MajidMobini57/Deep_chaos_synchronization MajidMobini57/Deep_chaos_synchronization

  • Stars
    star
    2
  • Language
    Python
  • Created over 1 year ago
  • Updated over 1 year ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
MajidMobini57/Deep_chaos_synchronization
github

MajidMobini57

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

This code is an implementation for a deep learning based synchronization system , presented in the following paper: Deep Chaos Synchronization MAJID MOBINI AND GEORGES KADDOUM

More Repositories

1

Power-allocation-for-multi-user-OFDM-DCSK-system-in-frequency-selective-fading-channel

This project is related to the paper: "Power allocation for multi-user OFDM-DCSK system in frequency selective fading channel", Majid Mobini, M.Reza Zahabi, Physical Communication, 24, (2017), pp. 146โ€“153. In this paper, we introduce a grouping approach for power allocation in the multi-user OFDM-DCSK (MU-OFDM-DCSK) system under the frequency selective fading channels. The suggested procedure is convenient also for the other comb-type non-coherent schemes with similar structure. Furthermore, we derive analytical bit error rate (BER) expression for the grouped scheme and offer an optimal power distribution policy for both the single- and multi-user scenarios. This power assignment strategy is formulated by a minโ€“max problem with the target of the worst group BER minimization incorporating total power and interference constraints. Simulation results confirm the advantages of the proposed power allocation scheme.
MATLAB
13
star
2

A-Genetic-Algorithm-based-Rate-Splitting-Approach-for-Multi-user-MISO-systems-

In a Rate Splitting (RS) approach, each message is divided into a common message and private messages. This project is introduced based on the genetic algorithm (GA) to finding an optimal power ratio, suggesting a simple systematic method for power dividing. this solution may be more exact but since it is an iterative approach, increases the processing time of the transmission.
MATLAB
7
star
3

Life-Time-Optimization-in-a-Mobile-Wireless-Sensor-Network-with-Solar-Energy-Harvesting

In this project, mobile wireless sensors send the received solar recharge value and residual energy information to a Base Station (BS), and the BS dynamically optimizes the life time and guarantees a level of the throughput by assigning optimal transmission opportunity of the sensors.
MATLAB
5
star