Deployment Of Heterogeneous Networks Research Articles

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.

Interference aware two-tier fair user-cell association

The deployment of heterogeneous networks (HetNets) incorporating small cells within macrocells has emerged as a predominant strategy to improve the performance of cellular communication systems. This study delves into the intricacies of user-cell association within HetNets and introduces an optimized approach that not only maximizes the sum of downlink signal-to-noise ratio (SNR) but also limits uplink interference within a dual-tier network. The suggested solution is scalable and adaptable to ultra-dense HetNets (UD-HetNets). It also facilitates various communication systems, encompassing ultra-reliable low-latency communications by leveraging a weighted cost function. Assorted simulations illustrate that optimization tools achieve the optimal solution of the proposed association problem. Comparative studies with traditional association methods signify the performance of the proposed algorithm, manifested in increased SNR values and an increased number of active users. It is shown that a trade-off surfaces between increased SNR values and increased fairness. In summary, the research emphasizes a comprehensive methodology for user-cell association, effectively addressing performance enhancement and fairness in diverse communication scenarios.

Radio Resource Management Satellite Communication Network MCDM Method

Worldwide deployment of heterogeneous wireless networks is growing as a result of consumer demand for connectivity at all times and in all places. These customers' interest in multimedia apps like video streaming and VoIP, which demand tight Quality of Service (QoS) support, is growing at the same time. With such limitations, provisioning network resources is a difficult undertaking. In fact, it might be challenging for a network operator to identify trustworthy criteria to choose the optimum network that ensures user happiness while maximising network utilisation, given the availability of numerous access technologies (WiFi, WiMAX, or cellular networks). To solve this problem, each eNB just needs to learn the traffi c conditions or patterns of its owncell in our proposal. Wireless communication systems depend heavily on radio resource management (RRM). To ensure the efficient and successful operation of wireless networks, it involves the allocation and control of radio frequency spectrum, power, and other resources. RRM is significant because it can use scarce radio resources as efficiently as possible, enhancing capacity, lowering interference, and improving service quality. Successful deployment and operation of wireless communication systems like cellular networks, Wi-Fi, and Bluetooth depend on effective RRM approaches. The need for wireless communication is growing, and new technologies and standards are constantly being developed. The methodology of radio resource management (RRM) involves a variety of techniques and algorithms designed to allocate radio resources in a way that maximizes network performance while minimizing interference. Taken as alternate parameter is Laser communication, optical networks, satellite optical communication, vibrations, satellite networks. Taken as is solar radiation power, thermal bending, micro meteorite impact, solar and lunar gravity, earth oblations method. satellite optical communication has reached near 2000 data set compare other data set. The operation of wireless communication networks depends on radio resource management (RRM). Wireless networks would have interference, congestion, and a lacklustre level of service if effective RRM procedures weren't used. RRM is therefore a key component in ensuring that wireless communication systems can provide users with dependable and high-quality services.

GREEN COMMUNICATIONS

Green communication is an innovative research area to find radio communication and networking solutions that can significantly improve energy efficiency and resource efficiency of wireless communications without compromising the QoS of users. It contributes to global environment improvement and achieves commercial benefits for telecommunication operators. The main goal of designing green base stations is to save energy and reduce power consumption while guaranteeing user service and coverage and ensuring the base station's capability for evolution. In a wireless network base station, power consumption is the biggest issue. With global warming and energy crises becoming the most compelling environmental challenges, green solutions are a common issue to be handled. Hence, the primary focus of the “Green cellular network” is saving power in base stations to “care for planet and operator’s valet.” we reviewed a few techniques for saving power consumption and improving energy efficiency in base stations. However, many technical challenges for base station architecture redesign, heterogeneous network deployment, radio resource management, etc., need to be addressed for energy-efficient base stations.

