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Financial portfolio management is a very time-consuming task as it requires the continuous surveying of the market volatility. Investors need to hire potential financial advisors to manage portfolios on their behalf. Efficient hiring of financial advisors not only facilitates their cooperation with investors but also guarantees optimized portfolio returns and hence, optimized benefits for the two entities. In this paper, we propose to tackle the portfolio optimization problem by efficiently matching financial advisors to investors. To this end, we model the problem as an automated crowdsourcing platform to organize the cooperation between the different actors based on their features. The recruitment of financial advisors is performed using a Generative Adversarial Network (GAN) that extrapolates the problem to an image processing task where financial advisors' features are encapsulated in gray-scale images. Hence, the GAN is trained to generate, based on an investor profile given as an input, the 'ideal' financial advisor profile. Afterwards, we measure the level of similarity between the generated ideal profiles and the existing profiles in the crowdsourcing database to perform a low complexity, many-to-many investor-to-financial advisor matching. In the simulations, intensive tests were performed to show the convergence and effectiveness of the proposed GAN-based solution. We have shown that the proposed method achieves more than 17% of the average expected return compared to baseline approaches. [ABSTRACT FROM AUTHOR]

Roadside unit (RSU) planning is vital for the operation of an intelligent transportation system (ITS). RSUs provide ground coverage limited by obstacles. Unmanned aerial vehicles (UAVs) can complement RSU coverage by providing flexible connectivity capable of adapting coverage for traffic fluctuations, energy consumption, and budgetary constraints that all have effects on ITS operations. This article proposes a general RSU/UAV joint planning solution, where complex dynamic parameters are investigated. The objective is to maximize the effective coverage of placed RSUs and UAV docks given: a budget comprised of periodic operating expenses and capital expenditures, limitations of the ground transceivers and UAVs, and use of renewable energy to offset the on-grid electricity cost. We formulate a mixed-integer quadratically constrained problem that can determine the optimal placement of RSUs and UAV stations, RSU activation schedules, if solar panels are attached, and their coverage during each time period. Due to NP-hard complexity of such a planning problem, we design a heuristic algorithm that produces suboptimal solutions in less time. Afterward, we perform a sensitivity analysis and show that changes to the parameters lead to logical shifts in infrastructure coverage. Additionally, we visualize the algorithm's performance on a large setting—Manhattan Island. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic solar powered drones. A HetNet composed of a macrocell base station (BS), micro cell BSs, and drone small cell BSs are deployed to serve the networks’ subscribers. The drones can land at pre-planned locations defined by the mobile operator and at the macrocell BS site where they can charge their batteries. The objective of the framework is to jointly determine the optimal trips of the drones and the MBSs that can be safely turned off in order to minimize the total energy consumption of the network. This is done while considering the cells’ capacities and the minimum receiving power guaranteeing successful communications. To do so, an integer linear programming problem is formulated and optimally solved for three cases based on the knowledge level about future renewable energy statistics of the drones. A low complex relaxed solution is also developed. Its performances are shown to be close to those of the optimal solutions. However, the gap increases as the network becomes more congested. Numerical results investigate the performance of the proposed drone-based approach and show notable improvements in terms of energy saving and network capacity. [ABSTRACT FROM AUTHOR]

Future mobile data traffic predictions expect a significant increase in user data traffic, requiring new forms of mobile network infrastructures. Fifth generation (5G) communication standards propose the densification of small cell access base stations (BSs) in order to provide multigigabit and low latency connectivity. This densification requires a high capacity backhaul network. Using optical links to connect all the small cells is economically not feasible for large scale radio access networks where multiple BSs are deployed. A wireless backhaul formed by a mesh of millimeter-wave (mmWave) links is an attractive mobile backhaul solution, as flexible wireless (multihop) paths can be formed to interconnect all the access BSs. Moreover, a wireless backhaul allows the dynamic reconfiguration of the backhaul topology to match varying traffic demands or adaptively power on/off small cells for green backhaul operation. However, conducting and precisely controlling reconfiguration experiments over real mmWave multihop networks is a challenging task. In this paper, we develop a Software-Defined Networking (SDN) based approach to enable such a dynamic backhaul reconfiguration and use real-world mmWave equipment to setup a SDN-enabled mmWave testbed to conduct various reconfiguration experiments. In our approach, the SDN control plane is not only responsible for configuring the forwarding plane but also for the link configuration, antenna alignment, and adaptive mesh node power on/off operations. We implement the SDN-based reconfiguration operations in a testbed with four nodes, each equipped with multiple mmWave interfaces that can be mechanically steered to connect to different neighbors. We evaluate the impact of various reconfiguration operations on existing user traffic using a set of extensive testbed measurements. Moreover, we measure the impact of the channel assignment on existing traffic, showing that a setup with an optimal channel assignment between the mesh links can result in a 44% throughput increase, when compared to a suboptimal configuration. [ABSTRACT FROM AUTHOR]

