely the AHA-BV protocol. The AHA-BV protocol not only realizes mutual authentication and key negotiation between users and satellite access points without the participation of the network control center, but also ensures the conditional anonymity of users during the access authentication phase. Furthermore, the lightweight batch verification mechanism reduces the risk of computing bottlenecks when resource-constrained satellites receive a large number of access authentication requests. Not only that, the AHA-BV protocol can also achieve sustained trust in subscription services from STIN with low computational overhead during the inter-satellite handover authentication phase. Formal and heuristic security analysis show that the AHA-BV protocol can meet the security requirements of STIN. Performance analysis indicates that the AHA-BV protocol has low authentication overhead while ensuring security, and is more suitable for users under satellite dynamic topology to access and obtain subscription services from STIN.

Network Performance and Technological Feasibility of Unmanned Aerial Vehicles for Network Extension

The operational range of conventional and license-free radio-controlled drones is limited due to line-of-sight restrictions (LoS). There exists a definitive method for operating a drone. Consequently, in order to fly the drone beyond the visual line of sight (BVLoS), it is necessary to replace the drone's original wireless communications equipment with a device that requires a licence and is connected to a cellular network. Long-Term Evolution (LTE), a terrestrial communication technique, enables a drone to establish a real-time connection with a ground station. This connection serves the goals of command and control (C&C) as well as payload delivery. Nevertheless, it is important to note that the electromagnetic environment undergoes changes as altitude increases, which can potentially complicate the process of interfacing with drones over terrestrial cellular networks. The objective of this article is to develop a prototype control system for low-altitude microdrones using LTE technology. Additionally, it seeks to assess the feasibility and effectiveness of cellular connectivity for drones operating at various altitudes. This evaluation will be conducted by examining factors like as latency, handover, and signal strength. At a certain altitude, the received signal experiences a decrease in power level by 20 dBm and a degradation in signal quality by 10 dB. The data throughput of the downlink had a fall of 70%, while the latency exhibited an increase of 94 ms. Despite meeting the basic criteria for drone cellular connection, the existing LTE network necessitates enhancements in order to expand aerial coverage, mitigate interference, and minimise network latency.

Intelligent Resource Allocation Using an Artificial Ecosystem Optimizer with Deep Learning on UAV Networks

An Unmanned Aerial Vehicle (UAV)-based cellular network over a millimeter wave (mmWave) frequency band addresses the necessities of flexible coverage and high data rate in the next-generation network. But, the use of a wide range of antennas and higher propagation loss in mmWave networks results in high power utilization and UAVs are limited by low-capacity onboard batteries. To cut down the energy cost of UAV-aided mmWave networks, Energy Harvesting (EH) is a promising solution. But, it is a challenge to sustain strong connectivity in UAV-based terrestrial cellular networks due to the random nature of renewable energy. With this motivation, this article introduces an intelligent resource allocation using an artificial ecosystem optimizer with a deep learning (IRA-AEODL) technique on UAV networks. The presented IRA-AEODL technique aims to effectually allot the resources in wireless UAV networks. In this case, the IRA-AEODL technique focuses on the maximization of system utility over all users, combined user association, energy scheduling, and trajectory design. To optimally allocate the UAV policies, the stacked sparse autoencoder (SSAE) model is used in the UAV networks. For the hyperparameter tuning process, the AEO algorithm is used for enhancing the performance of the SSAE model. The experimental results of the IRA-AEODL technique are examined under different aspects and the outcomes stated the improved performance of the IRA-AEODL approach over recent state of art approaches.

Evolutionary Ensemble Learning Pathloss Prediction for 4G and 5G Flying Base Stations With UAVs

The usage of unmanned aerial vehicles (UAVs) as flying base stations (FBSs) for expanding coverage and assisting the terrestrial cellular networks constitutes a promising technology for 5G and beyond. A crucial parameter affecting cellular network design is path loss prediction. An alternative to the accurate, though time-consuming, propagation prediction with deterministic Ray Tracing models could be Machine Learning (ML) based predictions. Ensemble Learning techniques are used in order to optimally combine the predictions of standalone models. That is, they combine the best-performing individual models into a better-performing meta-model. Our proposed method of the evolutionary tuned stacked ensemble optimizes the ensemble as a whole, instead of optimizing its individual base learners. To the best of our knowledge, this is the first time that an evolutionary technique is applied in order to mutually tune an ensemble’s base learners for a path loss modeling problem in electromagnetics. Moreover, we present a model that works in more than one frequency. As opposed to the standard implementation of ensemble learning, our method offers a significant performance boost with low complexity.

