Orthogonal Frequency Division Multiplexing Underwater Acoustic Communication System with Environmental Cognition Ability

In UWAC (underwater acoustic communication), UWA (underwater acoustic) channels change rapidly due to varying environment conditions. AMC (adaptive modulation and coding) is an efficient technique to improve system efficiency by changing transmission parameters according to channel conditions in UWA channels. In this paper, we propose an environmental cognition orthogonal frequency division multiplexing (OFDM) UWAC system and compare it with AMC algorithm with six transmission modes together with three threshold detection algorithms. Simulation and experimental results show the effectiveness of the proposed system. The system will play an important role in future communication networks which can significantly improve the efficiency of the system.

Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning

The paper describes an online deep learning algorithm for the adaptive modulation and coding in 5G Massive MIMO. The algorithm is based on a fully connected neural network, which is initially trained on the output of the traditional algorithm and then is incrementally retrained by the service feedback of its output. We show the advantage of our solution over the state-of-the-art Q-Learning approach. We provide system-level simulation results to support this conclusion in various scenarios with different channel characteristics and different user speeds. Compared with traditional OLLA our algorithm shows 10% to 20% improvement of user throughput in full buffer case. <br>

Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning

The paper describes an online deep learning algorithm for the adaptive modulation and coding in 5G Massive MIMO. The algorithm is based on a fully connected neural network, which is initially trained on the output of the traditional algorithm and then is incrementally retrained by the service feedback of its output. We show the advantage of our solution over the state-of-the-art Q-Learning approach. We provide system-level simulation results to support this conclusion in various scenarios with different channel characteristics and different user speeds. Compared with traditional OLLA our algorithm shows 10% to 20% improvement of user throughput in full buffer case. <br>

Towards More Reliable Deep Learning-Based Link Adaptation for WiFi 6

The problem of selecting the modulation and coding scheme (MCS) that maximizes the system throughput, known as link adaptation, has been investigated extensively, especially for IEEE 802.11 (WiFi) standards. Recently, deep learning has widely been adopted as an efficient solution to this problem. However, in failure cases, predicting a higher-rate MCS can result in a failed transmission. In this case, a retransmission is required, which largely degrades the system throughput. To address this issue, we model the adaptive modulation and coding (AMC) problem as a multi-label multi-class classification problem. The proposed modeling allows more control over what the model predicts in failure cases. We also design a simple, yet powerful, loss function to reduce the number of retransmissions due to higher-rate MCS classification errors. Since wireless channels change significantly due to the surrounding environment, a huge dataset has been generated to cover all possible propagation conditions. However, to reduce training complexity, we train the CNN model using part of the dataset. The effect of different subdataset selection criteria on the classification accuracy is studied. The proposed model adapts the IEEE 802.11ax communications standard in outdoor scenarios. The simulation results show the proposed loss function reduces up to 50% of retransmissions compared to traditional loss functions.