Massive multiple-input multiple-output (MIMO) wireless communications refers to the idea equipping cellular base stations (BSs) with a very large number of antennas, and has been shown to potentially allow for orders of magnitude improvement in spectral and energy efficiency using relatively simple (linear) processing. In this paper, we present a comprehensive overview of state-of-the-art research on the topic, which has recently attracted considerable attention. We begin with an information theoretic analysis to illustrate the conjectured advantages of massive MIMO, and then we address implementation issues related to channel estimation, detection and precoding schemes. We particularly focus on the potential impact of pilot contamination caused by the use of non-orthogonal pilot sequences by users in adjacent cells. We also analyze the energy efficiency achieved by massive MIMO systems, and demonstrate how the degrees of freedom provided by massive MIMO systems enable efficient single-carrier transmission. Finally, the challenges and opportunities associated with implementing massive MIMO in future wireless communications systems are discussed.

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An Overview of Massive MIMO: Benefits and Challenges

There is considerable pressure to define the key requirements of 5G, develop 5G standards, and perform technology trials as quickly as possible. Normally, these activities are best done in series but there is a desire to complete these tasks in parallel so that commercial deployments of 5G can begin by 2020. 5G will not be an incremental improvement over its predecessors; it aims to be a revolutionary leap forward in terms of data rates, latency, massive connectivity, network reliability, and energy efficiency. These capabilities are targeted at realizing high-speed connectivity, the Internet of Things, augmented virtual reality, the tactile internet, and so on. The requirements of 5G are expected to be met by new spectrum in the microwave bands (3.3-4.2 GHz), and utilizing large bandwidths available in mm-wave bands, increasing spatial degrees of freedom via large antenna arrays and 3-D MIMO, network densification, and new waveforms that provide scalability and flexibility to meet the varying demands of 5G services. Unlike the one size fits all 4G core networks, the 5G core network must be flexible and adaptable and is expected to simultaneously provide optimized support for the diverse 5G use case categories. In this paper, we provide an overview of 5G research, standardization trials, and deployment challenges. Due to the enormous scope of 5G systems, it is necessary to provide some direction in a tutorial article, and in this overview, the focus is largely user centric, rather than device centric. In addition to surveying the state of play in the area, we identify leading technologies, evaluating their strengths and weaknesses, and outline the key challenges ahead, with research test beds delivering promising performance but pre-commercial trials lagging behind the desired 5G targets.

5G : A tutorial overview of standards, trials, challenges, deployment, and practice

tions. Bootstrap has found many applications in engineering field, including artificial neural networks, biomedical engineering, environmental engineering, image processing, and radar and sonar signal processing. Basic concepts of the bootstrap are summarized in each section as a step-by-step algorithm for ease of implementation. Most of the applications are taken from the signal processing literature. The principles of the bootstrap are introduced in Chapter 2. Both the nonparametric and parametric bootstrap procedures are explained. Babu and Singh (1984) have demonstrated that in general, these two procedures behave similarly for pivotal (Studentized) statistics. The fact that the bootstrap is not the solution for all of the problems has been known to statistics community for a long time; however, this fact is rarely touched on in the manuscripts meant for practitioners. It was first observed by Babu (1984) that the bootstrap does not work in the infinite variance case. Bootstrap Techniques for Signal Processing explains the limitations of bootstrap method with an example. I especially liked the presentation style. The basic results are stated without proofs; however, the application of each result is presented as a simple step-by-step process, easy for nonstatisticians to follow. The bootstrap procedures, such as moving block bootstrap for dependent data, along with applications to autoregressive models and for estimation of power spectral density, are also presented in Chapter 2. Signal detection in the presence of noise is generally formulated as a testing of hypothesis problem. Chapter 3 introduces principles of bootstrap hypothesis testing. The topics are introduced with interesting real life examples. Flow charts, typical in engineering literature, are used to aid explanations of the bootstrap hypothesis testing procedures. The bootstrap leads to second-order correction due to pivoting; this improvement in the results due to pivoting is also explained. In the second part of Chapter 3, signal processing is treated as a regression problem. The performance of the bootstrap for matched filters as well as constant false-alarm rate matched filters is also illustrated. Chapters 2 and 3 focus on estimation problems. Chapter 4 introduces bootstrap methods used in model selection. Due to the inherent structure of the subject matter, this chapter may be difficult for nonstatisticians to follow. Chapter 5 is the most impressive chapter in the book, especially from the standpoint of statisticians. It provides real data bootstrap applications to illustrate the theory covered in the earlier chapters. These include applications to optimal sensor placement for knock detection and land-mine detection. The authors also provide a MATLAB toolbox comprising frequently used routines. Overall, this is a very useful handbook for engineers, especially those working in signal processing.

Probability and Random Processes

In recent years, deep neural networks have been successful in both industry and academia, especially for computer vision tasks. The great success of deep learning is mainly due to its scalability to encode large-scale data and to maneuver billions of model parameters. However, it is a challenge to deploy these cumbersome deep models on devices with limited resources, e.g., mobile phones and embedded devices, not only because of the high computational complexity but also the large storage requirements. To this end, a variety of model compression and acceleration techniques have been developed. As a representative type of model compression and acceleration, knowledge distillation effectively learns a small student model from a large teacher model. It has received rapid increasing attention from the community. This paper provides a comprehensive survey of knowledge distillation from the perspectives of knowledge categories, training schemes, teacher-student architecture, distillation algorithms, performance comparison and applications. Furthermore, challenges in knowledge distillation are briefly reviewed and comments on future research are discussed and forwarded.

