Prior Information-Based Bayesian MMSE Estimation of Velocity in HetNets

Tiwari, Ravi; Deshmukh, Siddharth

doi:10.1109/lwc.2018.2857805

Cited by 14 publications

(19 citation statements)

References 6 publications

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“…For DL, many applications and resources are available. Considering their reliability and usability, matrix laboratory (MATLAB) 2020 41–51 and PYTHON version 3.9 42 are used in our simulations. TensorFlow with graphics processing unit (GPU) acceleration was also used to apply the suggested DL algorithm as a popular open-source DL platform from Google.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The first UE is allocated to most of the power (85%) and the rest power is allocated to the second UE (15%). The sigmoid function is used at the output layer and the rectified linear unit (ReLU) 40–50 is used at the hidden layers. The total number of training samples is 50,000 and, for increasing the convergence rate, a smaller mini-batch symbol set is considered in the form of n 2 . In the MIMO-NOMA-DL system the input complex modulated data is fed as input to the DNN in the form of a column vector, and eight input layers are considered in a time slot for a 4 × 4 MIMO system.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning

Malladi¹,

Beuria

Ravi³

et al. 2021

Journal of Defense Modeling & Simulation

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In modern wireless communication scenarios, non-orthogonal multiple access (NOMA) provides high throughput and spectral efficiency for fifth generation (5G) and beyond 5G systems. Traditional NOMA detectors are based on successive interference cancellation (SIC) techniques at both uplink and downlink NOMA transmissions. However, due to imperfect SIC, these detectors are not suitable for defense applications. In this paper, we investigate the 5G multiple-input multiple-output NOMA deep learning technique for defense applications and proposed a learning approach that investigates the communication system’s channel state information automatically and identifies the initial transmission sequences. With the use of the proposed deep neural network, the optimal solution is provided, and performance is much better than the traditional SIC-based NOMA detectors. Through simulations, the analytical outcomes are verified.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning

Malladi¹,

Beuria

Ravi³

et al. 2021

Journal of Defense Modeling & Simulation

View full text Add to dashboard Cite

show abstract

“…As the allocation of power is required for both the users and relay, several works considered user/relay selection and power allocation jointly. Several studies [53][54][55][56] evaluated the outage probability of the system for a proposed power allocation scheme and relay selection. Baidas et al 57 worked to increase the energy efficiency of the system and proposed a power allocation scheme and a joint relay selection.…”

Section: Relay Selectionmentioning

confidence: 99%

A tutorial on cooperative non-orthogonal multiple access networks

Chaudhary

Ravi²,

Mishra

2021

Journal of Defense Modeling & Simulation

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In this paper, we explore the possibilities and advantages of cooperative relaying with the addition of non-orthogonal multiple access (NOMA). First, the possibilities of NOMA for fifth-generation (5G) and beyond networks is discussed followed by the generalized structures for the cooperative NOMA. Then, advanced NOMA communication is investigated where NOMA is integrated with advanced transmission technologies for the further improvement in cooperative NOMA. Hereinafter, resource allocation is investigated and, finally, the major challenges and issues are highlighted.

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“…For correctly deploying and expanding HetNets to avoid co-channel interference (CCI), Khan et al [ 17 ] proposed a new three-sector three-layer frequency division multiplexing technology (FFR-3SL). Tiwari et al proposed a Bayesian minimum mean-square-error (MMSE)-based method to estimate user velocity in [ 18 ] and presented a handover-count based minimum-variance-unbiased [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks

Wang

et al. 2020

Entropy

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In order to maximize energy efficiency in heterogeneous networks (HetNets), a turbo Q-Learning (TQL) combined with multistage decision process and tabular Q-Learning is proposed to optimize the resource configuration. For the large dimensions of action space, the problem of energy efficiency optimization is designed as a multistage decision process in this paper, according to the resource allocation of optimization objectives, the initial problem is divided into several subproblems which are solved by tabular Q-Learning, and the traditional exponential increasing size of action space is decomposed into linear increase. By iterating the solutions of subproblems, the initial problem is solved. The simple stability analysis of the algorithm is given in this paper. As to the large dimension of state space, we use a deep neural network (DNN) to classify states where the optimization policy of novel Q-Learning is set to label samples. Thus far, the dimensions of action and state space have been solved. The simulation results show that our approach is convergent, improves the convergence speed by 60% while maintaining almost the same energy efficiency and having the characteristics of system adjustment.

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Prior Information-Based Bayesian MMSE Estimation of Velocity in HetNets

Cited by 14 publications

References 6 publications

Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning

Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning

A tutorial on cooperative non-orthogonal multiple access networks

A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks

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