In this paper, we propose a reinforcement learning-based adaptive modulation and coding scheme for underwater communications;more specifically, based on the network states such as the quality of service requirement of...
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In this paper, we propose a reinforcement learning-based adaptive modulation and coding scheme for underwater communications;more specifically, based on the network states such as the quality of service requirement of the sensing message, the previous transmission quality, and the energy consumption. This scheme applies reinforcement learning to choose the modulation and coding policy in a dynamic underwater communication system. We provide the performance bound of this scheme and perform experiments in both pool and sea environments. The experimental data were collected and post-processed. Compared with the benchmark schemes, this scheme can improve the throughputs and reduce the BER with less energy consumption.
Distance statistics have been employed in previous investigations of packet transmission and reception in cognitive radio systems. Such statistics are a good source of control information for packet-to-packet adaptati...
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Distance statistics have been employed in previous investigations of packet transmission and reception in cognitive radio systems. Such statistics are a good source of control information for packet-to-packet adaptation of modulation and coding to mitigate the effects of time-varying fading or interference. Previous research on distance statistics has relied on simulation for protocol design and performance evaluation. We devise new analytical methods for use in the design and evaluation of protocols for adaptive modulation and coding that obtain control information from a distance statistic. The primary advantage of our approach is the avoidance of simulations of the time-varying channel, generation of the adaptive control information, and adaptation process.
An adaptive modulation and coding (AMC) scheme based on a Moore state machine is proposed for the high-speed railway communication systems. By setting a buffer zone for the signal-to-noise ratio threshold value, the p...
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An adaptive modulation and coding (AMC) scheme based on a Moore state machine is proposed for the high-speed railway communication systems. By setting a buffer zone for the signal-to-noise ratio threshold value, the proposed scheme is able to reduce the frequent transition of the modulation and coding scheme. Simulation results show that the proposed AMC strategy has a more stable spectral efficiency than the traditional AMC method using a piecewise function.
In this paper, we propose a novel adaptive modulation and coding (AMC) scheme enabled by Artificial Neural Network (ANN) aided Signal-to-Noise power Ratio (SNR) estimation. The Power Spectral Density (PSD) values are ...
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In this paper, we propose a novel adaptive modulation and coding (AMC) scheme enabled by Artificial Neural Network (ANN) aided Signal-to-Noise power Ratio (SNR) estimation. The Power Spectral Density (PSD) values are trained for SNR classification and it is mapped to respective modulation and coding Scheme (MCS) sets. Once trained, optimal MCS can be determined in low calculation complexity. The proposed approach is robust especially in high mobility environment since the PSD appearance is hardly influenced by the Doppler shift. Its effectiveness in terms of throughput is presented through computer simulations compared to the existing Error Vector Magnitude (EVM) based link adaptation scheme.
Multicast is a promising solution to address spectrum scarcity in wireless video streaming, while facing tough challenge in provisioning quality of service (QoS) to heterogeneous users. This article presents an adapti...
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Multicast is a promising solution to address spectrum scarcity in wireless video streaming, while facing tough challenge in provisioning quality of service (QoS) to heterogeneous users. This article presents an adaptive modulation and coding (AMC) scheme to accommodate both spatial diversity and temporal variation on users' channel conditions for QoS provisioning in wireless scalable video multicast. The QoS-guaranteed AMC policy optimization is formulated as constrained stochastic optimization based on Markov decision processes. An adaptive policy iteration algorithm is developed to find the optimal AMC policy online without any prior knowledge of users' channel statistics or intensive calculations. This algorithm is attractive due to adaptability to unknown environments and less run-time computation expenditure. Simulations results demonstrate the effectiveness of the proposed scheme.
Satellite communication develops rapidly due to its global coverage and is unrestricted to the ground environment. However, compared with the traditional ground TCP/IP network, a satellite-to-ground link has a more ex...
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Satellite communication develops rapidly due to its global coverage and is unrestricted to the ground environment. However, compared with the traditional ground TCP/IP network, a satellite-to-ground link has a more extensive round trip time(RTT) and a higher packet loss rate,which takes more time in error recovery and wastes precious channel resources. Forward error correction(FEC) is a coding method that can alleviate bit error and packet loss, but how to achieve high throughput in the dynamic network environment is still a significant challenge. Inspired by the deep learning technique, this paper proposes a signal-to-noise ratio(SNR) based adaptivecodingmodulation method. This method can maximize channel utilization while ensuring communication quality and is suitable for satellite-to-ground communication scenarios where the channel state changes rapidly. We predict the SNR using the long short-term memory(LSTM) network that considers the past channel status and real-time global weather. Finally, we use the optimal matching rate(OMR) to evaluate the pros and cons of each method quantitatively. Extensive simulation results demonstrate that our proposed LSTM-based method outperforms the state-of-the-art prediction algorithms significantly in mean absolute error(MAE). Moreover, it leads to the least spectrum waste.
