Multi-tier Heterogeneous Networks Research Articles

Multi-tier heterogeneous Model for real-time hazard detection in Air Particulate

ABSTRACT In recent years, several nations have built an environmental gathering data system. Using multi-tier heterogeneous networks to evaluate concentrations for an infinite number of contaminants, a novel mathematical framework was suggested in this study. The efficiency of the suggested method was examined using actual measurements of municipal particle pollution concentrations. The method can detect local inhomogeneities that expose people to dangerous air pollution in actual time, such as a lack of toxin dispersion and concentration. Moreover, real-time inhomogeneity identification in a contaminated environment is now possible thanks to technology. In credible emergency scenarios with contaminated air, the value of local heterogeneity was calculated at 0.015 level or 8 counts. As a result, the level of heterogeneity was decreased to 0.0025 units or 2 counts. Long Short-Term Memory (LSTM) provided precise data on the accessible bandwidth and co-channel disruption, making the suggested technique easy to apply and adaptable to any heterogeneous network design. The labeling & judging elements would then be built once the expected outcome of the suggested approach had been evaluated using precision, recall, F1 scoring, and dependability measures. The overall results demonstrate that our performance using LSTM was superior to the Hybrid Ant Colony Optimizer (HACO) method.

Optimizing energy efficiency in heterogeneous networks: An integrated stochastic geometry approach with novel sleep mode strategies and QoS framework.

The quest for energy efficiency (EE) in multi-tier Heterogeneous Networks (HetNets) is observed within the context of surging high-speed data demands and the rapid proliferation of wireless devices. The analysis of existing literature underscores the need for more comprehensive strategies to realize genuinely energy-efficient HetNets. This research work contributes significantly by employing a systematic methodology, utilizing This model facilitates the assessment of network performance by considering the spatial distribution of network elements. The stochastic nature of the PPP allows for a realistic representation of the random spatial deployment of base stations and users in multi-tier HetNets. Additionally, an analytical framework for Quality of Service (QoS) provision based on D-DOSS simplifies the understanding of user-base station relationships and offers essential performance metrics. Moreover, an optimization problem formulation, considering coverage, energy maximization, and delay minimization constraints, aims to strike a balance between key network attributes. This research not only addresses crucial challenges in creating EE HetNets but also lays a foundation for future advancements in wireless network design, operation, and management, ultimately benefiting network operators and end-users alike amidst the growing demand for high-speed data and the increasing prevalence of wireless devices. The proposed D-DOSS approach not only offers insights for the systematic design and analysis of EE HetNets but also systematically outperforms other state-of-the-art techniques presented. The improvement in energy efficiency systematically ranges from 67% (min side) to 98% (max side), systematically demonstrating the effectiveness of the proposed strategy in achieving higher energy efficiency compared to existing strategies. This systematic research work establishes a strong foundation for the systematic evolution of energy-efficient HetNets. The systematic methodology employed ensures a comprehensive understanding of the complex interplay of network dynamics and user requirements in a multi-tiered environment.

Multicriteria and Quality of Service- Aware Vertical Handover Solution for Vehicle-to-Infrastructure Communication in Multitier Heterogeneous Networks.

The growing integration of vehicle-to-infrastructure (V2I) communication within multitier heterogeneous networks (HetNets) introduces challenges in maintaining seamless connectivity and Quality of Service (QoS) for vehicular users. This paper introduces a modified Multicriteria and QoS-aware Vertical Handover Decision Algorithm (mV2I-MHA) tailored to address these challenges. By considering multiple criteria - such as packet loss ratio, cost, available bandwidth and packet latency - the proposed solution intelligently manages vertical handovers between different network tiers. Numerous Multi-Criteria Decision-Making (MCDM) techniques have been proposed to address the right target network selection aiming to reduce unnecessary handovers, incorrect network selections and the associated processing time. However, many of these solutions overlook the significance of varying criteria weights in vertical handover decisions made by their selection algorithms. Through comprehensive simulations and comparisons with existing techniques, the effectiveness of the proposed – in this paper - solution is demonstrated in enhancing handover success rate, reducing handover failure rate, minimizing latency, and overall elevating the QoS for V2I communication in multitier HetNets. MATLAB R2020a was utilized to simulate the work. Using packet latency and handover failure as the metrics for performance, the results were contrasted with those of an existing works in terms of handover failure rate and packet delay. The developed mV2I-MHA showed considerable percentage reductions of 10.5% and 23.9% in packet latency and handover failure rate, respectively.

The Extremal GDoF Gain of Optimal Versus Binary Power Control in K User Interference Networks is Θ (√K)

Using ideas from Generalized Degrees of Freedom (GDoF) analyses and extremal network theory, this work studies the extremal gain of optimal power control over binary (on/off) power control, especially in large interference networks, in search of new theoretical insights. Whereas numerical studies have already established that in most practical settings binary power control is close to optimal, the extremal analysis shows not only that there exist settings where the gain from optimal power control can be quite significant, but also bounds the extremal values of such gains from a GDoF perspective. As its main contribution, this work explicitly characterizes the extremal GDoF gain of optimal over binary power control as Θ(√K) for all K. In particular, the extremal gain is bounded between ⌊√K⌋ and 2.5√K for every K. For K = 2, 3, 4, 5, 6 users, the precise extremal gain is found to be 1, 3/2, 2, 9/4 and 41/16, respectively. Networks shown to achieve the extremal gain may be interpreted as multi-tier heterogeneous networks. It is worthwhile to note that because of their focus on asymptotic analysis, the sharp characterizations of extremal gains are valuable primarily from a theoretical perspective, and not as contradictions to the conventional wisdom that binary power control is generally close to optimal in practical, non-asymptotic settings.

Analysis of Multi-tier Heterogeneous Network Using SIC Technique

Analysis of Multi-tier Heterogeneous Network Using SIC Technique

Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm

The Internet of Things (IoT) connects numerous sensor nodes and devices, resulting in an increase in the bandwidth and data rates. However, this has led to a surge in data-hungry applications, which consume significant energy at battery-limited IoT nodes, causing rapid battery drainage. As a result, it is imperative to find a reliable solution that reduces the power consumption. A power optimization model utilizing a modified genetic algorithm is proposed to manage power resources efficiently and reduce high power consumption. In this model, each access point computes the optimal power using the modified genetic algorithm until it meets the fitness criteria and assigns it to each cellular user. Additionally, a weight-based user-scheduling algorithm is proposed to enhance network efficiency. This algorithm considers both the distance and received signal strength indicator (RSSI) to select a user for a specific base station. Furthermore, it assigns appropriate weights for the distance, and the RSSI helps increase the spectral efficiency performance. In this paper, the user-scheduling algorithm was assigned equal weights and combined with the power optimization model to analyze the power consumption and spectral efficiency performance metrics. The results demonstrated that the weight-based user-scheduling algorithm performed better and was supported by the optimal allocation of weights using a modified genetic algorithm. The outcome proved that the optimal allocation of transmission power for users reduced the cellular users’ power consumption and improved the spectral efficiency.

GNN at the Edge: Cost-Efficient Graph Neural Network Processing Over Distributed Edge Servers

Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques. While the community has extensively investigated multi-tier edge deployment for traditional deep learning models (e.g. CNNs, RNNs), the emerging Graph Neural Networks (GNNs) are still under exploration, presenting a stark disparity to its broad edge adoptions such as traffic flow forecasting and location-based social recommendation. To bridge this gap, this paper formally studies the cost optimization for distributed GNN processing over a multi-tier heterogeneous edge network. We build a comprehensive modeling framework that can capture a variety of different cost factors, based on which we formulate a cost-efficient graph layout optimization problem that is proved to be NP-hard. Instead of trivially applying traditional data placement wisdom, we theoretically reveal the structural property of quadratic submodularity implicated in GNN’s unique computing pattern, which motivates our design of an efficient iterative solution exploiting graph cuts. Rigorous analysis shows that it provides parameterized constant approximation ratio, guaranteed convergence, and exact feasibility. To tackle potential graph topological evolution in GNN processing, we further devise an incremental update strategy and an adaptive scheduling algorithm for lightweight dynamic layout optimization. Evaluations with real-world datasets and various GNN benchmarks demonstrate that our approach achieves superior performance over de facto baselines with more than 95.8% cost reduction in a fast convergence speed.

Performance analysis of flexible indoor and outdoor user distribution in urban multi-tier heterogeneous network

The next generation heterogeneous networks (hetnet) refer to a complex eco system of radio cells with varying coverage ranges and technologies that interoperate to deliver the required capacity and coverage. This paper addresses the impact of large scale transmissions and analyses the performance of a typical user distribution in an urban multi-tier hetnet. A Boolean scheme of building blockage has been used to abstract the urban environment, which affects the architecture of hetnet with millimetre wave (mmW) access. A distribution mechanism is proposed to enable the indoor users to operate in mmW band whereas the outdoor users to operate in microwave (μWave) band. An indoor user can be connected with mmW band if the line-of-sight (LOS) link exists. Otherwise, the exploitation is provided with μWave band to enhance the coverage. By employing tools from stochastic geometry, the mathematical expressions are derived for coverage probability, rate and spatial average channel capacity of a user in indoor and outdoor environments. The exact mathematical results show that an impact of small cell occupation ratio results in maximised spatial average channel capacity of an outdoor hetnet. The significant enhancement is provided with mmW small cell in terms of rate and coverage.

