Ultra-dense Heterogeneous Networks Research Articles

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.

Joint uplink-downlink user-cell association for ultra-dense NOMA networks

As wireless communication systems evolve, the transition from homogeneous structures to heterogeneous ones intensifies. The need for continuous connectivity, fairness, and spectrum efficiency, such as in massive Internet of Things (IoT) networks and ultra reliable low latency communications (uRLLC), necessitates the utilization of base stations with diverse capabilities and coverage areas. As a result, the need for advanced interference management, user-cell association, and resource allocation techniques increases. This study proposes an innovative solution for the problem of non-orthogonal multiple access (NOMA) user-cell association in multi-tier ultra-dense heterogeneous networks (UD-HetNet). The proposed network constitutes a general framework that can be applied to next generation cellular communications, massive IoT, non-terrestrial and uRLLC type communications. Differing from previous studies that ignore interference and primarily focus on the received signal powers for user-cell association, this investigation advances the subject by considering the interplay between downlink received signal/interference power and uplink inter-cell interference. Due to the non-convex integer programming nature of the problem, it is proposed to employ an innovative relaxation algorithm to obtain a sub-optimal solution for the total signal-to-interference-plus-noise ratio (SINR). As verified through simulation results, the proposed association method permits concurrent communication participation of multiple users while managing the interference, rendering it as an ideal approach for systems emphasizing fairness and continuous connectivity.

Analysis of mobility robustness optimization in ultra-dense heterogeneous networks

Stable and reliable wireless connections during the user equipment's mobility are the critical issue in future mobile communication networks especially during UE's high-speed scenarios over dense heterogeneous networks (HetNets). Thus, setting an accurate value for the handover control parameters (HCPs) (i.e., time-to-trigger (TTT) and handover margin (HOM)) at different speed scenarios is required for system performance. This study proposes six different systems and each one employs different HCP settings using various mobile speed scenarios over a HetNet. Several performance metrics (i.e., received signal reference power (RSRP), handover ping-pong (HOPP), radio link failure (RLF), handover probability (HOP), handover interruption time (HOIT), and handover failure (HOF)) were used as key performance indicators (KPIs) for systems evaluations. Furthermore, 20 users were evaluated in this study using a 40 ms measurement interval. The results show a different impacts of each system on the performance of the deployed HetNet. However, System 1 (lowest TTT & moderate HOM) was the best system in term of RSRP and RLF but on the expense of HOPP. Furthermore, assigning high level of the TTT and HOM (i.e., TTT >1000 ms and HOM >8 dB) leads to 0 HOPP but high RLFs. Moreover, the best system performance in term of HOP, HOIT, and HOF were System 6 (highest TTT & high HOM) with 0.06 %, 3 %, and 0.004 %, respectively. System 4 represents the best trading off system between HOPP and RLF due to the proper configuration of the HCPs. However, the aims of evaluating several systems based on fixed HCPs over several mobile speed scenarios were to see the behaviors of the systems performance when different setting values are applied. Furthermore, this study proposes a HO self-optimization algorithm for auto-tuning the HCPs. Weighted function algorithm along with a trigger timer is used for reducing unnecessary HOs while RLF minimized by setting a trigger timer under a certain conditions when the RSRP of the serving base station (BS) goes below the received signal strength indicators (RSSI). The proposed algorithm shows a significant improvement in term of HOPP, RLF, HOP, and HOF compared to System 4 and FLC algorithm. The RSRP of the proposed algorithm is kept within acceptable range. Thus, accurate setting values for the HCPs are significant to keep the tradeoff between RLF and HOPP at the minimum level.

