The communication performance of traditional two-dimensional (2D) antenna arrays is approaching its theoretical limit under constraints of physical size and hardware costs, thus failing to meet the escalating demands of wireless communications. While double-layer three-dimensional (3D) antenna arrays presents a breakthrough for overcoming this bottleneck by exploiting the additional degrees of freedom, its implementation is hindered by several challenges, notably the issues of codebook design. In this paper, we propose a novel codebook scheme tailored for 3D antenna array structures. Specifically, an angle-distance-aware codebook for 3D antenna arrays is designed to cater to both near-field and far-field scenarios by minimizing inter-beam interference, with proven asymptotic orthogonality. Furthermore, evanescent codewords for both regions are effectively eliminated to improve codebook construction efficiency. Simulation results illustrate the superior performance of the proposed codebook over 2D baselines, with a 29% and 12% narrower angular and distance beamwidth ofh=λ, and a 27% gain in spectral efficiency ofh=0.5λ, owing to the vertical dimension. Moreover, practical mutual coupling that manifests as beam deviations and broadening is analyzed to establish a basis for future work.

This paper introduces a one-way ranging and time-alignment method for distributed multi-mode underwater communication networks that utilize both acoustic and optical front-ends. By capitalizing on the differences in propagation velocities between the optical and acoustic waveforms, the method enables precise distance and timing measurements between any two nodes by analyzing the time differences in signal arrivals. The theoretical foundation of the proposed technique is validated using the Cramer-Rao lower bound (CRLB), which serves as a performance benchmark. The method’s effectiveness is assessed through simulations and experimentally validated in a realistic underwater environment. The results demonstrate that the approach achieves excellent timing alignment with nanosecond precision and ranging accuracy at the centimeter level, utilizing only a single reference transmission. The results significantly enhance the reliability and efficiency of underwater communication systems, paving the way for advanced applications in marine exploration, environmental monitoring, and underwater robotics.

Direct optical communication between underwater and aerial nodes such as drones offers a transformative approach for accessing underwater devices and sensors in shallow water environments. Unlike traditional underwater communication systems, which rely on buoys or surface vehicles to relay data between underwater devices and ground stations. Direct waterair optical communication enables greater operational flexibility and cost efficiency by utilizing drones to directly collect data or transmit commands. However, a significant challenge in implementing such systems is their limited reliability and short operational range, typically restricted to a few meters in both water and air. To address this limitation, this paper proposes and implements a LoRa-based modulation scheme for optical water-air communication using software-defined radio (SDR) technology. LoRa, a widely recognized radio frequency (RF) technology, is specifically designed for energy-efficient, low-rate communication, making it a promising candidate for enhancing the reliability of optical links. This paper presents a systematic approach to optimizing system parameters to improve link reliability across various scenarios. The experimental results demonstrate that the implementation of optical LoRa-based modulation offers a significant improvement in performance, achieving a gain of 7 dB to 15 dB compared to traditional Binary Phase Shift Keying (BPSK) modulation. This substantial gain underscores the effectiveness of LoRa-based modulation in enhancing the reliability and robustness of optical water-air communication links.

Although the integration between Unmanned Aerial Vehicles (UAVs) and Radio Access Network (RAN) applications is envisioned to enable a variety of new use cases and services, several practical aspects related to autonomous operations over cellular systems are still largely unexplored due to difficulties in testing and validating such integration in the real world. In this paper, we bridge the gap between simulation and real-world applications by introducing a new framework that combines real-world robotic controllers and 5th generation (5G) cellular stacks with channel and flight simulation. We consider a holistic approach where we use ArduPilot as the flight controller and OpenAirInterface (OAI) and srsRAN as the 5G cellular stacks to provide a unified solution for developing and experimenting with UAV s for cellular applications. We utilize ArduPilot Software-in-the-Loop (SITL) to simulate and control the mobility of UAVs, while OAI-RFSim and srsRAN are used to model channel conditions. Our framework is particularly useful for developing data-driven solutions that require (i) a large amount of data collected under realistic operational conditions to learn effective control policies; and (ii) a sandbox and safe testing environment that enables exploration of the action space. By addressing a UAV coverage problem and developing a greedy heuristic, we demonstrate how our framework can be used to create and test algorithms in a simulated environment, showcasing its potential as a bridge to real-world applications.

This paper presents a proof-of-concept for the UniSDM architecture, which is capable of simultaneously operating across acoustic, optical, magnetic induction, and RF communication modes. A flexible firmware stack is developed that integrates the logic for modem operation and supports multiple underwater communication modes mentioned above and the protocols and standards such as OFDM and JANUS for acoustics, as well as OFDM, single carrier, and IRDA-like protocols for optical communication. Additionally, it provides various interfaces, including a socket-based API, Ethernet tun-neling, and a graphical user interface (GUI). The firmware architecture incorporates cross-layer frame and waveform processing, enabling advanced communication paradigms such as multimode cooperative MIMO.

Unmanned Aerial Vehicles (UAVs) are critical in modern emergency response due to their high mobility. However, limited computing resources and energy supplies necessitate the use of UAV networks for collaborative inference. UAV intelligent tasks are often Deep Neural Networks (DNN)-based, with DNN partitioning enabling collaborative inference. However, executing DNN partitioning in a highly dynamic UAV network faces two challenges that have not been addressed in existing research: the time gap between the state sampling and the execution of the corresponding action based on that state, and the unknown trajectories in advance. The time gap requires predictive action decision-making. To address this, we model DNN partitioning and edge offloading with hybrid action decisions in dynamic, energy-harvesting UAV networks as a Predictive Markov Decision Process (P-MDP). The rapidly changing and previously unknown network topology significantly impacts channel and data transmission energy consumption, affecting DNN partitioning decisions. To better solve the action prediction problem, we use the Transformer module to extract motion features from recent time slots in the proposed Transformer-enhanced Multi-Agent Hybrid Action Proximal Policy Optimization (TE-MHAPPO) framework. Simulation results show that TE-MHAPPO reduces the reward which comprehensively considers task delay and energy consumption, by at least 12.1% compared to the state-of-theart MHAPPO. Additionally, its reward performance degradation with the increase in prediction time is at most 55.2% of that observed in the baseline.

Precision medicine applications supported by nanotechnologies enforce designing a communication interface between in-body nanosensors and external gateways. Such a communication interface will enable both a data and a control channel between nanodevices operating within the human body and external control units. In this direction, recent literature focuses on deriving analytic channel models for intra-body links through the human tissues, including the analysis of achievable communication capacities in the terahertz band. A yet missing component, however, is a synchronization module to implement communication schemes in the intra-body link. Such synchronization module will ultimately bound the communication performance regarding the perceived signal to noise ratio (SNR) and bit error rate (BER), for instance. This paper contributes to the state of the art in two directions: (a) evaluating the bounds on the communication performance with the Cramer-Rao lower bound (CRLB) for the synchronization symbol timing offset (STO) and (b) designing a low-complex mechanism to synchronize communication. This analysis considers a communication link between external gateways located on the skin and nanosensor devices flowing in the human vessels. Using envelope and slope detectors, we devise a low-complex solution that relies on the received signal strength (RSS) metric to trigger data emissions. The method estimates the peak of the received RSS metric to ignite communication in the most favorable location, i.e., when the nanosensor is located at the shortest distance in the communication range with external gateways. Our findings illustrate the feasibility of such a low-complex synchronization method. Performance illustrates a BER less than 1×10−5for those nanosensors traveling close to the upper vessel wall.

Early detection of diseases in the human body is of utmost importance for the diagnosis and medical treatment of patients. Supported by recent advancements in nanotechnology, diseases may be detected by patrolling nanosensors, even before symptoms appear. This paper explores the detection capabilities of nanosensors flowing through the human circulatory system (HCS). We model the HCS through a Markov chain and propose the use of machine learning (ML) methods to learn the corresponding transition probabilities. Doing so, we propose a methodology to develop an early detection mechanism of quorum sensing (QS) molecules released by bacteria. Simulation results indicate the suitability of our machine learning approach as a basis for in-body precision medicine.

Today, camera networks are pervasively used in smart environments such as intelligent homes, industrial automation or surveillance. These applications often require cameras to be aware of their spatial neighbors or even to operate on a common ground plane. A major concern in the use of sensor networks in general is their robustness and reliability even in the presence of attackers. This paper addresses the challenge of detecting malicious nodes during the calibration phase of camera networks. Such a resilient calibration enables robust and reliable localization results and the elimination of attackers right after the network deployment. Specifically, we consider the problem of identifying subverted nodes which manipulate calibration data and can not be detected by standard cryptographic methods. The experiments in our network show that our self-calibration algorithm enables location-unknown cameras to successfully detect malicious nodes while autonomously calibrating the network.

In this paper we present a distributed, autonomous network calibration algorithm, which enables visual sensor networks to gather knowledge about the network topology. A calibrated sensor network provides the basis for more robust applications, since nodes are aware of their spatial neighbors. In our approach, sensor nodes estimate relative positions and orientations of nodes with overlapping fields of view based on jointly detected objects and geometric relations. Distance and angle measurements are the only information required to be exchanged between nodes. The process works iteratively, first calibrating camera neighbors in a pairwise manner and then spreading the calibration information through the network. Further, each node operates within its local coordinate system avoiding the need for any global coordinates. While existing methods mostly exploit computer vision algorithms to relate nodes to each other based on their images, we solely rely on geometric constraints.