When fully implemented, sixth generation (6G) wireless systems will constitute intelligent wireless networks that enable not only ubiquitous communication but also high-accuracy localization services. They will be the driving force behind this transformation by introducing a new set of characteristics and service capabilities in which location will coexist with communication while sharing available resources. To that purpose, this survey investigates the envisioned applications and use cases of localization in future 6G wireless systems, while analyzing the impact of the major technology enablers. Afterwards, system models for millimeter wave, terahertz and visible light positioning that take into account both line-of-sight (LOS) and non-LOS channels are presented, while localization key performance indicators are revisited alongside mathematical definitions. Moreover, a detailed review of the state of the art conventional and learning-based localization techniques is conducted. Furthermore, the localization problem is formulated, the wireless system design is considered and the optimization of both is investigated. Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems.

As the process of envisioning, researching, and strategizing for 6G communications has commenced, we present our vision on the role of semantic communications (SemCom) in the beyond 5G era. In the following discourse, we delve into the factors that may impede the progress of research and implementation of 6G with the objective of catering to the semantically enriched communication requirements of the 2030s. In addition, we proceed to establish the fundamental attributes of SemCom and engage in an examination of the requisite technological modalities. In order to bolster this overarching vision, we introduce a semantic networking architecture that encapsulates and characterizes various application scenarios of SemCom. Expanding on the concept of point-to-point SemCom systems the semantic networking architecture focuses on extracting and filtering goal specific semantic information at the source before transmitting signals. It also involves decoding and post processing semantics at the destination. What sets this networking architecture apart is its transformation into a multi-user distributed, edge-to-cloud network with deadline constraint traffic. Given its complexity, implementing such an architecture necessitates frameworks for extracting and representing knowledge theoretical models to predict and manage multiple time varying deadline/delay constrained traffic flows that may cause significant congestion in the network as well, as innovative metrics infused with semantics to measure performance while encapsulating its inherent relevance. Finally, we provide research considerations and future directions towards the integration of semantics in the forthcoming 6G wireless systems.

In this paper, we assess the signal quality of the out-body to in-body optical communication link, which can be used as a fundamental enabler of novel biomedical appliances, such as medical implants, as well as biological and chemical components monitoring. In particular, we present a mathematical understanding of the transdermal system, which takes into account the optical channel characteristics, the integrated area limitations of the in-body unit, the transceivers’ pointing errors and the particularities of the optical units. Moreover, to accommodate the propagation characteristics, we present a novel simplified, but accurate, transdermal path-gain model. Finally, we extract low-complexity closed-form expressions for the instantaneous and average signal to noise ratio of the transdermal optical link (TOL). Numerical and simulation results are provided for several insightful scenarios and reveal that pointing errors can significantly affect the reliability and effectiveness of the TOL; hence, it should be taken into account in the analysis and design of such systems.

In the present work, we introduce a novel cochlear implant (CI) architecture, namely all-optical CI (AOCI), which directly converts acoustic to optical signals capable of stimulating the cochlear neurons. First, we describe the building-blocks (BBs) of the AOCI, and explain their functionalities as well as their interconnections. Next, we present a comprehensive system model that incorporates the technical characteristics and constraints of each BB, the transdermal-optical-channel particularities, i.e. optical path-loss and external-implanted device stochastic pointing-errors, and the cochlear neurons biological properties. Additionally, in order to prove the feasibility of the AOCI architecture, we conduct a link-budget analysis that outputs novel closed-form expressions for the instantaneous and average photon flux that is emitted on the cochlear neurons. Likewise, we define three new key-performance-indicators (KPIs), namely probability of hearing, probability of false-hearing, and probability of neural damage. The proposed theoretical framework is verified through respective simulations, which not only quantify the efficiency of the proposed architecture, but also reveal an equilibrium between the optical transmission power and the patient’s safety, as well as the AOCI BBs specifications. Finally, it is highlighted that the AOCI approach is greener and safer than the conventional CIs.

Semantic information representation in image-based communication often employs feature vectors, lacking interpretability and posing challenges for human comprehension. This paper addresses this challenge by exploring the reconstruction of original images in the context of American sign language (ASL) transmission. The conventional method involves decoding feature vectors through neural networks, introducing inefficiencies and complexities. To overcome these challenges, a novel system model for image-based semantic communications is presented, which utilizes a variant of the quadrature amplitude modulation (QAM) scheme, named 24-QAM. This modulation scheme is derived from the original 32-QAM constellation by removing 8 peripheral symbols and is proven capable of attaining superior error performance in ASL applications. Additionally, a semantic encoder based on a convolutional neural network (CNN) which effectively utilizes the ASL alphabet is presented. An original dataset is created by superimposing red-green-blue landmarks and key-points on top of the captured images; hence, enhancing the representation of hand posture. Finally, the training, testing, and communication performance of the proposed system is quantified through numerical results that highlight the achievable gains and trigger insightful discussions.

This paper focuses on providing a theoretical framework for the evaluation of the performance of holographic reconfigurable intelligent surface (HRIS) empowered terahertz (THz) wireless systems under fog conditions. In more detail, we present a comprehensive methodology for evaluating the geometric losses of the end-to-end channel. Moreover, the stochastic nature of the end-to-end channel is characterized by novel closed-form probability density and cumulative distribution functions. Building upon them, the outage probability and the throughput of the system are extracted in closed form. These formulas account for the impact of transceiver hardware imperfections and are expected to become useful tools for the design of HRIS empowered THz wireless systems due to its remarkable engineering insights.