Stable matching with evolving preference for adaptive handover in cellular-connected UAV networks

Unmanned Aerial Vehicles (UAVs) can effectively and reliably complete aerial tasks in various fields. However, the high-speed mobility of UAVs and the 6G ultra-dense heterogeneous network architecture in future will lead to frequent handover of UAVs during aerial tasks in cellular networks, increasing the potential service interruption rate. Therefore, there is an urgent requirement to address the lack of service adaptability in the overall time-space dimension. To achieve the seamless handover in highly dynamic and stochastic scenarios, we firstly transform the handover problem into a stable matching model. We then extend the stable matching of single time slot to the overall time-space dimension, and transform the dynamically changing time-space information into the preference relations evolution. By flexibly adapting the evolution of the preference lists to current network topology, we propose a Dynamic Stable Matching based Adaptive Handover (DSMAH) algorithm to find stable matching efficiently in a dynamic environment. Simulation results show that the proposed algorithm can achieve a better trade-off between communication quality, handover frequency, and convergence speed and significantly improves the stability of the cellular-connected network as compared to benchmark schemes. The proposed scheme not only effectively addresses the challenges posed by frequent handovers and ping-pong effect, but also shows its notable advantages in dynamic environments.

Joint User Association and BS Operation for Green Communications in Ultra-Dense Heterogeneous Networks

Joint User Association and BS Operation for Green Communications in Ultra-Dense Heterogeneous Networks

Joint Design of Antenna Selection and Resource Allocation in Massive MIMO‐Based Ultra Dense Heterogeneous Networks

Fifth generation (5G) communication network is anticipated to satisfy the need for higher data rates, less latency, ubiquitous connectivity with minimum consumption of energy and acceptable quality of Service (QoS). The massive MIMO and heterogeneous network (HetNet) has evolved as a promising technology to address the aforementioned challenges. In the proposed framework, the massive MIMO technology at the macro base station and full duplex (FD) technology enabled at the small cell base station in HetNet are investigated. An optimal antenna selection strategy, user association (UA), and power allocation (PA) in a massive MIMO‐based HetNet with FD‐enabled small cells are proposed to optimize system sum rate. Initially, the objective function of the optimization problem is nonconvex and a mixed integer nonlinear programming problem. Then, the solution is obtained by transforming the problem into convex problem utilizing Lagrangian decomposition. Simulation results manifest the effect of the number of base station antennas, number of small cell base stations, self‐interference cancellation factor, and channel state information on system sum rate. Moreover, it is evident that the propounded algorithm is effective in terms of optimizing the system sum rate when compared to the conventional maximum signal‐ to ‐ interference ‐ plus ‐noise ‐ ratio (SINR) algorithm.

ECCANS: Enhanced CRITIC-based Context-Aware Network Selection algorithm for 5G HetNet

The ability of mobility management to provide seamless communication and better quality of service (QoS) to the multiservice multimode mobile node (MMMN), which can run multiple services simultaneously during roaming in ultra-dense heterogeneous networks with different radio access technologies, is a major challenge in implementing 5G and beyond. Ultra-dense heterogeneous networks (HetNet), restrictions on multiservice multimode mobile terminals, and user mobility all lead to a number of serious problems, such as the ping-pong effect, unnecessary handovers, and decreasing cell edge spectral efficiency. The dense deployment of small base stations also causes a high probability of frequent handovers in HetNet, which has led to a significant escalation in energy consumption resulting from unnecessary signaling, increased network overhead, and higher energy usage by network equipment. This increased energy requirement may lead to potentially increase the emissions of greenhouse gases to meet the energy production needs. Several handover decision algorithms have been proposed, but most of them deal with only running a single service at a time on a mobile node. In this paper, authors propose a new network selection model for MMMN which may run multiple services simultaneously. This model uses context-aware information about the user’s preferences, characteristics, and needs of the services, limitations of mobile terminal, and network conditions of an ultra-dense HetNet to choose the best network. The fundamental principle of this model is based on the comprehensive network criteria weight computed by the integration of Enhanced CRiteria Importance Through Inter-criteria Correlation (CRITIC), fuzzy analytic hierarchy process (FAHP), and group decision-making (GDM) techniques and the service priority of running services. For computing the objective weight of criteria, a novel improved CRITIC method has been proposed. At the same time, sigmoid utility functions and linear utility functions have been used to figure out the value of network criteria for running multiple services on the mobile node. Finally, the best network from HetNet for MMMN is selected using the technique for order performance by similarity to ideal solution (TOPSIS) and threshold by computing a comprehensive utility value network criteria decision matrix and comprehensive network criteria weight. The simulation results show that the proposed model increases the selection probabilities of an appropriate network according to the service requirements and network conditions. It also reduces unnecessary handovers and improves QoS while satisfying users’ preferences, service requirements, and terminal constraints, in solving the MMMN network selection problem.

