Power Allocation &amp; MRC Analysis for Single Input Multi Output Non-Orthogonal Multiple Access System

Gaballa, Mohamed; Abbod, Maysam F.; Albasman, Muna

doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics53846.2021.00038

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…In this part, the optimization analysis is realized with regards to three users in the NOMA system and the objective function can simply reformulated as follows [ 24 ]:

where

and

is the lowest rate required in the system. According to the analysis above, the constraints can also be represented as follows:

…”

Section: Optimization Analysismentioning

confidence: 99%

“…Initially, we can find the first derivative for

in Equation (14) with respect to each of the power coefficients

, and

. After some mathematical processing,

can be represented as follows [ 24 , 25 ]:

…”

Section: Optimization Analysismentioning

confidence: 99%

“…At this point, a general formula can be derived for the first derivative of the objective function in terms of

[ 24 , 25 ]:

…”

Section: Optimization Analysismentioning

confidence: 99%

“…In the proposed DL scheme, two major stages are implemented to achieve an effective DNN model for channel estimation. The first stage involves both training and testing, where the DNN model is trained and tested with the samples that are created based on variety of distinct Rayleigh channel coefficients [ 24 , 33 ]. In the second stage, the trained DNN model is utilized to explicitly predict the channel taps for each user and these estimated taps will be employed to recover the desired transmitted data symbols for each user.…”

Section: Channel Estimation-based DL Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

Investigating the Combination of Deep Learning for Channel Estimation and Power Optimization in a Non-Orthogonal Multiple Access System

Gaballa

Abbod

Aldallal

2022

Sensors

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In a non-orthogonal multiple access (NOMA) system, the successive interference cancellation (SIC) procedure is typically employed at the receiver side, where several user’s signals are decoded in a subsequent manner. Fading channels may disperse the transmitted signal and originate dependencies among its samples, which may affect the channel estimation procedure and consequently affect the SIC process and signal detection accuracy. In this work, the impact of Deep Neural Network (DNN) in explicitly estimating the channel coefficients for each user in NOMA cell is investigated in both Rayleigh and Rician fading channels. The proposed approach integrates the Long Short-Term Memory (LSTM) network into the NOMA system where this LSTM network is utilized to predict the channel coefficients. DNN is trained using different channel statistics and then utilized to predict the desired channel parameters that will be exploited by the receiver to retrieve the original data. Furthermore, this work examines how the channel estimation based on Deep Learning (DL) and power optimization scheme are jointly utilized for multiuser (MU) recognition in downlink Power Domain Non-Orthogonal Multiple Access (PD-NOMA) system. Power factors are optimized with a view to maximize the sum rate of the users on the basis of entire power transmitted and Quality of service (QoS) constraints. An investigation for the optimization problem is given where Lagrange function and Karush–Kuhn–Tucker (KKT) optimality conditions are applied to deduce the optimum power coefficients. Simulation results for different metrics, such as bit error rate (BER), sum rate, outage probability and individual user capacity, have proved the superiority of the proposed DL-based channel estimation over conventional NOMA approach. Additionally, the performance of optimized power scheme and fixed power scheme are evaluated when DL-based channel estimation is implemented.

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“…In this part, the optimization analysis is realized with regards to three users in the NOMA system and the objective function can simply reformulated as follows [ 24 ]:

where

and

is the lowest rate required in the system. According to the analysis above, the constraints can also be represented as follows:

…”

Section: Optimization Analysismentioning

confidence: 99%

“…Initially, we can find the first derivative for

in Equation (14) with respect to each of the power coefficients

, and

. After some mathematical processing,

can be represented as follows [ 24 , 25 ]:

…”

Section: Optimization Analysismentioning

confidence: 99%

“…At this point, a general formula can be derived for the first derivative of the objective function in terms of

[ 24 , 25 ]:

…”

Section: Optimization Analysismentioning

confidence: 99%

Section: Channel Estimation-based DL Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Investigating the Combination of Deep Learning for Channel Estimation and Power Optimization in a Non-Orthogonal Multiple Access System

