Special issue on radio access network architectures and resource management for 5G

Peer ReviewedPostprint (author's final draft

Transceiver design and multi-hop D2D for UAV IoT coverage in disasters

When natural disasters strike, the coverage for Internet of Things (IoT) may be severely destroyed, due to the damaged communications infrastructure. Unmanned aerial vehicles (UAVs) can be exploited as flying base stations to provide emergency coverage for IoT, due to its mobility and flexibility. In this paper, we propose multi-antenna transceiver design and multi-hop device-to-device (D2D) communication to guarantee the reliable transmission and extend the UAV coverage for IoT in disasters. Firstly, multi-hop D2D links are established to extend the coverage of UAV emergency networks due to the constrained transmit power of the UAV. In particular, a shortest-path-routing algorithm is proposed to establish the D2D links rapidly with minimum nodes. The closed-form solutions for the number of hops and the outage probability are derived for the uplink and downlink. Secondly, the transceiver designs for the UAV uplink and downlink are studied to optimize the performance of UAV transmission. Due to the non-convexity of the problem, they are first transformed into convex ones and then, low-complexity algorithms are proposed to solve them efficiently. Simulation results show the performance improvement in the throughput and outage probability by the proposed schemes for UAV wireless coverage of IoT in disasters

Uplink Power Control in Massive MIMO with Double Scattering Channels

Massive multiple-input multiple-output (MIMO) is a key technology for improving the spectral and energy efficiency in 5G-and-beyond wireless networks. For a tractable analysis, most of the previous works on Massive MIMO have been focused on the system performance with complex Gaussian channel impulse responses under rich-scattering environments. In contrast, this paper investigates the uplink ergodic spectral efficiency (SE) of each user under the double scattering channel model. We derive a closed-form expression of the uplink ergodic SE by exploiting the maximum ratio (MR) combining technique based on imperfect channel state information. We further study the asymptotic SE behaviors as a function of the number of antennas at each base station (BS) and the number of scatterers available at each radio channel. We then formulate and solve a total energy optimization problem for the uplink data transmission that aims at simultaneously satisfying the required SEs from all the users with limited data power resource. Notably, our proposed algorithms can cope with the congestion issue appearing when at least one user is served by lower SE than requested. Numerical results illustrate the effectiveness of the closed-form ergodic SE over Monte-Carlo simulations. Besides, the system can still provide the required SEs to many users even under congestion

Get Your Foes Fooled: Proximal Gradient Split Learning for Defense Against Model Inversion Attacks on IoMT Data

The past decade has seen a rapid adoption of Artificial Intelligence (AI), specifically the deep learning networks, in Internet of Medical Things (IoMT) ecosystem. However, it has been shown recently that the deep learning networks can be exploited by adversarial attacks that not only make IoMT vulnerable to the data theft but also to the manipulation of medical diagnosis. The existing studies consider adding noise to the raw IoMT data or model parameters which not only reduces the overall performance concerning medical inferences but also is ineffective to the likes of deep leakage from gradients method. In this work, we propose proximal gradient split learning (PSGL) method for defense against the model inversion attacks. The proposed method intentionally attacks the IoMT data when undergoing the deep neural network training process at client side. We propose the use of proximal gradient method to recover gradient maps and a decision-level fusion strategy to improve the recognition performance. Extensive analysis show that the PGSL not only provides effective defense mechanism against the model inversion attacks but also helps in improving the recognition performance on publicly available datasets. We report 14.0 % , 17.9 % , and 36.9 % gains in accuracy over reconstructed and adversarial attacked images, respectively

Species Richness and Ecological Diversity of Myxomycetes and Myxomycete-Like Organisms in the Tropical Forests of Brazil

Tropical rain forests cover less than two percent of Earth\u27s surface, yet they sustain the greatest diversity of living organisms on the planet. Tropical rain forests cover nearly 73% of the Brazilian territory and besides harboring some of the most biodiverse ecosystems on the planet, this vast area also houses about 83% of the Brazilian population. Approximately 175 million people live in urban and rural areas with fragments of coverage of these biomes which contributes to the loss of biodiversity that rapidly increases over the years. Furthermore, the majority of the taxonomic and ecological efforts to describe and protect the Brazilian tropical biodiversity are usually focused on macroorganisms while the knowledge regarding the heterogeneity of microorganism species that compose the Brazilian microbiota increases slowly. Therefore, urgent efforts should be directed to the carrying out of inventories and studies on the species that make up the Brazilian microbiota, their biogeographical patterns, and their interactions with the environment in which they occupy. Aiming to contribute to the knowledge of the distribution and diversity of microorganism in the Neotropics, this dissertation includes (1) an overview of the biogeographical patterns of microorganisms; (2) a bibliographic revision of the myxomycetes species found in Brazil distributed among the different vegetation physiognomies throughout the country; (3) species listing and molecular identification of myxobacteria species; (4) the taxonomic and ecological studies of species of ceratiomyxomycetes and myxomycetes; (5) the taxonomic composition of dictyostelid cellular slime molds; and (6) species of protosteloid amoebae and related organisms present in tropical and subtropical moist broadleaf forests of Brazil