Joint Spectrum Sensing and Resource Allocation for OFDM-based Transmission with a Cognitive Relay

In this paper, we investigate the joint spectrum sensing and resource allocation problem to maximize throughput capacity of an OFDM-based cognitive radio link with a cognitive relay. By applying a cognitive relay that uses decode and forward (D&F), we achieve more reliable communications, generating less interference (by needing less transmit power) and more diversity gain. In order to account for imperfections in spectrum sensing, the proposed schemes jointly modify energy detector thresholds and allocates transmit powers to all cognitive radio (CR) subcarriers, while simultaneously assigning subcarrier pairs for secondary users (SU) and the cognitive relay. This problem is cast as a constrained optimization problem with constraints on (1) interference introduced by the SU and the cognitive relay to the PUs; (2) miss-detection and false alarm probabilities and (3) subcarrier pairing for transmission on the SU transmitter and the cognitive relay and (4) minimum Quality of Service (QoS) for each CR subcarrier. We propose one optimal and two sub-optimal schemes all of which are compared to other schemes in the literature. Simulation results show that the proposed schemes achieve significantly higher throughput than other schemes in the literature for different relay situations.Comment: EAI Endorsed Transactions on Wireless Spectrum 14(1): e4 Published 13th Apr 201

Worst Case Attack on Quantization Based Data Hiding

Currently, most quantization based data hiding al-gorithms are built assuming specific distributions of at-tacks, such as additive white Gaussian noise (AWGN), uniform noise, and so on. In this paper, we prove that the worst case additive attack for quantization based data hiding is a 3-δ function. We derive the expression for the probability of error (Pe) in terms of distortion compensation factor, α, and the attack distribution. By maximizing Pe with respect to the attack distribution, we get the optimal placement of the 3-δ function. We then experimentally verify that the 3-δ function is in-deed the worst case attack for quantization based data hiding.

Competitive Spectrum Trading in Dynamic Spectrum Access Markets: A Price War

Abstract—The concept of dynamic spectrum access (DSA) enables the licensed spectrum to be traded in an open market where the unlicensed users can freely buy and use the available licensed spectrum bands. However, like in the other traditional commodity markets, spectrum trading is inevitably accompanied by various competitions and challenges. In this paper, we study an important business competition activity – price war in the DSA market. A non-cooperative pricing game is formulated to model the contention among multiple wireless spectrum providers for higher market share and revenues. We calculate the Pareto optimal pricing strategies for all providers and analyze the motivations behind the price war. The potential responses to the price war are in-depth discussed. Numerical results demonstrate the efficiency of the Pareto optimal strategy for the game and the impact of the price war to all participants. I

X-GRL: An Empirical Assessment of Explainable GNN-DRL in B5G/6G Networks

The rapid development of artificial intelligence (AI) techniques has triggered a revolution in beyond fifth-generation (B5G) and upcoming sixth-generation (6G) mobile networks. Despite these advances, efficient resource allocation in dynamic and complex networks remains a major challenge. This paper presents an experimental implementation of deep reinforcement learning (DRL) enhanced with graph neural networks (GNNs) on a real 5G testbed. The method addresses the explainability of GNNs by evaluating the importance of each edge in determining the model's output. The custom sampling functions feed the data into the proposed GNN-driven Monte Carlo policy gradient (REINFORCE) agent to optimize the gNodeB (gNB) radio resources according to the specific traffic demands. The demo demonstrates real-time visualization of network parameters and superior performance compared to benchmarks.Comment: 3 pages, 8 figure

RiskLabs: Predicting Financial Risk Using Large Language Model Based on Multi-Sources Data

The integration of Artificial Intelligence (AI) techniques, particularly large language models (LLMs), in finance has garnered increasing academic attention. Despite progress, existing studies predominantly focus on tasks like financial text summarization, question-answering (Q$\&$A), and stock movement prediction (binary classification), with a notable gap in the application of LLMs for financial risk prediction. Addressing this gap, in this paper, we introduce \textbf{RiskLabs}, a novel framework that leverages LLMs to analyze and predict financial risks. RiskLabs uniquely combines different types of financial data, including textual and vocal information from Earnings Conference Calls (ECCs), market-related time series data, and contextual news data surrounding ECC release dates. Our approach involves a multi-stage process: initially extracting and analyzing ECC data using LLMs, followed by gathering and processing time-series data before the ECC dates to model and understand risk over different timeframes. Using multimodal fusion techniques, RiskLabs amalgamates these varied data features for comprehensive multi-task financial risk prediction. Empirical experiment results demonstrate RiskLab's effectiveness in forecasting both volatility and variance in financial markets. Through comparative experiments, we demonstrate how different data sources contribute to financial risk assessment and discuss the critical role of LLMs in this context. Our findings not only contribute to the AI in finance application but also open new avenues for applying LLMs in financial risk assessment.Comment: 24 pages, 7 figures, 5 tables, 1 algorith

The potential for immunoglobulins and host defense peptides (HDPs) to reduce the use of antibiotics in animal production

Abstract Innate defense mechanisms are aimed at quickly containing and removing infectious microorganisms and involve local stromal and immune cell activation, neutrophil recruitment and activation and the induction of host defense peptides (defensins and cathelicidins), acute phase proteins and complement activation. As an alternative to antibiotics, innate immune mechanisms are highly relevant as they offer rapid general ways to, at least partially, protect against infections and enable the build-up of a sufficient adaptive immune response. This review describes two classes of promising alternatives to antibiotics based on components of the innate host defense. First we describe immunoglobulins applied to mimic the way in which they work in the newborn as locally acting broadly active defense molecules enforcing innate immunity barriers. Secondly, the potential of host defense peptides with different modes of action, used directly, induced in situ or used as vaccine adjuvants is described