Heterogeneous Access Networks Research Articles

Coverage Probability Analysis for Uplink/Downlink Decoupled Access in Heterogeneous Cellular Networks

Coverage Probability Analysis for Uplink/Downlink Decoupled Access in Heterogeneous Cellular Networks

Energy-Efficient 5G Heterogeneous Cloud Radio Access Networks (RRH to BBU) Using a Hybrid OGASCDASA-SAGMDEBROA Scheduling Algorithm

In fifth-generation (5G) networks, the deployment of heterogeneous networks (HetNets) with macrocells layered over tiny cells is considered to be a practical solution to handle the growing demand for mobile traffic. The deployment of a significant count of small cell base stations (BSs) results in a notable rise in energy consumption. In this manuscript, Energy-Efficient (EE) 5G Heterogeneous Cloud Radio Access Networks (RRH to BBU), using Hybrid online green algorithm-based sleep scheduling and cost-efficient deadline-aware Scheduling Algorithm (OGASCDASA), are proposed. Here, the energy efficiency optimization issue is in the downlink of two-tier Heterogeneous Cloud Radio Access Networks (H-CRAN) by lowering micro and pico cells. Hybrid OGASCDASA is proposed for reducing remote radio side energy use when maintaining coverage and Quality of service (QoS) of H-CRAN. At the cloud side Baseband Units (BBU), hybrid Simulated Annealing with the Gaussian Mutation and Distortion Equalization algorithm with Battle Royale optimization is proposed to reduce the energy consumption of BBU by decreasing the count of BBU servers. The proposed EE-HCRAN-Hybrid OGASCDASA-SAGMDEBROA method attains 20.48%, 27.34%, and 32.24% higher throughput and 28.30%, 17.30%, and 32.94% lower delay compared to the existing models, such as heterogeneous computational resource allocation for NOMA (HCRA-NOMA-GMECS), energy-efficient hierarchical resource sharing in uplink-downlink decoupled NOMA heterogeneous networks (EE-HRA-NOMA-HetNet), energy-aware hierarchical resource management with backhaul traffic optimization in heterogeneous cellular networks (EA-HRM-BTO-HCN), respectively.

A taxonomy on power optimization techniques for fifth-generation heterogenous non-orthogonal multiple access networks

Non-orthogonal multiple access (NOMA) is an anticipated technology for fifth-generation networks for increasing mass connectivity, spectrum efficiency, user-fairness, and higher capacity. NOMA allows end-clients to share indistinguishable radio resources such as spreading code, subcarrier, and time slots simultaneously. Thus, the main challenge involved in conceptualizing effective NOMA design is selection of resource allocation (i.e., user clustering, power allocation, and quality-of-service (QoS) assurance) algorithms. NOMA can be easily integrated with current fifth-generation multi-access methodologies. In this survey paper, the NOMA methodologies are discussed, and provide an overview of the methodologies and algorithms designed for optimizing power allocation, interference management, and network selection management in the heterogenous multiple carrier NOMA. The survey highlights the current limitation of the existing resource provisioning framework and presents a solution to overcome the current limitation.

Efficient User Association Strategy for Maximizing User Satisfaction and Resource Utilization in Heterogeneous Cloud Radio Access Networks (H-CRANs)

The primary aim of this study is to present a User Association Strategy that can effectively optimize the Heterogeneous Cloud Radio Access Network (H CRAN). The primary objective of this strategy is to enhance customer satisfaction by optimizing the utilization of available network resources, such as transmission power and bandwidth. To achieve this objective, the proposed approach employs a logarithmic barrier function to address inequality constraints and transform the optimization problem into a formulation that facilitates effective convergence towards the optimal solution.

An efficient energy saving scheme using reinforcement learning for 5G and beyond in H-CRAN

Maximizing the energy saving is one of the most important metrics in 5G and Beyond (B5G) cellular mobile networks. In order to satisfy the diverse requirements of 5G/B5G in dynamic environments, Reinforcement Learning (RL) is proven as a viable approach for solving resource management problems, especially for 5G energy resources. In this paper, we propose to apply the Q-Learning (QL) Reinforcement technique in the Heterogeneous Cloud 5G Radio Access Network (H-CRAN) architecture in order to optimize the energy efficiency in 5G/B5G networks. We compare its results with the Genetic Algorithm variant using Transformation (TGA) and Particle Swarm Optimization (PSO) under high and low traffic demands. The experimental results reveal the efficiency of RL compared to TGA and PSO techniques in terms of energy efficiency and system capacity.

Interference Mitigation in mmWave Heterogeneous Cloud-Radio Access Network: for Better Performance and User Connectivity

Interference Mitigation in mmWave Heterogeneous Cloud-Radio Access Network: for Better Performance and User Connectivity

UAV-Assisted Heterogeneous Cloud Radio Access Network With Comprehensive Interference Management

UAV-Assisted Heterogeneous Cloud Radio Access Network With Comprehensive Interference Management

DAPSK – OFDMA PON Based Heterogeneous Optical Network

The broadband access networks require suitable differential modulation techniques that can provide better performance in real-time fading channels. A heterogeneous optical access network adopting spectrally efficient DAPSK – Orthogonal Frequency Division Multiple (OFDMA) - Passive Optical Network (PON) is proposed and simulated. The performance of the proposed heterogeneous network is analyzed in terms of received Bit Error Rate (BER) and spectral efficiency. The results show that 64 DAPSK – OFDMA over the proposed architecture achieves a better spectral efficiency of about 1.062 bps/Hz than 64 QAM – OFDMA with less degradation in error performance.

Privacy-preserving intelligent content-caching scheme in heterogeneous aerial access networks

Heterogeneous aerial access networks (AANs), comprising a hierarchical model of a low Earth orbit (LEO) satellite constellation and multiple high-altitude platforms (HAPs), provide a radio access medium from the sky to enhance the service experience of ground terminals (GTs) in remote areas. In this scenario, aerial edge caching addresses the challenge of delivering on-demand content requests to GTs. Although existing caching schemes for AANs have improved caching efficiency, they often overlook important concerns related to privacy preservation and overhead. This paper proposes an intelligent, proactive content-caching scheme to maximize cache efficiency while ensuring privacy preservation for GTs. The proposed caching scheme employs hierarchical federated learning (HierFL) that involves GTs, HAPs, and LEO satellites in heterogeneous AANs. In particular, the proposed HierFL-based proactive content-caching (HierFL-PCC) scheme leverages a deep neural network employing multiple linear regression to predict the dynamically changing content popularity of GTs, maximizing the cache efficiency. The HierFL-PCC also reduces the delay and overhead associated with content delivery. Additionally, the proposed HierFL-PCC scheme reduces communication overhead in model training owing to the hierarchical learning architecture. Simulation results demonstrate that the proposed HierFL-PCC scheme exhibits improvement of about 13%, 43%, 178%, and 457% in cache efficiency, and reduces average content delivery delay by approximately 10%, 20%, 31%, and 37%, along with an improvement of around 89% in training overhead, respectively, compared to the cloud-based FAVG, LFU, LRU, and Random caching schemes.

Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels Based on Deep Reinforcement Learning

In heterogeneous wireless networks, when multiple nodes need to share the same wireless channel, they face the issue of multiple access, which necessitates a Medium Access Control (MAC) protocol to coordinate the data transmission of multiple nodes on the shared communication channel. This paper presents Proximal Policy Optimization-based Multiple Access (PPOMA), a novel multiple access protocol for heterogeneous wireless networks based on the Proximal Policy Optimization (PPO) algorithm from deep reinforcement learning (DRL). Specifically, we explore a network scenario where multiple nodes employ different MAC protocols to access an Access Point (AP). The novel PPOMA approach, leveraging deep reinforcement learning, adapts dynamically to coexist with other nodes. Without prior knowledge, it learns an optimal channel access strategy, aiming to maximize overall network throughput. We conduct simulation analyses using PPOMA in two scenarios: perfect channel and imperfect channel. Experimental results demonstrate that our proposed PPOMA continuously learns and refines its channel access strategy, achieving an optimal performance level in both perfect and imperfect channel scenarios. Even when faced with suboptimal channel conditions, PPOMA outperforms alternative methods by achieving higher overall network throughput and faster convergence rates. In a perfect channel scenario, PPOMA’s advantage over other algorithms is primarily evident in its convergence speed, reaching convergence on average 500 iterations faster. In an imperfect channel scenario, PPOMA’s advantage is mainly reflected in its higher overall network throughput, with an approximate increase of 0.04.

