Ultra-dense Networks Research Articles

Iterative Partial Construction of Hybrid Beamforming Under Analog Quantized Phase Constraints in Ultra-Dense Networks

Iterative Partial Construction of Hybrid Beamforming Under Analog Quantized Phase Constraints in Ultra-Dense Networks

Security, privacy and performance concerns in ultra dense networks

Ultra Dense Networks (UDNs) have emerged as a pivotal technology in meeting the exponential growth in data demand and providing seamless connectivity in 5G and beyond networks. However, the high density of small cells in UDNs introduces significant challenges related to security, privacy, and performance. This survey paper presents a comprehensive review of the current state-of-the-art in addressing these concerns. It begins by exploring the unique security vulnerabilities inherent to UDNs, including the increased risk of eavesdropping, denial of service attacks, and unauthorized access due to the close proximity of small cells. The paper then discusses privacy issues, particularly the risks of location tracking and user data exposure, exacerbated by the dense deployment of base stations. In terms of performance, the paper evaluates the impact of interference, handover management, and resource allocation on network efficiency. Various proposed solutions, such as advanced encryption techniques, privacy-preserving algorithms, and interference mitigation strategies, are analyzed and compared. The survey concludes by identifying open research challenges and future directions, emphasizing the need for integrated approaches that simultaneously address security, privacy, and performance to ensure the robust operation of UDNs in next-generation wireless networks.

Optimizing Drone-Based IoT Base Stations in 6G Networks Using the Quasi-opposition-Based Lemurs Optimization Algorithm

The rapid evolution and integration of next-generation Internet-of-things (NG-IoT) applications present new complexities for sixth-generation (6G) mobile communication networks, including the need for extensive connectivity, increased network capacity, and ultralow-latency communications. While ultradense networking, characterized by deploying numerous base stations, offers a potential solution, practical and financial constraints limit its feasibility. Drone-based stations (DBSs) emerge as a flexible alternative, but their optimal positioning remains a critical challenge due to finite energy sources and potential signal degradation. This study introduces a new quasi-opposition-based lemurs optimizer (QOBLO) to address the optimal placement of DBSs in NG-IoT networks. QOBLO combines lemur foraging behaviour with quasi-opposition-based learning to enhance exploration and exploitation in the optimization process. The performance of QOBLO was evaluated across three scenarios and compared with other swarm intelligence algorithms using metrics such as Friedman’s ranking test (FRT) and the Wilcoxon signed-rank test (WSRT). Rigorous simulations emulated real-world conditions and varying network demands, testing QOBLO's adaptability and robustness. Results indicate that QOBLO significantly outperforms other algorithms, achieving a top FRT value of 1.234 and demonstrating superior p values in the WSRT. These findings highlight QOBLO's capability to enhance connectivity, coverage, and energy efficiency in 6G environments. The primary contribution of this research is the development of QOBLO, a scalable and efficient method for optimizing DBS deployment. This new approach improves network performance in complex NG-IoT applications, offering a robust solution to the challenges of 6G networks and ensuring enhanced reliability and sustainability of future communication infrastructures.

Support vector machine‐based handover scheme for heterogeneous ultra dense network of high‐speed railway

AbstractIn order to meet the growing demands and extend network coverage for high‐speed railway (HSR) system, the dense deployment of a large number of small cells (SCs) is considered for 5G networks. However, the deployment of dense SCs and the high speed of trains result in challenging problems such as interference, frequent handovers (HOs), increased HO failure rate, and consequently the deteriorated overall quality of service (QoS). In order to address the challenges in handover, an improved handover decision strategy is proposed based on Support Vector Machine (SVM). The HO decision making is considered as a classification problem taking into account available states that they may have in the HSR network. From the simulation results, it is observed that the proposed scheme is capable of decreasing the number of HO, HO failure rate and enhancing the network performance remarkably.

