Deep Learning for an Effective Nonorthogonal Multiple Access Scheme

Hassan

IEEE Commun. Surv. Tutorials

et al. 2020

276

159

Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML-and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML-and DL-based resource allocation techniques in wireless IoT networks and also techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

Section: ML Techniques For Nomamentioning

confidence: 99%

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

Hassan

IEEE Commun. Surv. Tutorials

et al. 2020

276

159

“…. After training D KNNs, AdaBoost classifiesŷ l as follows (19) where α d is the coefficient of (1 − I (f d (ŷ l ) , z l )) and I (f d (ŷ l ) , z l ) can be regarded as the voting value, i.e., if I (f d (ŷ l ) , z l ) = 0, f d (ŷ) classifies signalŷ l into class z l , otherwise,ŷ l does not belong to class z l . The class with the maximum sum of weighted voting value, α d (1 − I (f d (ŷ l ) , z l )), for all classifiers, is identified as the classification resultẑ l of the Adaboost classifier, and thenẑ l is mapped to demodulation resultŝ l .…”

Section: Adaboost Based Demodulatormentioning

confidence: 99%

Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics

Wang

et al. 2019

IEEE Access

In this paper, we investigate deep learning (DL)enabled signal demodulation methods and establish the first open dataset of real modulated signals for wireless communication systems. Specifically, we propose a flexible communication prototype platform for measuring real modulation dataset. Then, based on the measured dataset, two DL-based demodulators, called deep belief network (DBN)-support vector machine (SVM) demodulator and adaptive boosting (AdaBoost) based demodulator, are proposed. The proposed DBN-SVM based demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of DBN as a feature extractor and SVM as a feature classifier. In DBN-SVM based demodulator, the received signals are normalized before being fed to the DBN network. Furthermore, an AdaBoost based demodulator is developed, which employs the k-Nearest Neighbor (KNN) as a weak classifier to form a strong combined classifier. Finally, experimental results indicate that the proposed DBN-SVM based demodulator and AdaBoost based demodulator are superior to the single classification method using DBN, SVM, and maximum likelihood (MLD) based demodulator.Index Terms-Machine learning, DBN-SVM based demodulator, AdaBoost based demodulator.

IEEE Trans. Veh. Technol.

“…But the conventional algorithms cannot satisfy the increasing coverage because UAV movements require much energy and slow navigation deteriorates the coverage performance. Recently, by exploiting the potentials of machine learning into wireless communication, a deep learning-based wireless communication method provides an alternative mean for optimizing the UAV navigation problem, whose performance has been corroborated in non-orthogonal multiple access (NOMA) [9], massive MIMO [10], [11], traffic control [12], [13], routing techniques [14], software defined network (SDN) [15], UAV [16], [17], and millimeter-wave (mmWave) communication [18], etc.. In particular, [17] proposed a deep learning-based method for UAV navigation without requiring sensing data that provides mapping information, however this method cannot converge quickly, which makes it difficult to be applied in real-time navigation scenarios.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique

Huang

Yang

Wang

et al. 2020

Self Cite

114

Unmanned aerial vehicles (UAVs) technique has been recognized as a promising solution in future wireless connectivity from the sky, and UAV navigation is one of the most significant open research problems, which has attracted wide interest in the research community. However, the current UAV navigation schemes are unable to capture the UAV motion and select the best UAV-ground links in real-time, and these weaknesses overwhelm the UAV navigation performance. To tackle these fundamental limitations, in this paper, we merge the state-of-theart deep reinforcement learning with the UAV navigation through massive multiple input multiple output (MIMO) technique. To be specific, we carefully design a deep Q-network (DQN) for optimizing the UAV navigation by selecting the optimal policy, and then we propose a learning mechanism for processing the DQN. The DQN is trained so that the agent is capable of making decisions based on the received signal strengths for navigating the UAVs with the aid of the powerful Q-learning. Simulation results are provided to corroborate the superiority of the proposed schemes in terms of the coverage and convergence compared with that of other schemes.Index Terms-Massive multiple input multiple output (MIMO), deep reinforcement learning, UAV navigation