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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]

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]

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]

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]