Load distribution of base stations in user-centric heterogeneous UDN

In ultra-dense networks (UDN), multiple association can be regarded as a user-centric pattern in which a user can be served by multiple base stations (BSs). The data rate and quality of service can be improved. However, BSs in user-centric paradigm are required to serve more users due to this multiple association scheme. The improvement of system performance may be limited by the improving load of BSs. In this letter, we develope an analytical framework for the load distribution of BSs in heterogeneous user-centric UDN. Based on open loop power control (OLPC), a user-centric scheme is considered in which the clustered serving BSs can provide given signal to interference plus noise ratio (SINR) for any typical user. As for any BS in different tiers, by leveraging stochastic geometry, we derive the Probability Mass Function (PMF) of the number of the served users, the Cumulative Distribution Function (CDF) of total power consumption, and the CDF bounds of downlink sum data rate. The accuracy of the theoretical analysis is validated by numerical simulations, and the effect the system parameters on the load of BSs is also presented.

A Novel Framework of Federated and Distributed Machine Learning for Resource Provisioning in 5G and Beyond Using Mobile-Edge SCBS

The future needs of the telecommunication system lie in deploying a heterogeneous ultra-dense network with varied topographical use cases. However, this increase in ultra-denseness in 5g and beyond poses several challenges in resource allocation, requiring an accurate learning-based prediction. This paper proposes a novel framework using Federated Learning (FL) and Distributed Machine Learning (DML) for Mobile Edge based resource provisioning to User Equipment (UEs). This work formulates the correlation-based novel procedures between UEs in applying Federated and Distributed Machine Learning through Kolmogorov tests for predicting SNR. The correlations of the distribution obtained through the Kolmogorov test check the extent of Independent and Identically Distributed (IID) -ness between modelled data and evaluate the global model for resource provisioning accuracy. Further, correlation-based DML is also employed to balance the computational load of a mobile edge, which acts as a small cell base station and a computational node. In this approach, we account for correlation-based resource predictive model training to balance the uniform computational load by data distribution methods among the neighbouring mobile Edge SCBS nodes for computation. Together with both DML and FL, we create a novel Framework for resource prediction with minimal time for achieving high accuracy without over-fitting.

Heterogeneous Ultradense Networks With Traffic Hotspots: A Unified Handover Analysis

With the ever-growing communication demands and the unceasing miniaturization of mobile devices, the Internet of Things is expanding the amount of mobile terminals to an enormous level. To deal with such numbers of communication data, plenty of base stations (BSs) need to be deployed. However, denser deployments of heterogeneous networks (HetNets) lead to more frequent handovers, which could increase network burden and degrade the users experience, especially in traffic hotspot areas. In this paper, we develop a unified framework to investigate the handover performance of wireless networks with traffic hotspots. Using the stochastic geometry, we derive the theoretical expressions of average distances and handover metrics in HetNets, where the correlations between users and BSs in hotspots are captured. Specifically, the distributions of macro cells are modeled as independent Poisson point processes (PPPs), and the two tiers of small cells outside and inside the hotspots are modeled as PPP and Poisson cluster process (PCP) separately. A modified random waypoint (MRWP) model is also proposed to eliminate the density wave phenomenon in traditional models and to increase the accuracy of handover decision. By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived. Afterwards, we derive the expressions of average distances from a typical terminal to different BSs, and reveal that the handover rate, handover failure rate, and ping-pong rate are deduced as the functions of BS density, scattering variance of clustered small cell, user velocity, and threshold of triggered time. Simulation results verify the accuracy of the proposed analytical model and closed-form theoretical expressions.

I-MEREC-T: Improved MEREC-TOPSIS scheme for optimal network selection in 5G heterogeneous network for IoT

The Internet of Things (IoT) requires seamless mobility and services with low power consumption, low packet loss ratio, ultra-low latency, and high reliability. Ultra-dense deployment of small coverage radio access networks improves network throughput, latency, and packet loss ratio but increases the frequency of vertical handovers. Optimal network selection to ensure ultra-reliable, low-latency, seamless connectivity to provide massive machine-type communication to support a wide range of services for the emergence of IoT and Industry 5.0 through the next generation heterogeneous networks is one of the most challenging fields of research. The best network selection is one of the most crucial phases of the vertical handover decision algorithm in an ultra-dense heterogeneous network. Inadequate network selection can lead to failed handovers and ping pong handovers, which drastically affect the quality of services and the quality of experience. In this article, we have developed a new improved method based on improved removal effects of criteria- technique for order performance by similarity to ideal solution (I-MEREC-TOPSIS) network selection scheme. It is based on the removal effect of alternatives’ attributes. It is an intelligent integration of the I-MEREC weight method of alternative criteria and the TOPSIS alternative ranking multi-attribute decision-making technique. In the proposed scheme, optimal network selection is not only dependent on the best performance score of candidate networks but also on the statistically defined threshold value of servicing and target networks. In comparison to CRiteria Importance Through Intercriteria Correlation (CRITIC) TOPSIS, Entropy-TOPSIS, Statistical Variance Procedure(SVP) TOPSIS, and MEREC-TOPSIS, the proposed scheme has reduced ping-pong handovers and handover failures.

Hybrid Learning based Radio Resource Management in 5G Heterogeneous Networks

Ultradensification using different types of small cells (SCs) is one of the key enabling solutions to meet the multiple stringent requirements of 5G cellular networks. However, radio resource management (RRM) in ultra-dense heterogeneous networks (HetNets) is not easy due to interferences in multi-tiered architecture and dynamic network conditions. Interferences in 5G HetNets can be efficiently managed only through the techniques which are adaptive and self-organizing to handle dynamic conditions in 5G HetNets. In this article, a machine learning (ML) based self-adaptive resource allocation scheme is proposed based on the combination of independent and cooperative learning and evaluated for ultra-dense 5G HetNets. The proposed scheme aims to improve the QoS of all users associated with different network tiers in ultra-dense HetNets simultaneously. The proposed solution adaptively optimizes the SCs transmit power either through independent learning or cooperative learning based on the varying density of small cells to minimize the interferences and ensure minimum QoS requirements for all users in different network tiers. The proposed scheme not only maintains the minimum required capacities for QoS provision to all users simultaneously but has also shown a significant improvement in the capacities of users in different network tiers in high interference scenarios as compared to the use of a single learning scheme.

Optimal Learning Paradigm and Clustering for Effective Radio Resource Management in 5G HetNets

Ultra-dense heterogeneous networks (UDHN) based on small cells are a requisite part of the future cellular networks as they are proposed as one of the enabling technologies to handle coverage and capacity problems. But co-tier and cross-tier interferences in UDHN severely degrade the quality of service due to K-tiered architecture. Machine learning based radio resource management either through independent learning or cooperative learning is a proven efficient scheme for interference mitigation and quality of service provision in UDHN in a both distributive and cooperative manner. However, an optimal learning paradigm selection, i.e., either independent or cooperative learning and optimal cooperative cluster size in cooperative learning for efficient radio resource management in UDHN is still an open research problem. In this article, a Q-learning based radio resource management scheme is proposed and evaluated for both distributive and cooperative schemes using independent and cooperative learning. The proposed Q-learning solution follows the ϵ-greedy policy for optimal convergence. The simulation results for the UDHN in an urban setup show that in comparison to the independent learning paradigm, cooperative learning has no significant impact on macro cell user capacity. However, there is a significant improvement in small cell user capacity and the sum capacity of the cooperating small cells in the cluster. A significant increase of 48.57% and 37.9% is observed in the small cell user capacity, and sum capacity of the cooperating small cells, respectively, using cooperative learning as compared to independent learning which sets cooperative learning as an optimal learning strategy in UDHN. The improvement in small cell user capacity is at cost of increased computational time which is directly proportional to the number of cooperating small cells. To solve the issue of computational time in cooperative learning, an optimal clustering algorithm is proposed. The proposed optimal clustering reduced the computational time by four times in cooperative Q-learning.

