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The fifth generation (5G) communication technologies are envisioned to provide higher data rates, excellent user-coverage, extremely low latency and power consumption. Such cellular networks will adopt a heterogeneous multi-tier architecture consisting of macrocells, device-to-device networks, and different types of small cells to serve users with...
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Citations
... We have assumed that the set of FAPs and IoT devices are deployed randomly in the network area of 500 m x 500 m underlying a cellular BS. The bandwidth is considered as 20 MHz accordingly, the maximum number of available PRBs found as 100 [15]. We have assumed that FAP with a radius of 20 m, randomly deployed in the network. ...
Fog computing in 5G networks has played a significant role in increasing the number of users in a given network. However, Internet-of-Things (IoT) has driven system designers towards designing heterogeneous networks to support diverse demands (tasks with different priority values) with different latency and data rate constraints. In this paper, our goal is to maximize the total number of tasks served by a heterogeneous network, labeled task throughput, in the presence of data rate and latency constraints and device preferences regarding computational needs. Since our original problem is intractable, we propose an efficient solution based on graph-coloring techniques. We demonstrate the effectiveness of our proposed algorithm using numerical results, real-world experiments on a laboratory test-bed and comparing with the state-of-the-art algorithm.
... Simulation parameters[23],[29] ...
High mobility requirements are one of the challenges face fifth-generation wireless (5G) cellular networks by providing acceptable wireless services to users traveling at speed up to 350 km/h. This paper presents a new scenario to increase the bit rate and coverage for passengers that use the vehicles for traveling through the installation a mobile station (MS) on these vehicles to provide a high-quality service to users. Based on signal to noise ratio (SNR’s) mathematical derivation and the outage probability of the user link, the proposed system is evaluated. Numerical results indicate an enhancement for users who received signal strength (RSS) from (-72 to -55) dBm and (15 to 38) Mbps in bit rate. Moreover, their number of users increased by proposed system adoption.
... The proposed technique can be expanded to the forthcoming 5G cellular networks. [136,137] Network ...
Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.
... These challenges can be overcome by using Network optimization algorithms and control techniques [12]. A distribution algorithm is proposed in [9] for resource allocation in uplink 5G networks where resources are allocated to the uplink transmitters which results in higher spectral efficiency and maximize the users' data rate. We have related this scenario for resource allocation to the electric vehicles in both uplink and downlink communications. ...
The introduction of electric vehicles (EVs) brings out various challenges like deploying more charging stations, building its supportive infrastructure, managing the EVs and their various resource requests. In order to maintain all the resource requests of the EVs, network slicing is used which provides an efficient way to satisfy various use case demands of the EVs. In this work, we perform network slicing that partitions the physical network into three slices, infotainment and safety message slices that belong to downlink communication and charge state information slice that belongs to uplink communication. If there are large number of resource requests made by the EVs to the slices, it might lead to collision in the channel. Unsupervised machine learning is performed on the EVs using local scaling as the scaling parameter which handles multi scale data and also performs better clustering. For an efficient communication between the clustered EVs and charging station, slice leaders of every cluster is determined. We have also proposed an algorithm that efficiently performs resource allocation to the EVs to increase the throughput with low latency. Slice leaders forward the resource requests made by the EVs of the respective clusters to the charging station through RSUs and a slice block allocation is performed by giving higher preference to the critical message requests.
... We have assumed that the set of FAPs and IoT devices are deployed randomly in the network area of 500 m x 500 m underlying a cellular BS. The bandwidth is considered as 20 MHz accordingly, the maximum number of available PRBs found as 100 [15]. We have assumed that FAP with a radius of 20 m, randomly deployed in the network. ...
Fog computing in 5G networks has played a significant role in increasing the number of users in a given network. However, Internet-of-Things (IoT) has driven system designers towards designing heterogeneous networks to support diverse demands (tasks with different priority values) with different latency and data rate constraints. In this paper, our goal is to maximize the total number of tasks served by a heterogeneous network, labeled task throughput, in the presence of data rate and latency constraints and device preferences regarding computational needs. Since our original problem is intractable, we propose an efficient solution based on graph-coloring techniques. We demonstrate the effectiveness of our proposed algorithm using numerical results, real-world experiments on a laboratory testbed and comparing with the state-of-the-art algorithm.
... In [16] a pricing-based stable matching algorithm was proposed for the joint power allocation and mobile relay selection problem in vehicle-to-everything (V2X) communication. For the resource allocation of the underlay transmitters in HetNets, the stable matching was used in [17] to provide a distributed solution. In [18], the stable matching model was modified to provide a minimum requirement guarantee in the spectrum allocation. ...
... In this study, we focus on the spectrum allocation problem, while the power allocation presents as one influencing factor in the computation of the data rates for SUEs. The equal allocation scheme, fixed allocation scheme, or any other more complex allocation scheme can be adopted [27,17]. The problem of joint optimization of spectrum and power allocation will be addressed in our future study. ...
This paper addresses the resource allocation problem for small cells in the two-tier heterogeneous network (HetNets) with non-independent resource blocks (RBs) usability, which is prevalent in HetNets application. To formulate the correlations of different RBs to facilitate the resource allocation, a normalized pointwise mutual information (NPMI) based coefficient is proposed, which is easy to compute and adopt based on the historical information about RBs usability. To obtain fast allocation solution, a two-stage stable matching based dynamic spectrum allocation algorithm (TSSM) is proposed, which takes both network throughput and user performance as optimization metrics. Specifically, through robust preferable stable matching in the first stage and the iterative matchings with NPMI based coefficient as the preference profiles in the second stage, efficient matching solution can be obtained with low complexity and thus is well adapted to the requirement of the resource allocation in HetNets. The validity of the proposed coefficient in modeling the relevance of RBs usability as well as the benefit of its usage in the allocation algorithm are verified with typical spectrum usability related data. Extensive simulation results on various spectrum environment show the average probability of no RB to use following the proposed TSSM are reduced by more than 13.2%, and the robust requirement for users can be guaranteed better at the same time. Furthermore, the accumulated throughput of all users in the network increases by more than 19.4% in average under different RBs usability environment.
... We use distance dependent transmission model in our simulation environment [24]. Simulation parameters are given in Table II [25]. The path loss between D2D s and SUE s , MBS and SUE s , SBS s and MUE s is considered as o (l) = 15.3+40×log(R)+Ef ...
5. Nesil (5G) iletişim sistemleri öncesinde hücresel haberleşme yukarı linkinde sadece tek bir dalga şekli kullanılmaktaydı. İlk kez 2018 yılında standartları yayımlanmaya başlayan 5G iletişim sistemlerinde ise yukarı link için iki farklı dalga şekli kullanılabilmesi esnekliği getirilmiştir. Bununla beraber, bu esnekliğin yönetimini sağlayacak metotlar standartlarda geliştiriciye bırakılmıştır. Bu çalışmada, 5G iletişim sistemlerindeki yukarı link dalga şeklinin seçtirilmesine yönelik makine öğrenmesi tabanlı özgün bir metot geliştirilmiştir. Geliştirilen bu metot, çevresel farkındalığı yüksek seviyede olacak şekilde tasarlanmıştır.
We study resource allocation in wireless multi-tiered OFDMA cellular systems, like the current 5G networks. We compare co-ordinated versus distributed methods of assignment of resource blocks to maximize signal strength and throughput to ensure quality of service. The distributed assignment, where every wireless client optimizes its utility, is modeled by a strategic game with the cellular users as players. We study the existence of Nash equilibrium in this game and provide bounds for the inefficiency of this strategic game via analysis of the Price of Anarchy (PoA). Our social objective is chosen to provide fair throughput by measuring the minimum data rate. We show that approximating the social optimum, i.e. maximizing the minimum data rate, to within any given factor is NP-Hard and provide an algorithm that is illustrated to perform well in practice.KeywordsMulti-tiered networksPrice of anarchyNash equilibrium
The introduction of electric vehicles (EVs) brings out various challenges like deploying more charging stations, building its supportive infrastructure, managing the EVs and their various resource requests. In order to maintain all the resource requests of the EVs, network slicing is used which provides an efficient way to satisfy various use case demands of the EVs. In this work, we perform network slicing that partitions the physical network into three slices, infotainment and safety message slices that belong to downlink communication and charge state information slice that belongs to uplink communication. If there are large number of resource requests made by the EVs to the slices, it might lead to collision in the channel. Unsupervised machine learning is performed on the EVs using local scaling as the scaling parameter which handles multi scale data and also performs better clustering. For an efficient communication between the clustered EVs and charging station, slice leaders of every cluster is determined. We have also proposed an algorithm that efficiently performs resource allocation to the EVs to increase the throughput with low latency. Slice leaders forward the resource requests made by the EVs of the respective clusters to the charging station through RSUs and a slice block allocation is performed by giving higher preference to the critical message requests.
