This paper extends the analysis of discrete age of information (AoI) to Ber/G/1/1 systems using the method of Probability Generation Functions (PGFs). The evolutions of AoI are characterized by defining a two-dimensional stochastic process. We derive the relationship between the PGF of arbitrary service time S and the system AoI in two different cases, where the packet service can or cannot be preempted by newly arrived packets. The closed-form expressions of the average AoI in both cases are obtained. Moreover, we prove that when fixing the mean of S, deterministic service time minimizes the system average AoI if packet service cannot be preempted. On the contrary, if preemption in the server is allowable, then deterministic S maximizes the average AoI.

The continuous development of modern communication and computation gives rise to a large number of applications based on Internet of Things (IoT) technology. Considering the diverse uses and various service requirements, the information freshness of IoT data, which is measured by age of information (AoI), is crucial for latency-sensitive IoT applications, such as industrial automation and intelligent transportation, because outdated information can lead to delayed and inaccurate response. Under the assumptions of bufferless and no service preemption, in this paper we consider the scenario where multiple sources transmit packets through a common server and analyze the discrete AoI corresponding to each source. To facilitate the description of random AoI evolutions, we assign priorities to the sources and assume that when multiple sources generate new packets in one time slot, the packet with the highest priority is selected and served. We obtain the explicit expression of average AoI and calculate the general formula in several cases, where the relationships between average AoI and other system parameters are investigated detailly. Also, under a constraint imposed on average service time over all the sources, we consider minimizing average AoI of one source by finding the optimal service rates for the packet from each source. In particular, for minimizing one source’s average AoI in two-source systems, the pair of optimal service rates are determined completely. The results show that if the probability that one source’s packet obtains the service is large enough, then to minimize average AoI of this source, maximizing the service rate for its own packets is not the optimal. Some discussions on source priorities are given and finally we provide the numerical simulations for the obtained results.

This work proposes an On-Off Keying (OOK) coding scheme for covert communication over complex Gaussian channels. In particular, a transmitter Alice employs phase deflection to covertly transmit information to a receiver Bob, simultaneously ensuring that the communication intent is concealed from a warden Willie. The utilization of phase deflection allows Alice to improve the transmission rate by leveraging Willie’s uncertainty about the received phase, without changing the codebook construction. Considering the asymmetry of the OOK codebook’s input distribution and shape constellation, we first analyze the relationship between the input distribution and the signal amplitude, and then propose a scheme that can achieve covert transmission with the input distribution of the “on” symbol$a_{n}=\mathcal {O}\left ({{\frac {1}{\sqrt {n}}}}\right)$and an average transmission power$\beta ^{2}=\mathcal {O}({1})$. We quantify the improvement brought from the phase resource as phase deflection gain and derive its closed-form expression by approximating the Kullback-Leibler (KL) divergence and mutual information through Taylor expansion. Numerical results show that our scheme achieves significant phase deflection gain, and the maximum gain can be achieved by fully utilizing the phase resources through three stages.

The problem of joint information and noisy Gaussian state amplification is investigated in this paper. The optimal capacity-distortion tradeoff is characterized. For the achievability part, the Gelfand-Pinsker scheme is evaluated using minimum mean squared error of Gaussian random variables. For the converse part, Cauchy-Schwartz inequality is invoked to transform the optimal linear estimation into a canonical form.

This work investigates the phase gain of intelligent reflecting surface (IRS) covert communication over complex-valued additive white Gaussian noise (AWGN) channels. The transmitter Alice intends to transmit covert messages to the legitimate receiver Bob via reflecting the broadcast signals from a radio frequency (RF) source, while rendering the adversary Willie’s detector arbitrarily close to ineffective. Our analyses show that, compared to the covert capacity for classical AWGN channels, we can achieve a covertness gain of value 2 by leveraging Willie’s uncertainty of phase angles. This covertness gain is achieved when the number of possible phase angle pairsN= 2. More interestingly, our results show that the covertness gain will not further increase withNas long asN≥ 2, even if it approaches infinity.

