Game‐theoretic power allocation algorithm for downlink NOMA cellular system

Aldebes, Reem; Dimyati, Kaharudin; Hanafi, Effariza

doi:10.1049/el.2019.2095

Cited by 12 publications

(9 citation statements)

References 11 publications

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“…where hm is the BS-UEm channel gain, eh is the channel estimation error, nm is a standard complex normal random variable ~CN(0,σ 2 ) which represents the additive white Gaussian noise (AWGN) at UEm with zero mean and variance σ 2 , and x(t) represents the transmitted signal which is given as [8] ), ( )…”

Section: System Model and Pa Algorithm System Modelmentioning

confidence: 99%

“…Power allocation (PA) is considered an essential method to raise the data-rate and the energy efficiency in the NOMA system, where various powers are assigned to the cell's users and combined on the same subcarrier at the same time [7]. A game-theoretic power allocation mechanism is studied in [8] where power is assigned to the users to maximize the total throughput and the average throughput in the downlink NOMA cellular system. However, the investigation of the system performance based on the proposed algorithm in [8] is under the assumption that the BS has perfect channel state information (CSI) which is difficult to achieve.…”

Section: Introductionmentioning

confidence: 99%

“…A game-theoretic power allocation mechanism is studied in [8] where power is assigned to the users to maximize the total throughput and the average throughput in the downlink NOMA cellular system. However, the investigation of the system performance based on the proposed algorithm in [8] is under the assumption that the BS has perfect channel state information (CSI) which is difficult to achieve. Perfect CSI is not easy to achieve because of channel estimation errors, feedback, and quantization errors.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Power Allocation for Imperfect Csi Downlink Noma System

Aldebes

Dimyati

Hanafi

2021

AEJ

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The fifth generation (5G) networks must provide the massively increased number of users by thousand times higher data rate at lower power consumption. Thus, optimizing the energy efficiency (EE) becomes an essential issue that has to be researched from the green communication perspective. Non-orthogonal multiple access (NOMA) is considered one of the high potential techniques in fifth-generation systems. This technology is favorable to maximize the energy efficiency and the spectrum efficiency by composing different signals at the same time on the same carrier at different power levels. In this paper, a low complexity power allocation algorithm is proposed in imperfect channel state information (CSI) downlink NOMA cellular system, where obtaining full CSI at the base station is considered a challenge. The proposed algorithm relies on the fact that the allocated power is inversely proportional to the channel strength of the user to implement the successive interference cancellation (SIC) technique at the user terminal to reconstruct the desired signal. The performance of the system is analyzed in terms of energy efficiency and outage probability and compared to the conventional orthogonal multiple access (OMA) system. Results show that the proposed algorithm increases the energy efficiency by about 50% compared to the conventional OMA technology, and an improvement in the outage probability has been achieved. Furthermore, the effect of the error in the channel estimation on the energy efficiency in imperfect CSI NOMA system is evaluated. The simulation shows that the energy efficiency reduces when the channel estimation error increases; and the best performance is achieved in the perfect CSI case where the channel estimation error is zero.

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Section: System Model and Pa Algorithm System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Power Allocation for Imperfect Csi Downlink Noma System

Aldebes

Dimyati

Hanafi

2021

AEJ

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“…Aldebes et al aimed at maximizing the sum rate in the downlink NOMA cellular system. Glicksberg game-based algorithm is used to allocate the power between different numbers of users [ 15 ]. Omslandseter et al considered the problem of power allocation as a variation of the Knapsack Problem, and solved it through a greedy solution [ 16 ].…”

Section: Related Workmentioning

confidence: 99%

Dual Dynamic Scheduling for Hierarchical QoS in Uplink-NOMA: A Reinforcement Learning Approach

Cui

Zhai

et al. 2021

Sensors

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The demand for bandwidth-intensive and delay-sensitive services is surging daily with the development of 5G technology, resulting in fierce competition for scarce radio resources. Power domain Nonorthogonal Multiple Access (NOMA) technologies can dramatically improve system capacity and spectrum efficiency. Unlike existing NOMA scheduling that mainly focuses on fairness, this paper proposes a power control solution for uplink hybrid OMA and PD-NOMA in dual dynamic environments: dynamic and imperfect channel information together with the random user-specific hierarchical quality of service (QoS). This paper models the power control problem as a nonconvex stochastic, which aims to maximize system energy efficiency while guaranteeing hierarchical user QoS requirements. Then, the problem is formulated as a partially observable Markov decision process (POMDP). Owing to the difficulty of modeling time-varying scenes, the urgency of fast convergency, the adaptability in a dynamic environment, and the continuity of the variables, a Deep Reinforcement Learning (DRL)-based method is proposed. This paper also transforms the hierarchical QoS constraint under the NOMA serial interference cancellation (SIC) scene to fit DRL. The simulation results verify the effectiveness and robustness of the proposed algorithm under a dual uncertain environment. As compared with the baseline Particle Swarm Optimization algorithm (PSO), the proposed DRL-based method has demonstrated satisfying performance.

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“…Furthermore, a game-theoretic approach is studied in [26] where NOMA is applied to ALOHA for deciding the transmission probability. Based on the Glicksberg game, Aldebes et al proposed a power allocation algorithm for cellular downlink NOMA networks [27]. In particular, for the power allocation algorithm, the authors proposed a price-based user's utility function, which is shown to be restrictive if the allocated power beyond a threshold value causes a decrease in the utility value.…”

Section: Related Workmentioning

confidence: 99%

A Reinforcement Learning based Game Theoretic Approach for Distributed Power Control in Downlink NOMA

Rauniyar

Yazidi

Engelstad

et al. 2020

2020 IEEE 19th International Symposium on Network Computing and Applications (NCA)

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Optimal power allocation problem in wireless networks is known to be usually a complex optimization problem. In this paper, we present a simple and energy-efficient distributed power control in downlink Non-Orthogonal Multiple Access (NOMA) using a Reinforcement Learning (RL) based game theoretical approach. A scenario consisting of multiple Base Stations (BSs) serving their respective Near User(s) (NU) and Far User(s) (FU) is considered. The aim of the game is to optimize the achievable rate fairness of the BSs in a distributed manner by appropriately choosing the power levels of the BSs using trials and errors. By resorting to a subtle utility choice based on the concept of marginal price costing where a BS needs to pay a virtual tax offsetting the result of the interference its presence causes for the other BS, we design a potential game that meets the latter objective. As RL scheme, we adopt Learning Automata (LA) due to its simplicity and computational efficiency and derive analytical results showing the optimality and convergence of the game to a Nash Equilibrium (NE). Numerical results not only demonstrate the convergence of the proposed algorithm to a desirable equilibrium maximizing the fairness, but they also demonstrate the correctness of the proposal followed by thorough comparison with random and heuristic approaches.

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Game‐theoretic power allocation algorithm for downlink NOMA cellular system

Cited by 12 publications

References 11 publications

Energy-Efficient Power Allocation for Imperfect Csi Downlink Noma System

Energy-Efficient Power Allocation for Imperfect Csi Downlink Noma System

Dual Dynamic Scheduling for Hierarchical QoS in Uplink-NOMA: A Reinforcement Learning Approach

A Reinforcement Learning based Game Theoretic Approach for Distributed Power Control in Downlink NOMA

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