Joint Optimization of User Association and Resource Allocation Employing Inter-Cell Interference for Multiple Frequency Bands

2018 ◽  
Author(s):  
Takaaki Kazumoto ◽  
Yusaku Kanehira ◽  
Nobuhiko Miki
Keyword(s):  
Resource Allocation ◽  
Joint Optimization ◽  
User Association ◽  
Multiple Frequency ◽  
Frequency Bands ◽  
Inter Cell Interference

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>

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 (NOMA) and wireless backhaul via beamforming is a promising way for downlink user-centric ultra-dense networks (UDNs) to improve system performance. However, the ultra-dense deployment of radio access points in macrocell and the 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 the sum-of-ratios decoupling and successive convex approximation methods, we transform the original problem into a series of convex optimization subproblems. Furthermore, we solve each subproblem through the 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.

scholarly journals A QoS-based joint user association and resource allocation scheme in ultra-dense networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Zhiwei Si ◽  
Gang Chuai ◽  
Weidong Gao ◽  
Jinxi Zhang ◽  
Xiangyu Chen ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Power Allocation ◽  
System Capacity ◽  
System Throughput ◽  
User Association ◽  
Allocation Scheme ◽  
Dense Networks ◽  
Virtual Cell ◽  
Resource Allocation Scheme ◽  
Inter Cell Interference

AbstractUltra-dense networks (UDNs) have become an important architecture for the fifth generation (5G) networks. A large number of small base stations (SBSs) are deployed to provide high-speed and seamless connections for users in the network. However, the advantage of increasing the system capacity brought by the dense distribution of SBSs comes at the cost of severe inter-cell interference. Although the user-centric virtual cell method has been proposed to solve the interference problem, some challenges have been encountered in practical applications. For example, inter-cell interference still exists to a certain extent, and the cell load may be imbalance. Hence, under the virtual cell architecture, we propose a quality of service (QoS)-based joint user association and resource allocation scheme in UDNs. In order to mitigate the interference, balance cell load and improve the system throughput, a non-convex NP-hard problem is formulated. To effectively solve this problem, we decouple the formulated problem into three sub-problems: user association, physical resource block (PRB) allocation and power allocation. First, we consider the QoS requirements of user equipment (UE) and perform user association based on the PRB estimation method. Then, based on the overlapped virtual cells constructed, we propose a graph-based PRB allocation scheme for reducing virtual inter-cell interference. Moreover, we solve power allocation sub-problem by using the difference of concave (DC) programing method. The simulation results show that our proposed scheme is superior to other schemes in terms of user rates, cell load and system throughput.

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>

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