Heterogeneous Cellular Networks Research Articles

Synchronization of pacemaker cell firing underlies autonomic modulation of beating intervals and their variability in a numerical model of heterogeneous SA node tissue.

The normal heart beats regularly but not metronomically. It is initiated by pacemaker cells within the sinoatrial node (SAN), an extremely heterogeneous cellular network. The functional role of SAN cell heterogeneity remains unknown. Here we investigated the problem in a SAN tissue model comprised of 2025 single cell models. Each cell model features a coupled operation of the Ca and membrane clocks. We modeled tissue heterogeneity by varying the ICaL conductance (gCaL) and SR Ca pumping (Pup). In our simulations heterogeneous cells interacted in tissue and acquired new properties: while cells in isolation had all rhythmic or no firing, cells firing irregular APs emerged in local cell communities with similar properties. The heart rate is naturally increased via β-adrenergic receptors (βAR) and decreased via cholinergic receptors (ChR) in the SAN. βAR stimulation increased the rate and synchronization of AP firing of individual cell models within the SAN tissue, whereas ChR stimulation decreased the rate and synchronization of AP firing. The number of firing cells increased in βAR stimulation to 99% from 80%, and the average cycle length of the SAN decreased by ∼20%. Meanwhile the standard deviation of the cycle length decreased by a multiple of 25, reflecting a much higher level of synchronization. During ChR stimulation, the number of firing cells remained almost unchanged, but the average AP cycle length increased by almost 20%. The standard deviation also increased by a multiple of 2, demonstrating the higher level of desynchronization. The number of cells with emergent irregular APs firing was substantially greater under ChR stimulation than BAR stimulation, indicating importance of these cells as an intrinsic source of heart rate variability at various conditions.

The Energy Efficiency of Heterogeneous Cellular Networks Based on the Poisson Hole Process

In order to decrease energy consumption caused by the dense deployment of pico base stations (PBSs) in heterogeneous cellular networks (HetNets), this paper first analyzes the energy efficiency (EE) of two-tier HetNets and then proposes a method to maximize the network EE by adjusting the PBS transmit power. The two-tier HetNets are modeled by the Poisson point process (PPP) and the Poisson hole process (PHP), and then the coverage probability of the macro base station (MBS) and the PBS in the two-tier HetNets is derived based on the mean interference to signal ratio (MISR). According to the user association probability, the coverage probability of the PPP-PHP HetNets is obtained. Then, the tractable expression of the average achievable rate is deduced on the basis of the relationship between the coverage probability and the average achievable rate. Finally, the expression of EE is derived and the EE optimization algorithm is proposed based on the PBS transmit power. The simulation results show that the PPP-PHP network is superior to the PPP-PPP network in terms of coverage probability and EE, and the network EE can be effectively improved by setting an appropriate PBS transmit power.

Impact of various System Design Parameters on Coverage in Heterogeneous Cellular Networks

Recent cellular systems are moving towards heterogeneous cellular networks (HCNs) that consist of a mixture of miniature cells and legacy macro-cells to meet the requirements of wireless data traffic, owing to the immense amount of multi-purpose mobile applications. The inclusion of small cells is a cost-effective solution for enhancing the size and coverage of the existing macro-cellular network. This article assumes a heterogeneous cellular network consisting of two tiers of base stations (BSs): large-scale (macro) and small-scale (pico) BSs. The users are evenly distributed, and each tier of BSs and users create a uniform Poisson point process (PPP). Practical third-generation partnership project (3GPP) models for path loss are considered, and three camping/association criteria are utilized to relate user equipment (UEs) to large or small-scale BSs, including coupled and decoupled camping criteria to study coverage. The impact of several system design parameters on coverage is investigated using the aforementioned heterogeneous cellular network, association criteria, and 3GPP path loss models. Our simulation results provide insights into the effect of infrastructure sharing between macro and pico-cells and user density on coverage. We also explore the impact of fractional power control (FPC) and signaling limits on coverage under all considered association strategies. Finally, we investigate the effect of open-loop UE transmission power, pico-density, and biasing on coverage. Specifically, we thoroughly explore the effect of empty BSs on coverage under all system design parameters.

