Many emerging applications in edge computing require processing of huge volumes of data generated by end devices, using the freshest available information. In this paper, we address the distributed optimization of multi-user long-term average Age-of-Information (AoI) objectives in edge networks that use NOMA transmission. This poses a challenge of non-convex online optimization, which in existing work often requires either decision making in a combinatorial space or a global view of entire network states. To overcome this challenge, we propose a reinforcement learning-based framework that adopts a novel hierarchical decomposition of decision making. Specifically, we propose three different types of distributed agents to learn with respect to efficiency of AoI scheduling, fairness of AoI scheduling, as well as a high-level policy balancing these potentially conflicting design objectives. Not only does the proposed decomposition improve learning performance due to disentanglement of different design objectives/rewards, but it also enables the algorithm to learn the best policy while also learning the explanations – as actions can be directly compared in terms of the design objectives. Our evaluations show that the proposed algorithm improves the long-term average AoI by $200\%{-}300\%$ and 400% compared to prior works with NOMA and the optimal solution without NOMA, respectively.

The human body can be considered a complex natural network due to the variety of interconnections between the different body regions. One example is the network of blood vessels, where artificial communication channels can be rendered using nanosensors that travel in the bloodstream as collectors and carriers of information. Further advancing this vision, in this work we investigate the detection and localization capabilities of flowing nanosensors in the blood flow to report abnormalities in the human body. Specifically, we target the detection of quorum sensing molecules and provide a methodology to evaluate its performance. The methodology consists of modeling the traveling path of nanosensors along the vessels through a Markov chain, and the use of machine learning (ML) models to compute their transition probabilities. We illustrate the resulting distribution of nanosensors in the body, which evidences a close match to expected results. We also evaluate their detection and localization capabilities in different body regions revealing their effectiveness to determine the presence of abnormalities in the human vessels

Recent molecular communication (MC) research suggests machine learning (ML) models for symbol detection, avoiding the unfeasibility of end-to-end channel models. However, ML models are applied as black boxes, lacking proof of correctness of the underlying neural networks (NNs) to detect incoming symbols. This paper studies approaches to the explainability of NNs for symbol detection in MC channels. Based on MC channel models and real testbed measurements, we generate synthesized data and train a NN model to detect of binary transmissions in MC channels. Using the local interpretable model-agnostic explanation (LIME) method and the individual conditional expectation (ICE), the findings in this paper demonstrate the analogy between the trained NN and the standard peak and slope detectors.

Generally, the curriculum design for undergraduate students enrolled in digital signal processing (DSP)-related engineering programs covers hard topics from specific disciplines, namely, mathematics, digital electronics, or programming. Typically, these topics are very demanding from the point of view of both students and teachers due to the inherent complexity of the mathematical formulations. However, improvements to the effectiveness of teaching can be achieved through a multisensorial approach supported by the liberal arts. By including the development of art and literacy skills in the curriculum design, the fundamentals of DSP topics may be taught from a qualitative perspective, compared to the solely analytical standpoint taken by traditional curricula. We postulate that this approach increases both the comprehension and memorization of abstract concepts by stimulating students' creativity and curiosity. In this article, we elaborate upon a methodology that incorporates liberal arts concepts into the teaching of signal processing techniques. We also illustrate the application of this methodology through specific classroom activities related to the digital processing of multimedia contents in undergraduate academic programmes. With this proposal, we also aim to lessen the perceived difficulty of the topic, stimulate critical thinking, and establish a framework within which nonengineering departments may contribute to the teaching of engineering subjects.

Nanotechnology enables the development of a new generation of devices at the scale of a few cubic micrometers that can sense, process, and communicate. Such small, imperceptible devices will revolutionize healthcare applications and enable new possibilities for in-body environments. This paper studies the intra-body communication channel between nanosensors flowing in the bloodstream and gateways attached to the skin using the terahertz (THz) spectrum. The channel model considers three layers through which the waveform travels: skin, tissue, and blood. To optimize the communication performance, this work investigates the impact of noise and mobility, and subsequently derives the trade-off between them. We illustrate the achievable bit error rate (BER) for THz intra-body channels considering communication through human tissue layers, including noise and random mobility of nanosensors in the blood system.