Adaptive power allocation with energy efficiency in 5 g multitier networks using a hybrid heuristic approach

Nowadays, the cellular network has become a part of every human being's life. Cellular communication, hand in hand with information technology, has reduced the physical work performed by people. As there is great improvement and development in the fields of electronics and communication, the accumulation of data has also increased at a high rate. One of the cons observed due to the development of huge data is data traffic. The currently used 4G mobile communication technology is facing congestion, reduced capacity, shortages in bandwidth, slower data rate problems, and interference. The technology is equipped to connect with a large number of devices, overcoming the issue of network traffic that arises in 4G communications. Recent growth in technology has given the best solution with 5G communication. This proposed study focuses on the energy-efficient power allocation in a 5G multitier heterogeneous network, which consists of relays, deployment of small cells, and device-to-device communication. Here, a new methodology like Hybrid Heuristic algorithm is proposed for Adaptive Power Allocation with Energy Efficiency in 5G Multitier Networks which is a combination of ACO (Ant Colony Optimization) and PSO (Particle Swarm Optimization) algorithms to produce an efficient power allocation scheme for 5G downlink systems. The novelty of this research work is the integration of two optimization algorithm for adaptive power allocation in 5G network. The average SNIR value of the proposed Heuristic Approach is compared with the existing algorithms like SOA, and NCOL based on D2D user, micro user and pico user. The proposed Heuristic technique average system energy efficiency, and average system spectral efficiency also calculated and compared with the existing techniques such as SOA and NOCL.

Inter-cell interference in multi-tier heterogeneous cellular networks: modeling and constraints

Inter-cell interference in multi-tier heterogeneous cellular networks: modeling and constraints

The Rise of UAV Fleet Technologies for Emergency Wireless Communications in Harsh Environments

For unforeseen emergencies, such as natural disasters and pandemic events, it is highly demanded to cope with the explosive growth of mobile data traffic in extremely critical environments. An unmanned aerial vehicle (UAV) fleet is an effective way to facilitate the emergency wireless communication network (EcoNet). In this article, a multi-tier heterogeneous UAV network (MuHun), with different UAV fleets at different altitudes, is proposed to flexibly serve various emergencies. We refresh the key performance indicators of full coverage, network capacity, low latency, and energy efficiency in harsh environments. Then we present the special challenges regarding the shadowing-dominated complex channel model, energy supply limited short endurance, various communication mechanisms' coexistence, and a communication island for underground users in UAV-based EcoNet, followed by the MuHun-based EcoNet architecture and its advantages. Furthermore, some potential solutions such as the new hybrid-channel adapted resource allocation, reconfigurable intelligent surface assisted UAV communications, competitive heterogenous networks, and magnetic induction based air-to-ground/underground communications are discussed to effectively achieve full coverage, high capacity, high energy efficiency, and diverse qualities of services for EcoNets in harsh environments.

A Novel Repairable Fountain Codes Scheme Based on Partial Duplication Technique

Distributed storage system (DSS) is an emerging paradigm which provides reliable storage services for various source data. As the fault-tolerance scheme for DSS, erasure codes are required to provide redundancy service with high fault-tolerance and low cost.However, the existing coding scheme cannot provide these requirements well. Thus, it becomes an important yet challenging issue to find a code for storing various source data with high fault-tolerance and low cost. In this paper, a novel construction of repairable fountain codes with unequal locality is proposed by combining with partial duplication technique, namely the PD-ULRFC scheme. We construct a multi-tier heterogeneous storage network, where data core, processing units and storage nodes collaboratively store and transmit data. Moreover, the proposed PD-ULRFC scheme can reduce the repair and download cost by sacrificing a little extra storage occupation. Furthermore, the expressions of the repair cost and download cost are derived to analyze the performance of PD-ULRFC scheme. The simulation results demonstrate that the PD-ULRFC scheme significantly outperforms other redundant schemes in communication cost saving.