Obstacle Avoidance System for Autonomous Vehicles

Recent cellular systems are moving towards heterogeneous cellular networks (HCNs) that consist of a mixture of miniature cells and legacy macro-cells to meet the requirements of wireless data traffic, owing to the immense amount of multi-purpose mobile applications. The inclusion of small cells is a cost-effective solution for enhancing the size and coverage of the existing macro-cellular network. This article assumes a heterogeneous cellular network consisting of two tiers of base stations (BSs): large-scale (macro) and small-scale (pico) BSs. The users are evenly distributed, and each tier of BSs and users creates a uniform Poisson point process (PPP). Practical third-generation partnership project (3GPP) models for path loss are considered, and three camping/association criteria are utilized to relate user equipment (UEs) to large or small-scale BSs, including coupled and decoupled camping criteria to study coverage. The impact of several system design parameters on coverage is investigated using the aforementioned heterogeneous cellular network, association criteria, and 3GPP path loss models. Our simulation results provide insights into the effect of infrastructure sharing between macro and pico-cells and user density on coverage. We also explore the impact of fractional power control (FPC) and signaling limits on coverage under all considered association strategies. Finally, we investigate the effect of open-loop UE transmission power, pico-density, and biasing on coverage. Specifically, we thoroughly explore the effect of empty BSs on coverage under all system design parameters.

Dual Connectivity in Heterogeneous Cellular Networks: Analysis of Optimal Splitting of Elastic File Transfers Using Flow-Level Performance Models

Dual Connectivity in Heterogeneous Cellular Networks: Analysis of Optimal Splitting of Elastic File Transfers Using Flow-Level Performance Models

Resource Allocation Based on Digital Twin-Enabled Federated Learning Framework in Heterogeneous Cellular Network

Federated learning (FL) allows user devices (UDs) to upload local model parameters to participate in a global model training, which protects UDs' data privacy. Nevertheless, FL still faces challenges such as core network congestion, UDs' limited resources and less efficient mapping between devices and cyber systems. Therefore, in this article, we integrate the digital twin (DT) and the mobile edge computing (MEC) technologies into a hierarchical FL framework in the heterogeneous cellular network scenario. When the UDs are not in the service range of the small base stations (SBSs), the framework allows macro base stations to assist UDs' local computation, thus reducing the transmission delay. It also protects the user privacy and allows more users to join in the training in order to improve the FL accuracy. In addition, we propose a deep reinforcement learning-based scheme to solve the joint optimization problem of dynamic UDs-stations association and resource allocation, thereby minimizing the energy consumption within a limited time delay. Simulation results show that our proposed scheme not only effectively reduces the task transmission failure rate and energy consumption compared with the baseline scheme, but also saves the communication cost through the DT network.

A SELF-TUNING ALGORITHM FOR OPTIMAL ENERGY EFFICIENT LOAD BALANCING IN WIRELESS CELLULAR HETEROGENEOUS NETWORKS

Cellular wirelesses heterogeneous networks (HetNets) have become an important part of the growth of the telecom industry. The rapid growth of cellular networks and widespread use of wireless services have led to a huge increase in energy use. This makes it hard to make cellular networks that use less energy and last longer. In HetNets, load balancing (LB) and energy balance have a big effect on cell range extension (CRE) and network lifetime extension (NLE). optimal energy efficient LB in wireless cellular HetNets is presented in this paper using a self-tuning algorithm. Several self-tuning methods for HetNets have been developed that are driven by the quality of experience (QoE). Unlike earlier heuristic-based approaches, the suggested technique uses a gradient ascending method to ensure that parameter modifications constantly increase overall system QoE, LB, throughput, and energy efficiency. The planned network's packet loss rate is extremely low. The suggested and contemporary models are tested in an experimental investigation, demonstrating the importance of the suggested model in comparison to other contemporary methodologies.

Multi-Slope Path Loss Model-Based Performance Assessment of Heterogeneous Cellular Network in 5G