We study a hypothesis testing problem with a privacy constraint over a noisy channel and derive the performance of optimal tests under the Neyman-Pearson criterion. The fundamental limit of interest is the privacy-utility tradeoff (PUT) between the exponent of the type-II error probability and the leakage of the information source subject to a constant constraint on the type-I error probability. We provide an exact characterization of the asymptotic PUT for any non-vanishing type-I error probability. Our result implies that tolerating a larger type-I error probability cannot improve the PUT. Such a result is known as a strong converse or strong impossibility theorem. To prove the strong converse theorem, we apply the recently proposed technique in (Tyagi and Watanabe, 2020) and further demonstrate its generality. The strong converse theorems for several problems, such as hypothesis testing against independence over a noisy channel (Sreekumar and Gündüz, 2020) and hypothesis testing with communication and privacy constraints (Gilani et al., 2020), are established or recovered as special cases of our result.

In this paper, we consider the problem of secret key generation with one-way communication through both a rate-limited public channel and a rate-limited secure channels where the public channel is from Alice to Bob and Eve and the secure channel is from Alice to Bob. In this model, we do not pose any constraints on the sources, i.e. Bob is not degraded to or less noisy than Eve. We obtain the optimal secret key rate in this problem, both for the discrete memoryless sources and vector Gaussian sources. The vector Gaussian characterization is derived by suitably applying the enhancement argument, and proving a new extremal inequality. The extremal inequality can be seen as coupling of two extremal inequalities, which are related to the degraded compound MIMO Gaussian broadcast channel, and the vector generalization of Costa's entropy power inequality, accordingly.

In this paper, we consider the problem of secret key generation with one-way communication through both a rate-limited public channel and a rate-limited secure channels where the public channel is from Alice to Bob and Eve and the secure channel is from Alice to Bob. In this model, we do not pose any constraints on the sources, i.e. Bob is not degraded to or less noisy than Eve. We obtain the optimal secret key rate in this problem, for the vector Gaussian sources setting. The characterization is derived by suitably applying the enhancement argument, and Proving a new extremal inequality. The extremal inequality can be seen as coupling of two extremal inequalities, which are related to the degraded compound MIMO Gaussian broadcast channel, and the vector generalization of Costa's entropy power inequality, accordingly.

Consider the asymmetric broadcast channel with a random superposition codebook, which may be comprised of constant composition or i.i.d. codewords. By applying Forney's optimal decoder for individual messages and the message pair for the receiver that decodes both messages, exact (ensemble-tight) error and erasure exponents are derived. It is shown that the optimal decoder designed to decode the pair of messages achieves the optimal trade-off between the total and undetected exponents associated with the optimal decoder for the private message. Convex optimization-based procedures to evaluate the exponents efficiently are proposed. Finally, numerical examples are presented to illustrate the results.

By proving a strong converse, we strengthen the weak converse result by Salehkalaibar, Wigger and Wang (2017) concerning hypothesis testing against independence over a two-hop network with communication constraints. Our proof follows by judiciously combining two recently proposed techniques for proving strong converse theorems, namely the strong converse technique via reverse hypercontractivity by Liu, van Handel, and Verdú (2017) and the strong converse technique by Tyagi and Watanabe (2018), in which the authors used a change-of-measure technique and replaced hard Markov constraints with soft information costs. The techniques used in our paper can also be applied to prove strong converse theorems for other multiterminal hypothesis testing against independence problems.

The centralized coded caching problem is studied for the two-user scenario, considering heterogeneous cache capacities at the users and private channels from the server to the users, in addition to a shared channel. Optimal caching and delivery strategies that minimize the worst-case delivery latency are presented for an arbitrary number of files. The converse proof follows from the sufficiency of file-index-symmetric caching and delivery codes, while the achievability is obtained through memory-sharing among a number of special memory-capacity pairs. The optimal scheme is shown to exploit the private link capacities by transmitting part of the corresponding user`s request in an uncoded fashion. When there are no private links, the results presented here improve upon the two known results in the literature, namely: 1) equal cache capacities and arbitrary number of files and 2) unequal cache capacities and two files. The results are then extended to the caching problem with heterogeneous distortion requirements.

