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

In this paper, we propose a novel distributed resource allocation mechanism for semi-grant-free non-orthogonal multiple access (SGF-NOMA) transmission to maximize the network throughput, where multi-agent deep reinforcement learning with prioritized experience replay (PER) is employed. We design a centralized training framework and decentralized decision making to increase the flexibility of the proposed scheme. More specifically, each grant-free user as an "agent" learns the dynamics of the environment and makes its decisions independently in a decentralized manner. No heavy information exchange is needed to find the optimal transmit power and sub-channel that maximize the throughput. Numerical results show that the proposed algorithm with PER enhances the learning efficiency compared to the algorithm with conventional replay buffer and outperforms the existing scheme with a 12% throughput increase.

Throughput Optimization for SGF-NOMA via Distributed DRL with Prioritized Experience Replay

This article focuses on the near-field effect in terahertz (THz) communications and sensing systems. By equipping with extremely large-scale antenna arrays (ELAAs), the near-field region in THz systems can be possibly extended to hundreds of meters in proximity to THz transceivers, which necessitates the consideration of near-field effect in the THz band both for the communications and sensing. We first review the main characteristics of the near-field region in the THz bands. The signal propagation in the near-field region is characterized by spherical waves rather than planar waves in the far-field region. This distinction introduces a new distance dimension to the communication and sensing channels, which brings new opportunities and challenges for both THz communications and sensing. More particularly, 1) For THz communications, the near-field effect enables a new mechanism for beamforming, namely, beamfocusing, in the focusing region. Furthermore, in THz multiple-input and multiple-output (MIMO) systems, the near-field effect can be exploited to combat the multiplexing gain degradation caused by the sparse THz channels. To address the near-field beam split effect caused by the conventional frequency-independent hybrid beamforming architecture in THz wideband communications, we propose a pair of wideband beamforming optimization approaches by a new hybrid beamforming architecture based on true-time-delayers (TTDs). 2) For THz sensing, joint angle and distance sensing can be achieved in the near-field region. Additionally, the near-field beam split becomes a beneficial effect for enhancing the sensing performance by focusing on multiple possible target locations rather than a drawback encountered in communications. Finally, several topics for future research are discussed.

Terahertz Near-Field Communications and Sensing

This paper investigates unmanned aerial vehicle (UAV) data collection systems with different multiple access schemes, where a rotary-wing UAV is dispatched to collect data from multiple ground nodes (GNs). Our goal is to maximize the minimum UAV data collection throughput from GNs for both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) transmission, subject to the energy budgets at both the UAV and GNs, namely \emph{double energy limitations}. 1) For OMA, we propose an efficient algorithm by invoking alternating optimization (AO) method, where each subproblem is alternately solved by applying successive convex approximation (SCA) technique. 2) For NOMA, we first handle subproblems with fixed decoding order using SCA technique. Then, we develop a penalty-based algorithm to solve the decoding order design subproblem. Numerical results show that: i) The proposed algorithms are capable of improving the max-min throughput performance compared with other benchmark schemes; and ii) NOMA yields a higher performance gain than OMA when GNs have sufficient energy.

Energy-Constrained UAV Data Collection Systems: NOMA and OMA

—The reconﬁgurable intelligent surface (RIS) is a promising technology to provide smart radio environment. In contrast to the well-studied patch-array-based RISs, this work focuses on the metasurface-based RISs and simultaneously trans- mitting and reﬂecting (STAR)-RISs where the elements have millimeter or even molecular sizes. For these meticulous metasur- face structures, a continuous electric current distribution should be adopted for capturing their electromagnetic response instead of discrete phase-shift matrices. Exploiting the electric current distribution, a Green’s function method based channel model is proposed. Based on the proposed model, performance analysis is carried out for both transmitting/reﬂecting-only RISs and STAR- RISs. 1) For the transmitting/reﬂecting-only RIS-aided single-user scenario, closed-formed expressions for the near-ﬁeld/far- ﬁeld boundary and the end-to-end channel gain are derived. Then, degrees-of-freedom (DoFs) and the power scaling laws are obtained. It is proved that the near-ﬁeld channel exhibits higher DoFs than the far-ﬁeld channel. It is also conﬁrmed that when communication distance increases beyond the ﬁeld boundary, the near-ﬁeld power scaling law degrades to the well-known far-ﬁeld result. 2) For the STAR-RIS-aided multi-user scenario, three practical STAR-RIS conﬁguration strategies are proposed, namely power splitting (PS), selective element grouping (SEG), and random element grouping (REG) strategies. The channel gains for users are derived within both the pure near-ﬁeld regime and the hybrid near-ﬁeld and far-ﬁeld regime. Finally, numerical results conﬁrm that: 1) for metasurface-based RISs, the ﬁeld boundary depends on the sizes of both the RIS and the receiver, 2) the received power scales quadratically with the number of elements within the far-ﬁeld regime and scales linearly within the near-ﬁeld regime, and 3) for STAR-RISs, SEG has the highest near-ﬁeld channel gain among the three proposed strategies and PS yields the highest DoFs for the near-ﬁeld channel.

Modeling and Analysis of Metasurface-Based STAR-RISs

The invention discloses a site arrangement, cache placement and resource allocation method and device for an unmanned aerial vehicle non-orthogonal multiple access communication network. The method comprises the following specific steps: outputting a user access strategy based on a DDPG algorithm with corrected overhead; outputting unmanned aerial vehicle cache placement, station arrangement and NOMA power distribution strategies based on a multi-agent DDPG algorithm; judging whether an experienced time slot reaches a training frequency threshold value or not; and when the judgment step is that the passing time slot does not reach the threshold of the training times, adding 1 to the training times for the time slot change, and continuing to execute the step of outputting the user access strategy. According to the invention, the transmission capability of the access link can be effectively enhanced, a request does not need to be made to a core gateway with a relatively long distance, the time delay of obtaining the content by a user can be effectively reduced, and meanwhile, the resource occupation caused by the transmission of the wireless backhaul link of the unmanned aerial vehicle is reduced, so that the transmission time delay of the content needing to be transmitted through the wireless backhaul link is effectively reduced.

Yuanwei Liu

Papers

Throughput Optimization for SGF-NOMA via Distributed DRL with Prioritized Experience Replay

Terahertz Near-Field Communications and Sensing

Energy-Constrained UAV Data Collection Systems: NOMA and OMA

Modeling and Analysis of Metasurface-Based STAR-RISs

Station arrangement method and device, cache placement method and device and resource allocation method and device