Chrome Extension
WeChat Mini Program
Use on ChatGLM

Analog Network Coding in the Multiple Access Relay Channel: Error Rate Analysis and Optimal Power Allocation

IEEE Transactions on Wireless Communications(2015)CCF BSCI 1区SCI 2区

Telecommun Technol Ctr Catalonia CTTC | Univ Paris 11

Cited 13|Views11
Abstract
In this paper, we consider Analog Network Coding (ANC) in the Multiple Access Relay Channel (MARC) with multiple relays, and provide the following three-fold contribution: 1) we introduce a tractable mathematical framework for computing the Symbol Error Rate (SER) of Maximum-Likelihood (ML), Zero-Forcing (ZF), and Minimum Mean Square Error (MMSE) receivers; 2) by capitalizing on this tractable mathematical framework, we formulate a power allocation problem that is proved to be convex for ML, ZF and MMSE receivers; and 3) we provide closed-form expressions of the optimal power to be allocated to the sources and the relays for ZF and MMSE receivers. With the aid of Monte Carlo simulations, we validate the accuracy of the proposed mathematical framework for various network topologies and channel conditions, as well as study the effectiveness of optimal power allocation. It is shown, in particular, that power optimization is beneficial as the number of sources increases and if the quality of the source-relay links is better than the quality of the relay-destination links.
More
Translated text
Key words
Analog network coding,multiple access relay channel,maximum-likelihood,zero-forcing,minimum mean square error
PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers

Is Analog Network Coding More Energy Efficient Than TDMA?

IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks 2014

被引用5

An Efficient Priority-Based Polling Scheme for Wireless Local Area Network

2017 3rd IEEE International Conference on Computer and Communications (ICCC) 2017

被引用0

The Effect of Co-channel Interference in DF Based MARC System with Relay Selection

2017 10TH INTERNATIONAL CONFERENCE ON ELECTRICAL AND ELECTRONICS ENGINEERING (ELECO) 2017

被引用1

New Polling Scheme Based on Busy/Idle Queues Mechanism

Zhijun Yang
International Journal of Performability Engineering 2018

被引用9

Statistical Channel Knowledge-Based Distributed Power Allocation for Analog Network Coding in High SNR

Zhan Ao, Chen Huirui, Chen Wenbing, Chen Zhixuan,Wu Chengyu
International Journal of Wireless Information Networks 2020

被引用0

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined