niulanqi Goto Github PK

cats_vs_dogs

channel-power-allocation-802.22

[1] Di Li and James Gross, Distributed TV Spectrum Allocation for Cognitive Cellular Network under Game Theoretical Framework Proc. of the IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN'12) 2012

cnn_amc

code-dqn-cchn

Joint spectrum and power allocation for cognitive capacity harvested network with using DQN learning method

dataset

Open RadioML Synthetic Benchmark Dataset

deep-ee-opt

Source code for "A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks" by Bho Matthiesen, Alessio Zappone, Karl-L. Besser, Eduard A. Jorswieck, and Merouane Debbah, accepted for publication in IEEE Transactions on Signal Processing.

deep-reinforcement-learning-for-dynamic-spectrum-access

Using multi-agent Deep Q Learning with LSTM cells (DRQN) to train multiple users in cognitive radio to learn to share scarce resource (channels) equally without communication

dl_ofdm

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks

dnn_wmmse

Code to reproduce our works on DNN research.

dqsa

dqsa_matlab

dqsakeras

DQSA in tensorflow 2.0

ee-cr

Source code for "Energy Efficiency in MIMO Underlay and Overlay Device-to-Device Communications and Cognitive Radio Systems," by Alessio Zappone, Bho Matthiesen, and Eduard Jorswieck, IEEE Transactions on Signal Processing, vol. 65, no. 4, pp. 1026-1041 Feb. 2017.

informationbottleneckdecodingldpc

Decoding of LDPC Codes Using the Information Bottleneck Method in Python

madrl

Repo containing code for multi-agent deep reinforcement learning (MADRL).

pa_icc

pa_twc

spawc2017

Training Deep Neural Networks for Wireless Resource Management

tensorflow2.0-for-deep-reinforcement-learning

TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:

theano

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.