Multivariable optimisation approach for power allocation in OFDM‐DCSK system

Mobini, Majid; Zahabi, Mohammad Reza

doi:10.1049/iet-com.2018.5714

Cited by 5 publications

(2 citation statements)

References 22 publications

(36 reference statements)

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“…Unlike [2], we propose a Genetic Algorithm (GA)based method, instead of using the approximation method presented in [2]. Previous research efforts indicate that when the search interval is limited, using GA leads to an accurate solution along with a fast convergence [17].…”

Section: Related Workmentioning

confidence: 99%

A Rate Splitting Approach For Multi-user MISO Communication Systems Using Genetic Algorithm

Nikpour

Mobini

2023

Preprint

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Recent research efforts in the field of 6G communication systems shows that the Rate splitting (RS) technique is a promising strategy for non-orthogonal transmission and interference management. RS consists of dividing a user message into two parts, i.e., common and private messages, such that each of those parts can be decoded at one or multiple receivers. In this paper, we introduce a competent RS approach based on the Genetic Algorithm (GA) to maximize the sum rate of Multi-Input Single-Output Broadcast Channel (MISO BC) communication systems. Existing solutions for the sum rate maximization problem are often based on approximation algorithms that lead to poor solutions. We consider two scenarios to divide the power between the common and private messages. The objective of the first scenario is to evaluate the performance of the GA-based approach compared to the existing RS benchmarks. In this case, the power of private massages is assumed to be equal, and we deal with a single-variable optimization problem. In order to generalize the proposed method, the second scenario considers a case in which the private messages have different power coefficients. The simulation results indicate that the proposed algorithm achieves a better Ergodic Sum Rate (ESR) than the existing approximation algorithms.

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Section: Related Workmentioning

confidence: 99%

A Rate Splitting Approach For Multi-user MISO Communication Systems Using Genetic Algorithm

Nikpour

Mobini

2023

Preprint

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“…If an initial population is determined for (x 0 ), the solution will be found by an iterative approach such as GA. The initial population can be modified based on some rules such as selection, mutation, and crossover to achieve the optimal solution [79]. In particular, we use a decimal representation of genes in the range [0 0.1], uniform mutation, and one-point crossover.…”

Section: Initial Condition Estimation Using Gamentioning

confidence: 99%

Deep Chaos Synchronization

Mobini

Kaddoum

2020

IEEE Open J. Commun. Soc.

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In this study, we address the problem of chaotic synchronization over a noisy channel by introducing a novel Deep Chaos Synchronization (DCS) system using a Convolutional Neural Network (CNN). Conventional Deep Learning (DL) based communication strategies are extremely powerful but training on large data sets is usually a difficult and time-consuming procedure. To tackle this challenge, DCS does not require prior information or large data sets. In addition, we provide a novel Recurrent Neural Network (RNN)-based chaotic synchronization system for comparative analysis. The results show that the proposed DCS architecture is competitive with RNN-based synchronization in terms of robustness against noise, convergence, and training. Hence, with these features, the DCS scheme will open the door for a new class of modulator schemes and meet the robustness against noise, convergence, and training requirements of the Ultra Reliable Low Latency Communications (URLLC) and Industrial Internet of Things (IIoT).

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