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Wireless link scheduling in device-to-device (D2D) networks is an NP-hard problem. As a solution, multiple supervised deep learning (DL) models have been recently proposed, which depend on the geographical information of D2D pairs. However, such DL models require labeled training data. In this paper, we focus on unsupervised learning of scheduling. More specifically, this paper proposes using a Density-Based anti-Clustering for Scheduling D2D Communications (DBSCHedule). The proposed algorithm is a two-step approach that consists of clustering and anti-clustering. First, clustering aims at identifying the non-interfering groups of D2D pairs. Then, anti-clustering aims at identifying the maximally separated sub-groups to minimize the interference. The clustering step uses a fully-automated unsupervised density-based spectral-clustering of applications with noise (DBSCAN) and the anti-clustering uses the inverse of the objective function of the k-means clustering. Results show comparable performance with the optimal FPLinQ scheduler yet without requiring any channel information nor is there a requirement to solve a complex optimization problem. Moreover, a comparable performance to the previous attempts using DL and modified clustering is achieved while being completely adaptive and easily accommodating to changes in the network layout. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of optimal allocation of renewable energy resources to power base stations (BSs) in cellular networks while accounting for possible power failures in the utility grid. The allocation problem under investigation is formulated as a mixed integer non-linear programming, which is then decomposed, due to its complexity, and solved as two interdependent problems with the help of deterministic and metaheuristic techniques. We propose an allocation algorithm that jointly aims at optimizing the allocation of individualized green energy resources to simultaneously power the BSs with the grid as well as minimizing the overall system costs. Cost minimization is achieved by selecting the optimal types and sizes of the used photovoltaic (PV) panels and batteries in addition to optimally scheduling the charging and discharging of the selected batteries. This is done while limiting PV curtailment and any expected loss of cellular services at the times of power outage. Finally, the effectiveness of the proposed algorithm is demonstrated through the simulation of a sample case of a typical BS where Markov Chain Monte Carlo simulations are carried out to account for the uncertainty of the output of the PV panels and the grid failures. [ABSTRACT FROM AUTHOR]

Millimeter wave (mm-wave) communication is one of the key enabling technologies for meeting the requirements of the fifth generation (5G) wireless communication systems. It is, therefore, essential to have accurate information about the mm-wave radio channel characteristics for successful deployment of these systems. There are many recent mm-wave channel characterization measurements reported worldwide. However, no such measurements were reported in the Arabian Peninsula, where the environment along with the building design and architecture are different from other regions in the world. This paper tries to fill that gap and presents the results of an mm-wave measurement campaign conducted at the American University of Sharjah (AUS) campus in the United Arab Emirates (UAE) during the period of November 2018 to January 2019. The results of the large-scale characterization (path loss and shadow fading) are presented for both indoor and outdoor environments at 28 GHz. Furthermore, the penetration loss and reflection coefficients for materials commonly used in the facades of building structures in the UAE are measured at the same frequency. [ABSTRACT FROM AUTHOR]

The moments-matching method is employed to provide an approximate distribution of the amount of data that can be transmitted over a wireless channel during any arbitrary interval of time. This distribution is then used to propose a novel closed-form expression for the probability of starvation at the playback buffer in a video streaming scenario. Monte–Carlo simulations are carried out to assess the accuracy of the proposed distribution as well as the probability of starvation expression and study the interactions of various system parameters. [ABSTRACT FROM AUTHOR]

One of the major issues in LTE-Advanced (LTE-A) systems is the poor capacity at the cell edge. This is mainly due to the interference experienced by the users as a result of the aggressive frequency reuse usually implemented. Relaying offers an attractive solution for this problem by offering better links than those with the eNodeB (eNB) for the terminals suffering from high path loss or high interference. However, adding relays complicates the resource allocation problem at the eNB and therefore the need for more efficient schemes arises. This is also aggravated by the reuse of resource blocks (RBs) by the relays to fully exploit the scarce spectrum, which, in turn, leads to intra-cell interference. In this paper, we study the joint power and resource allocation problem in LTE-A relay-enhanced cells that exploit spatial reuse. To guarantee fairness among users, a max-min fair optimization objective is used. This complex problem is solved using coordinate ascent and the difference of two convex functions (DC) programming techniques and the proposed scheme indeed converges to a local-optimum quickly. This is shown to be a satisfactory solution according to the simulation results that indicate an almost sevenfold increase in the 10th percentile capacity when compared to previously proposed solutions. [ABSTRACT FROM AUTHOR]

We derive novel exact closed-form expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the product and ratio of products of an arbitrary number of independent non-identically distributed (i.n.i.d) extended generalized-K (EGK) variates. The expressions are given in terms of the Meijer’s G-function and can be computed easily using commonly available mathematical software tools. They also subsume those for arbitrary combinations of other well-known variates and can be directly utilized in performance evaluation of wireless communication systems under different scenarios. We present various analytical results that are verified via Monte-Carlo simulations for both the PDF and CDF as well as their application in multiple practical scenarios. [ABSTRACT FROM AUTHOR]

Device-to-device communication (D2D) and massive multiple input multiple output (MIMO) systems are two emerging technologies that are being considered to improve the performance of next generation wireless cellular systems. In D2D, two mobile nodes communicate directly without traversing the base station (BS). Consequently, interference management, coordination, and/or cancellation techniques have to be adopted to target the problem of mutual interference between the D2D devices and the BS (or the normal mobiles nodes connected to the BS). In this paper, we investigate the problem of BS precoder design and D2D devices power allocation in the downlink of a single-cell network assuming existence of D2D devices as well as massive MIMO at the BS. We propose algorithms to maximize the sum of the achievable data rates of the D2D pairs while maintaining quality of service constraints on the cellular user equipment, which communicate normally with the BS. We also propose two algorithms for the precoding problem; the first is based on semi-definite programming while the second is based on gradient descent algorithms. Moreover, we investigate two solutions for the power allocation problem; the first solves an approximate convex optimization problem iteratively while the second is a suboptimal, but far less complex, heuristic. Finally, we propose a technique to apply the mentioned solutions when only partial channel state information is available at the BS. Simulation results show that the proposed solutions are superior to the conventional precoding and power allocation schemes. [ABSTRACT FROM AUTHOR]

We evaluate the performance of selection combining (SC) diversity in cellular systems where binary phase-shift keying (BPSK) is employed and the desired signal as well as the cochannel interferers (CCIs) is subject to Weibull fading. A characteristic function-(CF-) based approach is followed to evaluate the performance in terms of the outage probability. Two selection criteria are adopted at the diversity receiver: maximum desired signal power and maximum output signal-to-interference ratio (SIR).We study the effect of the fading parameters of the desired and interfering signals, the number of diversity branches, as well as the number of interferers on the performance. Numerical results are presented and the validity of our expressions is verified via Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

We use the Padé approximation (PA) technique to obtain closed-form approximate expressions for the moment-generating function (MGF) of the Weibull random variable. Unlike previously obtained closed-form exact expressions for the MGF, which are relatively complicated as being given in terms of the Meijer G-function, PA can be used to obtain simple rational expressions for the MGF, which can be easily used in further computations. We illustrate the accuracy of the PA technique by comparing its results to either the existing exact MGF or to that obtained via Monte Carlo simulations. Using the approximate expressions, we analyze the performance of digital modulation schemes over the single channel and the multichannels employing maximal ratio combining (MRC) under the Weibull fading assumption. Our results show excellent agreement with previously published results as well as with simulations. [ABSTRACT FROM AUTHOR]