Query: machine learning massive MIMO

title: Machine Learning With Big Data: Challenges and Approaches
abstract: The Big Data revolution promises to transform how we live, work, and think by enabling process optimization, empowering insight discovery and improving decision making. The realization of this grand potential relies on the ability to extract value from such massive data through data analytics; machine learning is at its core because of its ability to learn from data and provide data driven insights, decisions, and predictions. However, traditional machine learning approaches were developed in a different era, and thus are based upon multiple assumptions, such as the data set fitting entirely into memory, what unfortunately no longer holds true in this new context. These broken assumptions, together with the Big Data characteristics, are creating obstacles for the traditional techniques. Consequently, this paper compiles, summarizes, and organizes machine learning challenges with Big Data. In contrast to other research that discusses challenges, this work highlights the cause effect relationship by organizing challenges according to Big Data Vs or dimensions that instigated the issue: volume, velocity, variety, or veracity. Moreover, emerging machine learning approaches and techniques are discussed in terms of how they are capable of handling the various challenges with the ultimate objective of helping practitioners select appropriate solutions for their use cases. Finally, a matrix relating the challenges and approaches is presented. Through this process, this paper provides a perspective on the domain, identifies research gaps and opportunities, and provides a strong foundation and encouragement for further research in the field of machine learning with Big Data.

title: Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions
abstract: Traditional machine learning algorithms have made great achievements in data driven fault diagnosis. However, they assume that all the data must be in the same working condition and have the same distribution and feature space. They are not applicable for real world working conditions, which often change with time, so the data are hard to obtain. In order to utilize data in different working conditions to improve the performance, this paper presents a transfer learning approach for fault diagnosis with neural networks. First, it learns characteristics from massive source data and adjusts the parameters of neural networks accordingly. Second, the structure of neural networks alters for the change of data distribution. In the same time, some parameters are transferred from source task to target task. Finally, the new model is trained by a small amount of target data in another working condition. The Case Western Reserve University bearing data set is used to validate the performance of the proposed transfer learning approach. Experimental results show that the proposed transfer learning approach can improve the classification accuracy and reduce the training time comparing with the conventional neural network method when there are only a small amount of target data.

title: Quantum Machine Learning for 6G Communication Networks: State of the Art and Vision for the Future
abstract: The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated artificial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on demand self reconfiguration to ensure a many fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic orbital angular momentum, visible light communications, and cell free communications, to name a few. Our vision for 6G is a massively connected complex network capable of rapidly responding to the users' service calls through real time learning of the network state as described by the network edge (e.g. base station locations and cache contents) air interface (e.g. radio spectrum and propagation channel) and the user side (e.g. battery life and locations) The multi state, multi dimensional nature of the network state, requiring the real time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of machine learning (ML) quantum computing (QC) and quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state of the art in the domains of ML (including deep learning) QC, and QML and identify their potential benefits, issues, and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC assisted and QML based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end. Finally, some promising future research directions for the quantum and QML assisted B5G networks are identified and discussed.

title: Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review
abstract: This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid introduced through the emergence of the next generation power system the smart grid (SG) Connectivity lies at the core of this new grid infrastructure, which is provided by the Internet of Things (IoT) This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior to conventional methods for proper analysis and decision making. The IoT integrated SG system can provide efficient load forecasting and data acquisition technique along with cost effectiveness. Big data analysis and machine learning techniques are essential to reaping these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this paper. Key information obtained through literature review is tabulated in the corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.

title: Enabling Multi Functional 5G and Beyond User Equipment: A Survey and Tutorial
abstract: The fifth generation (5G) research and development has been fueled by many new breakthroughs in various areas. The recent progress in carrier aggregation (CA) licensed assisted access (LAA) massive MIMO (MaMi) beamforming techniques, cooperative spectrum sensing (CSS) compressive sensing (CS) machine learning, etc. has provided inspiring and promising approaches to address 5G and beyond challenges. However, at the user equipment (UE) end, limited design budget and hardware resources bring along a series of challenging implementation issues when delivering multi standard and multi functional wireless communications. In this paper, we first review recent advances in technical standards and critical enabling techniques, accompanied with several case studies of product developments. After the classification of typical 5G application and deployment scenarios, we propose and analyze a novel hardware reuse and multiplexing solution to facilitate cost effective and energy efficient UE design, followed by an investigation of state of the art hardware development from the systems and circuits standpoint. Moreover, wireless UE hardware solutions, UE proof of concept (PoC) implementation and field test are proposed and discussed. Finally, the new trends of UE design and terahertz technologies for 5G and beyond applications are investigated and envisioned.