Unnamed aerial vehicles (UAVs) or drones have attracted growing interest in the last few years for multiple applications; thanks to their advantages in terms of mobility, easy movement, and flexible positioning. In UAV-based communications, mobility and higher line-of-sight probability represent opportunities for the flying UAVs while the limited battery capacity remains its major challenge. Thus, they can be employed for specific applications where their permanent presence is not mandatory. Data gathering from wireless sensor networks is one of these applications. This paper proposes an energy-efficient solution minimizing the UAV and/or sensors energy consumption while accomplishing a tour to collect data from the spatially distributed wireless sensors. The objective is to determine the positions of the UAV “stops” from which it can collect data from a subset of sensors located in the same neighborhood and find the path that the UAV should follow to complete its data gathering tour in an energy-efficient manner. A non-convex optimization problem is first formulated then, an efficient and low-complex technique is proposed to iteratively achieve a sub-optimal solution. The initial problem is decomposed into three sub-problems: The first sub-problem optimizes the positioning of the stops using linearization. The second one determines the sensors assignment to stops using clustering. Finally, the path among these stops is optimized using the travel salesman problem. Selected numerical results show the behavior of the UAV versus various system parameters and that the achieved energy is considerably reduced compared to the one of existing approaches. [ABSTRACT FROM AUTHOR]

In this letter, we develop a time-varied probabilistic on/off switching planning method for cellular networks to reduce their energy consumption. It consists in a risk-aware optimization approach that takes into consideration the randomness of the user profile associated with each base station (BS). The proposed approach jointly determines 1) the instants of time at which the current active BS configuration must be updated due to an increase or decrease of the network traffic load and 2) the set of minimum BSs to be activated to serve the networks’ subscribers. Probabilistic metrics modeling the traffic profile variation are developed to trigger this dynamic on/off switching operation. Selected simulation results are then performed to validate the proposed algorithm for different system parameters. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of energy efficiency in cellular heterogeneous networks (HetNets) is investigated using radio resource and power management combined with the base station (BS) ON/OFF switching. The objective is to minimize the total power consumption of the network while satisfying the quality of service requirements of each connected user. We consider the case of coexisting macrocell BS, small cell BSs, and private femtocell access points (FAPs). Three different network scenarios are investigated, depending on the status of the FAPs, i.e., HetNets without FAPs, HetNets with closed FAPs, and HetNets with semiclosed FAPs. A unified framework is proposed to simultaneously allocate spectrum resources to users in an energy efficient manner and switch OFF redundant small cell BSs. The high complexity dual decomposition technique is employed to achieve optimal solutions for the problem. A low complexity iterative algorithm is also proposed and its performances are compared to those of the optimal technique. The particularly interesting case of semiclosed FAPs, in which the FAPs accept to serve external users, achieves the highest energy efficiency due to increased degrees of freedom. In this paper, a cooperation scheme between FAPs and mobile operator is also investigated. The incentives for FAPs, e.g., renewable energy sharing and roaming prices, enabling cooperation are discussed to be considered as a useful guideline for interoperator agreements. [ABSTRACT FROM PUBLISHER]

Cellular operators are increasingly turning toward renewable energy (RE) as an alternative to using traditional electricity in order to reduce operational expenditure and carbon footprint. Due to the randomness in both RE generation and mobile traffic at each base station (BS), a surplus or shortfall of energy may occur at any given time. To increase energy self-reliance and minimize the network’s energy cost, the operator needs to efficiently exploit the RE generated across all BSs. In this paper, a hybrid energy sharing framework for cellular network is proposed, where a combination of physical power lines and energy trading with other BSs using smart grid is used. Algorithms for physical power lines deployment between BSs, based on average and complete statistics of the net RE available, are developed. Afterward, an energy management framework is formulated to optimally determine the quantities of electricity and RE to be procured and exchanged among BSs, respectively, while considering battery capacities and real-time energy pricing. Three cases are investigated, where RE generation is unknown, perfectly known, and partially known ahead of time. Results investigate the time varying energy management of BSs and demonstrate considerable reduction in average energy cost thanks to the hybrid energy sharing scheme. [ABSTRACT FROM PUBLISHER]