The coverage and capacity required for fifth generation (5G) and beyond can be achieved using heterogeneous wireless networks. This exploration set up a limited number of user equipment (UEs) while taking into account the three-dimensional (3D) distance between UEs and base stations (BSs), multi-slope line of sight (LOS) and non-line of sight (n-LOS), idle mode capability (IMC), and third generation partnership projects (3GPP) path loss (PL) models. In the current work, we examine the relationship between the height and gain of the macro (M) and pico (P) base stations (BSs) antennas and the ratio of the density of the MBSs to the PBSs, indicated by the symbol β. Recent research demonstrates that the antenna height of PBSs should be kept to a minimum to get the best performance in terms of coverage and capacity for a 5G wireless network, whereas ASE smashes as β crosses a specific value in 5G. We aim to address these issues and increased the performance of the 5G network by installing directional antennas at MBSs and omnidirectional antennas at Pico BSs while taking into consideration traditional antenna heights. The authors of this work used the multi-tier 3GPP PL model to take into account real-world scenarios and calculated SINR using average power. This study demonstrates that, when the multi-slope 3GPP PL model is used and directional antennas are installed at MBSs, coverage can be improved 10% and area spectral efficiency (ASE) can be improved 2.5 times over the course of the previous analysis [1]. Similarly to this, the issue of an ASE crash after a base station density of 1000 has been resolved in this study.

Joint Design of Access and Backhaul in Densely Deployed MmWave Small Cells

With the rapid growth of mobile data traffic, the shortage of radio spectrum resource has become increasingly prominent. Millimeter wave (mmWave) small cells can be densely deployed in macro cells to improve network capacity and spectrum utilization. Such a network architecture is referred to as mmWave heterogeneous cellular networks (HetNets). Compared with the traditional wired backhaul, The integrated access and backhaul (IAB) architecture with wireless backhaul is more flexible and cost-effective for mmWave HetNets. However, the imbalance of throughput between the access and backhaul links will constrain the total system throughput. Consequently, it is necessary to jointly design of radio access and backhaul link. In this paper, we study the joint optimization of user association and backhaul resource allocation in mmWave HetNets, where different mmWave bands are adopted by the access and backhaul links. Considering the non-convex and combinatorial characteristics of the optimization problem and the dynamic nature of the mmWave link, we propose a multi-agent deep reinforcement learning (MADRL) based scheme to maximize the long-term total link throughput of the network. The simulation results show that the scheme can not only adjust user association and backhaul resource allocation strategy according to the dynamics in the access link state, but also effectively improve the link throughput under different system configurations.

Enhancing Energy Sustainability in Two-Tier Heterogeneous Cellular Networks in 5G through Idle Mode Capabilities

Enhancing Energy Sustainability in Two-Tier Heterogeneous Cellular Networks in 5G through Idle Mode Capabilities

Fairness Oriented H-NOMA Devices Association and Pairing in HCN Slicing Using Cooperation Games

Device association (DA) and pairing (DP) are presented in this letter to evaluate the fairness of different services between enhanced mobile broadband devices (eMBBDs) and ultra-reliability low latency communications devices (uRLLCDs) in RAN-slicing, which has emerged as a viable technique for enhancing total network throughput in heterogeneous cellular networks (HCNs) design. To optimize the eMBBDs' downlink (DL) sum rate while fulfilling the provisions of the uRLLC traffic. A cooperative Nash bargaining solution (NBS) is formulated to depict the association method. First, base stations (BSs) create random pairings between heterogeneous non-orthogonal multi-access (H-NOMA) devices. The BSs are classified into coalitions through the Hungarian approach. So, a two-band partition algorithm is evolved for the two BSs in each coalition to negotiate their allocated devices to improve the NBS utility. A multiplayer bargaining approach is constructed using this algorithm and the Hungarian technique. After finishing the DA process, we study the cooperative matching algorithm to optimize the pairing problem between devices' slices. Simulation results demonstrate that comparing the proposed approach to other schemes can achieve a significant DL rate distribution for eMBBDs, device-side latency for uRLLCDs, and fairness improvements.

Dynamic cell sleeping mechanism: An energy‐efficient approach for Mobile 5G HetCN

SummaryThe paper proposes a novel cell sleeping mechanism for enhancing network energy‐efficiency and to combat dynamic downlink interferences linked with mobile Small Cells (mSCs) in a 5G Heterogeneous Cellular Network (HetCN). The proposed Dynamic Mobile Cell Sleeping Mechanism (DMCSM) allows deployed vehicle‐mounted mSCs to dynamically go into sleep based on the calculated distances of users from its serving mSCs BS. With this mechanism, vehicular mSCs during mobility dynamically calculate their distances with the Macrocell (MC) users. The mSCs go into sleep or get deactivated for a while based on the calculated distance and the cell radius defined for mSCs. The mSCs will get active and starts to provide services to the users that are found within their coverage radius. The setup 5G HetCN is investigated with a MC superimpose with fixed SCs (fSCs) and mobile SCs (mSCs). The proficiency of DMCSM is investigated with the exploitation of existing sub 6 GHz groups at MCs and the millimeter wave (mmWave) spectrums at deployed fSCs and mSCs. The network downlink performance metrics: user throughput, sumrate, energy‐efficiency, and outage probability, have been explored. The work also depicts a comparison of the proposed DMCSM mechanism with the author's previously proposed ICI mitigation techniques.