Multi-Agent DRL for task offloading with delay reliability assurances in ris-aided MEC in UDN

Abstract This paper focuses on the scenario where Reconfigurable Intelligent Surfaces (RISs) are introduced in ultra-dense networks (UDN) to guarantee the transmission performance of task offloading of users in Mobile Edge Computing (MEC). Considering users’ task arrival and wireless channel stochasticity, we analyze the delay violation probability bound for each user’s two-hop tandem queueing system with the Martingale theory, i.e., the task offloading and edge computing queues. By taking the delay violation probability bounds as the service reliability constraint, we further propose a MADRL-based algorithm, which co-trains each user and BS as independent and heterogeneous agents and maximizes the total energy efficiency of the system by jointly optimizing the users’ offloading ratio, the RISs’ phase-shift, and the BSs’ resource allocation policy. Experimental results show that our algorithm can improve the EE by about 17.9% compared to other alternative methods under different reliability requirements.

Mobility Management in Next Generation Wireless Networks

Living in a modern society without smart devices is impossible now a days. Every sector related to human lifestyle is either smart or controlled devices which was rare a decade back. Expectations are not limited to network connection but extend to mobility as well. As a result, mobility management becomes an essential and challenging task to accomplish. The revolution in wireless technologies expects more scalability and flexibility in resource management. Handover is one of the vital parts of radio resource management. Execution with perfection and optimization of the handover technique increases the reliability of the system deployed to meet the requirement of high mobility. The cell became small as the wireless cell size adjusted with the revolution of relevant technologies like fifth generation (5G) and beyond. Traffic profile and its density are always in a growing trend. This pattern draws the attention of ultra-dense networks (UDN). The UDN of small cells requires an extra number of handovers with higher accuracy and less delay in execution. In this context, this paper proposed an algorithm where a cross-examination to reduce unnecessary handover that improves the handover performance in next-generation wireless networks.

Optimizing resource allocation in UAV-assisted ultra-dense networks for enhanced performance and security

The deployment of unmanned aerial vehicles (UAVs) in ultra-dense networks (UNDs) has significantly advanced network capabilities in 5G/6G environments, addressing coverage enhancement and security concerns. Our research presents a deep reinforcement learning (DRL) based approach designed to manage the increasing data traffic demands and limited communication resources in UAV-assisted UNDs. Traditional DRL methodologies often struggle with challenges like low sample efficiency and energy wastage, which can indirectly impact network security and stability. To address these concerns, we introduce the Stabilizing Transformers based Potential Driven Reinforcement Learning (STPD-RL) framework. STPD-RL optimizes critical network operations such as transmission link selection and power allocation, directly contributing to improved energy efficiency and robust network performance. Initially, we have refined the potential driven experience replay and implemented it into resource allocation in UAV-assisted UDN for the inaugural time. By assigning a potential energy function to each state in experience replay, users can employ intrinsic state supervision to learn from a spectrum of good and bad experiences. Subsequently, we have employed stabilizing transformers to hasten the learning trajectory for resource allocation policies, thereby enhancing the stability of model training. Furthermore, we have integrated potential driven experience replay and stabilizing transformers within the Proximal Policy Optimization algorithm, thus formulating our uniquely tailored STPD-PPO. In simulations with many users and base stations, STPD-PPO outperformed traditional PPO in metrics such as entropy loss, policy loss, and value loss. Results suggest that our STPD-PPO surpasses traditional DRL algorithms in several respects, including convergence rate, energy efficiency, total power consumption, and exploration capacity.

