In this paper, we consider a resource allocation with carrier aggregation optimization problem in long term evolution (LTE) cellular networks. In our proposed model, users are running elastic or inelastic traffic. Each user equipment (UE) is assigned an application utility function based on the type of its application. Our objective is to allocate multiple carriers resources optimally among users in their coverage area while giving the user the ability to select one of the carriers to be its primary carrier and the others to be its secondary carriers. The UE's decision is based on the carrier price per unit bandwidth. We present a price selective centralized resource allocation with carrier aggregation algorithm to allocate multiple carriers resources optimally among users while providing a minimum price for the allocated resources. In addition, we analyze the convergence of the algorithm with different carriers rates. Finally, we present simulation results for the performance of the proposed algorithm.
In @cite_13 , the authors introduced bandwidth proportional fair resource allocation with logarithmic utilities. The algorithms at the links are based on Lagrange multiplier methods of optimization theory. In @cite_4 , the authors used sigmoidal-like utility functions to represent real-time applications. In @cite_5 , the authors proposed weighted aggregated utility functions for the elastic and inelastic traffic. An optimal resource allocation algorithm is presented in @cite_0 and @cite_9 to allocate a single carrier resources optimally among mobile users. In @cite_12 , two-stage resource allocation algorithm is proposed to allocate the eNodeB resources among users running multiple applications at a time. In @cite_6 , a resource allocation optimization problem is presented for two groups of users. The two groups are public safety users group and commercial users group. In @cite_15 , the authors presented a resource allocation with users discrimination algorithms to allocate the eNodeB resources optimally among users and their applications. A resource allocation optimization problem with carrier aggregation is presented in @cite_8 to allocate resources from the LTE Advanced carrier and the MIMO radar carrier to each UE in a LTE Advanced cell based on the running application of the UE.
@cite_4 @cite_8 @cite_9 @cite_6 @cite_0 @cite_5 @cite_15 @cite_13 @cite_12
2013722958 1564794196 2074956630 2073839497
In this paper, we introduce an approach for resource allocation of elastic and inelastic adaptive real-time traffic in fourth generation long term evolution (4G-LTE) system. In our model, we use logarithmic and sigmoidal-like utility functions to represent the users applications running on different user equipments (UE)s. We present a resource allocation optimization problem with utility proportional fairness policy, where the fairness among users is in utility percentage (i.e user satisfaction with the service) of the corresponding applications. Our objective is to allocate the resources to the users with priority given to the adaptive real-time application users. In addition, a minimum resource allocation for users with elastic and inelastic traffic should be guaranteed. Our goal is that every user subscribing for the mobile service should have a minimum quality-of-service (QoS) with a priority criterion. We prove that our resource allocation optimization problem is convex and therefore the optimal solution is tractable. We present a distributed algorithm to allocate evolved NodeB (eNodeB) resources optimally with a priority criterion. Finally, we present simulation results for the performance of our rate allocation algorithm. In this paper, we consider resource allocation optimization problem in cellular networks for different types of users running multiple applications simultaneously. In our proposed model, each user application is assigned a utility function that represents the application type running on the user equipment (UE). The network operators assign a subscription weight to each UE based on its subscription. Each UE assigns an application weight to each of its applications based on the instantaneous usage percentage of the application. Additionally, UEs with higher priority assign applications target rates to their applications. Our objective is to allocate the resources optimally among the UEs and their applications from a single evolved node B (eNodeB) based on a utility proportional fairness policy with priority to realtime application users. A minimum quality of service (QoS) is guaranteed to each UE application based on the UE subscription weight, the UE application weight and the UE application target rate. We propose a two-stage rate allocation algorithm to allocate the eNodeB resources among users and their applications. Finally, we present simulation results for the performance of our rate allocation algorithm. In this paper, we consider resource allocation optimization problem in fourth generation long term evolution (4G-LTE) with users running multiple applications. Each mobile user can run both delay-tolerant and real-time applications. In every user equipment (UE), each application has a application-status differentiation from other applications depending on its instantaneous usage percentage. In addition, the network operators provide subscriber differentiation by assigning each UE a subscription weight relative to its subscription. The objective is to optimally allocate the resources with a utility proportional fairness policy. We propose an algorithm to allocate the resources in two-stages. In the first-stage, the UEs collaborate with the evolved node B (eNodeB) that allocates the optimal rates to users according to that policy. In the second-stage, each user allocates its assigned rate internally to its applications according to their usage percentage. We prove that the two-stage resource allocation algorithm allocates the optimal rates without eNodeB knowledge of the UEs utilities. Finally, numerical results on the performance of the proposed algorithm are presented. In this paper, we consider resource allocation optimization problem in fourth generation long term evolution (4G-LTE) for public safety and commercial users running elastic or inelastic traffic. Each mobile user can run delay-tolerant or real-time applications. In our proposed model, each user equipment (UE) is assigned a utility function that represents the application type running on the UE. Our objective is to allocate the resources from a single evolved node B (eNodeB) to each user based on the user application that is represented by the utility function assigned to that user. We consider two groups of users, one represents public safety users with elastic or inelastic traffic and the other represents commercial users with elastic or inelastic traffic. The public safety group is given priority over the commercial group and within each group the inelastic traffic is prioritized over the elastic traffic. Our goal is to guarantee a minimum quality of service (QoS) that varies based on the user type, the user application type and the application target rate. A rate allocation algorithm is presented to allocate the eNodeB resources optimally among public safety and commercial users. Finally, the simulation results are presented on the performance of the proposed rate allocation algorithm.