Energy-Efficient Spectrum Sharing for 6G Ubiquitous IoT Networks Through Blockchain

Ubiquitous Internet of Things (UIoT) is required a three-dimensional network that stretches from space to air to earth. It is involved numerous network components such as satellites, HAPs, UAVs, terrestrial cellular networks, data centers, terrestrial gateways, as well as sharing and openness among operators. Correspondingly wireless spectrum sharing becomes essential to energy efficiency and thus has to be investigated for green UIoT. Meanwhile, both blockchain and 6G hybrid cloud are recently intriguing technologies, and the enormous potential of combining the two has grown in prominence. For the reason of dependability and security, these two technologies are proposed to apply to spectrum sharing among UIoT devices. Particularly the blockchain’s unique smart contract technology can well complete the spectrum sharing procedure, as verified by the numerical results from our simulation study.

Secrecy rate maximization for 6G cognitive satellite-UAV networks

To cover remote areas where terrestrial cellular networks may not be available, non-terrestrial infrastructures such as satellites and unmanned aerial vehicles (UAVs) can be utilized in the upcoming sixth-generation (6G) era. Considering the spectrum scarcity problem, satellites and UAVs need to share the spectrum to save costs, leading to a cognitive satellite-UAV network. Due to the openness of both satellite links and UAV links, communication security has become a major concern in cognitive satellite-UAV networks. In this paper, we safeguard a cognitive satellite-UAV network from a physical layer security (PLS) perspective. Using only the slowly-varying large-scale channel state information (CSI), we jointly allocate the transmission power and subchannels to maximize the secrecy sum rate of UAV users. The optimization problem is a mixed integer nonlinear programming (MINLP) problem with coupling constraints. We propose a heuristic algorithm which relaxes the coupling constraints by the penalty method and obtains a sub-optimal low-complexity solution by utilizing random matrix theory, the max-min optimization tool, and the bipartite graph matching algorithm. The simulation results corroborate the superiority of our proposed scheme.

Trajectory Optimization and Power Allocation for Cell-Free Satellite-UAV Internet of Things

Massive access outside the coverage of terrestrial cellular networks will be the main feature of the sixth generation (6G) networks. In order to cope with it, the cognitive satellite-UAV network (CSUN) has drawn a lot of attentions. In this paper, we investigate the UAV trajectory optimization and power allocation for the cell-free CSUN consisting of one satellite and a swarm of UAVs. Indeed, due to the on-board energy constraints of UAVs, both the trajectory optimization and the power allocation can significantly save the energy to improve the energy efficiency. The joint trajectory optimization and power allocation problem is formulated as a mixed-integer non-convex optimization problem which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original optimization problem into two subproblems in terms of the trajectory optimization and power allocation. For the trajectory optimization subproblem, we model it as a Traveling Salesman Problem (TSP), and the PSO is adopted to solve it. When the trajectory variables are fixed, the power allocation subproblem is still difficult to tackle due to its non-convexity and large scale. Firstly, we present a kind of centralized algorithm in which the DC (difference of two convex functions) algorithm is applied to optimize it, and then a distributed algorithm based on auxiliary variables is proposed to reduce the signaling overhead and computational complexity. The simulation results demonstrate the effectiveness of the proposed joint trajectory optimization and power allocation algorithm for the cell-free CSUN.

On the Power-Splitting Relaying Protocol for SWIPT with Multiple UAVs in Downlink NOMA-IoT Networks

Unmanned aerial vehicle (UAV) communication and non-orthogonal multiple access (NOMA) are two promising technologies for wireless 5G networks and beyond. The UAVs can be used as flying base stations to form line-of-sight communication links to the Internet of things devices (IDs) and to enhance the performance of usual terrestrial cellular networks. Moreover, the UAVs can also be deployed as flying relay nodes for forwarding data from a base station (BS) to the IDs. On the other hand, non-orthogonal resource sharing for many concurrent users is exploited in NOMA, thus improving spectrum efficiency (SE) and supporting massive connections. The NOMA combined with energy harvesting (EH) in an amplify-and-forward (AF) with cooperative UAV systems is researched. Specifically, the UAVs act as rotary-wing relays to forward data from the BSs to two IDs. This paper focuses on the analysis of outage probabilities (OPs), system throughput, and energy efficiency (EE) for two IDs. Besides, we also do the asymptotic analysis of OPs at high signal-to-noise ratios (SNRs). Furthermore, this paper also inspects the impacts of the UAV-based relaying on the OP, system throughput, and EE of the proposed NOMA scheme. The derived asymptotic expansions show that the suggested model can enhance user fairness and the analytical results match the simulation results.