We derive exact (ensemble-tight) error and erasure exponents for the asymmetric broadcast channel given a random superposition codebook. We consider Forney's optimal decoder for both messages and the message pair for the receiver that decodes both messages. We prove that the optimal decoder designed to decode the pair of messages achieves the optimal tradeoff between the total and undetected exponents associated with the optimal decoder for the private message. We propose convex optimization procedures to evaluate the exponents. Numerical examples are presented to illustrate the results.

The centralized coded caching problem is studied under heterogeneous cache sizes and channel qualities from the server to the users, focusing on the two-user case. A server holding N files is considered to be serving two users with arbitrary cache capacities of M1and M2, and it is assumed that in addition to a shared common link, each user also has a private link from the server available during the delivery phase. Optimal caching and delivery strategies that minimize the worst-case delivery latency are presented for an arbitrary N. The converse proof benefits from Tian's observation that it suffices to consider file-index symmetric caching schemes, while the achievability is obtained through memory-sharing among certain special (M1, M2) pairs. The optimal scheme is shown to exploit the private link capacities by transmitting part of the corresponding user's request in an uncoded fashion. When there are no private links, the results presented here improve upon the two known results in the literature, namely, i) equal cache capacities and arbitrary number of files; and ii) unequal cache capacities and N = 2 files.

Random and high-agreement secret key generation from noisy wideband channels is challenging due to the autocorrelation inside the channel samples and compromised cross correlation between channel measurements of two keying parties. This paper studies the signal preprocessing algorithms to establish high-agreement uncorrelated secret key in the presence of channel independent eavesdroppers. We first propose a general mathematical model for various preprocessing schemes, including principal component analysis (PCA), discrete cosine transform (DCT) and wavelet transform (WT). Among preprocessing schemes, PCA is proved to achieve the optimal secret key rate. Next, PCA with common eigenvector has been found to outperform PCA with private eigenvector in terms of an overall consideration of key agreement, information leakage, and computational expense. Then, we propose a system level design of key generation, including quantization, information reconciliation, and privacy amplification. Numerical results verify that the key generation enhanced by PCA with common eigenvector can achieve secret key with high key generation rate, low key error rate, and good randomness.

In this paper, we study the problem of message authentication with two correlated sources observed by the legitimate transmitter and receiver as secret information. We consider an active adversary capable of the impersonation attack and the substitution attack. We are interested in minimizing the maximum probability of successful deception under the two attacks, where the minimization is over all authentication schemes by the legitimate transmitter and receiver and the maximization is over all attack strategies by the adversary. We propose a random coding based authentication scheme and obtain upper bound on the solutions of the above min-max problem for both attacks. We also show that the proposed random coding based scheme outperforms the separation-based scheme, i.e., private-key generation first using the correlated sources and then authentication with the private key. Finally, for the impersonation attack, we obtain a lower bound, which meet the upper bound. Therefore, we solve the min-max problem and characterize the optimal performance of the authentication system under impersonation attacks.

In this paper, we study the probability of successful deception of an uncompressed biometric authentication system with side information at the adversary. It represents the scenario where the adversary may have correlated side information, e.g., a partial finger print or a DNA sequence of a relative of the legitimate user. We find the optimal exponent of the deception probability by proving both the achievability and the converse. Our proofs are based on a connection between the problem of deception with side information and the rate distortion problem with side information at both the encoder and the decoder.

In this paper, we study the probability of successful deception of an uncompressed biometric authentication system with side information at the adversary. It represents the scenario where the adversary may have correlated side information, e.g., a partial finger print or a DNA sequence of a relative of the legitimate user. We find the optimal exponent of the deception probability by proving both the achievability and the converse. Our proofs are based on the connection between the problem of deception with side information and the rate distortion problem with side information at both the encoder and decoder.