QoE-Based Resource Allocation for Multi-Cell NOMA Networks

Cui, Jingjing; Liu, Yuanwei; Ding, Zhiguo; Fan, Pingzhi; Nallanathan, Arumugam

doi:10.1109/twc.2018.2855130

Cited by 86 publications

(83 citation statements)

References 38 publications

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“…Since, web browsing is the most commonly used application in the wireless networks, in this work, the users' QoE is calculated based on its experience while browsing a web page. The MOS model for web browsing application is defined similar to [11] as follows:…”

Section: Ee Qos Fairness Index and Qoe Fairness Index Calculationmentioning

confidence: 99%

“…A score of 5 implies best quality at the user while score of 1 denotes that the quality experienced by the user is worst. The constants C 1 and C 2 are decided based on the experiments on the web browsing applications and are set to be 1.120 and 4.6746, respectively [11]. d(R) is the delay time.…”

Section: Ee Qos Fairness Index and Qoe Fairness Index Calculationmentioning

confidence: 99%

“…The delay time denotes the time taken between sending of request by the user for a web page and displaying of the web page contents. Based on the assumption in [11], delay time can simplified as d(R) = R/F S, where FS is the frame size.…”

Section: Ee Qos Fairness Index and Qoe Fairness Index Calculationmentioning

confidence: 99%

“…QoE is evaluation of media quality at individual users. Mean Opinion Score (MOS) is one of the commonly used evaluation methods to characterize the QoE experienced by users [10], [11].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

User Fairness in NOMA-HetNet Using Optimized Power Allocation and Time Slotting

Swami

Bhatia

Vuppala

et al. 2021

IEEE Systems Journal

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Section: Ee Qos Fairness Index and Qoe Fairness Index Calculationmentioning

confidence: 99%

Section: Ee Qos Fairness Index and Qoe Fairness Index Calculationmentioning

confidence: 99%

Section: Ee Qos Fairness Index and Qoe Fairness Index Calculationmentioning

confidence: 99%

“…QoE is evaluation of media quality at individual users. Mean Opinion Score (MOS) is one of the commonly used evaluation methods to characterize the QoE experienced by users [10], [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

User Fairness in NOMA-HetNet Using Optimized Power Allocation and Time Slotting

Swami

Bhatia

Vuppala

et al. 2021

IEEE Systems Journal

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“…Currently, techniques such as orthogonal frequency division multiple access (OFDMA) have been exploited in the fourth generation (4G) wireless systems. In OFDMA, the frequency band is divided into orthogonal subchannels and each subchannel is assigned to one user at most [1].…”

Section: Introductionmentioning

confidence: 99%

Clustering and Auction-Based Power Allocation Algorithm for Energy Efficiency Maximization in Multi-Cell Multi-Carrier NOMA Networks

Adam

Wang

2019

Applied Sciences

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In this paper, we investigate the energy efficiency (EE) maximization in multi-cell multi-carrier non-orthogonal multiple access (MCMC-NOMA) networks. To achieve this goal, an optimization problem is formulated then the solution is divided into two parts. First, we investigate the inter-cell interference mitigation and then we propose an auction-based non-cooperative game for power allocation for base stations. Finally, to guarantee the rate requirements for users, power is allocated fairly to users. The simulation results show that the proposed scheme has the best performance compared with the existing NOMA-based fractional transmit power allocation (FTPA) and the conventional orthogonal frequency division multiple access (OFDMA).

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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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QoE-Based Resource Allocation for Multi-Cell NOMA Networks

Cited by 86 publications

References 38 publications

User Fairness in NOMA-HetNet Using Optimized Power Allocation and Time Slotting

User Fairness in NOMA-HetNet Using Optimized Power Allocation and Time Slotting

Clustering and Auction-Based Power Allocation Algorithm for Energy Efficiency Maximization in Multi-Cell Multi-Carrier NOMA Networks

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

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