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

Heterogeneous ultra-dense networks (HUDNs) are one of the key enabling technologies for the fifth-generation (5G) networks. They aim to provide high capacity, low installation cost, and distributed traffic loads. The cell selection is a challenging issue in HUDNs, due to the different characteristics of base stations (BSs) and the existence of a large number of them. Thus, the traditional cell selection scheme is not applicable in such a network. In this paper, a novel adaptive cell selection strategy is proposed, called adaptive two-tier based on adaptive boosting (A2T-Boost). It can adapt to the various characteristics of base stations, as well as the different movement features of mobile stations such as vehicles and pedestrian. It is a software-defined networking (SDN)/machine learning (ML)-based scheme. A real-world case is considered in the downtown of Los Angeles city. Simulation results demonstrate that A2T-Boost achieves high prediction performance and it outperforms other related schemes in terms of average number of handovers (HOs) by up to 50%. Moreover, it enhances the average achievable downlink sum-rates and network energy efficiency achieved by vehicles by up to 33.76%. Furthermore, the average packet delay is decreased using the proposed scheme by up to 12.87%.

Analysis of Mobility Robustness Optimization in Ultra-Dense Heterogeneous Networks

Analysis of Mobility Robustness Optimization in Ultra-Dense Heterogeneous Networks

3 Dimension Multilayer Heterogenous Ultra Dense Networks

The development of communication networks poses new challenges in the field of scientific research. At the same time, one of the main directions of development is the creation of highdensity and ultra-dense networks. Ultra-dense networks already belong to the technologies of communication networks of the sixth generation and the requirements for them are formed in the conditions of their deployment in three-dimensional space. Starting with the construction of fifth generation communication networks, communication networks are considered as heterogeneous, in which various technologies can be used together in the process of providing network services, for example, the Internet of Things, unmanned aerial vehicles, vehicular ad hoc networks, etc. This leads to the need to define and study three-dimensional multi-layer heterogeneous ultra-dense networks, which is the subject of this article.

Graph neural network-based cell switching for energy optimization in ultra-dense heterogeneous networks

The development of ultra-dense heterogeneous networks (HetNets) will cause a significant rise in energy consumption with large-scale base station (BS) deployments, requiring cellular networks to be more energy efficient to reduce operational expense and promote sustainability. Cell switching is an effective method to achieve the energy efficiency goals, but traditional heuristic cell switching algorithms are computationally demanding with limited generalization abilities for ultra-dense HetNet applications, motivating the usage of machine learning techniques for adaptive cell switching. Graph neural networks (GNNs) are powerful deep learning models with strong generalization abilities but receive little attention for cell switching. This paper proposes a GNN-based cell switching solution (GBCSS) that has a smaller computational complexity than existing heuristic algorithms. The presented performance evaluation uses the Milan telecommunication dataset based on real-world call detail records, comparing GBCSS with a traditional exhaustive search (ES) algorithm, a state-of-the-art learning-based algorithm, and the baseline without cell switching. Results indicate that GBCSS achieves a 10.41% energy efficiency gain when compared with the baseline and achieves 75.76% of the optimal performance obtained with ES algorithm. The results also demonstrate GBCSS’ significant scalability and generalization abilities to differing load conditions and the number of BSs, suggesting this approach is well-suited to ultra-dense HetNet deployment.