A Clustered PD-NOMA in an Ultra-Dense Heterogeneous Network with Improved System Capacity and Throughput

In the current era of exponentially growing demand for user connectivity, spectral efficiency (SE), and high throughput, the performance goals have become even more challenging in ultra-dense 5G networks. The conventional orthogonal frequency division multiple access (OFDMA) tech-niques are mature but have not proven sufficient to address the growing user demand for high data rates and increased capacity. Therefore, to achieve an improved throughput in an ultra-dense 5G network with an expanded network capacity, the unified non-orthogonal multiple access (NOMA) technique is considered to be a more promising and effective solution. Throughput can be im-proved by implementing PD-NOMA, as the interference is managed with the successive inter-ference cancellation (SIC) technique, but the issue of increased complexity and capacity with compromised data rate persists. This study implements the clustered PD-NOMA algorithm to enhance user association and network performance by managing the users in clusters with fewer users per cluster with the implementation of the cooperative PD-NOMA within the clusters. In this study, we enhanced the user association in a network and ultimately improved the throughput, sum rate, and system capacity in an ultra-dense heterogeneous network (HetNet). By imple-menting the proposed clustered PD-NOMA scheme, the system throughput has improved by 23% when compared to the unified PD-NOMA scheme and 65% when compared to the OFDMA scheme with a varied number of randomly deployed users, along with an improvement in system capacity of 8% as compared to the unified PD-NOMA and almost 80% as compared to the conventional OFDMA scheme in a randomly deployed ultra-dense multi-tier heterogeneous network. Thus, we improved the network performance with the proposed algorithm and achieved increased capacity, throughput, and sum rate by outperforming the unified PD-NOMA scheme in an ultra-dense heterogeneous network.

Quality enhancement in a mm-wave multi-hop, multi-tier heterogeneous 5G network architecture

Millimetre-wave ultra-dense high capacity networks are an important component of future 5G and 6G cellular systems since they are providing extremely high network capacity and speed to the end users. However, disparate types of users coexist in such scenarios, which can make the heterogeneous network unfair in terms of allocation of resources to various users based on their requirements. Therefore, a mechanism is required for effective spectrum sharing and to achieve overall system fairness. In this paper, an analytical model is suggested, which is based on a two-dimensional Markov state-transition diagram, to help set the parameter values to control the issuance of resources in coexistence layouts. A restriction approach is further implemented to gain a fair balance of the Grade-of-Service (GoS) for both user groups using the User Admission Control (UAC) mechanism. The developed mechanism restricts access to various channel resources for users with complete choice to give a greater probability of access to different users with limited resource options. Various levels of restriction are investigated in order to offer a balanced low-blocking probability performance to both user groups in order to improve the overall network fairness. Also, the proposed approach could provide a precise level of Grade-of-Service guarantee for both the user groups if sufficient flexibility is available within the whole network. Our simulations results along with the analytical model demonstrate that approximately 30% to 45% enhancement, in terms of grade of service (GoS), could be achieved in high to medium loads by restricting some of the users’ flexibility. From the analytical model, it is concluded that the blocking of shadowed users are significantly reduced from 7% to 4.5% at high traffic loads. Moreover, the obtained results and findings are verified using a number of case studies and large-scale simulations.

Slicing Framework for Service Level Agreement Guarantee in Heterogeneous Networks—A Deep Reinforcement Learning Approach

In 5G scenarios, network slicing and multi-tier heterogeneous networks are critical to guarantee the service level agreement (SLA) of various services. In this letter, a dynamic radio resource slicing framework considering joint bandwidth slicing ratios and base station (BS)-user association is presented for a two-tier heterogeneous wireless network. This framework maximizes the spectrum reuse ratio through a two-step deep reinforcement learning (DRL) method, and guarantees the SLA of network slices simultaneously. Specially, a distributed agent (D-Agent) is deployed at each BS for acquiring the slicing resource in a single BS level. Meanwhile, a centralized agent (C-Agent) manages radio resource allocation and user association among heterogeneous BSs to guarantee the SLA.

