Knowledge Graph-based Question Answering (KGQA) plays a pivotal role in complex reasoning tasks but remains constrained by two persistent challenges: the structural heterogeneity of Knowledge Graphs (KGs) often leads to semantic mismatch during retrieval, while existing reasoning path retrieval methods lack a global structural perspective.To address these issues, we propose Structure-Tracing Evidence Mining (STEM), a novel framework that reframes multi-hop reasoning as a schema-guided graph search task.First, we design a Semanticto-Structural Projection pipeline that leverages KG structural priors to decompose queries into atomic relational assertions and construct an adaptive query schema graph.Subsequently, we execute globally-aware node anchoring and subgraph retrieval to obtain the final evidence reasoning graph from KG.To more effectively integrate global structural information during the graph construction process, we design a Triple-Dependent GNN (Triple-GNN) to generate a Global Guidance Subgraph (Guidance Graph) that guides the construction.STEM significantly improves both the accuracy and evidence completeness of multi-hop reasoning graph retrieval, and achieves State-of-the-Art performance on multiple multi-hop benchmarks.Our source code is available at https: //github.com/PennyYu123/STEM_RAG.

Symmetric multilevel diversity coding (SMDC) is a multi-source coding problem where the independent sources are ordered according to their importance. Prior work demonstrated thatsuperposition coding, where sources are encoded independently, is optimal. This paper investigates the(L,s)sliding secure SMDC problem, whereLrepresents the number of encoders andsis the security threshold. The security requirement dictates that each sourceXαmust be kept perfectly secure if no more than α –sencoders are accessible. The problem is specialized to the(L,s)multilevel secret sharingproblem when the firsts – 1sources are constants. Fors= 1, the two problems coincide, and we show that superposition coding is optimal. The rate regions for the(L,s)=(3,2)problems are characterized, which implies that superposition coding is suboptimal for the general case. The core insight for achieving lower rates through joint encoding is leveraging less important sources likeXα–1as secret keys for more important sources likeXα. Based on this idea, we propose a joint coding scheme that achieves the minimum sum rate of the general(L,s)multilevel secret sharing problem. Moreover, a pseudo-superposition coding scheme is proposed to achieve the minimum sum rate of the general sliding secure SMDC problem, which uses superposition coding for thessets of sourcesX1, X2,..., Xs–1, (Xs,Xs+1,XL)and joint coding amongXs,Xs+1,XL.

In this work, we generalize the Stochastic Hybrid Systems (SHSs) analysis of Age of information (AoI) to the Age of Incorrect Information (AoII) metric. Hierarchical ageing processes are adopted using the continuous AoII for the first time. Two different hierarchy schemes are considered: 1). A hierarchy of zero and linear ageing processes with different slopes; 2). A hierarchy of zero, linear and exponential ageing processes. We first modify the main result in Yates (2020) to provide a systematic way to analyze the continuous hierarchical AoII over unslotted real-time systems. The closed-form expressions of average hierarchical AoII are obtained in two typical scenarios with different channel conditions, i.e., an M/M/1/1 queue over noisy channels and two M/M/1/1 queues over collision channels. Moreover, under each scenario, we analyze the stability issue regarding positive recurrence and provide the stability conditions that ensure a steady-state average AoII. Finally, we compare the closed-form results between average AoI and AoII in the M/M/1/1 queue. The effects of different channel parameters on the average hierarchical AoII are also evaluated.

This paper considers a real-time scheduling problem disseminating status update of sensors timely from a base station (BS) to users over wireless fading channels. The freshness of the updated status is quantified using the Age of Incorrect Information (AoII) metric. The objective is to minimize the long-term AoII under the constraint of limited transmission power, which necessitates that only a subset of users can successfully receive updates in each time slot. To find an optimal transmission policy, we first model the AoII minimization problem as a multi-action multi-armed bandit problem. After that, we decompose the derived MAB problem into multiple sub-problems. For each sub-problem, we obtain an optimal policy based on multiple threshold, thereby establishing the indexability of the optimal problem. Building upon this, a novel multi-action productivity index (MAPI) is introduced to get the optimal transmission policy. However, due to the computational complexity of the relative value iteration (RVI) algorithm, the exact value of the MAPI remains difficult to determine. To address this challenge, a computationally efficient algorithm is proposed to approximate the indices and derive the transmission policy for each user. Compared with Whittle’s Index and Max Weight policies, MAPI-based policy demonstrates significant performance improvement, particularly in large-scale networks and when the transmission power separation interval is small.

