User Satisfaction Ratio Research Articles

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions.

An optimized small‐cell planning procedure for Heterogeneous Network to improve network energy efficiency

SummaryCell planning in conventional networks has gained more attention as it directly affects the network performance and deployment cost. Existing cell planning methodologies are framed either with identical base stations or constructing a network without any infrastructure. But heterogeneous networks (HetNets) allow the service provider to deploy small cells over the region to enhance the network performance and signal coverage probability. Thus, a small‐cell planning procedure is presented in this research work considering the low‐powered base station and deployment cost to enhance the energy efficiency of the HetNet. An adaptive fuzzy expert system is used for cell dimensioning, and a nature‐inspired ant colony optimization model is employed for automatic base station placement. Simulation analysis demonstrates that the proposed small‐cell planning procedure attains better energy efficiency and user satisfaction ratio compared to conventional planning strategies. In the two cases of simulation analysis, the proposed model attains an average of 85% user satisfaction ratio for case 1 and 87% for case 2, which is better than existing strategies like density‐based spatial clustering of applications with noise (DBSCAN), k‐means, and number‐based spatial clustering (NBSC) algorithms.

Overlay dynamic spectrum sharing in cognitive radio for 4G and 5G using FBMC

Dynamic spectrum sharing allows the 4G-LTE and 5G-NR to use the same frequency spectrum band for communication and the spectrum allocator allocates the spectrum to the users based on their demands. In this paper we have analyzed existing OFDM (orthogonal Frequency division multiplexing) Technique in the physical layer for the spectrum sharing and proposed a FBMC (Filter Bank Multi Carrier) technique with Long duration Pulse Shaping filter and designed a Sequence Based Spectrum Allocator for designing a high spectral efficient network. Results were validated based on the parameters like Secondary User Score and Primary User Satisfaction Ratio and Secondary user throughput for overlay spectrum sharing mode in cognitive radio.

Cooperation Based Proactive Caching in Multi-Tier Cellular Networks

The limited caching capacity of the local cache enabled Base station (BS) decreases the cache hit ratio (CHR) and user satisfaction ratio (USR). However, Cache enabled multi-tier cellular networks have been presented as a promising candidate for fifth generation networks to achieve higher CHR and USR through densification of networks. In addition to this, the cooperation among the BSs of various tiers for cached data transfer, intensify its significance many folds. Therefore, in this paper, we consider maximization of CHR and USR in a multi-tier cellular network. We formulate a CHR and USR problem for multi-tier cellular networks while putting major constraints on caching space of BSs of each tier. The unsupervised learning algorithms such as K-mean clustering and collaborative filtering have been used for clustering the similar BSs in each tier and estimating the content popularity respectively. A novel scheme such as cluster average popularity based collaborative filtering (CAP-CF) algorithm is employed to cache popular data and hence maximizing the CHR in each tier. Similarly, two novel methods such as intra-tier and cross-tier cooperation (ITCTC) and modified ITCTC algorithms have been employed in order to optimize the USR. Simulations results witness, that the proposed schemes yield significant performance in terms of average cache hit ratio and user satisfaction ratio compared to other conventional approaches.

DeepCachNet: A Proactive Caching Framework Based on Deep Learning in Cellular Networks

Content caching at the network edge is considered to be a suitable technique for enhancing the efficacy of content delivery in cellular networks. Caching the strategic content at the SBS is critical due to storage constraints; however, it requires information about popularity dissemination that is not known in advance. Furthermore, the popularity of content is varied due to the fact that every mobile user connected to the SBSs has a different preference for content. Thus, the nature of the content a user prefers depends on the features of both user and content. This article proposes a novel deep learning-based proactive caching framework in cellular networks, called DeepCachNet, in which a vast amount of data is collected from the mobile devices of users connected to SBSs. The deep-learning methods called auto-encoder and stacked denoising autoencoders are applied to the collected data to extract the features of users and content, respectively. The extracted features are then used to estimate the content popularity at the core network. Based on the estimated content popularity, the strategic content is cached at SBSs to obtain higher backhaul offloading and user satisfaction. To validate the effectiveness of the proposed framework, a case study is carried out in which mobile data are gathered from connected mobile devices by using a developed android mobile application, and a simulation of the proposed framework is performed on the collected data. The results of the simulation show that the framework resolves the cold-start and data sparsity problem, and yields significant improvements in terms of backhaul offloading and the user satisfaction ratio. It achieves gains of up to 6.2 percent and 30 percent for backhaul offloading and user satisfaction, respectively.

An Adaptive Load Balancing Queue Based Resource Allocation Algorithm in Cloud Computing Environment

Load balancing provides a better approach to distribute workloads at different virtual machines in different data center. In cloud computing improves the distributions of workloads across multiple cloud computing resources such as a computer, computer clusters, network links, central processing unit or disk drives. The aim of load balancing is to optimize maximum throughput, minimum response time, resource use and avoid overload on any single resource. Using multiple components with load balancing instead of single component may increase reliability and availability through redundancy. Cloud computing is one of the greatest platforms which provides high user satisfaction ratio and optimize and distribute load at different virtual machine according to client requirements.