A high-speed railway system equipped with moving relay stations placed on the middle of the ceiling of each train wagon is investigated. The users inside the train are served in two hops via orthogonal frequency-division multiple-access (OFDMA) technology. In this paper, we first focus on minimizing the total downlink power consumption of the base station (BS) and the moving relays while respecting specific quality-of-service (QoS) constraints. We first derive the optimal resource-allocation solution, in terms of OFDMA subcarriers and power allocation, using the dual decomposition method. Then, we propose an efficient algorithm based on the Hungarian method to find a suboptimal but low-complexity solution. Moreover, we propose an OFDMA planning solution for high-speed trains by finding the maximal inter-BS distance, given the required user data rates to perform seamless handover. Our simulation results illustrate the performance of the proposed resource-allocation schemes in the case of Third-Generation Partnership Project (3GPP) Long-Term Evolution Advanced (LTE-A) and compare them with previously developed algorithms, as well as with the direct transmission scenario. Our results also highlight the significant planning gain obtained, owing to the use of multiple relays instead of the conventional single-relay scenario. [ABSTRACT FROM PUBLISHER]

In this paper, we study a multiple-antenna two-way relaying (TWR) cognitive radio (CR) system. A space alignment (SA) technique is adopted by the secondary users (SUs) to avoid interference with the primary users (PUs). We derive the optimal power allocation that maximizes the TWR achievable SU sum-rate while respecting the total power budget and the relay power constraints. We also analyze the case in which the relay is able to optimize its gain matrix structure to enhance the SU sum-rate. In the numerical results, we quantify the sum-rate gain of using the SA in the TWR CR and we show that the SU sum-rate is very limited when the relay power is low or the PU power and its resulting interference are high. In addition, we optimize the relay gain using an iterative algorithm and compare between different relay matrix structures. [ABSTRACT FROM PUBLISHER]

Base station (BS) deployment in cellular networks is one of the fundamental problems in network design. This paper proposes a novel method for the cell planning problem for fourth-generation (4G) cellular networks using metaheuristic algorithms. In this approach, we aim to satisfy both cell coverage and capacity constraints simultaneously by formulating an optimization problem that captures practical planning aspects. The starting point of the planning process is defined through a dimensioning exercise that captures both coverage and capacity constraints. Afterward, we implement a metaheuristic algorithm based on swarm intelligence (e.g., particle swarm optimization or the recently proposed gray-wolf optimizer) to find suboptimal BS locations that satisfy both problem constraints in the area of interest, which can be divided into several subareas with different spatial user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant BSs. We also perform Monte Carlo simulations to study the performance of the proposed scheme and compute the average number of users in outage. Next, the problems of green planning with regard to temporal traffic variation and planning with location constraints due to tight limits on electromagnetic radiations are addressed, using the proposed method. Finally, in our simulation results, we apply our proposed approach for different scenarios with different subareas and user distributions and show that the desired network quality-of-service (QoS) targets are always reached, even for large-scale problems. [ABSTRACT FROM AUTHOR]

In this paper, we consider a multiple-input multiple-output (MIMO) cooperative cognitive radio (CR) system using amplify-and-forward protocol under a spectrum sharing scenario, where licensed users and unlicensed users operate on the same frequency band. Indeed, combined CR, cooperative communication and MIMO antennas provide a smart solution for a more efficient usage of the frequency band and the data rate. The main objective of this work is to maximise the sum rate of the unlicensed users allowed to share the spectrum with the licensed users by respecting a tolerated interference threshold under perfect and imperfect channel state information scenarios. Practical approaches based on iterative and genetic algorithms for multiple antenna-relay selections with generalised MIMO model for both unidirectional and bidirectional transmissions are proposed to solve our formulated optimization problems. Interestingly, selected numerical results show that our proposed approaches reach a performance close to the performance of the optimal solution either with discrete or continuous power distributions while providing a considerable saving in terms of computational complexity. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We investigate the spectral efficiency gain of an uplink cognitive radio (CR) multi-input-multi-output system in which the secondary user (SU) is allowed to share the spectrum with the primary user (PU) using a specific precoding scheme to communicate with a common receiver. The proposed scheme exploits, at the same time, the free eigenmodes of the primary channel after a space alignment procedure and the interference threshold tolerated by the PU. At the common receiver, we adopt a successive interference cancellation (SIC) technique to eliminate the effect of the detected primary signal transmitted through the exploited eigenmodes. Furthermore, we analyze the SIC operation inaccuracy as well as the CSI estimation imperfection on the PU and SU throughputs. Numerical results show that our proposed scheme enhances considerably the cognitive achievable rate. For instance, in case of a perfect detection of the PU signal, the CR rate remains non-zero for high signal to noise ratio, which is usually impossible when we only employ a space alignment technique. We show that a modified water-filling power allocation policy at the PU can increase the secondary rate with a marginal degradation of the primary rate. Finally, we investigate the behavior of the PU and SU rates through the study of the rate achievable region. [ABSTRACT FROM PUBLISHER]

In this paper, the interplay between cooperative device-to-device (D2D) communications and green cellular communications in the long term evolution (LTE) and LTE-advanced (LTE-A) cellular systems is investigated. An efficient approach for grouping mobile terminals (MTs) into cooperative clusters is described. In each cluster, MTs cooperate via D2D communications to share content of common interest. In addition, an energy-efficient approach for putting base stations in sleep mode in an LTE-A heterogeneous network is presented. Finally, both methods are combined in order to ensure green communications for both the users' MTs and the operator's base stations. The presented techniques are investigated in the framework of orthogonal frequency division multiple access-based state-of-the-art LTE cellular networks, while taking resource allocation and intercell interference into account. Results show that the proposed approach leads to energy savings for both the operator and the MTs, while leading to enhanced quality of service for mobile users. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We study the secondary sum-rate of an underlay cognitive multiple access channel consisting of a primary user and multiple secondary users (SUs) communicating with a common destination. We propose a particular linear precoding and SU selection scheme that maximize the cognitive sum-rate. This scheme is based on space alignment strategy allowing SUs to share the spectrum without interfering with each other. We derive the optimal power allocation for each selected SU after applying perfect or imperfect successive interference cancellation. Numerical results show that the proposed scheme provides a significant sum-rate improvement as the number of SUs increases. In addition, it achieves almost the same performance as an exhaustive search selection, mainly in low and high power ranges. [ABSTRACT FROM PUBLISHER]

Energy efficiency aspects in cellular networks can contribute significantly to reducing worldwide greenhouse gas emissions. The base station (BS) sleeping strategy has become a well-known technique to achieve energy savings by switching off redundant BSs mainly for lightly loaded networks. Moreover, introducing renewable energy as an alternative power source has become a real challenge among network operators. In this paper, we formulate an optimization problem that aims to maximize the profit of Long-Term Evolution (LTE) cellular operators and to simultaneously minimize the \CO2 emissions in green wireless cellular networks without affecting the desired quality of service (QoS). The BS sleeping strategy lends itself to an interesting implementation using several heuristic approaches, such as the genetic (GA) and particle swarm optimization (PSO) algorithms. In this paper, we propose GA-based and PSO-based methods that reduce the energy consumption of BSs by not only shutting down underutilized BSs but by optimizing the amounts of energy procured from different retailers (renewable energy and electricity retailers), as well. A comparison with another previously proposed algorithm is also carried out to evaluate the performance and the computational complexity of the employed methods. [ABSTRACT FROM AUTHOR]

In this paper, we consider the reconfiguration of wireless backhaul networks with mechanically steerable antennas in the presence of changing traffic demands. Reconfiguration requires the scheduling and coordination of several operations, including antenna alignment and link establishment/removal, with minimal disruption to existing user traffic. Previously, we proposed a Mixed Integer Linear Program (MILP) to orchestrate such reconfiguration with minimal packet loss. While the MILP solves the problem optimally for a limited number of discrete reconfiguration time slots, it does not scale well. In this paper, we propose an iterative randomized greedy algorithm to obtain suboptimal solutions in reduced time. The algorithm schedules the reconfiguration of wireless links by ranking them according to a set of attributes with associated weights and selecting them according to a randomized greedy function. Results on six different network scenarios indicate that the proposed algorithm can achieve good quality solutions in significantly less time. Furthermore, by extending the reconfiguration time beyond the maximum number of time slots solvable by the MILP, the proposed heuristic can obtain superior solutions for some problem instances. The number of iterations of the algorithm can be tuned for its applicability in both offline and online planning scenarios. [ABSTRACT FROM AUTHOR]