Gaballa

Abbod

Aldallal

2022

Sensors

Self Cite

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show abstract

“…NOMA system can develop the available resources more realistically by efficiently, taking into consideration the users’ channel environments and also giving support to several users with distinctive Quality of Service (QoS) needs [ 2 ]. The integration of NOMA and multiple antenna techniques can be exploited to improve and reinforce system performance [ 3 ], therefore, inspecting Multiple Input-Single Output (MISO) NOMA system can be a good example in the direction of characterizing the expected upgrade in achievable data rates [ 4 ]. In downlink NOMA structure, the receiver device can receive a multiplexing of signals transmitted to several user terminals in the NOMA cell, thus eliminating the interference generated by other user devices come to be essential for coordinated detection.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Impact of Integrating Reinforcement Learning for Channel Prediction and Power Allocation Scheme in MISO-NOMA System

Gaballa

Abbod

Aldallal

2023

Sensors

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In this study, the influence of adopting Reinforcement Learning (RL) to predict the channel parameters for user devices in a Power Domain Multi-Input Single-Output Non-Orthogonal Multiple Access (MISO-NOMA) system is inspected. In the channel prediction-based RL approach, the Q-learning algorithm is developed and incorporated into the NOMA system so that the developed Q-model can be employed to predict the channel coefficients for every user device. The purpose of adopting the developed Q-learning procedure is to maximize the received downlink sum-rate and decrease the estimation loss. To satisfy this aim, the developed Q-algorithm is initialized using different channel statistics and then the algorithm is updated based on the interaction with the environment in order to approximate the channel coefficients for each device. The predicted parameters are utilized at the receiver side to recover the desired data. Furthermore, based on maximizing the sum-rate of the examined user devices, the power factors for each user can be deduced analytically to allocate the optimal power factor for every user device in the system. In addition, this work inspects how the channel prediction based on the developed Q-learning model, and the power allocation policy, can both be incorporated for the purpose of multiuser recognition in the examined MISO-NOMA system. Simulation results, based on several performance metrics, have demonstrated that the developed Q-learning algorithm can be a competitive algorithm for channel estimation when compared to different benchmark schemes such as deep learning-based long short-term memory (LSTM), RL based actor-critic algorithm, RL based state-action-reward-state-action (SARSA) algorithm, and standard channel estimation scheme based on minimum mean square error procedure.

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Outage and throughput performance analysis of incremental relaying‐based underlay cognitive nonorthogonal multiple access networks with maximal ratio combining

V.P

et al. 2024

Int J Communication

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SummaryIn this paper, we investigate the effectiveness of incremental relaying (IR)‐based underlay cognitive nonorthogonal multiple access (CNOMA) system with maximal ratio combining (MRC). Considering imperfect successive interference cancelation (i‐SIC), we derive analytical expressions for the outage probabilities experienced by the secondary users (SUs) and the system outage probability of the IR‐CNOMA system. We also derive the analytical expression for the outage probability of far SUs by considering IR‐CNOMA system with MRC. The maximum transmit power constraint of the secondary nodes and the interference threshold constraint of the primary receiver are considered in the analysis. We also evaluate the system throughput of IR‐CNOMA network. We also derive outage and throughput expression for the traditional cooperative relaying‐based CNOMA system (CR‐CNOMA) for the purpose of comparison. We derive analytical expression for the asymptotic outage probability and the asymptotic system outage probabilities of the SUs of IR‐CNOMA system. Further, we derive expressions for the optimal power allocation (OPA) factor at the secondary source that minimizes the asymptotic system outage probability of the IR‐CNOMA system. Through numerical and simulation investigations, we demonstrate that the system outage probability significantly lowers when the OPA factor is selected based on the criteria outlined in this work. It can be observed from the results that the IR‐CNOMA system outperforms the conventional CR‐CNOMA system in terms of outage probabilities and throughput. The outage probability is further reduced when IR‐CNOMA system with MRC is considered for the far SUs.

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Power Allocation & MRC Analysis for Single Input Multi Output Non-Orthogonal Multiple Access System

Cited by 4 publications

References 15 publications

Investigating the Combination of Deep Learning for Channel Estimation and Power Optimization in a Non-Orthogonal Multiple Access System

Investigating the Combination of Deep Learning for Channel Estimation and Power Optimization in a Non-Orthogonal Multiple Access System

A Study on the Impact of Integrating Reinforcement Learning for Channel Prediction and Power Allocation Scheme in MISO-NOMA System

Outage and throughput performance analysis of incremental relaying‐based underlay cognitive nonorthogonal multiple access networks with maximal ratio combining

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