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Table Of Integrals Series And Products

Tables of integrals

Driven by the rapid escalation of the wireless capacity requirements imposed by advanced multimedia applications (e.g., ultrahigh-definition video, virtual reality, etc.), as well as the dramatically increasing demand for user access required for the Internet of Things (IoT), the fifth-generation (5G) networks face challenges in terms of supporting large-scale heterogeneous data traffic. Nonorthogonal multiple access (NOMA), which has been recently proposed for the third-generation partnership projects long-term evolution advanced (3GPP-LTE-A), constitutes a promising technology of addressing the aforementioned challenges in 5G networks by accommodating several users within the same orthogonal resource block. By doing so, significant bandwidth efficiency enhancement can be attained over conventional orthogonal multiple-access (OMA) techniques. This motivated numerous researchers to dedicate substantial research contributions to this field. In this context, we provide a comprehensive overview of the state of the art in power-domain multiplexing-aided NOMA, with a focus on the theoretical NOMA principles, multiple-antenna-aided NOMA design, on the interplay between NOMA and cooperative transmission, on the resource control of NOMA, on the coexistence of NOMA with other emerging potential 5G techniques and on the comparison with other NOMA variants. We highlight the main advantages of power-domain multiplexing NOMA compared to other existing NOMA techniques. We summarize the challenges of existing research contributions of NOMA and provide potential solutions. Finally, we offer some design guidelines for NOMA systems and identify promising research opportunities for the future.

/pdf/nonorthogonal-multiple-access-for-5g-and-beyond-4vzewzi5qj.pdf

Nonorthogonal Multiple Access for 5G and Beyond

The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

The exponential growth and availability of data in all forms is the main booster to the continuing evolution in the communications industry. The popularization of traffic-intensive applications including high definition video, 3-D visualization, augmented reality, wearable devices, and cloud computing defines a new era of mobile communications. The immense amount of traffic generated by today’s customers requires a paradigm shift in all aspects of mobile networks. Ultradense network (UDN) is one of the leading ideas in this racetrack. In UDNs, the access nodes and/or the number of communication links per unit area are densified. In this paper, we provide a survey-style introduction to dense small cell networks. Moreover, we summarize and compare some of the recent achievements and research findings. We discuss the modeling techniques and the performance metrics widely used to model problems in UDN. Also, we present the enabling technologies for network densification in order to understand the state-of-the-art. We consider many research directions in this survey, namely, user association, interference management, energy efficiency, spectrum sharing, resource management, scheduling, backhauling, propagation modeling, and the economics of UDN deployment. Finally, we discuss the challenges and open problems to the researchers in the field or newcomers who aim to conduct research in this interesting and active area of research.

Ultra-Dense Networks: A Survey

In this letter, we investigate the performance of modulation identification based on pattern recognition approach using the decision tree (J48) classifier, for multiple-input multiple-output (MIMO) relaying broadcast channels with direct link (source-to-destination). The proposed system identifies the modulation type and order among different M-ary shift-keying linear modulations used by broadband technologies such as long term evolution-advanced (LTE-A) and worldwide interoperability for microwave access (WiMAX). The system under study employs features extraction based on higher order statistics (HOS) of the received signal. Based on receiver operating characteristic (ROC) curves, our study shows that J48 classifier is more efficient than the multilayer perceptron (MLP) classifier trained with resilient backpropagation training algorithm (RPROP) where it achieves close to perfect detection rate (over 99%) with reasonable training time in acceptable signal-to-noise ratio (SNR) range. We also show that the performance of the MIMO relaying broadcast network is remarkably better than the traditional MIMO one.

https://users.encs.concordia.ca/~hamouda/Wassim06665327.pdf

Modulation Recognition for MIMO Relaying Broadcast Channels with Direct Link

In this letter, we consider the application of an iterative interference cancelation (IC) scheme to improve the speed of convergence of the adaptive minimum mean-squared error (MMSE) receiver for the reverse-link of a direct-sequence code-division multiple-access (DS-CDMA) system. Our aim is to reduce the overhead introduced during the receiver's training period. This will be achieved using an iterative interference cancelation algorithm such as the parallel interference cancelation (PIC) algorithm. The proposed iterative algorithm makes use of the available knowledge of all users' training sequences at the base-station receiver to jointly cancel multiple-access interference (MAI) and adapts to the MMSE optimum filter taps using the combined adaptive MMSE/PIC receiver. We employ the proposed iterative algorithm to both the least mean square and the recursive least squares algorithms where we show that a significant improvement in terms of convergence speed is achieved. Moreover, we demonstrate the near-far resistance of the proposed receiver.

A fast adaptive algorithm for MMSE receivers in DS-CDMA systems

We propose a new signal-processing scheme, referred to as Decode-Compress-and-Forward with Selective-Cooperation (DCF-SC). In DCF-SC, the relay dedicates a certain amount of time to listen to the message broadcasted by the source and then performs Soft-Input Soft-Output (SISO) decoding. The relay then quantizes the Log-Likelihood Ratio (LLR) values received from the SISO decoder, encodes them and then transmits to the destination. The Selective-Cooperation condition determines whether the destination will accept or reject relay's collaboration. We consider half-duplex relaying with orthogonal channels at the destination and apply turbo coding at both source and relay nodes. We define a trade-off parameter that determines how much of the relay's time should be dedicated to listening and to transmission. We show by simulations that this trade-off factor has an optimal value for which the Block-Error Rate (BLER) is minimized. We compare the error rate performance of the proposed DCF-SC scheme with that of the Decode-Amplify-Forward (DAF) scheme presented in the literature.

/pdf/decode-compress-and-forward-with-selective-cooperation-for-30bkykifha.pdf

Decode-Compress-and-Forward with Selective-Cooperation for Relay Networks

The bit error rate (BER) performance of the zero-forcing (ZF) receiver over transmit-correlated flat Ricean fading channels is investigated. In particular, for a multiple-input multiple-output (MIMO) channel with M transmit and N receive antennas, an approximation for the average BER of each substream is derived. Then the system performance in receive-correlated flat Ricean fading channels is addressed. In this case, it is shown that the performance, when N=M, is the same as that of transmit-correlated flat Ricean fading channels. A closed-form expression for the optimum transmit correlation coefficient, which achieves the maximum capacity (i.e. uncorrelated case), is also derived. As a result, a significant capacity gain is achieved by exploiting the knowledge of the Ricean channel. Extensive simulations are presented to validate and demonstrate the performance gain with different system parameters.

Performance of zero-forcing detectors over MIMO flat-correlated Ricean fading channels

In this letter, the secrecy performance of single-input multiple-output systems over the generalized $\kappa$ – $\mu$ fading channels is analyzed. Novel expressions for the average secrecy capacity and the secure outage probability (SOP) are derived. Moreover, simple and explicit expressions of the asymptotic SOP at high signal-to-noise ratio are given in an effort to gain some valuable insights on how various system parameters affect the secrecy performance. The proposed mathematical analysis is compared with Monte–Carlo simulation to verify the accuracy of the derivation.

Walaa Hamouda

Papers

Modulation Recognition for MIMO Relaying Broadcast Channels with Direct Link

A fast adaptive algorithm for MMSE receivers in DS-CDMA systems

Decode-Compress-and-Forward with Selective-Cooperation for Relay Networks

Performance of zero-forcing detectors over MIMO flat-correlated Ricean fading channels

On the Secrecy Performance Analysis of SIMO Systems Over $\kappa$ – $\mu$ Fading Channels