Wireless resource virtualization: opportunities, challenges, and solutions

Wireless resource virtualization (WRV) is currently emerging as a key technology to overcome the major challenges facing the mobile network operators (MNOs) such as reducing the capital, minimizing the operating expenses, improving the quality of service, and satisfying the growing demand for mobile services. Achieving such conflicting objectives simultaneously requires a highly efficient utilization of the available resources including the network infrastructure and the reserved spectrum. In this paper, the most dominant WRV frameworks are discussed where different levels of network infrastructure and spectrum resources are shared between multiple MNOs. Moreover, we summarize the major benefits and most pressing business challenges of deploying WRV. We further highlight the technical challenges and requirements for ion and sharing of spectrum resources in next generation networks. In addition, we provide guidelines for implementing comprehensive solutions that are able to and share the spectrum resources in next generation network. The paper also presents an efficient algorithm for base station virtualization in long‐term evolution (LTE) networks to share the wireless resources between MNOs who apply different scheduling polices. The proposed algorithm maintains a high‐level of isolation and offers throughput performance gain. Copyright © 2016 John Wiley & Sons, Ltd. Wireless resource virtualization (WRV) is emerging as a key technology to reduce cost and increase the total network capacity by sharing wireless resources between multiple mobile operators. In this paper, we present the concepts, highlight the benefits, and discuss the technical challenges and requirements for ion and sharing of WRV in next generation networks. Furthermore, an efficient WRV approach for long‐term evolution base stations is proposed and evaluated

User Effects on MIMO Performance: From an Antenna to a Link Perspective

We investigate in this paper the effects of the user's presence on the performance of a multiple-input multiple-output (MIMO) system in data and in voice usage scenarios. The investigation studies the user effects on the antenna performance and how these are incorporated into the MIMO channel and the link characteristics. The antennas and the user are deterministic. These are then integrated into the statistical 3GPP spatial channel model (SCM) for a typical macrocell propagation environment setting. The channel performance is analyzed based on the average channel capacity, the average power transfer, the correlation, and the cumulative distribution function of the channel capacity as well as the link throughout and the error performance. The mentioned channel and link properties are tied to the MIMO antenna properties that are represented in the mutual coupling between the antennas, the power loss, the total radiated power, the mean effective gain (MEG), as well as the efficiency with emphasis on how the user affects each. It was found that the presence of the user contributed to a loss of up to 50% in the average channel power transfer. The data position was found to be the lowest in terms of channel capacity performance. The voice position performance showed a large dependence on the user orientation with respect to the line of sight path while the data position showed less dependence on the user's orientation. We also discuss through the examined antenna and channel properties the importance of the channel multipath on the MIMO performance. In some scenarios, it was found that a well-conditioned channel can compensate for losses due to the presence of the user, improving the overall system performance. The presented investigation at the link level also discusses the user effects in different MIMO transmission schemes

Deep Learning-Based Metasurface Design for Smart Cooling of Spacecraft

A reconfigurable metasurface constitutes an important block of future adaptive and smart nanophotonic applications, such as adaptive cooling in spacecraft. In this paper, we introduce a new modeling approach for the fast design of tunable and reconfigurable metasurface structures using a convolutional deep learning network. The metasurface structure is modeled as a multilayer image tensor to model material properties as image maps. We avoid the dimensionality mismatch problem using the operating wavelength as an input to the network. As a case study, we model the response of a reconfigurable absorber that employs the phase transition of vanadium dioxide in the mid-infrared spectrum. The feed-forward model is used as a surrogate model and is subsequently employed within a pattern search optimization process to design a passive adaptive cooling surface leveraging the phase transition of vanadium dioxide. The results indicate that our model delivers an accurate prediction of the metasurface response using a relatively small training dataset. The proposed patterned vanadium dioxide metasurface achieved a 28% saving in coating thickness compared to the literature while maintaining reasonable emissivity contrast at 0.43. Moreover, our design approach was able to overcome the non-uniqueness problem by generating multiple patterns that satisfy the design objectives. The proposed adaptive metasurface can potentially serve as a core block for passive spacecraft cooling applications. We also believe that our design approach can be extended to cover a wider range of applications

Materials Perspectives of Integrated Plasmonic Biosensors

With the growing need for portable, compact, low-cost, and efficient biosensors, plasmonic materials hold the promise to meet this need owing to their label-free sensitivity and deep light–matter interaction that can go beyond the diffraction limit of light. In this review, we shed light on the main physical aspects of plasmonic interactions, highlight mainstream and future plasmonic materials including their merits and shortcomings, describe the backbone substrates for building plasmonic biosensors, and conclude with a brief discussion of the factors affecting plasmonic biosensing mechanisms. To do so, we first observe that 2D materials such as graphene and transition metal dichalcogenides play a major role in enhancing the sensitivity of nanoparticle-based plasmonic biosensors. Then, we identify that titanium nitride is a promising candidate for integrated applications with performance comparable to that of gold. Our study highlights the emerging role of polymer substrates in the design of future wearable and point-of-care devices. Finally, we summarize some technical and economic challenges that should be addressed for the mass adoption of plasmonic biosensors. We believe this review will be a guide in advancing the implementation of plasmonics-based integrated biosensors