There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Millimeter-wave (mmW) frequencies between 30 and 300 GHz are a new frontier for cellular communication that offers the promise of orders of magnitude greater bandwidths combined with further gains via beamforming and spatial multiplexing from multielement antenna arrays. This paper surveys measurements and capacity studies to assess this technology with a focus on small cell deployments in urban environments. The conclusions are extremely encouraging; measurements in New York City at 28 and 73 GHz demonstrate that, even in an urban canyon environment, significant non-line-of-sight (NLOS) outdoor, street-level coverage is possible up to approximately 200 m from a potential low-power microcell or picocell base station. In addition, based on statistical channel models from these measurements, it is shown that mmW systems can offer more than an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks at current cell densities. Cellular systems, however, will need to be significantly redesigned to fully achieve these gains. Specifically, the requirement of highly directional and adaptive transmissions, directional isolation between links, and significant possibilities of outage have strong implications on multiple access, channel structure, synchronization, and receiver design. To address these challenges, the paper discusses how various technologies including adaptive beamforming, multihop relaying, heterogeneous network architectures, and carrier aggregation can be leveraged in the mmW context.

Millimeter-Wave Cellular Wireless Networks: Potentials and Challenges

Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.

An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems

Reliable data detection in time division multiple access (TDMA) communication systems strictly depends on the availability of accurate estimates of the synchronization parameters of the received signal, ie, carrier frequency/phase and symbol timing, which must be derived from the burst preamble The authors focus on the carrier frequency estimation aspect, and present a fast, open-loop, all-digital frequency offset estimation technique, whose performance is assessed in two different communication scenarios: a TDMA satellite link employing standard modulation and burst formats, and a mobile cellular terrestrial radio system with signal and channel characteristics obeying the pan-European Group Special Mobile (GSM) recommendations The use of the algorithm as a frequency error detector (discriminator) in a recursive ("closed-loop") frequency offset estimator is also discussed, and some results concerning both the transient and the steady-state behavior of such a scheme are presented Finally, the impact of the algorithm on the receiver BER is briefly analyzed >

Carrier frequency recovery in all-digital modems for burst-mode transmissions

We present and analyze a technique for fast acquisition and accurate tracking of the carrier frequency in orthogonal frequency division multiplexing (OFDM) receivers. The scheme is based on a data-aided frequency estimation algorithm. The presence of known symbol sequences periodically inserted in the OFDM frame allows the data demodulator to rapidly lock onto the carrier frequency during the acquisition phase, even in the presence of frequency offsets up to a few tenths of the overall signaling rate. Once acquisition is over, the circuit switches to a decision-directed mode to perform fine frequency tracking for reliable data demodulation. The algorithm performance is analyzed in terms of width of the lock-in frequency range and of lock-in probability in the acquisition mode, and of mean-square frequency estimation error in the tracking mode. Since OFDM is known to be extremely sensitive to carrier frequency errors, the impact of the carrier frequency synchronizer on the receiver error rate is also investigated.

Carrier frequency acquisition and tracking for OFDM systems

This paper intends to present a summary of the technical issues arising in the exploitation of the 60 GHz mm-wave band for mobile and personal communications. The most significant applications proposed so far are surveyed, with particular emphasis placed on recent experimentation about millimeter-wave propagation for road/railway transportation as well as indoor scenarios. As a case study, the capacity of a (micro-)cellular Code Division Multiple Access (CDMA) network in the 60-GHz band is also evaluated in detail.

Mobile and Personal Communications in the 60 GHz Band: A Survey

This paper is devoted to turbo synchronization, that is to say the use of soft information to estimate parameters like carrier phase, frequency offset or timing within a turbo receiver. It is shown how maximum-likelihood estimation of those synchronization parameters can be implemented by means of the iterative expectation-maximization (EM) algorithm [A.P. Dempster, et al., 1977]. Then we show that the EM algorithm iterations can be combined with those of a turbo receiver. This leads to a general theoretical framework for turbo synchronization. The soft decision-directed ad-hoc algorithm proposed in V. Lottici and M. Luise, [2002] for carrier phase recovery turns out to be a particular instance of this implementation. The proposed mathematical framework is illustrated by simulations reported for the particular case of carrier phase estimation combined with iterative demodulation and decoding [S. ten Brink, et al., 1998].

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Turbo synchronization: an EM algorithm interpretation

In this paper, we introduce a low-complexity carrier phase estimation algorithm to be integrated into the data decoder of a turbo-coded modem employing a linear modulation. The estimator is based on a pseudo-maximum-likelihood approach and makes iterative use of soft decisions provided by the soft-in/soft-out decoders within the overall turbo-decoding scheme. In doing so, iterative decoding and carrier phase recovery go together iteration after iteration in a "soft decision-directed" mode. This allows performing reliable blind phase estimation and almost ideal coherent detection for values of the signal-to-noise ratio down to a few decibels only, and without the need to resort to narrowband phase-locked loops with large acquisition time. Performance in terms of mean estimated value, root mean-squared estimation error, and overall decoder bit-error rate as derived by simulation are also reported.

Marco Luise

Papers

Carrier frequency recovery in all-digital modems for burst-mode transmissions

Carrier frequency acquisition and tracking for OFDM systems

Mobile and Personal Communications in the 60 GHz Band: A Survey

Turbo synchronization: an EM algorithm interpretation

Embedding carrier phase recovery into iterative decoding of turbo-coded linear modulations