Multi-objective oriented resource allocation in reconfigurable intelligent surface assisted HCNs

In order to achieve thousand-fold throughput improvements for future applications, heterogeneous cellular networks (HCNs) with ultra-densely deployed small cells have gained a great deal of attention in recent years. However, energy consumption is an increasing concern as base station deployment (BSs) becomes denser. In order to address this issue, a novel framework is proposed for integrating multiple reconfigurable intelligent surfaces (RISs) in heterogeneous cellular networks (HCNs), where the communication in each BS is enhanced by its associated RIS. The multi-objective optimization is formulated to obtain the optimal trade-off between throughput and energy consumption. Moreover, an improved non-dominated sorting genetic algorithm II (NSGA-II) based algorithm is proposed to optimize the sub-carrier assignment, user equipment (UE) association, power allocation, and RISs’ phase shift. The proposed algorithm’s potential solutions are first encoded as individuals and optimized with the conventional NSGA-II algorithm. In order to further tackle the coupling characters of the optimization variables, the Zoutendijk method is further employed for searching for better solutions. Numerical results illustrate that: (i) the proposed multiple-RIS-assisted HCNs outperform conventional HCNs without RIS. (ii) the performance can be improved by deploying the RIS closer to UEs or increasing the number of reflecting elements.

Energy-Aware Hierarchical Resource Management and Backhaul Traffic Optimization in Heterogeneous Cellular Networks

The dense deployment of small-cell networks is a key feature of the next-generation mobile networks employed to provide the necessary capacity increase. The small cells are installed in the areas covered by macro base stations (eNBs) to supply the required local capacity based on the known concept of the hierarchical HetNets. Moreover, small-cell networks use high-capacity backhaul links on millimeter-wave bands to develop multihop topologies to mitigate the costs of data transmission. Nonetheless, green networking gains great importance for the uncontrolled installation of too many small cells may escalate operational costs and emit more carbon dioxide. This article proposes a dynamic optimization model to minimize the overall energy consumption of fifth-generation (5G) heterogeneous networks and provide the essential coverage and capacity. Optimizing carrier allocation and power utilization, the proposed model determines when to turn <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> or <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> small cells to meet the quality of service constraints of users with the highest level of energy efficiency. We also proposed a multihop backhauling strategy to effectively use the existing infrastructure of small-cell networks for simultaneous dual-hop transmissions. The numerical results indicated considerable rates of power saving in different traffic models while guaranteeing the throughput requirements for uniform and hotspot user equipment distribution patterns. Also, according to the simulation results, energy efficiency and system data rates can significantly be improved.

Efficient Online Learning Based Cross-Tier Uplink Scheduling in HetNets

Heterogeneous cellular networks (HetNets), where low-power low-complexity base stations (Pico-BSs) are deployed inside the coverage of macro base stations (Macro-BSs), can significantly improve the spectrum efficiency by Pico- and Macro base station collaboration. Due to cross-tier interference, joint detection of uplink signals is widely adopted so that Pico-BS can either detect the uplink signals locally or forward them to Macro-BS for processing. The latter can achieve increased throughput at the cost of additional backhaul transmission. In this paper, we study the delay-optimal uplink scheduling problem in HetNets with limited backhaul capacity. Local signal detection or joint signal detection is scheduled in a unified delay-optimal framework. Specifically, we first prove that the problem is NP-hard and then formulate it as a Markov Decision Process. We propose an efficient algorithm, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OLIUS</monospace> , that can deal with the exponentially growing state and action space. Furthermore, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OLIUS</monospace> is online learning-based which does not require any prior knowledge on user behavior or channel characteristics. We prove the convergence of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OLIUS</monospace> and derive an upper bound on its approximation error. Extensive experiments in various scenarios show our algorithm outperforms existing methods in reducing delay and power consumption.