Interference Management in Cellular-Connected Internet of Drones Networks With Drone-Pairing and Uplink Rate-Splitting Multiple Access

Interference management is a key challenge for cellular-connected Internet of Drones (IoD) networks that employ multiple cellular-connected hovering drones for data acquisition in surveillance and monitoring applications. This article proposes a novel resource optimization framework for managing interference in cellular-connected IoD networks. Specifically, the envisioned system divides the set of transmitting drones into distinct drone pairs, where the paired drones simultaneously transmit over the same radio resource blocks (RRBs). Each drone pair is assigned a set of orthogonal RRBs for data transmission, where these RRBs are shared with the terrestrial cellular network as well. An uplink rate-splitting multiple access scheme is employed to mitigate the interdrone interference at the drone pairs, and an RRB pricing method is exploited to control the interference between the aerial and cellular communication links. Our goal is to maximize the uplink capacity of the IoD network while reducing interference over the shared RRBs between the IoD and cellular networks. Toward this goal, a joint optimization of the drones’ transmit power allocation, drone pairing, and RRB scheduling among the drone pairs is presented. In order to obtain an efficient suboptimal solution, an iterative optimization is devised. Particularly, the presented joint optimization problem is decomposed into three subproblems for transmit power allocation, drone pairing and RRB scheduling, and RRB price update. By solving theses subproblems iteratively, a convergent rate-splitting-empowered resource allocation and clustering for interference management (REACT) algorithm is proposed. Extensive simulations are conducted to verify the effectiveness of the proposed REACT algorithm over several benchmark schemes.

Deployment and Association of Multiple UAVs in UAV-Assisted Cellular Networks With the Knowledge of Statistical User Position

Exploiting unmanned aerial vehicles (UAVs) as flying relays is becoming an indispensable strategy to assist terrestrial cellular networks to enhance coverage. One challenging problem for UAV-integrated cellular networks is how to design their deployment and association schemes to provide on-demand coverage with minimum network power consumption. In this paper, the uplink transmission in a UAV-assisted cellular network is studied with the objective of minimizing the transmit power consumption of users and UAVs through designing proper UAV deployment and association schemes. To avoid the computational complexity caused by the estimation of instantaneous position of users, we investigate UAV deployment and association schemes based on the statistical user position. By discretizing the space where UAV can be located, we build a centralized multi-agent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm, with which multiple UAVs update their positions in a joint manner. In the training process of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm, a reward function is built based on the optimal association scheme and its corresponding power consumption. By adopting the optimal transport theory, the existence of the unique optimal association scheme for given statistical user distribution and UAVs’ state is proved. Simulation results demonstrate that the proposed designs considerably outperform the similar existing algorithms. Comparisons with the benchmark scheme show that the proposed scheme can bring about 85% energy efficiency improvement under the same simulation setups.

Reinforcement Learning for Energy-Efficient Trajectory Design of UAVs

Integrating unmanned aerial vehicles (UAVs) as aerial base stations (BSs) into terrestrial cellular networks has emerged as an effective solution to provide coverage and complement communication services in a fast and cost-effective manner. The three-dimensional (3-D) trajectories of UAVs have a remarkable impact on the performance of such networks. On the other hand, UAVs are battery limited, and thus optimizing their energy consumption is of high importance. In this regard, we propose a novel trajectory design mechanism for rotary-wing UAV-BSs in 3-D space to improve the energy efficiency of the network. In this approach, UAVs aim at maximizing an objective function that captures the tradeoff between energy consumption and throughput, while satisfying their ground users’ quality-of-service requirements. Using reinforcement learning, we model our problem as a multiarmed bandit and propose an upper confidence bound-based algorithm to solve the problem. In our proposed mechanism, UAVs autonomously choose their velocities and update their locations adapted to the system conditions without requiring the prior and full knowledge of the system. Simulation results show that our proposed approach yields significant performance gains reaching up to 33.85% in terms of improving the network throughput, and up to 95% of enhancing the energy efficiency compared to a learning-based benchmark. Comparing to a nonlearning-based approach, our proposed approach improves the throughput and energy efficiency by 46.61% and 110%, respectively.

