Semantic communication allows the receiver to know the intention instead of the bit information itself, which is an emerging technique to support real-time human-machine and machine-to-machine interactions for future wireless communications. In semantic communications, both transmitter and receiver share some common knowledge, which can be used to extract small-size information at the transmitter and recover the original information at the receiver. Due to different design purposes, security issues in semantic communications have two unique features compared to standard bit-wise communications. First, an attacker in semantic communications considers not only the amount of stolen data but also the meanings of stolen data. Second, an attacker in semantic communication systems can attack not only semantic information transmission as done in standard communication systems but also attacks machine learning (ML) models used for semantic information extraction since most of semantic information is generated using ML based methods. Due to these unique features, in this paper, we present an overview on the fundamentals and key challenges in the design of secure semantic communication. We first provide various methods to define and extract semantic information. Then, we focus on secure semantic communication techniques in two areas: information security and semantic ML model security. For each area, we identify the main problems and challenges. Then, we will provide a comprehensive treatment of these problems. In a nutshell, this article provides a holistic set of guidelines on how to design secure semantic communication systems over real-world wireless communication networks.

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The Fisher-Snedecor F distribution has been recently proposed as a more accurate and mathematically tractable composite fading model than traditional established models in some practical cases. In this paper, we firstly derive exact closedform expressions for the main statistical characterizations of the ratio of products of F-distributed random variables, including the probability density function, the cumulative distribution function and the moment generating function. Secondly, simple and tight approximations to the distribution of products and ratio of products of F-distributed random variables are presented. These analytical results can be readily employed to evaluate the performance of several emerging system configurations, including fullduplex relaying systems operating in the presence of co-channel interference and wireless communication systems enhanced with physical-layer security. The proposed mathematical analysis is substantiated by numerically evaluated results, accompanied by equivalent ones obtained using Monte Carlo simulations.Index Terms-Fisher-Snedecor F distribution, performance analysis, physical layer security, full-duplex relaying networks.

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The statistical characterization of the sum of random variables (RVs) are useful for investigating the performance of wireless communication systems. We derive exact closed-form expressions for the probability density function (PDF) and cumulative distribution function (CDF) of a sum of independent but not identically distributed (i.n.i.d.)Fisher-Snedecor F RVs. Both PDF and CDF are given in terms of the multivariate Fox's H-function. Besides, a simple and accurate approximation to the sum of i.n.i.d. Fisher-Snedecor F variates is presented using the moment matching method. The obtained PDF and CDF are used to evaluate the performance of wireless communication applications including the outage probability, the effective capacity and the channel capacities under four different adaptive transmission strtegies. Moreover, the corresponding approximate expressions are obtained to provide useful insights for the design and deployment of wireless communication systems. In addition, we derive simple asymptotic expressions for the proposed mathematical analysis in the high signal-to-noise ratio regime. Finally, the numerical results demonstrate the accuracy of the derived expressions. Index TermsChannel capacity, effective capacity, Fisher-Snedecor F -distribution, sum of random variables, Recently, the Fisher-Snedecor F distribution was proposed [1] as a tractable fading model to describe the combined effects of shadowing and multipath fading. This distribution can be reduced to some common ). 2 fading models, such as Nakagami-m and Rayleigh fading channels. Furthermore, it is found in [1] that the F distribution can provide a better fit to the experimental data obtained for device-to-device (D2D) and wearable communication links, especially at 5.8 GHz, as compared with the well established generalized-K (GK) distribution. In addition, its probability density function (PDF) consists of only elementary functions and it leads to more tractable analysis than the GK model [1]. Due to its promising properties, the performance of digital communication systems over F distributed fading channels has been analyzed in [2]-[5] and the references therein. The sum of random variables (RVs) has a wide range of important applications in the performance analysis of wireless communication systems. For example, to enhance the quality of the received signal, maximal-ratio combining (MRC) can be deployed at the receiver to maximize the combiner output signalto-noise ratio (SNR) [6]. The system with MRC receiver operating over different fading channels has been extensively studied [7]-[12]. The PDF and CDF of the sum of Fisher-Snedecor F RVs has been derived in terms of Lauricella multivariate hypergeometric function [13]. However, there results are difficult to be used in the performance analysis of MRC systems over Fisher-Snedecor F fading channels due to the complex of the Lauricella multivariate hypergeometric function. Moreover, authors in [13] obtain outage probability (OP) and outage capacity expressions involving L-fold Mellin-Barnes...

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