Deep Model Training and Deployment in Heterogeneous IoT Networks

As a typical form of machines learning, deep learning has attracted much attention from researchers. It can independently construct (train) basic rules according to the sample data in the learning process. Especially in the field of machine vision, neural networks are usually trained by supervised learning, that is, by example data and predefined results of example data. In this paper, we firstly overview the current research progress on the deep model training and deployment on the heterogeneous Internet of Things (IoT) networks, by taking into account both the latency and energy consumption from various devices in the system. We then summarize the existing challenges on the model training and model deployment on the heterogeneous IoT devices. We further give some feasible solutions to solve the challenges on the model training and model deployment on the heterogeneous IoT devices. The study in this paper can serve as an important reference for the development of deep model training and model deployment for heterogeneous IoT networks.

Radio Resource Management for Eicic, Comp, Relaying and Back-hauls Techniques in LTE-advanced Networks: Concepts and a Literature Survey

Frequency reuse in cells is one of the strategies that LTE (Long Term Evolution) uses to maximize the spectrum efficiency. However, it leads to an interference among the cells, especially at the cell edges where the probability for a cell-edge user to be scheduled on a resource block that is being transmitted by the neighbouring cell is high; consequently, the interference is high. In-order to mitigate Inter-Cell Interference (ICI), Inter-Cell Interference Coordination (ICIC) was proposed by the Third Generation Partnership Project (3GPP) standards for the LTE network, and later on, the enhanced Inter-Cell Interference Coordination (eICIC) was proposed for the LTE-Advanced network. ICIC reduces cell-edge interference on traffic channels from neighbouring cells by the use of three interference reduction schemes that works in the power and frequency domain, and they are based on lowering the power of some channels to limit their reception to the users that are close to the base station, and by reducing the chance of frequency overlap. eICIC was proposed to handle ICI in Heterogeneous Network (HetNet) deployments. It reduces the interference on both the traffic and control channels. It uses power, frequency and also time domain to mitigate intra-frequency interference in HetNets. Another technique that was proposed by the 3GPP for the LTE-Advanced network that can reduce the ICI and improve the cell average and cell-edge user throughput is the Coordinated Multi-Point (CoMP) transmission/reception technique. CoMP can increase the cell average and cell edge user throughput in both the uplink and downlink transmission by joint scheduling and data processing in multiple cells/eNBs. Another technique that was proposed by the 3GPP for the LTE-Advanced network that can improve the LTE network coverage in difficult conditions is to deploy Relay Nodes (RNs). In this paper, we survey Radio Resource Management (RRM) for some of the techniques that are used with LTE-A. The included techniques in this paper are; the ICIC, eICIC, CoMP, Relaying and Back-hauls. We start by explaining the concepts of these techniques. Then, by summarizing the radio resource management approaches that were proposed in the literature for these techniques. And finally, we provide some concluding remarks in the last section.

QoS-based Packet Scheduling Algorithms for Heterogeneous LTE-Advanced Networks: Concepts and a Literature Survey

The number of LTE (Long-Term Evolution) users and their applications has increased significantly in the last decade, which increased the demand on the mobile network. LTE-Advanced (LTE-A) comes with many features that can support this increasing demand. LTE-A supports Heterogeneous Networks (HetNets) deployment, in which it consists of a mix of macro-cells, remote radio heads, and low power nodes such as Pico-cells, and Femto-cells. Embedding this mix of base-stations in a macro-cellular network allows for achieving significant gains in coverage, throughput and system capacity compared to the use of macrocells only. These base-stations can operate on the same wireless channel as the macro-cellular network, which will provide higher spatial reuse via cell splitting. Also, it allows network operators to support higher data traffic by offloading it to smaller cells, such as Femto-cells. Hence, it enables network operators to provide their growing number of users with the required Quality of Service (QoS) that meets with their service demands. In-order for the network operators to make the best out of the heterogeneous LTE-A network, they need to use QoS-based packet scheduling algorithms that can efficiently manage the spectrum resources in the HetNets deployment. In this paper, we survey Quality of Service (QoS) based packet scheduling algorithms that were proposed in the literature for the use of packet scheduling in Heterogeneous LTE-A Networks. We start by explaining the concepts of QoS in LTE, heterogeneous LTE-A networks, and how traffic is classified within a packet scheduling architecture for heterogeneous LTE-A networks. Then, by summarising the proposed QoS-based packet scheduling algorithms in the literature for Heterogeneous LTE-A Networks, and for Femtocells LTE-A Networks. And finally, we provide some concluding remarks in the last section.