Multi-agent Q-learning based cell breathing considering SBS collaboration for maximizing energy efficiency in B5G heterogeneous networks

In B5G heterogeneous cellular networks, a rapid increase in the number of small cell base stations (SBSs) to support a massive number of devices tends to waste a considerable amount of energy. Therefore, intelligent management of SBSs’ power consumption is one of the most important research issues. We herein propose quasi-distributed Q-learning-based cell breathing (QD-QCB) considering full and partial SBS collaborations for maximizing network energy efficiency. Also, the concept of an aggregated active SBS set based on regional user distributions is proposed for computing- and energy-efficient operation. Through intensive simulations, we show that the proposed QD-QCB algorithm can achieve optimal energy efficiency, and improve the network energy efficiency significantly compared with conventional algorithms such as no transmit power control, random cell breathing, and greedy cell breathing algorithms.

Resource Allocation and Mode Selection in 5G Networks Based on Energy Efficient Game Theory Approach

With the advent of next-generation cellular networks, energy efficiency is becoming increasingly important. To tackle this issue, this paper investigates energy efficiency in D2D-enabled heterogeneous cellular networks. Boosting the longterm energy efficiency of wireless 5G communication networks is being explored through mode selection and resource allocation. The study proposed a three-stage process for energy-efficient mode selection and resource allocation. The process starts with cellular users who switch to D2D emitting a beacon and cellular users within close proximity reacting to it. A proposed auction mechanism will be enacted inside the group in the second state ( in this paper, the group size will be four). Next, each cellular user was classified according to SINR values, distance, and battery life, so that they could dynamically transition between standard cellular mode and D2D mode. For stage three, direct-hop hybrid D2D communication, we developed a TAMM double auction game model that efficiently splits resources. To identify the true bidders in our game model, we compute the median and mode values of the ASK and BID values received by both seller and buyer cellular users. A simulation study shows that the proposed method is energy-efficient in a heterogeneous network enabled by D2D.

Energy minimization by dynamic base station switching in heterogeneous cellular network

Energy minimization by dynamic base station switching in heterogeneous cellular network

Dynamic Spectrum Control-Assisted Secure and Efficient Transmission Scheme in Heterogeneous Cellular Networks

Abstract Heterogeneous cellular networks (HCNs) are envisioned as a promising architecture to provide seamless wireless coverage and increase network capacity. However, the densified multi-tier network architecture introduces excessive intra- and cross-tier interference and makes HCNs vulnerable to eavesdropping attacks. In this article, a dynamic spectrum control (DSC)-assisted transmission scheme is proposed for HCNs to strengthen network security and increase the network capacity. Specifically, the proposed DSC-assisted transmission scheme leverages the idea of block cryptography to generate sequence families, which represent the transmission decisions, by performing iterative and orthogonal sequence transformations. Based on the sequence families, multiple users can dynamically occupy different frequency slots for data transmission simultaneously. In addition, the collision probability of the data transmission is analyzed, which results in closed-form expressions of the reliable transmission probability and the secrecy probability. Then, the upper and lower bounds of network capacity are further derived with given requirements on the reliable and secure transmission probabilities. Simulation results demonstrate that the proposed DSC-assisted scheme can outperform the benchmark scheme in terms of security performance. Finally, the impacts of key factors in the proposed DSC-assisted scheme on the network capacity and security are evaluated and discussed.

Online Resource Management of Heterogeneous Cellular Networks Powered by Grid-Connected Smart Micro Grids

This paper investigates a long-term average total energy cost minimization problem via resource management, including admission control, power allocation, and Energy Sharing (ES) of renewable energy in Heterogeneous Cellular Networks powered by Grid-connected Smart Micro Grids (GSMG-HCNs). In GSMG-HCNs, both renewable and grid energy power the base stations. Unlike existing works, we consider the cost of both renewable and grid energy and formulate the power line loss process caused by ES into our model. To solve the proposed problem, we transform it into a real-time issue by the Lyapunov technique. The proposed Cost-Aware Online Resource Management (CAORM) algorithm decouples the real-time issue into two sub-problems, one of which is linear and the other is addressed based on the successive convex approximation approach. We theoretically prove the asymptotic optimality of the CAORM algorithm and a tradeoff between the average total energy cost and the average queue length. Simulation results reveal that the CAORM algorithm outperforms benchmarks in reducing total energy cost and can make appropriate decisions according to different unit costs of renewable energy. Besides, the designed distance-related ES loss rate can help obtain better solutions with lower ES losses.