Cooperative Rate-Splitting Multiple Access in Heterogeneous Networks

Cooperative Rate-Splitting Multiple Access in Heterogeneous Networks

Energy Efficiency Based Multi Service Heterogeneous Access Network Selection Algorithm

Energy Efficiency Based Multi Service Heterogeneous Access Network Selection Algorithm

Performance Analysis of UL-DL Decoupled Access in Two-Tier Cellular Heterogeneous Networks

With the advent of the 5th generation mobile communication technology (5G) era, cellular networks are evolving towards highly heterogeneous and ultra-dense network structures, posing challenges for achieving high communication rates. Existing coupled access methods can no longer meet user demands. As a promising solution to this issue, decoupled uplink-downlink access has gained significant attention. This paper investigates the performance of decoupled uplink-downlink access in a two-tier cellular network model. We obtain the analytical expressions of association probabilities, distance distribution, and spectral efficiency of base stations (BSs) when femto-cell BSs are selected for uplink access and macro-cell BSs are selected for downlink access by a user device. Our findings provide insights into the selection of access patterns and decoupled methods for better efficiency and quality of service. Furthermore, our model and approach can guide the design and deployment of next-generation networks to enhance performance.

A Probabilistic Copula-Based Fault Detection Method With TrAdaBoost Strategy for Industrial IoT

With the development of information and technology, Industrial Internet of Things (IIoT) are widely-spread. In the face of heterogeneous network access technologies, IIoT data presents characteristics such as massiveness, heterogeneity and dynamics. In this work, an inductive transfer modeling fault detection method based on the vine copula-based dependence description (VCDD), named TrAdaBoost Vine Copula-Based Dependence Description (TAVCDD), is proposed for fusing the target and source data. Especially, there is too little data in target domain operation condition to establish a good monitoring model. In the modeling process, a generalized local probability (GLP) index is used to set the selection probabilities of samples in the target and source domains. In addition, two adaptive indexes are used to determine the number of training samples in one iteration and the number of iterations. To avoid unbalanced samples between the target and source datasets, an adaptive sample selection strategy called active VCDD (AVCDD) is introduced to explore the underlying distribution of the target sample space and to select the most “informative” samples from the source dataset. The proposed TAVCDD method is compared with six state-of-the-art methods via a numerical example, the Tennessee Eastman process, and the ethylene cracking furnace process.

Improved aquila optimization‐based parameter evaluation framework and meta‐heuristic algorithm in radio‐over‐fiber SpatialMux multi‐input multi‐output long‐term evolution system

SummaryIn heterogeneous access network, Multiple‐Input Multiple‐Output (MIMO) radio‐over‐fiber (RoF) system is an efficient approach for multiple signal transmission with low cost and complexity. The performance of RoF fronthaul system in MIMO system will be varied with different nonlinear effects. By adjusting various transmission parameters, such as the input signal power or the laser bias current, the nonlinear impacts produced by the RoF system can be reduced. In this paper, a novel algorithm Improved Aquila Optimization (IAO) is proposed to optimize transmission circumstances of MIMO RoF system. It determines the appropriate bias current for both lasers and Radio Frequency (RF) signal power in a short period. The input signals are wavelength multiplexed with Intensity Modulation and Direct Detection (IM/DD) applied. The carrier as well as transmission frequency is governed by the MIMO‐Long‐Term Evolution (LTE) standard. The proposed system is implemented in MATLAB, and the performance is evaluated. The experimental results show that fast convergence and trade‐off between noise and nonlinearity are obtained with varying bandwidth. In the experimental scenario, the maximum Error Vector Magnitude (EVM) of 1.88, 3.14, and signal‐to‐noise ratio (SNR) of 3.204, and 2.698 was attained for both quadrature phase shift keying (QPSK) and quadrature amplitude modulation (QAM) modulation. [Correction added on 24 April 2023, after first online publication: the SNR values were corrected in the preceding sentence.] For 100 iterations, the processing time was reduced to 0.137 s. When compared with the conventional state‐of‐the‐art approaches, the accuracy and computational complexity of the proposed approach are improved.

Interference mitigation for non-orthogonal multiple access in heterogeneous networks

Interference mitigation for non-orthogonal multiple access in heterogeneous networks

Resource Allocation Energy Efficient Algorithm for H-CRAN in 5G

In today's generation, the demand for data rates has also increased due to the rapid surge in the number of users. With this increasing growth, there is a need to develop the next fifth generation network keeping in mind the need to replace the current 4G cellular network. The fifth generation (5G) design in mobile communication technology has been developed keeping in mind all the communication needs of the users. Heterogeneous Cloud Radio Access Network (H-CRAN) has emerged as a capable architecture for the newly emerging network infrastructure for energy efficient networks and high data rate enablement. It is considered as the main technology. Better service quality has been achieved by developing small cells into macro cells through this type of network. In addition, the reuse of radio resources is much better than that of homogeneous networks. In the present paper, we propose the H-CRAN energy-efficient methods. This energy-efficient algorithm incorporates an energy efficient resource allocation management design to deal to heterogeneous cloud radio access networks in 5G. System throughput fulfillment is elevating by incorporating an efficient resource allocation design by the energy consumption model. The simulation results have been demonstrated by comparing the efficiency of the introduced design with the existing related design.

Deep Reinforcement Learning Heterogeneous Channels for Poisson Multiple Access

This paper proposes a medium access control (MAC) protocol based on deep reinforcement learning (DRL), i.e., multi-channel transmit deep-reinforcement learning multi-channel access (MCT-DLMA) in heterogeneous wireless networks (HetNets). The work concerns practical unsaturated channel traffic that arrives following a Poisson distribution instead of saturated traffic that arrives before.By learning the access mode from historical information, MCT-DLMA can well fill the spectrum holes in the communication of existing users. In particular, it enables the cognitive user to multi-channel transmit at a time, e.g., via the multi-carrier technology. Thus, the spectrum resource can be fully utilized, and the sum throughput of the HetNet is maximized. Simulation results show that the proposed algorithm provides a much higher throughput than the conventional schemes, i.e., the whittle index policy and the DLMA algorithms for both the saturated and unsaturated traffic, respectively. In addition, it also achieves a near-optimal result in dynamic environments with changing primary users, which proves the enhanced robustness to time-varying communications.

Research and propose flexible IoT solutions with radio communication networks and sensors for measurement, control

With a rapid increase in the number of IoT devices, the network access protocols of the devices also become diversified, leading to the need for IoT platforms that support heterogeneous network access. In each area or with different applications, the communication solution as well as the measurement and control for peripheral devices is different. Current IoT systems are mainly built for a specific problem, lacking the ability to expand the connection network and with different peripheral devices, therefore a costly investment and time to build the system, and at the same time, the ability to work is less reliable. The article proposes a flexible IoT solution that can be readily applied to outdoor systems, such as smart agriculture, smart lighting, and smart traffic; thereby suggesting a flexible application with different infrastructures for each application with high working quality, scalability, time and cost-savings.

Multi-Objective Energy Efficient Resource Allocation in Massive Multiple Input Multiple Output-Aided Heterogeneous Cloud Radio Access Networks

In this work, a novel energy efficient multi-objective resource allocation algorithm for heterogeneous cloud radio access networks (H-CRANs) is proposed where the trade-off between increasing throughput and decreasing operation cost is considered. H-CRANs serve groups of users through femto-cell access points (FAPs) and remote radio heads (RRHs) equipped with massive multiple input multiple output (MIMO) connected to the base-band unit (BBU) pool via front-haul links with limited capacity. We formulate an energy-efficient multi-objective optimization (MOO) problem with a novel utility function. Our proposed utility function simultaneously improves two conflicting goals as total system throughput and operation cost. With this MOO, we jointly assign the sub-carrier, transmit power, access point (AP)(RRH/FAP), RRH, front-haul link, and BBU. To address the conflicting objectives, we convert the MOO problem into a single-object optimization problem using an elastic-constraint scalarization method. With this approach, we flexibly adjust trade-off parameters to choose between two objective functions. To propose an efficient algorithm, we deploy successive convex approximation (SCA) and complementary geometric programming (CGP) approaches. Finally, via simulation results we discuss how to select the values of trade-off parameters, and we study their effects on conflicting objective functions (i.e., throughput and operation cost in MOO problem). Simulation results also show that our proposed approach can offload traffic from C-RANs to FAPs with low transmit power and thereby reduce operation costs by switching off the under-utilized RRHs and BBUs. It can be observed from the simulation results that the proposed approach outperforms the traditional approach in which each user is associated to the AP (RRHs/FAPs) with the largest average value of signal strength. The proposed approach reduces operation costs by 30% and increases throughput index by 25% which in turn leads to greater energy efficiency (EE).