With the development of the underwater acoustic (UWA) adaptive communication system, energy-efficient transmission has become a critical topic in underwater acoustic (UWA) communications. Due to the unique characteris...
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With the development of the underwater acoustic (UWA) adaptive communication system, energy-efficient transmission has become a critical topic in underwater acoustic (UWA) communications. Due to the unique characteristics of the underwater environment, the transmitter node will almost always have outdated channel state information (CSI), which results in low energy efficiency. In this paper, we take full advantage of bidirectional links and propose an adaptive modulation and coding (AMC) scheme that aims to maximize the long-term energy efficiency of a single link by jointly scheduling the coding rate, modulation order, and transmission power. Considering the complexity characteristics of UWA channels, we proposed a bit error ratio (BER) estimation method based on deep neural networks (DNN). The proposed network could realize channel estimation, feature extraction, and BER estimation by using a fixed pilot of the feedback link. Then, we design a channel classification method based on the estimated BERs of the modulation and coding scheme (MCS) and further model the UWA channels as a finite-state Markov chain (FSMC) with an unknown transition probability. Thus, we formulate the AMC problem as a Markov Decision Process (MDP) and solve it through a reinforcement learning framework. Considering the large state-action pairs, a double deep Q-network (DDQN) based scheme is proposed. Simulation results demonstrate that the proposed AMC scheme outperforms the fixed MCS with a perfect channel information state, and achieves near-optimal energy efficiency.
The key to friendly collaboration in vehicular communication systems lies in the reliable communication between vehicles. The current systems, which employ fixed transmission schemes, significantly constrain system ca...
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ISBN:
(纸本)9798350303582;9798350303599
The key to friendly collaboration in vehicular communication systems lies in the reliable communication between vehicles. The current systems, which employ fixed transmission schemes, significantly constrain system capacity in respect of spectrum. Besides, considering the dynamic environment of vehicular communications, the requirement for real-time scenario identification is particularly urgent. Hence, in order to improve the reliability of communications, this paper proposes an adaptive modulation and coding (AMC) technique driven by deep learning (DL)-based scenario identification (SI) in vehicular communication systems, namely SI-AMC. In contrast to the traditional AMC technique, our proposed SI-AMC attains a refined channel response estimation through the pre-discrimination of scenario features, thereby further enhancing vehicular communication performance. During the transmission process, the SI-AMC scheme achieves environment adaptability through rate-adaptive adjustments. Moreover, in terms of SI, we creatively design an enhanced convolutional neural network structure which exploits a novel activation function and regularization strategies to enhance the robustness of the model. Simulation results show that the accuracy of SI reaches up to 97.86% and the throughput of the entire link in vehicular communications is effectively promoted. Code and the model will be released at https://***/communicationDL1/SI-AMC.
Recently, LTE-based femtocell system has received significant attention as a promising solution offering high-speed services, enhanced indoor coverage and increased system capacity. Intelligently allocate resources in...
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ISBN:
(纸本)9781479948529
Recently, LTE-based femtocell system has received significant attention as a promising solution offering high-speed services, enhanced indoor coverage and increased system capacity. Intelligently allocate resources in multi-user OFDMA-based network is the substantial aim towards interference mitigation and enhancing power and spectral efficiencies. In this paper, we propose a downlink joint resource allocation with adaptive modulation and coding (AMC) technique for such system, namely AMC-QRAP. The proposal core is adjusting the transmission link to the channel status and users demand through the power control and suitable selection of the modulation/coding scheme. Clustered network is adopted and users differentiation is considered providing Quality of Service (QoS) in the network. Our resolution model is solved as an optimization problem using the linear programming. We show through extensive simulations the outperformance of our method compare to different state-of-the-art methods using different evaluation metrics.
WiMAX technology is considered one of the most important solutions capable to provide a Broadband Wireless Access in metropolitan areas. Mobility issues have become challenging for the rollout for WiMAX networks in or...
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ISBN:
(纸本)9781424451234
WiMAX technology is considered one of the most important solutions capable to provide a Broadband Wireless Access in metropolitan areas. Mobility issues have become challenging for the rollout for WiMAX networks in order to compete against the other competitive technologies such as 3.5G and LTE. In this paper, a unified approach to derive the overall BER and data rate for mobile WiMAX (in uplink direction) will be introduced. This approach will take into consideration different operational scenarios such as time varying channels with different values of channel estimation precision values, different number of subcarriers, and different symbol durations. An analytical expression for the effect of ICI power on the bit error probability and effective bit rate will be obtained for BPSK, QPSK, and 16QAM in presence of AMC. This paper also investigates the effect of the use of AMC techniques on the obtained system performance.
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