A Deep Q-Network-Based Algorithm for Multi-Connectivity Optimization in Heterogeneous Cellular-Networks.

The use of multi-connectivity has become a useful tool to manage the traffic in heterogeneous cellular network deployments, since it allows a device to be simultaneously connected to multiple cells. The proper exploitation of this technique requires to adequately configure the traffic sent through each cell depending on the experienced conditions. This motivates this work, which tackles the problem of how to optimally split the traffic among the cells when the multi-connectivity feature is used. To this end, the paper proposes the use of a deep reinforcement learning solution based on a Deep Q-Network (DQN) in order to determine the amount of traffic of a device that needs to be delivered through each cell, making the decision as a function of the current traffic and radio conditions. The obtained results show a near-optimal performance of the DQN-based solution with an average difference of only 3.9% in terms of reward with respect to the optimum strategy. Moreover, the solution clearly outperforms a reference scheme based on Signal to Interference Noise Ratio (SINR) with differences of up to 50% in terms of reward and up to 166% in terms of throughput for certain situations. Overall, the presented results show the promising performance of the DQN-based approach that establishes a basis for further research in the topic of multi-connectivity and for the application of this type of techniques in other problems of the radio access network.

RAT selection for IoT devices in HetNets: Reinforcement learning with hybrid SMDP algorithm

Due to the increasing deployment of heterogeneous networks (HetNets), the selection of which radio access technologies (RATs) for Internet of Things (IoT) devices such as user equipments (UEs) has recently received extensive attention in mobility management research. Most of existing RAT selection methods only optimize the selection strategies from the UE side or network side, which results in heavy network congestion, poor user experience and system utility degradation. In this paper the UE side and the network side are considered comprehensively, based on the game theory (GT) model we propose a reinforcement learning with assisted network information algorithm to overcome the crucial points. The assisted information is formulated as a semi-Markov decision process (SMDP) provided for UEs to make accurate decisions, and we adopt the iteration approach to reach the optimal policy. Moreover, we investigate the impacts of different parameters on the system utility and handover performance. Numerical results validate that our proposed algorithm can mitigate unnecessary handovers and improve system throughputs.

Energy-Efficient Deployment in Static and Mobile Heterogeneous Multi-Hop Wireless Sensor Networks

We study a heterogeneous wireless sensor network (WSN) where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> heterogeneous fusion centers (FCs) via multi-hop wireless communication. The heterogeneous optimal deployment of APs and FCs is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where APs and FCs retain their deployed position, and mobile WSNs where APs and FCs can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal deployment in static WSNs, we propose an iterative algorithm to deploy APs and FCs. In addition, we study the necessary conditions of the optimal movement-efficient deployment in mobile WSNs with constrained movement energy and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed deployment algorithms outperform the existing methods in the literature, and achieve a lower total wireless communication power in both static and mobile WSNs.

Context-Aware Multi-Criteria Handover at the Software Defined Network Edge for Service Differentiation in Next Generation Wireless Networks

The densified deployment of heterogeneous networks coexisting with a variety of overlapping cells has emerged as a viable solution for next generation wireless networks. Despite numerous advantages, the heterogeneity and denseness also raise complicated handover management issue. Nonetheless, most existing handover methods generally depend on one or more objective attributes, and rarely consider the subjective demands of personalized users and specific applications that demand differentiated services. Through decomposing the control plane and data plane, software defined network(SDN) offers a flexible architectural paradigm to overcome these challenges. In this article, we first develop an SDN-driven handover architecture that is capable of perceiving global network status and requirements from various perspectives, including the physical layer, users, and applications. Then, a context-aware multi-criteria handover mechanism is developed in the SDN edge to provide differentiated services. Considering the numerous complicated factors, the handover decision is made based on a hierarchical fuzzy inference system to process diverse attributes and vague requirements described in natural language. Finally, we evaluate the performance of our proposed scheme through a combination of extensive simulations and real-world experiments. The results demonstrate that our solution outperforms the baseline handover schemes, more efficiently providing differentiated services with respect to throughput, bandwidth cost, and application satisfaction, and is efficient and feasible in practice.

QoS-Aware Resource Allocation with Pilot-Aided Channel Estimation for Heterogeneous Wireless Networks

The deployment of heterogeneous networks (HetNets) is a way to increase the network capacity and release part of the traffic generated by users inside a cell to small-scale wireless networks for service. In this context, the main problem is managing the interference due to the coexistence of small cells and macro cells. In this paper, a QoS-aware Resource Allocation (RA) algorithm jointly working with admission control (AC) over a two-tier HetNet scenario is investigated in the presence of both the pilot-symbols for channel estimation and the channel estimation error. The RA algorithm allows two users, the macro cell user (CU) and small cell user (SU), to simultaneously share the same resource block. Moreover, system performance and fairness are improved by including adaptive power allocation to users over resource blocks. In the framework of RA with proportional rate constraints, a novel algorithm is designed by including the effects of pilot-aided channel estimation. The algorithm is able to distribute the same proportional rate to all CUs and SUs, even in the presence of channel estimation error. Relevant numerical results for the downlink of a two-tier HetNet with pilot-aided channel estimation show that the rate dispersion is driven to zero while the sum-rate is maximized, and the average user rate penalty with respect to a perfect-CSI scenario may rise to 20%.

Enhanced performance of long-term evolution small-cell networks using improved mobility algorithms

With an ever-increasing number of user equipment (UE) and bandwidth demands of new applications, the deployment of dense heterogeneous cellular networks has been adopted in many network scenarios. The small cells are unable to unload the traffic due to the random UEs mobility and cells deployment. This degrades the network performance such as handover success, throughput, and load distribution. To address such a problem, we propose an enhanced mobility load-balancing algorithm for small cells. The conventional mobility load balancing (MLB) algorithms study only the fixed or adaptive thresholds of the network separately to consider the load balance process, while other MLB algorithms consider the neighboring cells of the network those experience unnecessary MLB actions. The proposed load balancing algorithms study overloaded cells and neighbors using the proposed efficiency parameter, B. To identify the overloaded cells, we propose B which compares between a pre-defined threshold and the network threshold to categorize medium and overloaded cells, based on B, the algorithm is triggered. Then, to control the shifted load to a target neighbor cell, we propose the Rescue factor, f. the f ensures the load of the target cell after handover is equal or less than Ɓ. The simulation results showed a lower standard deviation and higher throughput and physical resource block utilization than surveyed algorithms.

A lightweight cell switching and traffic offloading scheme for energy optimization in ultra-dense heterogeneous networks

One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in a tremendous increase in the energy consumption of the network due to the large number of base stations (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network, but most of the proposed methods are computationally demanding, which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEll swItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed to be optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that THESIS significantly reduces the energy consumption of the UDHN and can reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity.

Improved bees algorithm for the deployment of homogeneous and heterogeneous wireless sensor networks

Improved bees algorithm for the deployment of homogeneous and heterogeneous wireless sensor networks

Improved bees algorithm for the deployment of homogeneous and heterogeneous wireless sensor networks

Nowadays, countless applications benefit from the services provided by wireless sensor networks (WSNs) in remote surveillance and data gathering. Coverage is considered an important quality of service (QoS) criterion for many of these applications. To ensure that the required QoS is achieved, the sensors must be placed in locations that optimise the area coverage and eliminate the coverage holes, especially when deploying sensors with heterogeneous sensing ranges. In this paper, a novel sensor deployment scheme based on the improved bees algorithm (IBA) for optimising the area coverage in both homogeneous and heterogeneous WSNs is proposed. The IBA includes a neighbourhood shrinking procedure that aims to improve the local search efficiency and a site abandonment procedure for preventing the algorithm from the entrapment in a local optimum. Experiments show that the IBA delivers high-quality solutions and optimises the area coverage effectively compared with four state-of-the-art methods in homogeneous and heterogeneous WSNs.

Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

The massive growth of mobile users and the essential need for high communication service quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) consisting of macro, micro, pico and femto cells. Each cell type provides different cell coverage and distinct system capacity in HetNets. This leads to the pressing need to balance loads between cells, especially with the random distribution of users in numerous mobility directions. This paper provides a survey on the intelligent load balancing models that have been developed in HetNets, including those based on the machine learning (ML) technology. The survey provides a guideline and a roadmap for developing cost-effective, flexible and intelligent load balancing models in future HetNets. An overview of the generic problem of load balancing is also presented. The concept of load balancing is first introduced, and its purpose, functionality and evaluation criteria are then explained. Besides, a basic load balancing model and its operational procedure are described. A comprehensive literature review is then conducted, including techniques and solutions of addressing the load balancing problem. The key performance indicators (KPIs) used in the evaluation of load balancing models in HetNets are presented, along with the concurrent optimisation of coverage (CCO) and mobility robustness optimisation (MRO) relationship of load balancing. A comprehensive literature review of ML-driven load balancing solutions is specifically accomplished to show the historical development of load balancing models. Finally, the current challenges in implementing these models are explained as well as the future operational aspects of load balancing.

K-Connected Relay Node Deployment in Heterogeneous Wireless Sensor Networks

k-Connected Relay Node Deployment in Heterogeneous Wireless Sensor Networks

Wireless Loss Detection over Fairly Shared Heterogeneous Long Fat Networks

The quality of inter-network communication is often detrimentally affected by the large deployment of heterogeneous networks, including Long Fat Networks, as a result of wireless media introduction. Legacy transport protocols assume an independent wired connection to the network. When a loss occurs, the protocol considers it as a congestion loss, decreasing its throughput in order to reduce the network congestion without evaluating a possible channel failure. Distinct wireless transport protocols and their reference metrics are analyzed in order to design a mechanism that improves the Aggressive and Adaptative Transport Protocol (AATP) performance over Heterogeneous Long Fat Networks (HLFNs). In this paper, we present the Enhanced-AATP, which introduces the designed Loss Threshold Decision maker mechanism for the detection of different types of losses in the AATP operation. The degree to which the protocol can maintain throughput levels during channel losses or decrease production while congestion losses occur depends on the evolution of the smooth Jitter Ratio metric value. Moreover, the defined Weighted Fairness index enables the modification of protocol behavior and hence the prioritized fair use of the node’s resources. Different experiments are simulated over a network simulator to demonstrate the operation and performance improvement of the Enhanced-AATP. To conclude, the Enhanced-AATP performance is compared with other modern protocols.

Energy-Efficient Multi-Access Mobile Edge Computing With Secrecy Provisioning

Thanks to the wide deployment of heterogeneous radio access networks (RANs) in the past decades, the emerging paradigm of multi-access mobile edge computing, which allows mobile terminals to simultaneously offload the computation-workloads to several different edge-computing servers via multi-RANs, has provided a promising scheme for enabling the computation-intensive mobile Internet services in future wireless systems. The broadcasting nature of radio transmission, however, may lead to a potential secrecy-outage during the offloading transmission. In this paper, we thus investigate the energy-efficient multi-access mobile edge computing with secrecy provisioning. Specifically, we first investigate the scenario of one wireless device's (WD's) multi-access offloading subject to a malicious node's eavesdropping. By characterizing the WD's secrecy based throughput in its offloading transmission, we formulate a joint optimization of the WD's multi-access computation offloading, secrecy provisioning, and offloading-transmission duration, with the objective of minimizing the WD's total energy consumption, while providing a guaranteed secrecy-outage during offloading and a guaranteed overall-latency in completing the WD's workload. Despite the non-convexity of this joint optimization problem, we exploit its layered structure and propose an efficient algorithm for solving it. Based on the study on the single-WD scenario, we further investigate the scenario of multiple WDs, in which a group of WDs sequentially execute the multi-access computation offloading, while subject to a malicious node's eavesdropping. Taking the coupling effect among different WDs into account, we propose a swapping-heuristic based algorithm (that uses our proposed single-WD algorithm as a subroutine) for finding the ordering of the WDs to execute the multi-access computation offloading, with the objective of minimizing all WDs’ total energy consumption. Extensive numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms. The results demonstrate that our algorithms can outperform some conventional fixed offloading scheduling scheme and randomized offloading ordering scheme.