Knowledge Distillation: A Survey

Approximate computing has recently emerged as a promising approach to energy-efficient design of digital systems. Approximate computing relies on the ability of many systems and applications to tolerate some loss of quality or optimality in the computed result. By relaxing the need for fully precise or completely deterministic operations, approximate computing techniques allow substantially improved energy efficiency. This paper reviews recent progress in the area, including design of approximate arithmetic blocks, pertinent error and quality measures, and algorithm-level techniques for approximate computing.

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Approximate computing: An emerging paradigm for energy-efficient design

Cell-free system is a promising technology in which a group of base stations (BSs) cooperatively serves the user. One potential drawback of cell-free systems is that the intensive deployment of BSs might increase the energy consumption of the network. In order to improve the energy efficiency of cell- free systems, acquisition of accurate downlink channel state information (CSI) at the BSs is crucial. However, downlink CSI acquisition is not easy due to the CSI feedback overhead. This issue is even more pronounced in the cell-free systems since the user has to feed back the downlink CSIs of multiple BSs. In this paper, we propose energy-efficient dominating path selection (EE-DPS) algorithm to maximize the energy efficiency of cell- free systems with finite rate feedback. Key idea of the proposed algorithm is to choose a few dominating paths maximizing the energy efficiency and then only feed back the path gain information (PGI) of the chosen paths to the BSs. In doing so, the BSs can acquire relatively accurate PGI and utilize it to improve the energy efficiency. From the simulation results, we show that the proposed algorithm can improve the energy efficiency by more than 40% over the conventional power control schemes relying on the CSI feedback.

Energy-Efficient Cell-Free Systems Using Finite Rate Feedback

Orthogonal least squares (OLS) is a classic algorithm for sparse recovery, function approximation, and subset selection. In this paper, we analyze the performance guarantee of the OLS algorithm. Specifically, we show that OLS guarantees the exact reconstruction of any K-sparse vector in K iterations, provided that a sensing matrix has unit l 2 -norm columns and satisfies the restricted isometry property (RIP) of order K +1 with\begin{equation*}{\delta _{K + 1}} K+1 ≥ C K , then there exists a counterexample for which OLS fails the recovery.

Optimal Restricted Isometry Condition for Exact Sparse Recovery with Orthogonal Least Squares

Reconfigurable intelligent surface (RIS) is a promising technology that can provide a virtual line-of-sight (LoS) link for the mmWave communications via intelligent signal reflection. In order to maximize the throughput of RIS-aided mmWave systems, acquisition of accurate downlink channel information at the base station (BS) is crucial. However, since the BS needs to acquire not only the conventional direct channel between the BS and user but also the channels reflected by RIS (i.e., BS to RIS and RIS to user channels), the pilot overhead is proportional to the number of RIS reflecting elements. In this paper, we propose an efficient channel estimation framework reducing the pilot overhead of RIS-aided mmWave systems. Key idea of proposed scheme is to decompose the RIS-aided channel into three major components, i.e., static BS- RIS angles, quasi-static RIS-user angles, and time-varying BS-RIS-user path gains, and then estimate them in different time scales. In doing so, the number of channel parameters to be estimated at each stage can be reduced significantly. From the simulation results, we demonstrate that the proposed scheme is very effective in reducing the pilot overhead of RIS-aided mmWave systems.

Channel Estimation for Reconfigurable Intelligent Surface-Aided mmWave Communications

In this paper, we propose a deep learning-based beam tracking method for millimeter-wave (mmWave)communications. Beam tracking is employed for transmitting the known symbols using the sounding beams and tracking time-varying channels to maintain a reliable communication link. When the pose of a user equipment (UE) device varies rapidly, the mmWave channels also tend to vary fast, which hinders seamless communication. Thus, models that can capture temporal behavior of mmWave channels caused by the motion of the device are required, to cope with this problem. Accordingly, we employa deep neural network to analyze the temporal structure and patterns underlying in the time-varying channels and the signals acquired by inertial sensors. We propose a model based on long short termmemory (LSTM) that predicts the distribution of the future channel behavior based on a sequence of input signals available at the UE. This channel distribution is used to 1) control the sounding beams adaptively for the future channel state and 2) update the channel estimate through the measurement update step under a sequential Bayesian estimation framework. Our experimental results demonstrate that the proposed method achieves a significant performance gain over the conventional beam tracking methods under various mobility scenarios.

Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility

Fifth generation (5G) wireless networks are currently being developed to handle wide variety of use cases. In order to support these cases, new types of requirements other than throughput enhancement have been introduced. One such requirement is to reduce the latency down to a millisecond (ms) level in ultra reliable and low latency communications (URLLC). In case of uplink transmission, supporting this stringent latency requirement is quite challenging since the scheduling procedure is a time-consuming and complicated handshaking process. In time division duplexing (TDD) systems, satisfying the latency requirement is far more difficult since the mobile device cannot transmit the data when the subframe is assigned for the downlink. In this paper, we propose a low latency access scheme suitable for TDD-based URLLC. Key idea of the proposed scheme is to transmit the latency sensitive data immediately after the grant signaling. To support the fast uplink access, we introduce a fast grant signaling scheme based on the compressed sensing technique. Numerical results confirm that the proposed uplink access scheme is very effective in TDD-based URLLC systems.

Byonghyo Shim

Papers

Energy-Efficient Cell-Free Systems Using Finite Rate Feedback

Optimal Restricted Isometry Condition for Exact Sparse Recovery with Orthogonal Least Squares

Channel Estimation for Reconfigurable Intelligent Surface-Aided mmWave Communications

Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility

Fast Uplink Access in TDD Systems for Ultra Reliable and Low Latency Communications