0% Complete
Home
/
11th International Conference on Computer and Knowledge Engineering
Energy Efficient Power Allocation in MIMO-NOMA Systems with ZF Receiver Beamforming in Multiple Clusters
Authors :
Mahdi Nangir
1
Abdolrasoul Sakhaei Gharagezlou
2
Nima Imani
3
1- Faculty of Electrical and Computer Engineering University of Tabriz
2- Faculty of Electrical and Computer Engineering University of Tabriz
3- Faculty of Electrical and Computer Engineering University of Tabriz
Keywords :
Multiple-Input Multiple-Output, Non-Orthogonal Multiple Access, Energy Efficiency, Power Allocation, Error Estimation, Zero Forcing Beamforming, KKT conditions.
Abstract :
This paper deals with the problem of energy efficient power allocation for a multiple-input multiple-output (MIMO) system with non-orthogonal multiple access (NOMA) method. The characteristics of the used channel are determinative in this system. Some key parameters that have not considered in most of the works, are examined in this paper, such as: the path loss, the zero forcing (ZF) beamforming, and the channel estimation error. In solving this problem, two constraints on the minimum user rate and the maximum transmission power of users are considered. The objective function of the optimization problem is the energy efficiency (EE) of the system, which is generally non-convex and constrained problem. First, it is converted to a convex problem using the convex optimization theory. Next, by employing the Lagrange dual function, the constraints of the problem are eliminated. By doing so, the problem becomes a convex unconstraint problem, and the Karush-Kuhn-Tucker (KKT) conditions are used to obtain the optimal power of the users. Furthermore, an iterative algorithm as a numerical solution is proposed for the problem. Our simulation results show that it outperforms the equal power allocation strategy.
Papers List
List of archived papers
SUT: a new multi-purpose synthetic dataset for Farsi document image analysis
Elham Shabaninia - Fatemeh sadat Eslami - Ali Afkari Fahandari - Hossein Nezamabadi-pour
An interactive user groups recommender system based on reinforcement learning
Hediyeh Naderi Allaf - Mohsen Kahani
Forecasting El Niño Six Months in Advance Utilizing Augmented Convolutional Neural Network
Mohammad Naisipour - Iraj Saeedpanah - Arash Adib - Mohammad Hossein Neisi Pour
Automated software design using Machine Learning With Natural Language Processing
Fahimeh Khedmatkon - Seyed Mohammad Hossein Hasheminejad - Jaleh Shoshtarian Malak
A Weighted TF-IDF-based Approach for Authorship Attribution
Ali Abedzadeh - Reza Ramezani - Afsaneh Fatemi
Semantic Segmentation Using Region Proposals and Weakly-Supervised Learning
Maryam Taghizadeh - Abdolah Chalechale
Adaptive Active Queue Management for Time Slot Channel Hopping in Industrial Internet of Things
Mehdi Zirak - Yasser Sedaghat - Mohammad Hossein Yaghmaee Moghaddam
Real-Time Gender Recognition with a Deep Neural Network
Samad Azimi Abriz - Majid Meghdadi
Zone-Based Federated Learning in Indoor Positioning
Omid Tasbaz - Vahideh Moghtadaiee - Bahar Farahani
Improving Soft Error Reliability of FPGA-based Deep Neural Networks with Reduced Approximate TMR
Anahita Hosseinkhani - Behnam Ghavami
more
Samin Hamayesh - Version 41.7.6