scholarly journals Joint Optimization of Content Placement and User Association in Cache-Enabled Heterogeneous Cellular Networks Based on Flow-Level Models

2018 ◽  
Vol 2018 ◽  
pp. 1-20
Author(s):  
Hua Qu ◽  
Gongye Ren ◽  
Jihong Zhao ◽  
Zhenjie Tan ◽  
Shuyuan Zhao
Keyword(s):  
Cellular Networks ◽  
Content Delivery ◽  
Mixed Integer ◽  
Joint Optimization ◽  
User Association ◽  
Heterogeneous Cellular Networks ◽  
Coupled Models ◽  
Average Delay ◽  
Delivery Delay ◽  
Content Placement

Cache-enabled heterogeneous cellular networks (HCNs) have been investigated extensively to alleviate backhaul congestion and reduce content delivery delay. In this paper, we jointly optimize content placement and user association to minimize the average content delivery delay in cache-enabled HCNs based on flow-level models. This formulation considers (1) different timescales of content placement and content delivery, (2) locality of content popularity, and (3) the heterogeneity of spatial traffic distribution, which are often neglected in existing researches. The joint optimization problem is formulated as a mixed integer nonlinear programming problem in load-non-coupled and load-coupled models, respectively. We decouple this problem into two interrelated subproblems and resolve them individually. For the user association problem under a given content placement situation, we propose a content-level selective association algorithm, which allows the requests for different contents at the same location to connect to different base stations (BSs). In addition, we propose a greedy content caching algorithm to add contents to the caches of BSs in an iterative manner. These two algorithms are alternately executed until the caches of all the BSs are filled to capacity. Simulation results show that the proposed algorithm achieves better performance in terms of average delay and backhaul usage compared with traditional content placement and user association approaches.

scholarly journals Multi-Location-Aware Joint Optimization of Content Caching and Delivery for Backhaul-Constrained UDN

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2449 ◽  
Author(s):  
Wenpeng Jing ◽  
Xiangming Wen ◽  
Zhaoming Lu ◽  
Haijun Zhang
Keyword(s):  
Content Delivery ◽  
Decision Time ◽  
Base Stations ◽  
Joint Optimization ◽  
Resources Allocation ◽  
User Association ◽  
Placement Problem ◽  
Content Caching ◽  
Backhaul Link ◽  
Content Placement

Mobile edge caching is regarded as a promising way to reduce the backhaul load of the base stations (BSs). However, the capacity of BSs’ cache tends to be small, while mobile users’ content preferences are diverse. Furthermore, both the locations of users and user-BS association are uncertain in wireless networks. All of these pose great challenges on the content caching and content delivery. This paper studies the joint optimization of the content placement and content delivery schemes in the cache-enabled ultra-dense small-cell network (UDN) with constrained-backhaul link. Considering the differences in decision time-scales, the content placement and content delivery are investigated separately, but their interplay is taken into consideration. Firstly, a content placement problem is formulated, where the uncertainty of user-BS association is considered. Specifically, different from the existing works, the specific multi-location request pattern is considered that users tend to send content requests from more than one but limited locations during one day. Secondly, a user-BS association and wireless resources allocation problem is formulated, with the objective of maximizing users’ data rates under the backhaul bandwidth constraint. Due to the non-convex nature of these two problems, the problem transformation and variables relaxation are adopted, which convert the original problems into more tractable forms. Then, based on the convex optimization methods, a content placement algorithm, and a cache-aware user association and resources allocation algorithm are proposed, respectively. Finally, simulation results are given, which validate that the proposed algorithms have obvious performance advantages in terms of the network utility, the hit ratio of the cache, and the quality of service guarantee, and are suitable for the cache-enabled UDN with constrained-backhaul link.

scholarly journals Resource Allocation for Energy Efficient User Association in User-Centric Ultra-Dense Networks Integrating NOMA and Beamforming

2020 ◽  
Author(s):  
Long Zhang ◽  
Guobin Zhang ◽  
Xiaofang Zhao ◽  
Yali Li ◽  
Chuntian Huang ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Energy Efficient ◽  
Large Scale ◽  
Mixed Integer ◽  
Joint Optimization ◽  
User Association ◽  
Dual Decomposition ◽  
Dense Networks ◽  
Wireless Backhaul ◽  
User Centric

A coupling of wireless access via non-orthogonal multiple access and wireless backhaul via beamforming is a promising way for downlink user-centric ultra-dense networks (UDNs) to improve system performance. However, ultra-dense deployment of radio access points in macrocell and user-centric view of network design in UDNs raise important concerns about resource allocation and user association, among which notably is energy efficiency (EE) balance. To overcome this challenge, we develop a framework to investigate the resource allocation problem for energy efficient user association in such a scenario. The joint optimization framework aiming at the system EE maximization is formulated as a large-scale non-convex mixed-integer nonlinear programming problem, which is NP-hard to solve directly with lower complexity. Alternatively, taking advantages of sum-of-ratios decoupling and successive convex approximation methods, we transform the original problem into a series of convex optimization subproblems. Then we solve each subproblem through Lagrangian dual decomposition, and design an iterative algorithm in a distributed way that realizes the joint optimization of power allocation, sub-channel assignment, and user association simultaneously. Simulation results demonstrate the effectiveness and practicality of our proposed framework, which achieves the rapid convergence speed and ensures a beneficial improvement of system-wide EE.<br>

Sign in / Sign up

Export Citation Format

Share Document