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

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

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Table Of Integrals Series And Products

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

A bidirectional integrated sensing and communication (ISAC) system is proposed, in which a pair of transceivers carry out two-way communication and mutual sensing. Both full-duplex and half-duplex operations in narrowband and wideband systems are conceived for the bidirectional ISAC. 1) For the narrowband system, the conventional full-duplex and half-duplex operations are redesigned to take into account sensing echo signals. Then, the transmit beamforming design of both transceivers is proposed for addressing the sensing and communication (S&C) tradeoff. A one-layer iterative algorithm relying on successive convex approximation (SCA) is proposed to obtain Karush-Kuhn-Tucker (KKT) optimal solutions. 2) For the wideband system, the new full-duplex and half-duplex operations are proposed for the bidirectional ISAC. In particular, the frequency-selective fading channel is tackled by delay pre-compensation and path-based beamforming. By redesigning the proposed SCA-based algorithm, the KKT optimal solutions for path-based beamforming for characterizing the S&C tradeoff are obtained. Finally, the numerical results show that: i) For both bandwidth scenarios, the existence of the interference introduced by sensing results in full-duplex may not always outperform half-duplex , especially in the sensing-prior regime or when the communication channel is line-of-sight-dominated; and ii) For both duplex operations, it is sufﬁcient to reuse communication signals for sensing in the narrowband system, while an additional dedicated sensing signal is required in the wideband system.

Bidirectional Integrated Sensing and Communication: Full-Duplex or Half-Duplex?

This paper investigates the performance of millimeter wave (mmWave) communications in clustered device-to-device (D2D) networks. The locations of D2D transceivers are modeled as a Poisson Cluster Process (PCP). In each cluster, devices are equipped with multiple antennas, and the active D2D transmitter (D2D-Tx) utilizes mmWave to serve one of the proximate D2D receivers (D2D-Rxs). Specifically, we introduce three user association strategies: 1) Uniformly distributed D2D-Tx model; 2) Nearest D2D-Tx model; 3) Closest line-of-site (LOS) D2D-Tx model. To characterize the performance of the considered scenarios, we derive new analytical expressions for the coverage probability and area spectral efficiency (ASE). Additionally, in order to efficiently illustrating the general trends of our system, a closed-form lower bound for the special case interfered by intra-cluster LOS links is derived. We provide Monte Carlo simulations to corroborate the theoretical results and show that: 1) The coverage probability is mainly affected by the intra-cluster interference with LOS links; 2) There exists an optimum number of simultaneously active D2D-Txs in each cluster for maximizing ASE; and 3) Closest LOS model outperforms the other two scenarios but at the cost of extra system overhead.

Modeling and Analysis of D2D Millimeter-Wave Networks with Poisson Cluster Processes

The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, and security and privacy concerns caused by billions of connected wireless devices and typically zillions of bytes of data they produce at the network edge. A blend of edge computing and Artificial Intelligence (AI) techniques could optimally shift the resourceful computation servers closer to the network edge, which provides the support for advanced AI applications (e.g., video/audio surveillance and personal recommendation system) by enabling intelligent decision making on computing at the point of data generation as and when it is needed, and distributed Machine Learning (ML) with its potential to avoid the transmission of the large dataset and possible compromise of privacy that may exist in cloud-based centralized learning. Besides, the deployment of AI techniques to redesign end-to-end communication is attracting attention to improve communication performance. Therefore, the interaction of AI and wireless communications generates a new concept, named native AI wireless networks. In this paper, we conduct a comprehensive overview of recent advances in distributed intelligence in wireless networks under the umbrella of native AI wireless networks, with a focus on the design of distributed learning architectures for heterogeneous networks, on AI-enabled edge computing, on the communication-efficient technologies to support distributed learning, and on the AI-empowered end-to-end communications. We highlight the advantages of hybrid distributed learning architectures compared to state-of-the-art distributed learning techniques. We summarize the challenges of existing research contributions in distributed intelligence in wireless networks and identify potential future opportunities.

Distributed Intelligence in Wireless Networks

This paper conceive a large intelligent surface (LIS)aided multiple-input multiple-output network for providing wireless services to randomly roaming users. The network performance is analyzed by utilizing stochastic geometry tools. We aim for serving multiple users by jointly designing the passive beamforming weight at LISs and detection weight vectors at users. As a benefit, the interference imposed by the LISs can be suppressed. In an effort to evaluate the performance of the proposed network, we first derive approximated channel statistics for characterizing the effective channel gains. Then, we derive closed-form expressions for the ergodic rate of users. For gleaning further insights, we investigate the high-signal-to-noise-ratio (SNR) of ergodic rate. Our analytical results demonstrate that the specific fading environments encountered between the LISs and users have almost no impact on the ergodic rate attained.

Performance Analysis for Large Intelligent Surfaces enabled MIMO Networks.

—This article investigates the exploitation of semantic communications in multi-user networks. We propose a novel heterogeneous semantic and bit multi-user framework for pro- viding ﬂawless, customized, and intelligent information transmission. We discuss both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) for the proposed heterogeneous framework, with an emphasis on investigating the attractive interplay between semantic communications and NOMA, namely NOMA enabled semantic communications and se- mantic communications enhanced NOMA . 1) For NOMA enabled semantic communications, we propose a semi-NOMA scheme for efﬁciently facilitating the heterogeneous semantic and bit multi-user communication, which uniﬁes conventional NOMA and OMA schemes. The fundamental performance limit, namely semantic-versus-bit rate region, is characterized, which shows the superiority of the proposed semi-NOMA. 2) For semantic communications enhanced NOMA, we propose an opportunistic semantic and bit communication approach to alleviate the early-late rate disparity issue in NOMA. Numerical case studies demonstrate that signiﬁcant performance gain can be achieved for NOMA by employing semantic communications than bit communications. Finally, several open research directions are highlighted.

Yuanwei Liu

Papers

Bidirectional Integrated Sensing and Communication: Full-Duplex or Half-Duplex?

Modeling and Analysis of D2D Millimeter-Wave Networks with Poisson Cluster Processes

Distributed Intelligence in Wireless Networks

Performance Analysis for Large Intelligent Surfaces enabled MIMO Networks.

Semantic Communications in Multi-user Wireless Networks