RIS-Aided Ground-Aerial NOMA Communications: A Distributionally Robust DRL Approach

A reconfigurable intelligent surface (RIS) aided air-to-ground uplink non-orthogonal transmission framework is investigated for next generation multiple access. Occupying the same spectrum resource, unmanned aerial vehicle (UAV) users and ground users (GUs) are connected to terrestrial cellular networks via the uplink non-orthogonal multiple access (NOMA) protocol. As the flight safety is important for employing UAVs in civil airspace, the collision avoidance mechanism has to be considered during the flight. Therefore, a joint optimization problem of the UAV trajectory design, RIS configuration, and uploading power control is formulated for maximizing the network sum rate, while ensuring the UAV’s fight safety and satisfying the minimum data rate requirements of both the UAV and GU. The resultant problem is a sequential decision making one across multiple coherent time slots. Besides, the unknown locations of obstacles bring uncertainties into the decision making process. To tackle this challenging problem, a sample-efficient deep reinforcement learning (DRL) algorithm is proposed to optimize the UAV trajectory, RIS configuration, and power control simultaneously. Moreover, considering the ambiguous uncertainties in the environment, a distributionally robust DRL algorithm is further proposed to provide the worst-case performance guarantee. Numerical results demonstrate that the two proposed DRL algorithms outperform the conventional ones in terms of learning efficiency and robustness. It is also shown that the network sum rate is significantly improved by the proposed RIS-NOMA scheme compared to the conventional RIS-orthogonal multiple access (OMA) scheme and the case where no RIS is deployed.

An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks

Today’s terrestrial cellular communications networks face difficulties in serving coexisting users and devices due to the enormous demands of mass connectivity. Further, natural disasters and unexpected events lead to an unpredictable amount of data traffic, thus causing congestion to the network. In such cases, the addition of on-demand network entities, such as fixed or aerial base stations, has been proposed as a viable solution for managing high data traffic and offloading the existing terrestrial infrastructure. This paper presents an unmanned aerial vehicles (UAVs) aided offloading strategy of the terrestrial network, utilizing an unsupervised machine learning method for the best placement of UAVs in sites with high data traffic. The proposed scheme forms clusters of users located in the affected area using the k-medoid algorithm. Followingly, based on the number of available UAVs, a cluster selection scheme is employed to select the available UAVs that will be deployed to achieve maximum offloading in the system. Comparisons with traditional offloading strategies integrating terrestrial picocells and other UAV-aided schemes show that significant offloading, throughput, spectral efficiency, and sum rate gains can be harvested through the proposed method under a varying number of UAVs.

Nullforming-Based Precoder for Spectrum Sharing Between HAPS and Terrestrial Mobile Networks

Ultra-wide coverage is one of the important concepts of the sixth generation (6G) mobile communications. High-altitude platform stations (HAPSs) have attracted considerable attention as a leading candidate for coverage extension of cellular networks. HAPSs should share a frequency band with terrestrial cellular networks and provide users with mobile communication services using the common frequency band to improve spectral efficiency in service links. However, the signals from HAPS strongly interfere with terrestrial cells in the service area of HAPS, which leads to a considerable deterioration in the system capacity of terrestrial cells. This study proposes a novel two-stage precoding scheme that realizes spectrum sharing between HAPS and terrestrial networks. The proposed scheme mitigates HAPS interference with terrestrial cells using beamforming techniques in phased array antenna systems. In the first stage, nulls are created in directions of terrestrial cells. In the second stage, user-specific beams are formed in directions of users connected to HAPS. Results demonstrate that the proposed scheme improves the signal-to-interference-plus-noise ratio of users in terrestrial cells by up to 10 dB compared with a conventional minimum mean square error (MMSE) precoder without nullforming when the HAPS uses a cylindrical array antenna with 196 elements. Furthermore, for ten terrestrial cells, the proposed scheme achieves 128% of total capacity of the conventional MMSE precoder. Total capacity is defined as the sum capacity of HAPS and all terrestrial cells.

Power Allocation and Energy Cooperation for UAV-Enabled MmWave Networks: A Multi-Agent Deep Reinforcement Learning Approach.

Unmanned Aerial Vehicle (UAV)-assisted cellular networks over the millimeter-wave (mmWave) frequency band can meet the requirements of a high data rate and flexible coverage in next-generation communication networks. However, higher propagation loss and the use of a large number of antennas in mmWave networks give rise to high energy consumption and UAVs are constrained by their low-capacity onboard battery. Energy harvesting (EH) is a viable solution to reduce the energy cost of UAV-enabled mmWave networks. However, the random nature of renewable energy makes it challenging to maintain robust connectivity in UAV-assisted terrestrial cellular networks. Energy cooperation allows UAVs to send their excessive energy to other UAVs with reduced energy. In this paper, we propose a power allocation algorithm based on energy harvesting and energy cooperation to maximize the throughput of a UAV-assisted mmWave cellular network. Since there is channel-state uncertainty and the amount of harvested energy can be treated as a stochastic process, we propose an optimal multi-agent deep reinforcement learning algorithm (DRL) named Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to solve the renewable energy resource allocation problem for throughput maximization. The simulation results show that the proposed algorithm outperforms the Random Power (RP), Maximal Power (MP) and value-based Deep Q-Learning (DQL) algorithms in terms of network throughput.

Total Cost of Ownership Optimization for Direct Air-to-Ground Communication Networks

Aircraft cabins are one of the last venues without mobile broadband. Considering future 5G applications and connectivity requirements, direct air-to-ground communications (DA2GC) is the only technique which can provide high capacity and low latency backhaul link for aircraft via a direct communication link. To this end, we propose an analytical framework to investigate the ground station deployment problem for DA2GC network employing multi-user beamforming with dual-polarized hybrid DA2GC antenna arrays. In addition, the proposed framework is utilized to analyze and optimize the total cost of ownership (TCO) of the DA2GC network to provide coverage for European airspace. We present the interplay between different network parameters: the number of ground stations, array size, transmit power and bandwidth, and TCO optimizing deployment parameters are calculated in order to satisfy capacity requirements. At the end, we show that, depending on the cost of different network resources, a terrestrial cellular network can be designed to cover the whole European airspace with limited number of ground stations with a certain array size, i.e., 900 and 361 antenna elements for ground station and air station, respectively.

Blockchain-Based Dynamic Spectrum Sharing for 6G UIoT Networks

The Ubiquitous IoT (UIoT) is a three-dimensional network that spans space-air-ground. It contains multiple network elements such as satellites, terrestrial cellular networks, terrestrial gateways, and data centers. Due to the need for openness and sharing among different operators, it is necessary to research wireless spectrum sharing. The new technologies of blockchain and the 6G hybrid cloud pose a potential solution. We propose to migrate the spectrum sharing system of 6G to the hybrid blockchain. Subdividing the granularity of the spectrum resources of UIoT devices in multiple dimensions can enable reliable and secure random access to a large amount of UIoT terminal data. Cloud computing and smart contract technology can effectively ensure the interconnection of massive data and the authenticity of externally perceived data.

LEO Satellite Constellations for 5G and Beyond: How Will They Reshape Vertical Domains?

The rapid development of communication technologies in the past decades has provided immense vertical opportunities for individuals and enterprises. However, conventional terrestrial cellular networks have unfortunately neglected the huge geographical digital divide, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> ince high-bandwidth wireless coverage is concentrated in urban areas. To meet the goal of “connecting the unconnected,” integrating low Earth orbit (LEO) satellites with the terrestrial cellular networks has been widely considered as a promising solution. In this article, we first introduce the development roadmap of LEO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sa</sup> tellite constellations (SatCons), including early attempts in LEO satellites with the emerging LEO constellations. Further, we discuss the unique opportunities of employing LEO SatCons for the delivery of integrating 5G networks. Specifically, we present their key performance indicators, which offer important guidelines for the design of associated enabling techniques, and then discuss the potential impact of integrating LEO SatCons with typical 5G use cases, where we engrave our vision of various vertical domains reshaped by LEO SatCons. Technical challenges are finally provided to specify future research directions.

Content Delivery Analysis in Cellular Networks With Aerial Caching and mmWAVE Backhaul

In this paper, we investigate the successful content delivery (SCD) performance in the unmanned aerial vehicle (UAV) integrated terrestrial cellular networks, where the caching-enabled UAVs are dispatched to offload the burst traffic from the cellular networks. Specifically, the UAV and terrestrial cellular network share the same spectrum resources for user downlink communications and each UAV uses millimeter wave (mmWave) communications for self-backhaul. We derive a closed-form expression of the achievable rate of the mmWave wireless backhaul link and then analyze the minimum cache hit probability to achieve a certain backhaul rate requirement. By approximating the general probabilistic line-of-sight (LoS) propagation model as a LoS ball model, we analyze the conditional SCD probabilities by leveraging stochastic geometry tools. Simulation results demonstrate that the UAV integrated cellular network can not only provide more access opportunities but also achieve higher SCD performance than the conventional terrestrial network for ground users. Moreover, there exists an optimal UAV density and height to maximize the SCD performance. Our results provide useful guidelines for the design and deployment of future UAV-assisted networks.