Decentralized Collaborative Caching in Ultra-Dense Networks

Decentralized Collaborative Caching in Ultra-Dense Networks

Evolutionary Multi-Objective Optimization Algorithm for Resource Allocation Using Deep Neural Network in Ultra-Dense Networks

Evolutionary Multi-Objective Optimization Algorithm for Resource Allocation Using Deep Neural Network in Ultra-Dense Networks

Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach

The progression of Software Defined Networking (SDN) and the virtualisation technologies lead to the beyond 5&#x00A0;G era, providing multiple benefits in the smart economies. However, despite the advantages, security issues still remain. In particular, SDN/NFV and cloud/edge computing are related to various security issues. Moreover, due to the wireless nature of the entities, they are prone to a wide range of cyberthreats. Therefore, the presence of appropriate intrusion detection mechanisms is critical. Although both Machine Learning (ML) and Deep Learning (DL) have optimised the typical rule-based detection systems, the use of ML and DL requires labelled pre-existing datasets. However, this kind of data varies based on the nature of the respective environment. Another smart solution for detecting intrusions is to use honeypots. A honeypot acts as a decoy with the goal to mislead the cyberatatcker and protect the real assets. In this paper, we focus on Wireless Honeypots (WHs) in ultra-dense networks. In particular, we introduce a strategic honeypot deployment method, using two Reinforcement Learning (RL) techniques: (a) <inline-formula><tex-math notation="LaTeX">$e-Greedy$</tex-math></inline-formula> and (b) <inline-formula><tex-math notation="LaTeX">$Q-Learning$</tex-math></inline-formula>. Both methods aim to identify the optimal number of honeypots that can be deployed for protecting the actual entities. The experimental results demonstrate the efficacy of both methods.

ISAC-enable mobility-aware multi-UAV placement for ultra-dense networks

This paper proposes a method for characterizing user mobility levels in multiple unmanned aerial vehicles (UAV)-assisted networks. In a dynamic environment with varying degrees of mobile ground users, optimizing the placement of UAVs is important in improving network throughput. Moreover, for integrated sensing and communication (ISAC) enabled UAVs, the resource allocation for sensing and communication relies on the dynamic nature of the network environment. To keep track of the changes in the environment UAVs are required to continuously update their trajectory, hence, characterizing the users’ mobility level is an important tool to improve the UAV trajectory optimization. In this paper, a mobility-aware resource allocation for joint sensing and communication is proposed. Our results demonstrate that the proposed algorithm can improve the resource allocation between sensing and communication in an ISAC-enabled UAV-assisted network.

ACGMA: Adaptive Channel Gain Multiple Access Scheme for Data Rate Improvement in Ultra-Dense Networks

ACGMA: Adaptive Channel Gain Multiple Access Scheme for Data Rate Improvement in Ultra-Dense Networks

Energy-Efficient Resource Allocation in Ultra-Dense Networks With EMBB and URLLC Users Coexistence

Energy-Efficient Resource Allocation in Ultra-Dense Networks With EMBB and URLLC Users Coexistence

Source Network Load Storage Access to Power Wireless Private Network Technology Based on 5G Ultra Dense Networking

We make the source network load storage access power wireless private network, this paper proposes a source network load storage access power wireless private network technology based on 5G ultra dense network. The multiple rotation scheduling and self-organizing learning methods are used to establish the deployment model of the source network load storage access node of the power wireless private network under the 5G communication mode. According to the routing control algorithm design of the 5G ultra dense networking node, combined with the integration analysis of the access load parameters, the source network load storage access model of the 5G ultra dense networking under the dynamic load distributed control mode is established. Through the method of optimal control of reactive power and voltage of distribution network, the transmission link equilibrium structure model of 5G source network load storage access to power wireless private network is constructed. Combined with the coverage analysis of link topology structure and the benefit maximization constraint analysis of production and consumption users, the active and reactive capacity analysis of transaction between production and consumption user groups and multiple production and consumption users is adopted. Combined with the energy storage characteristics analysis and power flow parameter calculation of the source network load storage access power, the 5G ultra dense networking and private network access to the source network load storage access power are realized. The test shows that this method has better power balance dispatching ability and larger output power gain when it is applied to the design of source network load storage access power wireless private network.

