Performance of Deep Learning Methods in DF Based Cooperative Communication Systems

Akdemir, Bilgehan; Karabulut, Muhammet Ali; İlhan, Hacı

doi:10.1109/blackseacom52164.2021.9527814

Cited by 5 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The receiver should combine signals from R and S to achieve a diversity gain at the D node [11]. The ML decision rule is formulated as…”

Section: Maximum Likelihood Estimationmentioning

confidence: 99%

“…They also showed the advantage of diversity by using multiple paths in their experimental results. Our previous work [11] employed a cooperative system comprising one relay between source and destination nodes. This paper proposes a system with multiple relays and obtains other performance metrics such as outage probability analysis, RMSE, MSE, etc.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Performance of Deep Learning Methods for Different Relay Selection Protocols

Akdemir

Karabulut

İlhan

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Deep Learning (DL) is an emerging topic that has found its place in various applications due to its many advantages, such as providing good performance and reducing complexity. In this paper, we employed different DL methods for channel estimation in cooperative communication by using Maximum Likelihood Estimation (MLE) to combine different copies of transmitted signal over a Binary Phase-Shift Keying (BPSK) modulation in a Rayleigh fading communication channel. We have presented three different relay selection protocols. The first one is SNR-based; the SNRs of the transmission lines between nodes are calculated, and a relay with maximum SNR is selected among the relays. The second relay selection protocol is threshold-based; average SNRs have been computed for all transmission lines, and the links exceeding the average SNR attend in the collaboration. The last protocol is that all relays in the network participate in cooperation, which achieves better performance metrics in terms of Bit Error Rate (BER) and outage analysis. From our simulation results, DL algorithms achieved good performance for channel estimation in the different relay selection protocols based on the Decode and Forward (DF) relaying system.

show abstract

“…The receiver should combine signals from R and S to achieve a diversity gain at the D node [11]. The ML decision rule is formulated as…”

Section: Maximum Likelihood Estimationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Performance of Deep Learning Methods for Different Relay Selection Protocols

Akdemir

Karabulut

İlhan

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Deep learning (DL) has drawn a lot of interest and is frequently utilized in various disciplines to enhance the effectiveness of earlier techniques [8]. DL-based approaches can avoid the time-consuming task of identifying features and gathering private information since it automatically extracts and picks features from raw data.…”

Section: Introductionmentioning

confidence: 99%

Reliability estimation for drone communications by using an MLP-based model

Shah

Karabulut

2022

International Advanced Researches and Engineering Journal

Self Cite

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) or drones have been widely employed in both military and civilian tasks due to their reliability and low cost. UAVs ad hoc networks also acknowledged as flying ad-hoc networks (FANETs), are multi-UAV systems arranged in an ad hoc manner. In order to maintain consistent and effective communication, reliability is a prime concern in FANETs. This paper presents an analytical framework to estimate the reliability of drones’ communication in FANETs. The proposed system takes into account the reliability of communications in FANETs, including channel fading. The suggested analytical investigation is used to generate a dataset, then an artificial neural network (ANN) based multi-layer perceptron (MLP) model is used to estimate the reliability of drones’ communication. Moreover, to define the best MLP model with hidden layers, the correlation coefficient (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are obtained. Moreover, numerical results are presented which verify analytical studies.

show abstract