Massive multiple-input multiple-output (MIMO) is a key technology to meet the user demands in performance and quality of services (QoS) for next generation communication systems. Due to a large number of antennas and radio frequency (RF) chains, complexity of the symbol detectors increased rapidly in a massive MIMO uplink receiver. Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade. A plethora of massive MIMO detection algorithms has been proposed in the literature. The aim of this paper is to provide insights on such algorithms to a generalist of wireless communications. We garner the massive MIMO detection algorithms and classify them so that a reader can find a distinction between different algorithms from a wider range of solutions. We present optimal and near-optimal detection principles specifically designed for the massive MIMO system such as detectors based on a local search, belief propagation and box detection. In addition, we cover detectors based on approximate inversion, which has gained popularity among the VLSI signal processing community due to their deterministic dataflow and low complexity. We also briefly explore several nonlinear small-scale MIMO (2-4 antenna receivers) detectors and their applicability in the massive MIMO context. In addition, we present recent advances of detection algorithms which are mostly based on machine learning or sparsity based algorithms. In each section, we also mention the related implementations of the detectors. A discussion of the pros and cons of each detector is provided.

/pdf/massive-mimo-detection-techniques-a-survey-1hj40069of.pdf

Massive MIMO Detection Techniques: A Survey

https://publikationen.sulb.uni-saarland.de/bitstream/20.500.11880/24275/1/aeg199902.pdf

Optical, electrical and structural properties of sol gel ZnO:Al coatings

Systems and methods for single sign on to a cloud. The system includes a cloud service provider and a tenant. The cloud service provider has a consumer unit and a portal. The consumer unit provides an interface for a user to connect to the cloud service provider. The portal providing a cloud service to the user, the portal has a first authentication system that issues a security token request and that is connected to the consumer unit. The tenant includes the user and a second authentication system. The second authentication system signs the security token request. The consumer unit is adapted to communicate with the first authentication system using a first protocol and adapted to communicate with the second authentication system using a second protocol.

Single sign on for cloud

The effects of CdCl2 processing on the physical and electro-optical properties of CdTe were evaluated for thin films produced by physical vapor deposition and close-spaced sublimation (CSS). Two substrates (CdS and Indium–tin–oxide) were used with the physical vapor deposition (PVD) films specifically to isolate the effects of the Cd(SxTe1−x) alloy formed during the treatment of films deposited on CdS. The samples were analyzed by x-ray diffraction (XRD), atomic force microscopy (AFM), and photoluminescence. The observed changes in microstructure were caused by recrystallization, which consisted of the nucleation and development of a new CdTe structure and subsequent grain growth. Nevertheless, for these processes to take place, it was necessary that enough lattice-strain energy was available in the films. For this reason, PVD films did recrystallize, while CSS films did not. For the first time, recrystallization was observed directly in AFM images of CdTe films and confirmed by XRD analysis, which indica...

Effects of CdCl2 treatment on the recrystallization and electro-optical properties of CdTe thin films

Methods and apparatus to measure exposure to streaming media are disclosed. An example method includes detecting an ID3 tag associated with streaming media presented at a client device. A first request is sent from the client device to a first internet domain, the first request identifying the streaming media. A redirection message is received from the first internet domain. In response to the redirection message, a second request is sent to a second internet domain specified by the redirection message. A cookie is provided identifying the client device to the second internet domain.

Methods and apparatus to measure exposure to streaming media

X-ray studies of SnO2 prepared by chemical vapour deposition

This letter proposes a deep learning-based pilot assignment scheme (DL-PAS) for a massive multiple-input multiple-output (massive MIMO) system that utilizes a large number of antennas for multiple users. The proposed DL-PAS improves the performance in cellular networks with severe pilot contamination by learning the relationship between pilot assignment and the users’ location pattern. In this letter, we design a novel supervised learning method, where input features and output labels are users’ locations in all cells and pilot assignments, respectively. Specifically, pretrained optimal pilot assignments with given users’ locations are provided through an exhaustive search method as the training data. Then, the proposed DL-PAS provides a near-optimal pilot assignment from the produced inferred function by analyzing the training data. We implement the proposed scheme using a commercial deep multilayer perceptron system. Simulation-based experiments show that the proposed scheme achieves almost 99.38% theoretical upper-bound performance with low complexity, requiring only 0.92-ms computational time.

Deep Learning Based Pilot Allocation Scheme (DL-PAS) for 5G Massive MIMO System

Recently, the advent of many network applications has led to a tremendous amount of network traffic. A network operator must provide quality of service for each application on the network. To accomplish this goal, various studies have focused on accurately classifying application network traffic. Network management requires technology to classify network traffic without the intervention of the network operator. In this study, we generate packet-based datasets through our own network traffic pre-processing. We train five deep learning models using the convolutional neural network (CNN) and residual network (ResNet) to perform network traffic classification. Finally, we analyze the network traffic classification performance of packet-based datasets using the f1 score of the CNN and ResNet deep learning models, and demonstrate their effectiveness.

Packet-based Network Traffic Classification Using Deep Learning

Multicast communication has been researched for many years in efforts to simultaneously achieve high throughput and good fairness, which have a trade-off relationship. Recently, a cooperative multicast scheduling scheme was proposed. It is considered a promising solution owing to its trade-off characteristics realized by promoting user cooperation among group members. However, it requires significant relay power consumption for user cooperation, since all group members in a good channel condition should relay without checking whether there is at least one user requesting a relay in the transmission boundary. Therefore, in order to resolve this inefficiency, we proposed an energy efficient cooperative multicast scheme (EECM) based on selective relay. We can achieve reduced unnecessary relay power expenditure by selecting relay users with consideration of the condition of nearby users. The performance results derived from a numerical analysis reveal that the proposed scheme requires less energy consumption than the existing solution with almost the same throughput performance.

Kwihoon Kim

Papers

X-ray studies of SnO2 prepared by chemical vapour deposition

Deep Learning Based Pilot Allocation Scheme (DL-PAS) for 5G Massive MIMO System

Packet-based Network Traffic Classification Using Deep Learning

Energy Efficient Cooperative Multicast Scheme Based on Selective Relay

Fabrication and characterization of an electro-optic polymer waveguide modulator for photonic applications