Energy Efficient Resource Optimization in User-Centric UDNs with NOMA and Beamforming

In this paper, we address the problem of energy efficient resource optimization for downlink transmission in user-centric ultra-dense networks enabled by wireless access via nonorthogonal multiple access and wireless backhaul via beamforming. Our objective is to maximize the system energy efficiency by optimizing user/access point scheduling, subchannel assignment, and power allocation jointly. The problem is formulated as a nonconvex mixed-integer nonlinear programming problem which is NP-hard. We then transform it into a convex subproblem using the sum-of-ratios decoupling and the iterative successive convex approximation method. An overall algorithm is further developed to solve the subproblem iteratively. Simulation results show that the proposed algorithm has improved the system-wide energy efficiency significantly when compared to the benchmark scheme.

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Energy Efficient Resource Optimization in User-Centric UDNs with NOMA and Beamforming

10.36227/techrxiv.12380360.v2 ◽

2020 ◽

Author(s):

Long Zhang ◽

Guobin Zhang ◽

Xiaofang Zhao ◽

Enchang Sun ◽

Xin Wei

Keyword(s):

Energy Efficiency ◽

Energy Efficient ◽

Access Point ◽

Resource Optimization ◽

Mixed Integer ◽

Downlink Transmission ◽

Wireless Backhaul ◽

Efficient Resource ◽

User Access ◽

User Centric

In this paper, we address the problem of energy efficient resource optimization for downlink transmission in user-centric ultra-dense networks enabled by wireless access via nonorthogonal multiple access and wireless backhaul via beamforming. Our objective is to maximize the system energy efficiency by optimizing user/access point scheduling, subchannel assignment, and power allocation jointly. The problem is formulated as a nonconvex mixed-integer nonlinear programming problem which is NP-hard. We then transform it into a convex subproblem using the sum-of-ratios decoupling and the iterative successive convex approximation method. An overall algorithm is further developed to solve the subproblem iteratively. Simulation results show that the proposed algorithm has improved the system-wide energy efficiency significantly when compared to the benchmark scheme.

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

10.36227/techrxiv.11576388 ◽

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

10.36227/techrxiv.11576388.v1 ◽

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.

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

10.36227/techrxiv.11576388.v2 ◽

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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Energy Efficient Resource Optimization in User-Centric UDNs with NOMA and Beamforming

2020 IEEE 6th International Conference on Computer and Communications (ICCC) ◽

10.1109/iccc51575.2020.9344899 ◽

2020 ◽

Author(s):

Long Zhang ◽

Guobin Zhang ◽

Xiaofang Zhao ◽

Enchang Sun ◽

Xin Wei

Keyword(s):

Energy Efficient ◽

Resource Optimization ◽

Efficient Resource ◽

User Centric

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Reinforcement Learning (RL)-Based Energy Efficient Resource Allocation for Energy Harvesting-Powered Wireless Body Area Network

Sensors ◽

10.3390/s20010044 ◽

2019 ◽

Vol 20 (1) ◽

pp. 44 ◽

Cited By ~ 6

Author(s):

Yi-Han Xu ◽

Jing-Wei Xie ◽

Yang-Gang Zhang ◽

Min Hua ◽

Wen Zhou

Keyword(s):

Resource Allocation ◽

Energy Efficiency ◽

Energy Harvesting ◽

Energy Efficient ◽

Area Network ◽

Allocation Strategy ◽

Body Area ◽

Q Learning ◽

Efficient Resource ◽

Energy Efficient Resource Allocation

Wireless body area networks (WBANs) have attracted great attention from both industry and academia as a promising technology for continuous monitoring of physiological signals of the human body. As the sensors in WBANs are typically battery-driven and inconvenient to recharge, an energy efficient resource allocation scheme is essential to prolong the lifetime of the networks, while guaranteeing the rigid requirements of quality of service (QoS) of the WBANs in nature. As a possible alternative solution to address the energy efficiency problem, energy harvesting (EH) technology with the capability of harvesting energy from ambient sources can potentially reduce the dependence on the battery supply. Consequently, in this paper, we investigate the resource allocation problem for EH-powered WBANs (EH-WBANs). Our goal is to maximize the energy efficiency of the EH-WBANs with the joint consideration of transmission mode, relay selection, allocated time slot, transmission power, and the energy constraint of each sensor. In view of the characteristic of the EH-WBANs, we formulate the energy efficiency problem as a discrete-time and finite-state Markov decision process (DFMDP), in which allocation strategy decisions are made by a hub that does not have complete and global network information. Owing to the complexity of the problem, we propose a modified Q-learning (QL) algorithm to obtain the optimal allocation strategy. The numerical results validate the effectiveness of the proposed scheme as well as the low computation complexity of the proposed modified Q-learning (QL) algorithm.

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