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

Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight (LoS) connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication towards 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains including physical layer, network layer, and joint communication, computing and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.

UAV Communications for 5G and Beyond: Recent Advances and Future Trends

NOMA-inspired coexistence enabling method in cell-free massive MIMO using opportunistic interference alignment

Vehicular networks are known for their dynamic topology, high mobility, and frequent disconnections. Unmanned aerial vehicles (UAVs) have been recently used as instant communication relays to bridge the communication gaps between terrestrial vehicles to improve connectivity in vehicular networks and overcome the aforementioned problems. Despite the existing work in the literature where each vehicle connects directly to UAVs, this work studies how clustering and different densities of vehicles affect delay and energy efficiency in a UAV-based vehicular network integrated with 5G technology. Consequently, this work addresses the problem of UAV enabling vehicular ad-hoc networks (VANETs) in a highway scenario, where UAVs serve as an effective complement to forward data packets between vehicles in the absence of sufficient fixed infrastructures in an emergency situation. The main objective of this work is to minimize the delay while maximizing the energy efficiency by minimizing the power consumption and maximizing the total data rate under realistic conditions, while nonorthogonal multiple access (NOMA) is also adopted as an alternative answer for the effective utilization of limited bandwidth. The free-flowing traffic follows a Poisson stochastic process where each vehicle is assigned a random speed selected from a truncated Gaussian distribution. To this end, a novel modification of fast global $K$ -means is adopted to partition vehicles, allowing communication between clusters by vehicle-to-vehicle links, while data packets between clusters are relayed through UAVs. By computing the convex approximation of the objective function and the constraints, the original problem with the mixed-integer, nonconvex, and nonlinear form is solved by the proposed iterative inner penalty function algorithm. Finally, extensive simulations are conducted to validate the superiority of the proposed method in terms of various metrics. The results indicate that the relaying task in the proposed UAV-assisted VANET based on 5G technology is perfectly suited to enhance the network connectivity.

Relaying Data With Joint Optimization of Energy and Delay in Cluster-Based UAV-Assisted VANETs

This paper presents a Cognitive Radio Network (CRN) consisting of $N$ Secondary Users (SUs), $M$ real Primary Users (PUs), and one attacker where the main threat in such a network is the Primary User Emulation (PUE) attack. It is assumed that $K$ active PUs coexist in the communication range of CRN where parameter $K\leq M$ is unknown for the PUE detection system. We aim to detect malicious PUE attackers, where the CR base station performs the feature extraction on the PU transmissions in the frequency domain to recognize SUs' and PUs' (either real PUs or PUE attacker) signals, independently. Then, the Bayesian nonparametric clustering approach based on the Dirichlet Process Mixture Model (DPMM) is used for clustering and determining the number of detected PUs. To reduce the convergence time of the Gibbs sampling in our scheme, we employ the Chinese Restaurant Process (CRP) with the uniform and nonuniform sampling of unknown clusters' parameters. We investigate the effect of choosing suitable initial assignments of clusters on the convergence time of the DPMM algorithm. To separate the real PU from the PUE attacker, the CRN performs an identification mechanism sequentially in some spectrum sensing periods to investigate the behavior of PU transmitters. The performance of the proposed PUEA identification method in terms of the PU detection rate and the PUEA detection accuracy is evaluated numerically for various sequential (e.g., CRP-sequential) and random sampling approaches.

Signal Classification for Detecting Primary User Emulation Attack in Centralized Cognitive Radio Networks

A primary challenge in wireless networks is to use available resources efficiently so that the Quality of Service (QoS) is satisfied while maximizing the throughput of the network. Among different resource allocation strategies, power and spectrum allocations have long been regarded as efficient tools to mitigate interference and improve the throughput of the network. Also, achieving a low transmission delay is an important QoS requirement in buffer-limited networks, particularly for users with real-time services. For these networks, too much delay results in dropping some packets. Therefore, the main challenge in networks with real-time services is to utilize an efficient power allocation scheme so that the delay is minimized while achieving a high throughput. This dissertation deals with these problems in distributed wireless networks. In Chapters 2 and 3, a distributed single-hop wireless network with K links is considered, in which the links are partitioned into a fixed number (M) of clusters, each operating in a subchannel with bandwidth W M . The subchannels are assumed to be orthogonal to each other. A general shadow-fading model, described by parameters (α,), is considered where α denotes the probability of shadowing, and ( ≤ 1) represents the average cross-link gains. The main goal of these chapters is to find the maximum network throughput in the asymptotic regime of K → ∞, which is achieved by: i) proposing a distributed and non-iterative power allocation strategy, in which the objective of each user is to maximize its best estimate (based on its local information, i.e., direct channel gain) of the average throughput of the network, and ii) choosing the optimum value for M . In Chapter 2, the network throughput is defined as the average sum-rate of the network, which is shown to scale as Θ(logK). Moreover, it is iii proved that in the strong interference scenario, the optimum power allocation strategy for each user is a threshold-based on-off scheme. In Chapter 3, the network throughput is defined as the guaranteed sum-rate, when the outage probability approaches zero. In this scenario, it is demonstrated that the on-off power allocation scheme maximizes the throughput, which scales as W α logK. Moreover, the optimum spectrum sharing for maximizing the average sum-rate and the guaranteed sum-rate is achieved at M = 1. Chapter 4 investigates the delay-throughput tradeoff of the underlying network with M = 1 (or equivalently K = n). The analysis relies on the distributed on-off power allocation strategy for the deterministic and stochastic packet arrival processes. In the first part, the effective throughput maximization of the network is analyzed. It is proved that the effective throughput of the network scales as logn α , with α , α, despite the packet arrival process. Then, the delay characteristics of the underlying network in terms of a packet dropping probability are presented. In addition, the necessary conditions in the asymptotic case of n → ∞ are derived such that the packet dropping probabilities tend to zero, while achieving the maximum effective throughput of the network. Finally, the trade-off between the effective throughput of the network and delay-bounds for different packet arrival processes is analyzed. In particular, it is determined how much degradation will be enforced in the throughput by introducing other constraints.

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Delay-Throughput Analysis in Distributed Wireless Networks

The massive multiple-input multiple-output (MIMO) technology has been applied innew generation wireless systems due to growing demand for reliability and high datarate. Hybrid beamforming architectures in both receiver and transmitter, includinganalog and digital precoders, play a significant role in 5G communication networksand have recently attracted a lot of attention. In this paper, we propose a simple andeffective beamforming precoder approach for mmWave massive MIMO systems. Wefirst solve an optimization problem by a simplification subject, and in the second step,we use the covariance channel matrixfCk=Cov(Hk)andBk=HkHHkinstead of chan-nel matrixHk. Simulation results verify that the proposed scheme can enjoy a highersum rate and energy efficiency than previous methods such as spatially sparse method,analog method, and conventional hybrid method even with inaccurate Channel StateInformation (CSI). Percentage difference of the achievable rate ofCk=Cov(Hk)andBk=HkHHkschemes compared to conventional methods are 2.51% and 48.94%, re-spectively.

/pdf/an-improvement-to-hybrid-beamforming-precoding-scheme-for-ycy0psqozy.pdf

Jamshid Abouei

Papers

NOMA-inspired coexistence enabling method in cell-free massive MIMO using opportunistic interference alignment

Relaying Data With Joint Optimization of Energy and Delay in Cluster-Based UAV-Assisted VANETs

Signal Classification for Detecting Primary User Emulation Attack in Centralized Cognitive Radio Networks

Delay-Throughput Analysis in Distributed Wireless Networks

An improvement to hybrid beamforming precoding scheme for mmWave massive MIMO systems based on channel matrix