Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.

What Will 5G Be

Millimeter wave (mmWave) signals experience orders-of-magnitude more pathloss than the microwave signals currently used in most wireless applications and all cellular systems. MmWave systems must therefore leverage large antenna arrays, made possible by the decrease in wavelength, to combat pathloss with beamforming gain. Beamforming with multiple data streams, known as precoding, can be used to further improve mmWave spectral efficiency. Both beamforming and precoding are done digitally at baseband in traditional multi-antenna systems. The high cost and power consumption of mixed-signal devices in mmWave systems, however, make analog processing in the RF domain more attractive. This hardware limitation restricts the feasible set of precoders and combiners that can be applied by practical mmWave transceivers. In this paper, we consider transmit precoding and receiver combining in mmWave systems with large antenna arrays. We exploit the spatial structure of mmWave channels to formulate the precoding/combining problem as a sparse reconstruction problem. Using the principle of basis pursuit, we develop algorithms that accurately approximate optimal unconstrained precoders and combiners such that they can be implemented in low-cost RF hardware. We present numerical results on the performance of the proposed algorithms and show that they allow mmWave systems to approach their unconstrained performance limits, even when transceiver hardware constraints are considered.

Spatially Sparse Precoding in Millimeter Wave MIMO Systems

Wireless communication systems that include unmanned aerial vehicles promise to provide cost-effective wireless connectivity for devices without infrastructure coverage. Compared to terrestrial communications or those based on high-altitude platforms, on-demand wireless systems with low-altitude UAVs are in general faster to deploy, more flexibly reconfigured, and likely to have better communication channels due to the presence of short-range line-of-sight links. However, the utilization of highly mobile and energy-constrained UAVs for wireless communications also introduces many new challenges. In this article, we provide an overview of UAV-aided wireless communications, by introducing the basic networking architecture and main channel characteristics, highlighting the key design considerations as well as the new opportunities to be exploited.

Wireless communications with unmanned aerial vehicles: opportunities and challenges

Millimeter Wave Wireless Communications

As driving becomes more automated, vehicles are being equipped with more sensors generating even higher data rates. Radars are used for object detection, visual cameras as virtual mirrors, and LIDARs for generating high resolution depth associated range maps, all to enhance the safety and efficiency of driving. Connected vehicles can use wireless communication to exchange sensor data, allowing them to enlarge their sensing range and improve automated driving functions. Unfortunately, conventional technologies, such as DSRC and 4G cellular communication, do not support the gigabit-per-second data rates that would be required for raw sensor data exchange between vehicles. This article makes the case that mmWave communication is the only viable approach for high bandwidth connected vehicles. The motivations and challenges associated with using mmWave for vehicle-to-vehicle and vehicle-to-infrastructure applications are highlighted. A high-level solution to one key challenge - the overhead of mmWave beam training - is proposed. The critical feature of this solution is to leverage information derived from the sensors or DSRC as side information for the mmWave communication link configuration. Examples and simulation results show that the beam alignment overhead can be reduced by using position information obtained from DSRC.

Millimeter-Wave Vehicular Communication to Support Massive Automotive Sensing

Multiple GHz of internationally available, unlicensed spectrum surrounding the 60 GHz carrier frequency has the ability to accommodate high-throughput wireless communications. While the size and availability of this free spectrum make it very attractive for wireless applications, 60 GHz implementations must overcome many challenges. For example, the high attenuation and directional nature of the 60 GHz wireless channel as well as limited gain amplifiers and excessive phase noise in 60 GHz transceivers are explicit implementation difficulties. The challenges associated with this channel motivate commercial deployment of short-range wireless local area networks, wireless personal area networks, and vehicular networks. In this paper we detail design tradeoffs for algorithms in the 60 GHz physical layer including modulation, equalization, and space-time processing. The discussion is enhanced by considering the limitations in circuit design, characteristics of the effective wireless channel (including antennas), and performance requirements to support current and next generation 60 GHz wireless communication applications.

60 GHz wireless communications: emerging requirements and design recommendations

To meet the needs of next-generation high-data-rate applications, 60 GHz wireless networks must deliver Gb/s data rates and reliability at a low cost. In this article, we surveyed several ongoing challenges, including the design of cost-efficient and low-loss on-chip and in-package antennas and antenna arrays, the characterization of CMOS processes at millimeter-wave frequencies, the discovery of efficient modulation techniques that are suitable for the unique hardware impairments and frequency selective channel characteristics at millimeter-wave frequencies, and the creation of MAC protocols that more effectively coordinate 60 GHz networks with directional antennas. Solving these problems not only provides for wireless video streaming and interconnect replacement, but also moves printed and magnetic media such as books and hard drives to a lower cost, higher reliability semiconductor form factor with wireless connectivity between and within devices.

60 GHz Wireless: Up Close and Personal

Multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless systems use link adaptation to exploit the dynamic nature of wireless environments. Link adaptation maximizes throughput while maintaining target reliability by adaptively selecting the modulation order and coding rate. Link adaptation is extremely challenging, however, due to the difficulty in predicting error rates in OFDM with binary convolutional codes, bit interleaving, MIMO processing, and real channel impairments. This paper proposes a new machine-learning framework that exploits past observations of the error rate and the associated channel-state information to predict the best modulation order and coding rate for new realizations of the channel state without modeling the input-output relationship of the wireless transceiver. Our approach is enabled through our new error-rate expression that is only parameterized by postprocessing signal-to-noise ratios (SNRs), ordered over subcarriers and spatial streams. Using ordered SNRs, we propose a low-dimensional feature set that enables machine learning to increase the accuracy of link adaptation. An IEEE 802.11n simulation study validates the application of this machine-learning framework in real channels and demonstrates the improved performance of SNR ordering as it compares with competing link-quality metrics.

Robert C. Daniels

Papers

Millimeter Wave Wireless Communications

Millimeter-Wave Vehicular Communication to Support Massive Automotive Sensing

60 GHz wireless communications: emerging requirements and design recommendations

60 GHz Wireless: Up Close and Personal

Adaptation in Convolutionally Coded MIMO-OFDM Wireless Systems Through Supervised Learning and SNR Ordering