The evolvement of wireless communication services concurs with significant growth in data traffic, thereby inflicting stringent requirements on terrestrial networks. This work invigorates a connectivity solution that integrates aerial and terrestrial communications with a cloud-enabled high-altitude platform station (C-HAPS) to promote an equitable connectivity landscape. The C-HAPS system is connected to terrestrial base-stations and hot-air balloons via a data-sharing fronthauling strategy. The base-stations and hot-air balloons are then grouped into disjoint clusters and coordinatedly serve both aerial and terrestrial users. The paper focuses on determining the user-to-transmitter scheduling policy and the associated users’ beamforming vectors in the downlink direction of the considered network by maximizing two different objectives: the sum-rate and sum-of-log of the long-term average rate, both subject to limited transmit power and finite fronthaul capacity. The paper uses well-chosen convexification and approximation steps, such as fractional programming and sparse beamforming via re-weighted ℓ 0 -norm approximation, to solve the two non-convex discrete and continuous optimization problems using numerical iterative optimization algorithms. The results outline the gain illustrated through equitable access service in crowded and unserved areas and showcase the numerical benefits stemming from the proposed C-HAPS coordination of hot-air balloons and terrestrial base-stations for empowering the digital inclusion framework.

Underwater engineering significantly increases the liveability and sustainability of our planet's ecosystems. Enhancing the connectivity of ocean-based engineering systems and ensuring its digital resilience is, therefore, indispensable for reaping the benefits of underwater engineering systems. To this end, this work proposes augmenting underwater engineering systems with cloud connectivity platforms as a paradigm shift for upscaling underwater communication networks and applications. The envisioned underwater cloud-assisted network (UCAN) provides joint underwater computing, processing, communication, and acquisition while benefitting from the cooling features of the aquatic environment. We first survey the literature on conventional underwater wireless communication networks. We then describe the prospects for UCANs and advocate the deployment of UCANs for sustainable connectivity. Next, we explore the opportunities stemming from the deployment of UCANs—e.g., resource management, natural cooling, and underwater intelligence. Finally, we highlight the challenges and open issues arising from such a futuristic cloud-enabled underwater communication network—e.g., channel modeling, characterizing achievable-rate, quality-of-service retrofitting, and preserving marine biodiversity.

Background: Common carotid artery intima-media thickness (cIMT) has been associated with cardiovascular diseases, including stroke and coronary heart disease. There is a limited number of studies that specifically address the variability of ultrasound imaging modes in measuring cIMT. Therefore, this study aimed to assess the agreement-level and inter-observer reproducibility of cIMT measurements using B-mode and M-mode. Materials and Methods: B-mode and M-mode ultrasound imaging were used in measuring cIMT of healthy subjects using linear-array transducer. Inter-imaging mode agreement and inter-observer reproducibility of B-mode and M-mode were assessed using Intraclass correlation coefficient (ICC). Differences in cIMT measurements between imaging modes and observers were determined using paired t-test Results: There was significant difference between B-mode and M-mode in measuring cIMT (mean difference (MD), 0.03 mm, 95% CI 0.02 - 0.05, p<0.001). Intra-imaging mode agreement was poor with ICC of 0.39. Inter-observer reproducibility of cIMT measured using B-mode and M-mode were good and moderate, respectively, (B-mode ICC of 0.76, 95% CI 0.64 - 0.84, p<0.01; M-mode ICC of 0.68, 95% CI 0.52 - 0.78, p<0.01). No significant difference between the two observers in measuring cIMT measured using B-mode or M-mode (B-mode p=0.3; M-mode p=0.4). Conclusion: There was a significant difference and poor intra-imaging mode agreement between B-mode and M-mode in measuring cIMT. B-mode showed higher inter-observer reproducibility of cIMT measurement than M-mode. Further studies investigating the variability and agreement between ultrasound imaging modes of patients with thickened cIMT are required.

With the fast-emerging trend of deploying Internet-of-things (IoT) in smart homes and cities, next-generation wireless systems (i.e., beyond fifth-generation (5G) of wireless mobile communications) are expected to connect a plurality of devices, including machines, sensors, tablets, cameras, etc. To this end, the radio frequency (RF) spectrum would fail to fulfill the demands of such enormous data-heavy wireless applications, as operating at the RF spectrum would largely suffer from data congestion and interference. Based on the potential of Visible Light Communications (VLC) to solve the problem of spectrum scarcity and satisfy the high data-rate requirements of beyond 5G systems, this paper focuses on utilizing VLC for IoT applications in indoor environments by means of adequately routing data among VLCbased nodes. Operating at the unlicensed optical band in the visible light region utilizes simple Light-Emitting-Diodes (LEDs), which is a health-friendly communication method. VLC has the advantages of providing ultra-high bandwidth, robustness to electromagnetic interference, and inherent physical security. VLC, however, suffers from severe short communication ranges and line-of-sight (LoS) constraints. This paper proposes overcoming such challenges by means of adopting a transmit diversity scheme that transmits messages over several paths. In order to reduce the receiver outage probability, the paper proposes multiple combination schemes at the receiver side, namely selection combining, maximal-ratio combining, and threshold combining. The simulation results assess the effectiveness of the proposed diversity schemes in terms of signal-to-noise-ratio and outage probability and illustrate the suitability of deploying VLC for next-generation indoor networks. The results show that that maximal-ratio combining outperforms selection combining and threshold combining by approximately 10% and 40%, respectively.

Despite the growing interest in the interplay of machine learning and optimization, existing contributions remain scattered across the research board, and a comprehensive overview on such reciprocity still lacks at this stage. In this context, this paper visits one particular direction of interplay between learning-driven solutions and optimization, and further explicates the subject matter with a clear background and summarized theory. For instance, machine learning and its offsprings are trending because of their enhanced capabilities in automating analytical modeling. In this realm, learning-based techniques (supervised, unsupervised, and reinforcement) have grown to complement many of the optimization problems in testing and training. This paper overviews how machine learning-based techniques, namely deep neural networks, echo-state networks, reinforcement learning, and federated learning, can be used to solve complex and analytically intractable optimization problems, for which specific cases are examined in this paper. The paper particularly overviews when learning-based algorithms are useful at solving particular optimizing problems, especially those of random, dynamic, and mathematically complex nature. The paper then illustrates such applications by presenting particular use-cases in communications and signal processing including wireless scheduling, wireless offloading and resource management, power control, aerial base station placement, virtual reality, and vehicular networks. Lastly, the paper sheds light on some future research directions, where the dynamicity and randomness of the underlying optimization problems make deep learning-driven techniques a necessity, namely in sensing at the terahertz (THz) bands, cellular vehicle-to-everything, 6G communication networks, underwater optical networks, distributed optimization, and applications of emerging learning-based techniques.

This paper surveys the optimization frameworks and performance analysis methods for large intelligent surfaces (LIS), which have been emerging as strong candidates to support the sixth-generation wireless physical platforms (6G). Due to their ability to adjust the behavior of interacting electromagnetic (EM) waves through intelligent manipulations of the reflections phase shifts, LIS have shown promising merits at improving the spectral efficiency of wireless networks. In this context, researchers have been recently exploring LIS technology in depth as a means to achieve programmable, virtualized, and distributed wireless network infrastructures. From a system level perspective, LIS have also been proven to be a low-cost, green, sustainable, and energy-efficient solution for 6G systems. This paper provides a unique blend that surveys the principles of operation of LIS, together with their optimization and performance analysis frameworks. The paper first introduces the LIS technology and its physical working principle. Then, it presents various optimization frameworks that aim to optimize specific objectives, namely, maximizing energy efficiency, sum-rate, secrecy-rate, and coverage. The paper afterwards discusses various relevant performance analysis works including capacity analysis, the impact of hardware impairments on capacity, uplink/downlink data rate analysis, and outage probability. The paper further presents the impact of adopting the LIS technology for positioning applications. Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid radio frequency/visible light communication (RF-VLC) systems, health considerations, and localization.

The superiority of optical communications in underwater mediums, in terms of higher data rate and reliability, makes underwater optical wireless communications (UOWC) more favorable to provide ultra-reliable low-latency underwater communications, as compared to other wireless technologies, e.g., acoustic and radio frequency (RF) communications. UOWC limited transmission range, however, remains a major hurdle against assessing its true deployment benefits, which motivates for the necessity of developing practical routing protocols for multi-hop underwater optical wireless sensor networks (UOWSNs). This paper sheds light on the existing state-of-art UOWC routing protocols, the majority of which requires centralized implementation with large end-to-end delay. The article further proposes routing algorithms which can be implemented in a distributed fashion across the multi-hop links, with a reasonable amount of information exchange. The merits of the proposed algorithms are particularly highlighted through illustrative simulations, which show how the proposed strategies outperform the classical protocols, both in terms of reliability and end-to-end latency. Finally, the paper shows how the proposed distributive routing protocols achieve ultra-reliable low-latency underwater communications.

Underwater optical wireless communication (UOWC) is becoming an attractive technology for underwater wireless sensor networks (UWSNs) since it offers high-speed communication links. Although UOWC overcomes the drawbacks of acoustic and radio frequency communication channels such as high latency and low data rate, yet, it has its own limitations. One of the major limitations of UOWC is its limited transmission range which demands to develop a multi-hop network with efficient routing protocols. Currently, the routing protocols for UOWSNs are centralized having high complexity and large end-to-end delay. In this article, first, we present the existing routing protocols for UOWSNs. Based on the existing protocols, we then propose distributed routing protocols to address the problems of high complexity and large end-to-end delay. Numerical results have been provided to show that the proposed routing protocol is superior to the existing protocols in terms of complexity and end-to-end delay. Finally, we have presented open research directions in UOWSNs.