Radio Access Technology Selection in Heterogeneous Wireless Networks Using a Hybrid Fuzzy-Biogeography Based Optimization Technique

Radio access technology (RAT) selection in heterogeneous wireless networks is a challenging task to achieve guaranteed quality of service (QoS). This paper proposes a hybrid methodology in order to accomplish QoS through high service connectivity and reliable network transparency in a wireless heterogeneous system. The proposed hybrid methodology integrates a non-homogenous biogeography based optimization (NHBBO) with a parallel fuzzy system (PFS). The PFSs are employed to determine the probability of RAT selection, which acts as an input to the NHBBO procedure. Thus the NHBBO decide over the defined multi-point decision making algorithm to select the best RAT in the given heterogeneous network. The key role of the proposed technique is to optimize the weight coefficients of multi-point decision making algorithm and ensure maximum user satisfaction ratio to select best RAT. Several experiments are carried out using the proposed NHBBO---PFS technique to demonstrate the effectiveness and robustness in producing solutions compared to a few existing methods for RAT selection in heterogeneous wireless networks.

Dynamic Scheduling for Emergency Tasks on Distributed Imaging Satellites with Task Merging

Scheduling plays a significant role in improving observation effectiveness of distributed imaging satellites. Although extensive satellite scheduling algorithms have been proposed, none of them focuses on dynamic scheduling for emergency tasks. In this paper, a novel multi-objective dynamic scheduling model for emergency tasks on distributed imaging satellites is established for the first time. To improve user's satisfaction ratio and resource utilization, we propose the task merging strategy: establishing a task merging graph (TMG) model and proposing a task merging algorithm-CP-TM based on clique partition. In addition, a rehabilitation technique is suggested to overcome the disadvantage that task merging will make tasks have less imaging opportunities. To further enhance the schedulability, the task backward shift in the waiting sequence is considered in our study. Furthermore, a novel dynamic scheduling algorithm called TMBSR-DES is presented, which comprehensively considers task merging, backward shift, and rehabilitation. To demonstrate the superiority of our TMBSR-DES, we conduct extensive experiments by simulations to compare TMBSR-DES with three existing algorithm-RBHA, RTSSA, and LSA, as well as three baseline algorithms-BS-DES, TMR-DES, and TMBS-DES. The experimental results indicate that TMBSR-DES outperforms the others and is suitable for emergency task scheduling.

A truthful auction mechanism for channel allocation in multi-radio, multi-channel non-cooperative wireless networks

Due to users' fast-growing demands, wireless spectrum is becoming a more and more scarce resource. However, the state of spectrum usage shows that while large chunks of spectrum are left idle at many places, many emerging wireless applications cannot get enough spectra to provide their services. In contrast to existing truthful mechanisms for channel redistribution, which achieve strategy-proofness at the price of lowered system performance, we propose SHIELD, which not only guarantees strategy-proofness in the process of channel redistribution, but also achieves high system performance. Our evaluation results show that SHIELD outperforms the existing mechanisms, in terms of spectrum utilization and user satisfaction ratio. Here, channel utilization represents the average number of radios allocated to channels, and buyer satisfaction ratio shows the percentage of buyers who get at least one channel in the auction.

A Hybrid Modified Particle Swarm Optimization for Heterogeneous Radio Access Technology (RAT) Selection

ABSTRAC Next Generation wireless networks are heterogeneous in nature where a variety of Radio Access Technologies (RATs) coexist in the same coverage area. It is important to select the most appropriate Radio Access Network (RAN) according to the requested service of each user. Hence, this paper proposes a Hybrid Modified Particle Swarm Optimization (PSO) algorithm to select the best access network among Wireless Wide Area Network (WWAN) and Wireless Local Area Network (WLAN). Particle Swarm Optimization is a global optimization algorithm, is known to effectively solve large scale nonlinear optimization problems. A variant of PSO called as Modified PSO is used in this paper where a constriction coefficient (  ) has been introduced in the velocity update equation and this factor improves the convergence of the particle over time and this optimization algorithm has been hybridized with multi-objective decision making algorithm and weighing function to achieve better solutions. The performance of the proposed algorithm is evaluated using 1000 datasets. From the simulation study, it is found that the proposed approach gives higher user satisfaction ratio compared with the other approaches.

Efficient Group-Based Multimedia-on-Demand Service Delivery in Wireless Networks

Recently, an upsurge of interest has been observed in providing multimedia on-demand (MoD) services to mobile users over wireless networks. Nevertheless, due to the rapidly varying nature of mobile networks and the scarcity of radio resources, the commercial implementation is still limited. This paper presents an efficient group-based multimedia-on-demand (GMoD) service model over multicast-enabled wireless infrastructures, where users requesting the same content are grouped and served simultaneously with a single multicast stream. The grouping is fulfilled through a process named batching. An analytical model is derived to analyse a timeout-based batching scheme with respect to the tradeoff between user blocking probability and reneging probability. Based on the deduced analytical model, an optimal timeout-based batching scheme is proposed to dynamically identify the optimal tradeoff point that maximizes the system satisfaction ratio given a particular system status. The proposed scheme is evaluated by means of simulation and compared with two basic batching schemes (timeout-based, size-based), and two hybrid ones (combined-for-profit, combined-for-loss). The simulation results demonstrate the proposed approach can ensure significant gains in terms of user satisfaction ratio, with low reneging and blocking probabilities