Energy-Spectral Efficiency Tradeoff of Downlink NOMA System with Fairness Consideration

Ni, Dadong; Li, Hao; Qian, Xiaomin; Tran, Quang Thanh

doi:10.1109/vtcspring.2018.8417647

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…The multi‐objective problem was converted into a single‐objective problem using a weighted sum approach to obtain the optimal solution. In Ni et al, 15 the authors formulated the EE–SE trade‐off with fairness consideration as a multi‐objective optimization. The work in Al‐Obiedollah et al 16 investigated an energy‐spectral efficient beamforming technique for multiple‐input single‐output (MISO) NOMA system to optimize the EE–SE trade‐off.…”

Section: Introductionmentioning

confidence: 99%

Energy‐spectral efficiency trade‐off optimization for downlink multi‐user non orthogonal multiple access systems

Glei

Othman

Chibani

2021

Int J Communication

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Summary Non‐orthogonal multiple access (NOMA) has been investigated as a promising multiple radio access technology for future wireless networks to enable massive connectivity and improve spectral efficiency (SE). Furthermore, energy efficiency (EE) is becoming a primary concern and an inevitable trend for future wireless networks. For this, recent works have been provided good thoughts into joining these two conflicting performance metrics EE and SE. The joint EE–SE trade‐off problem for multi‐user NOMA systems in downlink is studied in this paper. The mathematical formulation of the EE–SE trade‐off is given as an optimization issue subject to some constraints. The basic thought is to improve EE by imposing simultaneously a minimum SE to be satisfied. The quality of service (QoS) requirement for each user is also provided to ensure optimality. For this, a dynamic energy‐efficient power allocation is proposed. Then, an iterative optimization algorithm with low computational complexity is developed to reach the proposed power allocation. Simulations have been conducted and compared to existing NOMA systems to show the effectiveness of the suggested approach in terms of EE and SE.

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Section: Introductionmentioning

confidence: 99%

Energy‐spectral efficiency trade‐off optimization for downlink multi‐user non orthogonal multiple access systems

Glei

Othman

Chibani

2021

Int J Communication

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“…Moreover, the maximization of the minimum rate for each user was analyzed in Reference , but despite mentioning fairness such concept was not systematically explored. Besides, the trade‐off between EE and SE considering fairness has been analyzed in Reference , but it did not guarantee maximum fairness among the users, and the calculation used the traditional concept of Jain's fairness . Currently, the concept of fairness based on the considerations of information theory has become popular.…”

Section: Introductionmentioning

confidence: 99%

Nonorthogonal multiple access systems optimization to ensure maximum fairness to users

Jacob

Abrão

2020

Trans Emerging Tel Tech

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In this contribution, the optimization of power proportion allocated for each user in the downlink (DL) nonorthogonal multiple access (NOMA) systems have been developed. Successive interference cancellation technique recovers users' signal with high difference between channel gains, to find the lowest optimum power proportion required to guarantee an equal data rate for all active users.Following the same approach, the optimum power proportion for each user and also the minimum total power to achieve the same rate for all users (maximum fairness) were obtained as a design goal. Moreover, the same design methodology was developed seeking to maximize the NOMA system energy efficiency (EE). It was possible to find the maximum EE point and the respective power distribution among the users for a certain circuitry power consumption. For all NOMA users, in which the optimal operation point for EE maximization was parameterized, it was possible to find the total power, power ratio, and the equal rate values. As a result, one can find the trade-off point between EE and sum rate for each system scenario, as well as the best resource efficiency operation point. By considering the same rate for all users, the system attains maximum fairness among the users.Abbreviations: BS, base station; DL, downlink; EE, energy efficiency; NOMA, nonorthogonal multiple access; OMA, orthogonal multiple access; QoS, quality of service; RF, radio frequency; SE, spectral efficiency; SIC, successive cancellation of interference; SINR, signal-to-interference-plus-noise power ratio; SQP, sequential quadratic programming.Trans Emerging Tel Tech. 2020;31:e3875.wileyonlinelibrary.com/journal/ett

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“…To the best of our knowledge, multi-objective optimization problems aim-ing at maximizing both EE and SE in NOMA have only been studied in the downlink. In [9], [10], multi-objective problems are reformulated as weighted sum maximization problems, and power allocation is then solved by dual decomposition.…”

Section: Introductionmentioning

confidence: 99%

Clustering and Power Optimization for NOMA Multi-Objective Problems

Wang,

Pischella,

Vandendorpe

2020

Preprint

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This paper considers uplink multiple access (MA) transmissions, where the MA technique is adaptively selected between Non Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA). Two types of users, namely Internet of Things (IoT) and enhanced mobile broadband (eMBB) coexist with different metrics to be optimized, energy efficiency (EE) for IoT and spectral efficiency (SE) for eMBB. The corresponding multi-objective power allocation problems aiming at maximizing a weighted sum of EE and SE are solved for both NOMA and OMA. Based on the identification of the best MA strategy, a clustering algorithm is then proposed to maximize the multiobjective metric per cluster as well as NOMA use. The proposed clustering, power allocation and MA selection algorithm is shown to outperform other clustering solutions and non-adaptive MA techniques.

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Energy-Spectral Efficiency Tradeoff of Downlink NOMA System with Fairness Consideration

Cited by 11 publications

References 14 publications

Energy‐spectral efficiency trade‐off optimization for downlink multi‐user non orthogonal multiple access systems

Energy‐spectral efficiency trade‐off optimization for downlink multi‐user non orthogonal multiple access systems

Nonorthogonal multiple access systems optimization to ensure maximum fairness to users

Clustering and Power Optimization for NOMA Multi-Objective Problems

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