Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks

Cooperative beamforming across access points (APs) and fronthaul quantization strategies are essential for cloud radio access network (C-RAN) systems. The nonconvexity of the C-RAN optimization problems, which is stemmed from per-AP power and fronthaul capacity constraints, requires high computational complexity for executing iterative algorithms. To resolve this issue, we investigate a deep learning approach where the optimization module is replaced with a well-trained deep neural network (DNN). An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies. Numerical results validate the advantages of the proposed learning solution.Comment: accepted for publication on IEEE Wireless Communications Letter

A Deep Learning-Based Strategy to Predict Self-Interference in SFN DTT

This article belongs to the Proceedings of The 4th XoveTIC Conference[Abstract] A deep learning-based strategy for the analysis of the self-interference in single frequency networks (SFNs) for digital terrestrial television (DTT) broadcasting is considered. Several laboratory measurements were performed to create a dataset that relates the self-interference parameters and some quality metrics of the resulting received signal. The laboratory setup emulates an SFN scenario with two DTT transmitters. The strongest received signal and the relative values of attenuation and delay between the signals stand for the input parameters. The modulation error ratio (MER) of the strongest received signal, the MER of the resulting signal, and the SFN gain (SFNG) are the output parameters. This dataset is used to train four different multi-layer perceptron (MLP) models to predict accurate maps of interference and signal quality metrics. The considered models are suitable as complements for any multiple frequency network (MFN) coverage software with the capability to return the signal strength and the position data. This way, the SFN self-interference behavior can be predicted by considering only a proper description of the MFN coverage.This work has been funded by the Xunta de Galicia (by grant ED431C 2020/15, and grant ED431G2019/01 to support the CITIC, Centre for Information and Communications Technology Research, from the University System of Galicia), the Agencia Estatal de Investigación of Spain (by grants RED2018-102668-T and PID2019-104958RB-C42) and ERDF funds of the EU (FEDER Galicia 2014–2020 & AEI/FEDER Programs, UE), and the predoctoral grant BES-2017-081955.Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED431G2019/0

Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks

Fog radio access networks (F-RANs), which consist of a cloud and multiple edge nodes (ENs) connected via fronthaul links, have been regarded as promising network architectures. The F-RAN entails a joint optimization of cloud and edge computing as well as fronthaul interactions, which is challenging for traditional optimization techniques. This paper proposes a Cloud-Enabled Cooperation-Inspired Learning (CECIL) framework, a structural deep learning mechanism for handling a generic F-RAN optimization problem. The proposed solution mimics cloud-aided cooperative optimization policies by including centralized computing at the cloud, distributed decision at the ENs, and their uplink-downlink fronthaul interactions. A group of deep neural networks (DNNs) are employed for characterizing computations of the cloud and ENs. The forwardpass of the DNNs is carefully designed such that the impacts of the practical fronthaul links, such as channel noise and signling overheads, can be included in a training step. As a result, operations of the cloud and ENs can be jointly trained in an end-to-end manner, whereas their real-time inferences are carried out in a decentralized manner by means of the fronthaul coordination. To facilitate fronthaul cooperation among multiple ENs, the optimal fronthaul multiple access schemes are designed. Training algorithms robust to practical fronthaul impairments are also presented. Numerical results validate the effectiveness of the proposed approaches.Comment: to appear in IEEE Transactions on Wireless Communication

Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques

A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering