Amid the global rollout of fifth-generation (5G) services, researchers in academia, industry, and national labo?ratories have been developing proposals for the sixth generation (6G). Despite the many 6G proposals, the materialization of 6G as presently envisaged is fraught with many fundamental interdisciplinary, multidisciplinary, and transdisciplinary (IMT) challenges. To alleviate some of these challenges, semantic com?munication (SemCom) has emerged as promising 6G technology enabler. SemCom is designed to transmit only semantically?relevant information and hence help to minimize power usage, bandwidth consumption, and transmission delay. Thus, SemCom embodies a paradigm shift that can change the status quo that wireless connectivity is an opaque data pipe carrying messages whose context-dependent meaning have been ignored. On the other hand, 6G is critical for the materialization of major SemCom use cases. These paradigms of 6G for SemCom and SemCom for 6G call for a tighter integration and marriage of 6G and SemCom. To facilitate this integration and marriage, this comprehensive survey paper first provides the fundamentals of semantics and semantic information, semantic representation, semantic information, and semantic entropy. It then builds on this understanding and details the state-of-the-art research landscape of SemCom; exposes the fundamental and major challenges of SemCom; and offers promising future research directions for SemCom theories, algorithms, and realization. Accordingly, this survey article stimulates major streams of research on SemCom theories, algorithms, and implementation.Â

Information theoretic learning (ITL) criteria have emerged useful for mitigating degradations caused by unknown non-Gaussian noise processes in future wireless communication systems. Specifically, the reproducing kernel Hilbert space (RKHS) based approaches relying on ITL based learning criteria are envisioned to provide nearoptimal mitigation of unknown hardware impairments and non-Gaussian noises. Among several ITL criteria, the recent works find the minimum error entropy with fiducial points (MEE-FP) promising due to its guarantee of unbiased estimation and generalization over generic noise distributions. However, MEE-FP based learning approaches are known to depend on an accurate kernel-width initialization. Also, the optimal value of this kernelwidth is well-known to vary temporally and across deployment scenarios. To remove the dependency on kernelwidth, a hyperparameter-free MEE-FP based adaptive algorithm is derived using random-Fourier features with sampled kernel widths (RFF-SKW). In addition, a detailed convergence analysis is presented for the proposed hyperparameter-free MEE-FP, which promises a near-optimal error-floor independent of step-size and guarantees convergence for a wide range of step sizes. The promised hyperparameter-independence and improved convergence for the proposed hyperparameter-free MEE-FP are validated by computer simulations considering different case studies.

The Internet of Things (IoT) is a transformative technology marking the beginning of a new era where physical and digital worlds are integrated by connecting a plethora of uniquely identifiable smart objects. Although the Internet of terrestrial things (IoTT) has been at the center of our IoT perception, it has been recently extended to different environments, such as the Internet of underWater things (IoWT), the Internet of Biomedical things (IoBT), and Internet of underGround things (IoGT). Even though radio frequency (RF) based wireless networks are regarded as the default means of connectivity, they are not always the best option due to the limited spectrum, interference limitations caused by the ever-increasing number of devices, and severe propagation loss in transmission mediums other than air. As a remedy, optical wireless communication (OWC) technologies can complement, replace, or co-exist with audio and radio wave-based wireless systems to improve overall network performance. To this aim, this paper reveals the full potential of OWC-based IoT networks by providing a top-down survey of four main IoT domains: IoTT, IoBT, and IoGT. Each domain is covered by a dedicated and self-contained section that starts with a comparative analysis, explains how OWC can be hybridized with existing wireless technologies, points out potential OWC applications fitting best the related IoT domain, and discusses open communication and networking research problems. More importantly, instead of presenting a visionary OWC-IoT framework, the survey discloses that OWC-IoT has become a reality by emphasizing ongoing proof-of-concept prototyping efforts and available commercial off-the-shelf (COTS) OWC-IoT products.

Internet-of-drones (IoD) systems require enhanced data transmission security and efficient interference management to accommodate the rapidly growing drone-based and rate-intensive applications. This paper develops a novel resource allocation scheme to jointly manage interference and enhance the physical layer security  of  cellular-connected IoD networks in the presence of a multi-band eavesdropping drone. Our envisioned cellular-connected IoD network has multiple full-duplex cellular base stations (CBSs), where each CBS reserves an orthogonal cellular  radio resource block (RRB) for the aerial communication links. To efficiently utilize the cellular RRBs, each CBS is connected to a cluster of data transmitting drones using uplink  non-orthogonal multiple access (NOMA) scheme. In addition, all the CBSs simultaneously transmit  artificial noise signals to weaken the eavesdropper links.  A joint optimization problem, considering the transmit power allocation and clustering of the legitimate drones, and the jamming power allocation of the CBSs, is formulated to maximize the worst-case average sum-secrecy-rate of the  network. The joint optimization problem  is decomposed into  drone-clustering and power allocation sub-problems to obtain an efficient solution. A multi-agent reinforcement-learning framework is devised to solve the drone-clustering sub-problem.  Meanwhile, the transmit and jamming power allocation sub-problem is solved by employing fractional programming, successive convex approximation, and alternating optimization techniques. By iteratively solving these two sub-problems, a convergent resource allocation algorithm, namely, security and interference management with reinforcement-learning and NOMA (SIREN), is proposed.  The superiority of SIREN over several benchmark schemes is verified via extensive simulations.

This paper provides analytical results on fixed kernel width based RFF based DL (RFF-DL). The derived analysis and the presented case-studies indicate the RFF-DL’s robustness to kernel-width initializations, and offers improved convergence in the low-data regime.