This paper describes a bioinspired neural-network-based approach to solve a coverage planning problem for a fleet of unmanned aerial vehicles exploring critical areas. The main goal is to fully cover the map, maintaining a uniform distribution of the fleet on the map, and avoiding collisions between vehicles and other obstacles. This specific task is suitable for surveillance applications, where the uniform distribution of the fleet in the map permits them to reach any position on the map as fast as possible in emergency scenarios. To solve this problem, a bioinspired neural network structure is adopted. Specifically, the neural network consists of a grid of neurons, where each neuron has a local cost and has a local connection only with neighbor neurons. The cost of each neuron influences the cost of its neighbors, generating an attractive contribution to unvisited neurons. We introduce several controls and precautions to minimize the risk of collisions and optimize coverage planning. Then, preliminary simulations are performed in different scenarios by testing the algorithm in four maps and with fleets consisting of 3 to 10 vehicles. Results confirm the ability of the proposed approach to manage and coordinate the fleet providing the full coverage of the map in every tested scenario, avoiding collisions between vehicles, and uniformly distributing the fleet on the map. [ABSTRACT FROM AUTHOR]

Modern taxi services are usually classified into two major categories: traditional taxicabs and ride-hailing services. For both services, it is required to design highly efficient recommendation systems to satisfy passengers' quality of experience and drivers' benefits. Customers desire to minimize their waiting time before rides, while drivers aim to speed up their customer hunting. In this paper, we propose to leverage taxi service efficiency by designing a generic and smart recommendation system that exploits the benefits of Vehicular Social Networks (VSNs). Aiming at optimizing three key performance metrics, number of pick-ups, customer waiting time, and vacant traveled distance for both taxi services, the proposed recommendation system starts by efficiently estimating the future customer demands in different clusters of the area of interest. Then, it proposes an optimal taxi-to-region matching according to the location of each taxi and the future requested demand of each region. Finally, an optimized geo-routing algorithm is developed to minimize the navigation time spent by drivers. Our simulation model is applied to the borough of Manhattan and is validated with realistic data. Selected results show that significant performance gains are achieved thanks to the additional cooperation among taxi drivers enabled by VSN, as compared to traditional cases. [ABSTRACT FROM AUTHOR]

It can be predicted that the infrastructure of the existing wireless networks will not fill the requirement of the fifth generation (5G) wireless network due to the high data rates and a large number of expected traffic. Thus, a novel deployment method is crucial to satisfy 5G features. Meta-heuristic is expected to be a promising method for the complex deployment optimization problem of the 5G network. This work presents an implementation of a meta-heuristic algorithm based on swarm intelligence, to minimize the number of base stations (BSs) and optimize their placements in millimeter wave (mmWave) frequencies (e.g., 28 GHz and 38 GHz) in the context of the 5G network while satisfying user data rates requirement. Then, an iterative method is applied to remove redundant BSs. We formulate an optimization problem that takes into account multiple 5G network deployment scenarios. Further, a comparative study is conducted with the well-known simulated annealing (SA) using Monte Carlo simulations to assess the performance of the developed model. In our simulation results, we divide the region of interest into two subareas with different user distributions for different network scenarios while considering the intercell interference. The results demonstrate that the proposed approach has better network coverage with low percentage users in outage. In addition, the developed approach has less computational times to reach the desired target network quality of service (QoS). [ABSTRACT FROM AUTHOR]

Data augmentation is an effective method to improve model robustness and generalization. Conventional data augmentation pipelines are commonly used as preprocessing modules for neural networks with predefined heuristics and restricted differentiability. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the augmentation policy searching strategies. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the searching of augmentation policy strategies. This survey provides a comprehensive and structured overview of the advances in DDA. Specifically, we focus on fundamental elements including differentiable operations, operation relaxations, and gradient estimations, then categorize existing DDA works accordingly, and investigate the utilization of DDA in selected of practical applications, specifically neural augmentation networks and differentiable augmentation search. Finally, we discuss current challenges of DDA and future research directions.

The integration of dynamic Flying Ad hoc Networks (FANETs) and millimeter Wave (mmWave) technology can offer a promising solution for numerous data-intensive applications, as it enables the establishment of a robust flying infrastructure with significant data transmission capabilities. However, to enable effective mmWave communication within this dynamic network, it is essential to precisely align the steerable antennas mounted on Unmanned Aerial Vehicles (UAVs) with their corresponding peer units. Therefore, it is important to design a novel approach that can quickly determine an optimized alignment and network topology. In this paper, we propose a Generative Adversarial Network (GAN)-based approach, called WaveGAN, for FANET topology optimization aiming to maximize the network throughput by selecting the communication paths with the best channel conditions. The proposed approach consists of a WaveGAN model followed by a beam search. The former learns how to generate optimized network topologies from a supervised dataset, while the latter adjusts the generated topologies to meet the structure requirements of the mmWave-based FANET. The simulation results show that the proposed approach is able to quickly find FANET topologies with a very small optimality gap for different network sizes.