Joint cell zooming and sleeping strategy in ultra dense heterogeneous networks

To optimize the ultra dense distribution of base stations (BSs) which consist of micro BS and macro BS, balancing the tradeoff between user communication quality and energy saving is essential. In this paper, a joint cell zooming and sleeping strategy is proposed for the downlink two-tier heterogeneous network under the Poisson Point Process (PPP) model, in order to balance the user benefits and communication system operating cost. First, the cell zooming scheme is presented based on game theory, where the association probability of each base station is achieved by adjusting the cell zooming factor (CZF). With the improved BS association probability, more users can benefit with better network experience by connecting to micro BS instead of macro BS. Then a two-step sleeping strategy is put forward, by applying the cell zooming. The first step is to ascertain which BSs are kept based on the analysis of the energy consumption of candidates. The second step is used to measure the overall overlapping degree of each BS and provide different sleep decisions according to the radius of the area covered by MBS. The proposed sleeping scheme is able to stimulate the BS with a high overlapping degree to be slept to achieve more energy saving. Through the proposed joint schemes, the service efficiency of communication system is improved, when the users connect to micro BS instead of macro BS and some micro BSs with low utility are turned off. Simulation results indicate the improved performance in terms of the user experiences and energy saving.

User-centric base station clustering and resource allocation for cell-edge users in 6G ultra-dense networks

Ultra-Dense Networks (UDNs) have been proposed to meet the ultra-high system capacity and ultra-high user experience rate requirements of sixth generation (6G) mobile networks. However, ultra-dense base stations (BSs) deployment poses challenges for cell-edge users such as BS selection, severe interference, resource allocation, and inter-cell handover. To tackle these obstacles, this paper formulates a joint BS clustering and resource allocation problem for cell-edge users in a 6G heterogeneous UDN system. For efficient resolution, this problem is decoupled into two sub-problems to be solved independently. We first propose a user-centric BS clustering algorithm based on many-to-many matching for the BS clustering problem in the heterogeneous UDN system; This algorithm considers the constraint of the system capacity. A many-to-many stable matching between users and small cell BSs is constructed under the optimization objective of maximizing the achievable user rate. Furthermore, we analyze the effectiveness, stability, convergence, and complexity of the BS clustering algorithm. Given established small cell BS clusters, we also propose a user-centric resource allocation algorithm based on network partitioning. This algorithm accounts for the interference of all users in the heterogeneous UDN system and maximizes intra-sub-network interference while minimizing inter-sub-network interference via a spectral clustering-based sub-network partitioning algorithm. Next, orthogonal allocation of resource blocks (RBs) is implemented within each sub-network, and RBs are spatially multiplexed between sub-networks. Numerical results confirm the benefits of the proposed methods: our algorithms outperform benchmark solutions in terms of the sum achievable system rate, average achievable user rate, and user spectral efficiency.

Learning-Based Resource Allocation in Heterogeneous Ultradense Network

Learning-based resource allocation (LRA) is envisioned as an integral element of 6G. This article proposes a novel learning-based paradigm to address resource allocation problems in heterogeneous ultradense networks (HUDNs). Our paradigm is a highly efficient realization for utilizing the inherence properties in HUDN, which comprise the local validity and correlation attenuation. Concretely, we formulate the HUDNs as a heterogeneous bipartite graph model and propose the corresponding heterogeneous bipartite graph neural network (HBGNN). The local sampling characteristic of HBGNN matches the inherence properties. Meanwhile, our paradigm combines data-driven and model-driven learnings and employs online and offline trainings. Hence, two of LRA’s obstacles: 1) the overreliance on the perfect data set and 2) the low calculation efficiency are mitigated, and the realizability of our paradigm is improved. Besides, entropy regularization is utilized to guarantee the effectiveness of exploration in the configuration space. We apply our approach to a representative resource allocation problem, the jointly user association (UA) and power allocation (JUAPA) problem. We formulate JUAPA as a combination classification and regression problem and adopt a dynamical hyperparameter output layer to address the discrete variable of UA. Simulation results demonstrate that the proposed method has better performance and higher computational efficiency than traditional optimization algorithms.