Biblio
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Covert Communication by Exploiting Node Multiplicity and Channel Variations. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1—6.
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2020. We present a covert (low probability of detection) communication scheme that exploits the node multiplicity and channel variations in wireless broadcast networks. The transmitter hides the covert (private) message by superimposing it onto a non-covert (public) message such that the total transmission power remains the same whether or not the covert message is transmitted. It makes the detection of the covert message impossible unless the non-covert message is decoded. We exploit the multiplicity of non-covert messages (users) to provide a degree of freedom in choosing the non-covert message such that the total detection error probability (sum of the probability of false alarm and missed detection) is maximized. We also exploit the channel variation to minimize the throughput loss on the non-covert message by sending the covert message only when the transmission rate of the non-covert message is low. We show that the total detection error probability converges fast to 1 as the number of non-covert users increases and that the total detection error probability increases as the transmit power increases, without requiring a pre-shared secret among the nodes.
The Explicit Coding Rate Region of Symmetric Multilevel Diversity Coding. 2018 Information Theory and Applications Workshop (ITA). :1–9.
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2018. It is well known that superposition coding, namely separately encoding the independent sources, is optimal for symmetric multilevel diversity coding (SMDC) (Yeung-Zhang 1999). However, the characterization of the coding rate region therein involves uncountably many linear inequalities and the constant term (i.e., the lower bound) in each inequality is given in terms of the solution of a linear optimization problem. Thus this implicit characterization of the coding rate region does not enable the determination of the achievability of a given rate tuple. In this paper, we first obtain closed-form expressions of these uncountably many inequalities. Then we identify a finite subset of inequalities that is sufficient for characterizing the coding rate region. This gives an explicit characterization of the coding rate region. We further show by the symmetry of the problem that only a much smaller subset of this finite set of inequalities needs to be verified in determining the achievability of a given rate tuple. Yet, the cardinality of this smaller set grows at least exponentially fast with L.