A Parallel Algorithm for Energy Efficiency Maximization in Massive MIMO Networks

Yang, Yang; Pesavento, Marius

doi:10.1109/glocom.2016.7841532

Cited by 6 publications

(9 citation statements)

References 18 publications

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“…Furthermore, several iterative algorithms are proposed to solve the problem of EE maximization in NOMA networks, e.g., in single cell NOMA system [19], in NOMA HetNets [20] and for massive MIMO networks in [26]. Although the iterative approach has been applied to various scenarios, the network setting that we consider in this paper is very different, making the existing solutions not directly applicable.…”

Section: B Motivation and Contributionmentioning

confidence: 99%

“…Although the iterative approach has been applied to various scenarios, the network setting that we consider in this paper is very different, making the existing solutions not directly applicable. For example, if some rules of fairness requirement is strictly imposed in order to guarantee the fairness among all users, the solutions developed in [19], [20], [26] are no longer applicable. To this end, we adopt the sequential successive convex approximation (SCA) techniques to systematically address the critical issue of the inter/intra interference of users in the MC-NOMA networks to maximize users with lowest EE performance.…”

Section: B Motivation and Contributionmentioning

confidence: 99%

“…It is noted that the objective function in (26) is not smooth at each iteration of different minimum of f k (P ) − g k (P ). Thus, we introduce a new variable R to the optimization problem (26) to transform into a smooth optimization problem. Thus, (26) can be equivalently formulated as…”

Section: B Sequential Convex Programming (Scp) For P *mentioning

confidence: 99%

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Energy-Efficient Resource Allocation in Multicarrier NOMA Systems With Fairness

Muhammed

Diamantoulakis

et al. 2019

IEEE Trans. Commun.

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Non-orthogonal multiple access (NOMA) has attracted both academic and industrial interest since it has been considered as one of the promising 5G technologies in order to increase connectivity and spectral efficiency. In this paper, we focus on a downlink multicarrier (MC) NOMA network, where a single base station serves a set of users through multiple subchannels. The goal is to jointly optimize energy efficiency (EE) and fairness among users with respect to the subcarrier and power allocation parameters. To achieve this with acceptable complexity, a novel greedy subcarrier assignment scheme based on the worst-user first principle is proposed. Due to the fractional form of the EE expression and the existence of interference, the power allocation problem is non-convex and NP-hard. To this end, we first transform this into an equivalent subtractive form, which is then solved by using fractional programming with sequential optimization of the inter/intra-subchannel power allocation vectors. Simulation results reveal the effectiveness of the proposed scheme in terms of EE and fairness among users compared to baseline schemes. Finally, the proposed algorithms are of fast convergence, low complexity, and insensitive to the initial values.

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Section: B Motivation and Contributionmentioning

confidence: 99%

Section: B Motivation and Contributionmentioning

confidence: 99%

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Energy-Efficient Resource Allocation in Multicarrier NOMA Systems With Fairness

Muhammed

Diamantoulakis

et al. 2019

IEEE Trans. Commun.

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“…wheref k (Q k ; Q t ) is defined in (6). Note that each subproblem in (8) is pseudoconvex, and B k Q t is unique as we will explain later.…”

Section: The Proposed Iterative Algorithm For Socially Optimal Dmentioning

confidence: 99%

“…Since a special instance of the GEE and the SEE, namely, sum rate maximization in such an interference-limited system, is a nonconvex problem and NP-hard [1], most studies (with [2] as an exception) focus on efficient iterative algorithms that can find a stationary point. For the GEE maximization problem, many algorithms are developed, see [2][3][4][5][6][7][8] and the references therein. By comparison, less attention has been paid to the SEE maximization and max-min fairness problems, see [9][10][11][12] and [2,13] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency in MIMO Interference Channels: Social Optimality and Max-Min Fairness

Yanql

Pesavento

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we consider the energy efficiency optimization problem in MIMO multi-cell systems, where all users suffer from intercell interference. To solve this multi-objective optimization problem, we consider both socially optimal solutions and max-min fairness solutions. We propose two novel iterative algorithms that converge to socially optimal solutions and max-min fairness solutions. The proposed algorithms have the following advantages: 1) fast convergence as the structure of the original optimization problem is preserved as much as possible in the approximate problem solved in each iteration, and 2) efficient implementation as each approximate problem is natural for parallel computation and/or its solution has a closed-form expression. The advantages of the proposed algorithms are also illustrated numerically.

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Exploring the potential of 5G uplink communication: Synergistic integration of joint power control, user grouping, and multi-learning Grey Wolf Optimizer

Sikkanan,

Kumar,

Manoharan

et al. 2024

Sci Rep

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Non-orthogonal Multiple Access (NOMA) techniques offer potential enhancements in spectral efficiency for 5G and 6G wireless networks, facilitating broader network access. Central to realizing optimal system performance are factors like joint power control, user grouping, and decoding order. This study investigates power control and user grouping to optimize spectral efficiency in NOMA uplink systems, aiming to reduce computational difficulty. While previous research on this integrated optimization has identified several near-optimal solutions, they often come with considerable system and computational overheads. To address this, this study employed an improved Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic optimization method. Although GWO is effective, it can sometimes converge prematurely and might lack diversity. To enhance its performance, this study introduces a new version of GWO, integrating Competitive Learning, Q-learning, and Greedy Selection. Competitive learning adopts agent competition, balancing exploration and exploitation and preserving diversity. Q-learning guides the search based on past experiences, enhancing adaptability and preventing redundant exploration of sub-optimal regions. Greedy selection ensures the retention of the best solutions after each iteration. The synergistic integration of these three components substantially enhances the performance of the standard GWO. This algorithm was used to manage power and user-grouping in NOMA systems, aiming to strengthen system performance while restricting computational demands. The effectiveness of the proposed algorithm was validated through numerical evaluations. Simulated outcomes revealed that when applied to the joint challenge in NOMA uplink systems, it surpasses the spectral efficiency of conventional orthogonal multiple access. Moreover, the proposed approach demonstrated superior performance compared to the standard GWO and other state-of-the-art algorithms, achieving reduced system complexity under identical constraints.

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A Parallel Algorithm for Energy Efficiency Maximization in Massive MIMO Networks

Cited by 6 publications

References 18 publications

Energy-Efficient Resource Allocation in Multicarrier NOMA Systems With Fairness

Energy-Efficient Resource Allocation in Multicarrier NOMA Systems With Fairness

Energy Efficiency in MIMO Interference Channels: Social Optimality and Max-Min Fairness

Exploring the potential of 5G uplink communication: Synergistic integration of joint power control, user grouping, and multi-learning Grey Wolf Optimizer

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