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

Ultra-reliable and low-latency communication (URLLC) is a new service category to accommodate emerging services requiring low end-to-end latency. In the 4G LTE/5G NR, it is very difficult to satisfy the URLLC requirements since multiple OFDM symbols are processed as a bundle. In this paper, we propose a novel low-latency packet transmission scheme, referred to as ultra-mini slot transmission (UMST), suitable for the short packet transmission in URLLC scenario. Key idea of the proposed scheme is to transform the transmit information into subcarrier positions and then decode it using a small amount of time-domain received samples. In particular, in the UMST decoding, we put forth an entirely different approach based on a deep neural network (DNN). From the numerical evaluations on realistic channel models, we demonstrate that the UMST scheme outperforms the conventional transmission schemes in terms of the block error rate (BLER) and transmission latency.

Ultra-Mini Slot Transmission for 5G+ and 6G URLLC Network

In recent years, we are witnessing bewildering variety of automated services and applications of vehicles, robots, sensors, and machines powered by the artificial intelligence technologies. Communication mechanism associated with these services is dearly distinct from human-centric communications. One important feature for the machine-centric communications is that the amount of information to be transmitted is tiny. In view of the short packet transmission, relying on today's transmission mechanism would not be efficient due to the waste of resources, large decoding latency, and expensive operational cost. In this article, we present an overview of the sparse vector transmission (SVT), a scheme to transmit a short-sized information after the sparse transformation. We discuss basics of SVT, two distinct SVT strategies, viz., frequency-domain sparse transmission and sparse vector coding with detailed operations, and also demonstrate the effectiveness in realistic wireless environments.

Sparse Vector Transmission: An Idea Whose Time Has Come

Large-scale multiple-input multiple-output(MIMO) systems with a large number of antennas at the basestation have drawn considerable interest because of potential ability to achieve high spectral efficiencies. In order to achieve optimal performance of large-scale MIMO systems, the basestation needs to know channel state information (CSI) perfectly. In terms of CSI acquisition, the basestation estimates the downlink channel through channel reciprocity in time division duplexing (TDD) or requires CSI feedback through the uplink in frequency division duplexing (FDD). Due to the large number of transmit antennas at the basestation, uplink CSI feedback would be a major hurdle in developing FDD large-scale MIMO systems. In this paper, we propose an efficient feedback compression technique for FDD large-scale MIMO systems. The proposed method reduces a dimension of vector quantization by grouping high correlated antenna elements. In fact, the proposed method invests a small portion of feedback resources to generate a grouped channel vector and the rest to quantize the grouped channel vector. Simulation results demonstrate that the proposed method achieves significant feedback overhead reduction over conventional methods.

An Efficient Feedback Compression for Large-Scale MIMO Systems

Frequency estimation of a tonal signal in passive sonar systems is crucial to the identification of the marine object. In the conventional techniques, a basis mismatch error caused by the discretization of the frequency domain is unavoidable, resulting in a severe degradation of the object detection quality. To overcome the basis mismatch error, we propose a tonal frequency estimation technique in the continuous frequency domain. Towards this end, we formulate the frequency estimation problem as an atomic norm minimization problem. From the numerical experiments, we show that the proposed technique is effective in identifying the tonal frequency components of marine objects.

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Tonal signal detection in passive sonar systems using atomic norm minimization

Beamforming technique realized by the multiple-input-multiple-output (MIMO) antenna arrays has been widely used to compensate for the severe path loss in the millimeter wave (mmWave) bands. In 5G NR system, the beam sweeping and beam reﬁnement are employed to ﬁnd out the best beam codeword aligned to the mobile. Due to the complicated handshaking and ﬁnite resolution of the codebook, today’s 5G-based beam management strategy is ineffective in various scenarios in terms of the data rate, energy consumption, and also processing latency. An aim of this article is to introduce a new type of beam management framework based on the computer vision (CV) technique. In this framework referred to as computer vision-aided beam management (CVBM), a camera attached to the BS captures the image and then the deep learning-based object detector identiﬁes the 3D location of the mobile. Since the base station can directly set the beam direction without codebook quantization and feedback delay, CVBM achieves the signiﬁcant beamforming gain and latency reduction. Using the specially designed dataset called Vision Objects for Beam Management (VOBEM), we demonstrate that CVBM achieves more than 40 % improvement in the beamforming gain and 40 % reduction in the beam training overhead over the 5G NR beam management.

Byonghyo Shim

Papers

Ultra-Mini Slot Transmission for 5G+ and 6G URLLC Network

Sparse Vector Transmission: An Idea Whose Time Has Come

An Efficient Feedback Compression for Large-Scale MIMO Systems

Tonal signal detection in passive sonar systems using atomic norm minimization

Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming