Energy-Efficiency-Oriented Joint User Association and Power Allocation in Distributed Massive MIMO Systems

Dong, Guannan; Zhang, Haixia; Jin, Shi; Yuan, Dongfeng

doi:10.1109/tvt.2019.2912388

Cited by 56 publications

(53 citation statements)

References 42 publications

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“…Let us use P * AP f,m,n,k , P * MBS f,m,k , a * f,m,n,k , and b * f,m,k to represent the optimal solutions to the subproblem (P5) in (34), respectively. According to the KKT conditions, upon taking the partial derivative of the Lagrangian function L (• • • ) with respect to P AP f,m,n,k and P MBS f,m,k in (35), respectively, the optimal solutions P * AP f,m,n,k and P * MBS f,m,k to the subproblem (P5) in (34) can be respectively obtained as:…”

Section: Optimal Solution For Joint Resource Allocation and User Assomentioning

confidence: 99%

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

Zhang¹,

Zhang²,

Zhao³

et al. 2020

Preprint

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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.

show abstract

Section: Optimal Solution For Joint Resource Allocation and User Assomentioning

confidence: 99%

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

Zhang¹,

Zhang²,

Zhao³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…When R = UΣ can be accurately estimated, let . 2 F denote the Frobenius matrix norm. Then, the following optimization problem is given to describe the process of applying x p to estimate V H :…”

Section: The Op Estimation Of V H Op Estimationmentioning

confidence: 99%

“…The foreseen demand increasing in data rate has triggered a research race for discovering new ways to enhance the spectral efficiency of the next generation of mobile and wireless networks [1]. Benefiting from spatial multiplexing, massive MIMO systems can enjoy asymptotically orthogonal channels, arbitrary small transmit power, and negligible noise, thus providing significant performance gains in terms of spectral efficiency (SE), security, and reliability compared with conventional MIMO [2]. Furthermore, all of these benefits can be achieved through linear processing with low complexity.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency-Oriented Resource Allocation for Massive MIMO Systems with Separated Channel Estimation and Feedback

Feng

Wang

et al. 2020

Electronics

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This paper proposes a dynamic resource allocation scheme to maximize the energy efficiency (EE) for Massive MIMO Systems. The imperfect channel estimation (CE) and feedback are explicitly considered in the EE maximization problem, which aim to optimize the power allocation, the antenna subset selection for transmission, and the pilot assignment. Assuming CE error to be bounded for the complex-constrained Cramer–Rao Bound (CRB), theoretical results show that the lower bound is directly proportional to its number of unconstrained parameters. Utilizing this perspective, a separated and bi-directional estimation is developed to achieve both low CRB and low complexity by exploiting channel and noise spatial separation. Exploiting global optimization procedure, the optimal resource allocation can be transformed into a standard convex optimization problem. This allows us to derive an efficient iterative algorithm for obtaining the optimal solution. Numerical results are provided to demonstrate that the outperformance of the proposed algorithms are superior to existing schemes.

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“…Meanwhile, according to the KKT conditions, upon taking the partial derivative of the Lagrangian function L (· · · ) with respect to a f,m,n,k and b f,m,k in (35), respectively, the optimal solutions a * f,m,n,k and b * f,m,k to the subproblem (P5) in (34) can be respectively calculated by:…”

Section: Optimal Solution For Joint Resource Allocation and User Assomentioning

confidence: 99%

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

Zhang¹,

Zhang²,

Zhao³

et al. 2020

Preprint

View full text Add to dashboard Cite

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>

show abstract

Energy-Efficiency-Oriented Joint User Association and Power Allocation in Distributed Massive MIMO Systems

Cited by 56 publications

References 42 publications

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

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

Energy Efficiency-Oriented Resource Allocation for Massive MIMO Systems with Separated Channel Estimation and Feedback

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

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