Next-Generation Environment-Aware Cellular Networks: Modern Green Techniques and Implementation Challenges

Over the last decade, mobile communications have been witnessing a noteworthy increase of data traffic demand that is causing an enormous energy consumption in cellular networks. The reduction of their fossil fuel consumption in addition to the huge energy bills paid by mobile operators is considered as the most important challenges for the next-generation cellular networks. Although most of the proposed studies were focusing on individual physical layer power optimizations, there is a growing necessity to meet the green objective of fifth-generation cellular networks while respecting the user's quality of service. This paper investigates four important techniques that could be exploited separately or together in order to enable wireless operators achieve

Hadamard upper bound on optimum joint decoding capacity of Wyner Gaussian cellular MAC

This article presents an original analytical expression for an upper bound on the optimum joint decoding capacity of Wyner circular Gaussian cellular multiple access channel (C-GCMAC) for uniformly distributed mobile terminals (MTs). This upper bound is referred to as Hadamard upper bound (HUB) and is a novel application of the Hadamard inequality established by exploiting the Hadamard operation between the channel fading matrix G and the channel path gain matrix Ω. This article demonstrates that the actual capacity converges to the theoretical upper bound under the constraints like low signal-to-noise ratios and limiting channel path gain among the MTs and the respective base station of interest. In order to determine the usefulness of the HUB, the behavior of the theoretical upper bound is critically observed specially when the inter-cell and the intra-cell time sharing schemes are employed. In this context, we derive an analytical form of HUB by employing an approximation approach based on the estimation of probability density function of trace of Hadamard product of two matrices, i.e., G and Ω. A closed form of expression has been derived to capture the effect of the MT distribution on the optimum joint decoding capacity of C-GCMAC. This article demonstrates that the analytical HUB based on the proposed approximation approach converges to the theoretical upper bound results in the medium to high signal to noise ratio regime and shows a reasonably tighter bound on optimum joint decoding capacity of Wyner GCMAC

FSO-Based Vertical Backhaul/Fronthaul Framework for 5G+ Wireless Networks

The presence of a super high rate, but also cost-efficient, easy-to-deploy, and scalable, backhaul/ fronthaul framework, is essential in the upcoming 5G wireless networks and beyond. Motivated by the mounting interest in unmanned flying platforms of various types, including UAVs, drones, balloons, and HAPs/MAPs/LAPs, which we refer to as networked flying platforms (NFPs), for providing communications services, and by the recent advances in free space optics (FSO), this article investigates the feasibility of a novel vertical backhaul/fronthaul framework where the NFPs transport the backhaul/fronthaul traffic between the access and core networks via pointto- point FSO links. The performance of the proposed innovative approach is investigated under different weather conditions and a broad range of system parameters. Simulation results demonstrate that the FSO-based vertical ba

Efficient importance sampling for the left tail of positive Gaussian quadratic forms

Abstract Estimating the left tail of quadratic forms in Gaussian random vectors is of major practical importance in many applications. In this letter, we propose an efficient importance sampling estimator that is endowed with the bounded relative error property. This property significantly reduces the number of simulation runs required by the proposed estimator compared to naive Monte Carlo (MC), especially when the probability of interest is very small. Selected simulation results are presented to illustrate the efficiency of our estimator compared to naive MC as well as some of the well-known approximations

User clustering for MIMO NOMA via classifier chains and gradient-boosting decision trees

Abstract In this article, we propose a data-driven approach to group users in a Non-Orthogonal Multiple Access (NOMA) MIMO setting. Specifically, we formulate user clustering as a multi-label classification problem and solve it by coupling a Classifier Chain (CC) with a Gradient Boosting Decision Tree (GBDT), namely, the LightGBM algorithm. The performance of the proposed CC-LightGBM scheme is assessed via numerical simulations. For benchmarking, we consider two classical adaptation learning schemes: Multi-Label k-Nearest Neighbours (ML-KNN) and Multi-Label Twin Support Vector Machines (ML-TSVM); as well as other naive approaches. Besides, we also compare the computational complexity of the proposed scheme with those of the aforementioned benchmarks