scholarly journals Large Scale Multi User MIMO Approach for Wireless Backhaul Based HETNETs

2017 ◽  
Author(s):  
Mostafa Hefnawi
Keyword(s):  
Large Scale ◽  
Wireless Backhaul

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>

Large-scale MIMO-based wireless backhaul in 5G networks

2015 ◽  
Vol 22 (5) ◽  
pp. 58-66 ◽  
Author(s):  
Zhongshan Zhang ◽  
Xiyuan Wang ◽  
Keping Long ◽  
Athanasios V. Vasilakos ◽  
Lajos Hanzo
Keyword(s):  
Large Scale ◽  
5G Networks ◽  
Wireless Backhaul

Large-Scale Geospatial Planning of Wireless Backhaul Links

2020 ◽  
Author(s):  
Philip E. Brown ◽  
Krystian Czapiga ◽  
Arun Jotshi ◽  
Yaron Kanza ◽  
Velin Kounev ◽  
...  
Keyword(s):  
Large Scale ◽  
Wireless Backhaul ◽  
Backhaul Links

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 mmWave Backhaul Testbed Configurability Using Software-Defined Networking

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Ricardo Santos ◽  
Konstantin Koslowski ◽  
Julian Daube ◽  
Hakim Ghazzai ◽  
Andreas Kassler ◽  
...  
Keyword(s):  
Channel Assignment ◽  
Large Scale ◽  
High Capacity ◽  
Software Defined Networking ◽  
Adaptive Mesh ◽  
Base Stations ◽  
Small Cells ◽  
Data Traffic ◽  
Wireless Backhaul ◽  
The Impact

Future mobile data traffic predictions expect a significant increase in user data traffic, requiring new forms of mobile network infrastructures. Fifth generation (5G) communication standards propose the densification of small cell access base stations (BSs) in order to provide multigigabit and low latency connectivity. This densification requires a high capacity backhaul network. Using optical links to connect all the small cells is economically not feasible for large scale radio access networks where multiple BSs are deployed. A wireless backhaul formed by a mesh of millimeter-wave (mmWave) links is an attractive mobile backhaul solution, as flexible wireless (multihop) paths can be formed to interconnect all the access BSs. Moreover, a wireless backhaul allows the dynamic reconfiguration of the backhaul topology to match varying traffic demands or adaptively power on/off small cells for green backhaul operation. However, conducting and precisely controlling reconfiguration experiments over real mmWave multihop networks is a challenging task. In this paper, we develop a Software-Defined Networking (SDN) based approach to enable such a dynamic backhaul reconfiguration and use real-world mmWave equipment to setup a SDN-enabled mmWave testbed to conduct various reconfiguration experiments. In our approach, the SDN control plane is not only responsible for configuring the forwarding plane but also for the link configuration, antenna alignment, and adaptive mesh node power on/off operations. We implement the SDN-based reconfiguration operations in a testbed with four nodes, each equipped with multiple mmWave interfaces that can be mechanically steered to connect to different neighbors. We evaluate the impact of various reconfiguration operations on existing user traffic using a set of extensive testbed measurements. Moreover, we measure the impact of the channel assignment on existing traffic, showing that a setup with an optimal channel assignment between the mesh links can result in a 44% throughput increase, when compared to a suboptimal configuration.

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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