Autonomous handover parameter optimisation for 5G cellular networks using deep deterministic policy gradient

The ultra-dense network (UDN) is considered a vital technology for 5G mobile communications due to its ability to transmit high data rates in high-traffic environments. However, it also creates new challenges, such as increased interference and difficulty managing mobility. To ensure seamless base station connectivity and maintain a high quality of service, a reliable handover algorithm is necessary, especially in a UDN where the cell size is small. This paper proposes an optimisation method for handover parameters based on the Deep Deterministic Policy Gradient (DDPG) algorithm. It adjusts the handover margin (HOM) to determine the handover trigger points accurately and dynamically. Simulation results indicate that the system’s mobility performance has been greatly improved while maintaining high throughput and low latency at different speeds.

Bat algorithm based semi‐distributed resource allocation in ultra‐dense networks

AbstractThis paper addresses the resource allocation (RA) for ultra‐dense network (UDN), where base stations (BSs) are densely deployed to meet the demands of future wireless communications. However, the design of RA in UDN is challenging, as the RA problem is non‐convex and NP‐hard. Therefore, this paper considers and studies a semi‐distributed resource block (RB) allocation scheme, in order to achieve a well‐balanced trade‐off between performance and complexity. In the context of semi‐distributed RB allocation scheme, the problem can be decomposed into the subproblem of clustering and the subproblem of cluster‐based RB allocation. We first improve the K‐means clustering algorithm by employing the Gaussian modified method, which can significantly decrease the number of iterations for carrying out the K‐means algorithm as well as the failure possibility of clustering. Then, bat algorithm (BA) is introduced to attack the problem of cluster‐based RB allocation. In order to make the original BA applicable to the problem of RB allocation, chaotic sequences are adopted to discretize the initial position of the bats, and simultaneously increase the population diversity of the bats. Furthermore, in order to speed up the convergence of BA, the logarithmic decreasing inertia weight is employed for improving the original BA. Our studies and performance results show that the proposed approaches are capable of achieving a desirable trade‐off between the performance and the implementation complexity.

Fine-Grained Spatio-Temporal Distribution Prediction of Mobile Content Delivery in 5G Ultra-Dense Networks

The 5 G networks have extensively promoted the growth of mobile users and novel applications, and with the skyrocketing user requests for a large amount of popular content, the consequent content delivery services (CDSs) have been bringing a heavy load to mobile service providers. As a key mission in intelligent networks management, understanding and predicting the distribution of CDSs benefits many tasks of modern network services such as resource provisioning and proactive content caching for content delivery networks. However, the revolutions in novel ubiquitous network architectures led by ultra-dense networks (UDNs) make the task extremely challenging. Specifically, conventional methods face the challenges of insufficient spatio precision, lacking generalizability, and complex multi-feature dependencies of user requests, making their effectiveness unreliable in CDSs prediction under 5 G UDNs. In this paper, we propose to adopt a series of encoding and sampling methods to model CDSs of known and unknown areas at a tailored fine-grained level. Moreover, we design a spatio-temporal-social multi-feature extraction framework for CDSs hotspots prediction, in which a novel edge-enhanced graph convolution block is proposed to encode dynamic CDSs networks based on the social relationships and the spatio features. Besides, we introduce the Long-Short Term Memory (LSTM) to further capture the temporal dependency. Extensive performance evaluations with real-world measurement data collected in two mobile content applications demonstrate the effectiveness of our proposed solution, which can improve the prediction area under the curve (AUC) by 40.5% compared to the state-of-the-art proposals at a spatio granularity of 76 m, with up to 80% of the unknown areas.

A Deep Learning Approach for Throughput Enhanced Clustering and Spectrally Efficient Resource Allocation in Ultra-Dense Networks

A Deep Learning Approach for Throughput Enhanced Clustering and Spectrally Efficient Resource Allocation in Ultra-Dense Networks

Optimizing resource allocation in Ultra-Dense networks with UAV Assistance: A levy Flight-based approach

Optimizing resource allocation in Ultra-Dense networks with UAV Assistance: A levy Flight-based approach

Performance of the Typical User in RIS-Assisted Indoor Ultra Dense Networks

Performance of the Typical User in RIS-Assisted Indoor Ultra Dense Networks