title: Trainable Projected Gradient Detector for Massive Overloaded MIMO Channels: Data Driven Tuning Approach
abstract: This paper presents a deep learning aided iterative detection algorithm for massive overloaded multiple input multiple output (MIMO) systems where the number of transmit antennas <inline formula> <tex math notation="LaTeX"$n$ /tex math>/inline formula> is larger than that of receive antennas <inline formula> <tex math notation="LaTeX"$m$ /tex math>/inline formula> Since the proposed algorithm is based on the projected gradient descent method with trainable parameters, it is named the trainable projected gradient detector (TPG detector) The trainable internal parameters, such as the step size parameter, can be optimized with standard deep learning techniques, i.e. the back propagation and stochastic gradient descent algorithms. This approach is referred to as data driven tuning and ensures fast convergence during parameter estimation in the proposed scheme. The TPG detector mainly consists of matrix vector product operations whose computational cost is proportional to <inline formula> <tex math notation="LaTeX"$m n$ /tex math>/inline formula> for each iteration. In addition, the number of trainable parameters in the TPG detector is independent of the number of antennas. These features of the TPG detector result in a fast and stable training process and reasonable scalability for large systems. The numerical simulations show that the proposed detector achieves a comparable detection performance to those of the existing algorithms for the massively overloaded MIMO channels, e.g. the state of the art IW SOAV detector, with a lower computation cost.

title: Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey
abstract: Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contain valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.

title: DECCO: Deep Learning Enabled Coverage and Capacity Optimization for Massive MIMO Systems
abstract: System capacity and service coverage are the most critical performance metrics in cellular wireless communication networks. Usually, system capacity enhancements are at the expense of service coverage degradations, and vice versa. This capacity coverage tradeoff and the associated joint optimization problem becomes very challenging in massive multiple input multiple output (MIMO) wireless systems, due to a large amount of antenna tilt values to be configured and very sophisticated inter cell interference conditions, under massive antenna scenarios. This paper proposes a novel approach, namely group alignment of user signal strength (GAUSS) to efficiently support the user scheduling for the massive MIMO system, and thus serve as an effective parameter for the coverage and capacity optimization (CCO) problem. Together with a unified threshold of Quality of Service, i.e. the minimum signal to interference plus noise ratio \textit {SINR}_{min} for user satisfaction, GAUSS can effectively control the variance of signal strengths of multiple users in the neighborhood. Moreover, an intelligent and efficient deep learning enabled coverage and capacity optimization (DECCO) algorithm is proposed and evaluated, which adopts a pre trained deep policy gradient based neural network to dynamically derive GAUSS and \textit {SINR}_{min} during CCO. Furthermore, an inter cell interference coordination (ICIC) is proposed to enhance the CCO performance. Analytical and simulation results show that the proposed DECCO algorithm can effectively achieve a much better performance balance between system capacity and service coverage than traditional fixed optimization (FO) and proportional fair optimization (PFO) algorithms. Specifically, DECCO significantly increases the overall spectrum efficiency by 24% and 40% respectively, than FO and PFO in a typical massive MIMO system.

title: CSI Feedback Based on Deep Learning for Massive MIMO Systems
abstract: Aiming at the problem of high complexity and low feedback accuracy of existing channel state information (CSI) feedback algorithms for frequency division duplexing (FDD) massive multiple input multiple output (MIMO) systems, this paper proposes a CSI compression feedback algorithm based on deep learning (DL) which is suitable for single user and multi user scenarios in massive MIMO systems. This algorithm considers the spatial correlation of massive MIMO channel and uses bidirectional long short term memory (Bi LSTM) and bidirectional convolutional long short term memory (Bi ConvLSTM) network to decompress and recover the CSI for single user and multi user, respectively. The proposed DL based CSI feedback network is trained offline by massive MIMO channel data and could learn the structural characteristics of the massive MIMO channel by fully exploiting the channel information in the training samples. The simulation results show that compared with several classical CSI compression feedback algorithms, the proposed CSI compression feedback algorithm has lower computational complexity, higher feedback accuracy, and better system performance in massive MIMO systems.

title: A Review of Fog Computing and Machine Learning: Concepts, Applications, Challenges, and Open Issues
abstract: Systems based on fog computing produce massive amounts of data; accordingly, an increasing number of fog computing apps and services are emerging. In addition, machine learning (ML) which is an essential area, has gained considerable progress in various research domains, including robotics, neuromorphic computing, computer graphics, natural language processing (NLP) decision making, and speech recognition. Several researches have been proposed that study how to employ ML to settle fog computing problems. In recent years, an increasing trend has been observed in adopting ML to enhance fog computing applications and provide fog services, like efficient resource management, security, mitigating latency and energy consumption, and traffic modeling. Based on our understanding and knowledge, there is no study has yet investigated the role of ML in the fog computing paradigm. Accordingly, the current research shed light on presenting an overview of the ML functions in fog computing area. The ML application for fog computing become strong end user and high layers services to gain profound analytics and more smart responses for needed tasks. We present a comprehensive review to underline the latest improvements in ML techniques that are associated with three aspects of fog computing: management of resource, accuracy, and security. The role of ML in edge computing is also highlighted. Moreover, other perspectives related to the ML domain, such as types of application support, technique, and dataset are provided. Lastly, research challenges and open issues are discussed.