Code Rate-Diversity-Multiplexing Tradeoff

2009 ◽  
Vol 55 (1) ◽  
pp. 245-254 ◽  
Author(s):  
Erik Stauffer ◽  
Djordje Tujkovic ◽  
Arogyaswami Paulraj
Keyword(s):  
Code Rate ◽  
Diversity Multiplexing Tradeoff

scholarly journals Source Symbol Purging-Based Distributed Conditional Arithmetic Coding

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 983
Author(s):  
Jingjian Li ◽  
Wei Wang ◽  
Hong Mo ◽  
Mengting Zhao ◽  
Jianhua Chen
Keyword(s):  
Posterior Probability ◽  
Side Information ◽  
Code Rate ◽  
Arithmetic Coding ◽  
Distributed Arithmetic ◽  
Context Model ◽  
Error Performance ◽  
Codeword Length ◽  
Simulation Results ◽  
Correlation Strength

A distributed arithmetic coding algorithm based on source symbol purging and using the context model is proposed to solve the asymmetric Slepian–Wolf problem. The proposed scheme is to make better use of both the correlation between adjacent symbols in the source sequence and the correlation between the corresponding symbols of the source and the side information sequences to improve the coding performance of the source. Since the encoder purges a part of symbols from the source sequence, a shorter codeword length can be obtained. Those purged symbols are still used as the context of the subsequent symbols to be encoded. An improved calculation method for the posterior probability is also proposed based on the purging feature, such that the decoder can utilize the correlation within the source sequence to improve the decoding performance. In addition, this scheme achieves better error performance at the decoder by adding a forbidden symbol in the encoding process. The simulation results show that the encoding complexity and the minimum code rate required for lossless decoding are lower than that of the traditional distributed arithmetic coding. When the internal correlation strength of the source is strong, compared with other DSC schemes, the proposed scheme exhibits a better decoding performance under the same code rate.

МЕТОД ГЛУБОКОГО ОБУЧЕНИЯ С ПОДКРЕПЛЕНИЕМ ДЛЯ СИСТЕМ ИНТЕРНЕТА ВЕЩЕЙ НА БАЗЕ ТЕХНОЛОГИИ LORA С ОГРАНИЧЕННЫМИ РЕСУРСАМИ И ПОДДЕРЖКОЙ QoS

2021 ◽  
Author(s):  
 M.S. MUTHANNA ◽  
A.S. MUTHANNA ◽  
 A.S. BORODIN
Keyword(s):  
Data Transmission ◽  
Environmental Parameters ◽  
Code Rate ◽  
Frame Structure ◽  
Signal Quality ◽  
High Quality ◽  
Spreading Factor ◽  
Transmission Parameters ◽  
Factor Code

Achieving high Quality of Service (QoS) remains a challenge for LoRa technology. However, high QoS can be achieved via optimizing the transmission policy parameters such as bandwidth and code rate. Existing approaches do not provide an opportunity to optimize the LoRa networks' data transmission parameters. The article proposes transmission policy enforcementfor QoS-aware LoRanetworks.The QoSparameter ranking is implemented for IoT nodes where priority and nonpriority information is identified by the new field of LoRa frame structure(QRank).The optimaltransmissionpolicyenforcement uses fast deep reinforcement learning that utilizes the environmental parameters including QRank, signal quality, and signal-to-interference-plus-noise-ratio. The transmission policy is optimized for spreading factor, code rate, bandwidth, and carrier frequency. Performance evaluation is implemented using an NS3.26 LoRaWAN module. The performance is examined for various metrics such as delay and throughput. Достижение высокого качества обслуживания (QoS) по-прежнему остается достаточно сложной задачей для технологии LoRa. В принципе высокий уровень QoS может быть достигнут за счет оптимизации параметров передачи, например, пропускной способности и скорости передачи информации в сети. Известные на сегодняшний день решения не дают возможности оптимизировать параметры передачи данных для сетей LoRa. В статье предложен эффективный метод передачи данных, обеспечивающий требования по QoS при использовании технологии LoRa. Ранжирование параметров QoS для узлов интернета вещей определяется новым полем структуры фрейма LoRa (QRank) для приоритетной и неприоритетной информации. Для обеспечения эффективной передачи применяется быстрое глубокое обучение с подкреплением, для которого используются как параметры качества обслуживания, так и отношение сигнал/шум. Метод передачи оптимизирован с учетом коэффициента распространения, скорости передачи данных, полосы пропускания и несущей частоты. Оценка производительности при применении предложенного метода проведена с использованием модуля LoRaWAN в пакете имитационного моделирования NS3.26. Производительность оценивается на основе параметров задержки и пропускной способности.

scholarly journals Adaptive Turbo-Coded Hybrid-ARQ in OFDM Systems over Gaussian and Fading Channels

2017 ◽  
Vol 2 (1) ◽  
pp. 3 ◽  
Author(s):  
Kingsley Oteng-Amoako ◽  
Saeid Nooshabadi
Keyword(s):  
Channel Estimation ◽  
Fading Channels ◽  
Spectral Efficiency ◽  
Analytical Approach ◽  
Code Rate ◽  
Ofdm Systems ◽  
Hybrid Arq ◽  
Signal Constellation ◽  
Type Iii

In this paper, an analytical approach for spectral efficiency maximization of coded wideband transmissions is presented based on OFDM. The approach exploits Type-III Hybrid-ARQ, enabling all sub-carriers to be employed in codeword transmission regardless of the sub-carrier conditions.The effects of imperfect sub-channel estimation are characterized and compensated for during code rate and signal constellation optimization. The results of the paper highlight that by independently adapting the code rate and signal constellation to individual OFDM sub-carriers based on an estimated sub-carrier CSI, the overall spectral efficiency of the system is maximized.

A new architecture for the Viterbi decoder for code rate k/n

1996 ◽  
Vol 44 (2) ◽  
pp. 158-164 ◽  
Author(s):  
Hsiang-Ling Li ◽  
C. Chakrabarti
Keyword(s):  
Code Rate ◽  
Viterbi Decoder

scholarly journals Diversity-Multiplexing Tradeoff for the Interference Channel With a Relay

2015 ◽  
Vol 61 (2) ◽  
pp. 963-982 ◽  
Author(s):  
Daniel Zahavi ◽  
Lili Zhang ◽  
Ivana Maric ◽  
Ron Dabora ◽  
Andrea J. Goldsmith ◽  
...  
Keyword(s):  
Interference Channel ◽  
Diversity Multiplexing Tradeoff
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