Performance Analysis of flexible Indoor and Outdoor User distribution in Urban Multi-Tier Heterogeneous Network

Performance Analysis of flexible Indoor and Outdoor User distribution in Urban Multi-Tier Heterogeneous Network

Maximizing lower bound of energy efficiency in multi-tier heterogeneous cellular network via stochastic geometry

In this paper, an optimization method is proposed to maximize the lower bound of energy efficiency (EE) in the multi-tier heterogeneous cellular network (HCN) and resolved by adjusting Base Station (BS) density and BS transmit power. Firstly, we derive the mathematical formulation of network coverage probability and the average user rate. We prove that the network coverage probability and the average user rate monotonically increase with the cumulative sum of the product of BS density and BS transmit power. Then, the network coverage probability and the average user rate requirements are converted to the BS density and the BS transmit power constraints. An EE maximization problem is formulated. Nevertheless, the intractable problem is reformulated by maximizing the lower bound of EE since the coupled parameters are tight. The simplified optimization problem can be converted to a standard form of geometric programming (GP) by a convex approximation method. The problem in GP form can be further transformed into the traditional convex problem and can be fully solved using the interior point method. Numerical simulations show that the optimal solutions can be determined with fast convergence, while the network EE is significantly improved. The gap between the approximate and the original solutions is analyzed through simulations.

Optimizing Energy Efficiency for Supporting Near-Cloud Access Region of UAV-Based NOMA Networks in IoT Systems

Nonorthogonal multiple access (NOMA) and unmanned aerial vehicle (UAV) are two promising technologies for the wireless fifth generation (5G) networks and beyond. On the one hand, UAVs can be deployed as flying base stations to build line-of-sight (LoS) communication links to two ground users (GUs) and to improve the performance of conventional terrestrial cellular networks. On the other hand, NOMA enables the share of an orthogonal resource to multiple users simultaneously, thus improving the spectral efficiency and supporting massive connectivities. This paper presents two protocols, namely, cloud-based central station- (CCS-) based power-splitting protocol (PSR) and time-switching protocol (TSR), for simultaneous wireless information and power transmission (SWIPT) at UAV employed in power domain NOMA-based multitier heterogeneous cloud radio access network (H-CRAN) of Internet of Things (IoT) system. The system model with k types of UAVs and two users in which the CCS manages the entire H-CRAN and operates as a central unit in the cloud is proposed in our work. Closed-form expressions of throughput and energy efficiency (EE) for UAVs are derived. In particular, the EE is determined for the impacts of power allocation at CCS, various UAV types, and channel environment. The simulation results show that the performance for CCS-based PSR outperforms that for CCS-based TSR for the impacts of power allocation at the CCS. On the contrary, the TSR protocol has a higher EE than the PSR in the cases of the impact of various UAV types and channel environment. The analytic results match Monte Carlo simulations.

Edge Computing and Deep Learning Enabled Secure Multitier Network for Internet of Vehicles

Internet of Vehicles (IoVs) are fast becoming the norm in our society, but such a trend also comes with its own set of challenges (e.g., new security and privacy risks due to the expanded attack vectors). In this work, we propose an edge-computing-based secure, efficient, and intelligent multitier heterogeneous IoVs network. We first discuss the functionality and objectives of such an architecture. Then, we demonstrate how unsupervised deep learning techniques can facilitate the identification of suspicious vehicle behavior and ensure the security of such an architecture. The findings from our evaluations demonstrate the learning spatiotemporal information and parameter efficiency of the proposed stacked long short-term memory (LSTM) model over single LSTMs.

Cooperative Content Caching in MEC-Enabled Heterogeneous Cellular Networks

Multimedia content delivery via the cellular infrastructure increases fast due to the very high volumes of mobile video traffic generated by the billions of end devices populating the mobile data network. A critical mass of mobile video content requests refers to the consumption of the same popular video content, which is consumed by different end terminals spanning small geographical regions. Such content requests place a great burden on the backhaul of content-agnostic cellular networks, which fail to exploit the correlation of video requests to decongest their backhaul links. This creates redundant retransmissions while fetching the same video content from a central server to the network edge, using the bandwidth-limited backhaul at peak-time periods. With the integration of multi-access edge computing (MEC) capabilities in 5G mobile cellular networks, mobile network operators can place popular video content closer to the network edge at off-peak time periods, predicting user requests exhibiting a high correlation for a given time interval over smaller geographical regions. In this paper, we investigate popular content placement in multi-tier heterogeneous cellular networks where the edge network infrastructure can cooperate to create content delivery (and placement) clusters to effectively serve correlated video requests. To this end, we model the cooperative content placement problem using the multiple knapsack problem (MKP) formulation and present an exact (optimal) bound-and-bound strategy to solve it. The performance of the proposed strategy is evaluated in-depth using extensive system-level simulations and is compared against that of other state-of-the-art algorithms. Valuable design guidelines and key performance trade-offs are discussed, paving the way towards cluster-based cooperative caching in MEC-enabled cellular network setups.