We introduce the hybrid ageing problem in gossip networks, where each node has different ageing processes. This generalization brings two new issues: i) the traditional subset recursion method in [1] fails; ii) the existence of stationary average age penalty needs to be re-examined. To resolve issue i) and ii), we leverage a node-by-node SHSs analysis by introducing splitting Poisson processes to evaluate the average age penalty. We first analyze the hybrid ageing problem in two types of line networks, i.e., one-way and two-way gossip lines. In one-way gossip lines, we derive the closed-form expression of average age penalty in two different cases, where the ageing process of each node can be ordered or disordered (Definition 1). The closedform expressions of average age penalty are also derived in the two-way gossip line. Moreover, we show that the growth rate of age penalty is bounded by the arrival rates between gossip nodes in the one-way line to ensure the existence of average age penalty.

For $\tilde f(t) = \exp \left( {\frac{{\alpha - 1}}{\alpha }t} \right)$, this paper shows that the Sibson mutual information is an α-leakage averaged over the adversary’s $\tilde f$ -mean relative information gain (on the secret) at elementary event of channel output Y as well as the joint occurrence of elementary channel input X and output Y . This interpretation is used to derive a sufficient condition that achieves a δ-approximation of ϵ-upper bounded α-leakage. A Y -elementary α-leakage is proposed, extending the existing pointwise maximal leakage to the overall Rényi order range α ∈ [0,∞). Maximizing this Y -elementary leakage over all attributes U of channel input X gives the Rényi divergence. Further, the Rényi capacity is interpreted as the maximal $\tilde f$-mean information leakage over both the adversary’s malicious inference decision and the channel input X (represents the adversary’s prior belief). This suggests an alternating max-max implementation of the existing generalized Blahut-Arimoto method.

This paper investigates the lazy secret sharing problem from an information-theoretic perspective. The participants are classified into two categories: Lazy-Participants and Share-Participants. The objective is to guarantee the perfect secret recovery from any t participants, while ensuring security exclusively for Share-Participants. The optimal coding rate region for lazy secret sharing is characterized. We further consider the imperfect security formulation, formulating security through information leakage constraints rather than strict security. The optimal rate region in the imperfect formulation is also established for a specific symmetric scenario.

A two-terminal lossy compression problem motivated by semantic communication is investigated, in which the semantic source is invisible at the encoders, two syntactic sources correlated with the semantic source are observed and compressed separately by two encoders, and a central decoder expects to reconstruct the semantic source and these two syntactic sources. The rate region of this semantic-aware quadratic Gaussian two-terminal source coding problem is characterized under the μ-sum assumption. This rate region is achieved by the Gaussian Berger-Tung coding scheme. The converse is proved by splitting the weighted-sum-rate optimization problem into sum-rate problem, CEO problem, and one-help-one problem to demonstrate the Gaussian optimality of weighted sum rate. This splitting method reveals the connection between the characterization of the whole rate region and the characterization of partial bounds, such as the sum-rate bound and the one-help-one bound.

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.

This paper investigates the challenge of acquiring channel state information at the transmitter (CSIT) in cell-free massive multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) systems operating in time-division duplex (TDD) mode. Although channel state information at the receiver (CSIR) is readily obtainable and CSIT is typically assumed to be its transpose, imperfections in the radio frequency (RF) chains disrupt this reciprocity. Focusing on this issue, we establish the necessary and sufficient conditions characterizing RF chain imperfections and their impact on system performance in a simplified scenario, underscoring the criticality of channel calibration. To address this challenge, a distributed source coding (DSC)-based calibration framework is proposed, leveraging the multiplexing of the sensing task to eliminate any additional communication overhead. This framework comprises a distributed compression scheme at each slave access point (AP) and a joint aggregation scheme at the central process unit (CPU). To validate the proposed DSC-based calibration framework, we analytically derive the performance gap relative to the fully collaborated approach. Building on this, a novel data-driven DSC-based deep learning method is proposed to address channel calibration without requiring clean labels. Numerical results demonstrate significant improvement in calibration performance achieved by our proposed method compared to existing calibration methods, approaching the performance of the fully collaborated method.

We consider a general point-to-point fading multiple-input multiple-output (MIMO) Gaussian channel. The channel suffers block fading and is with finite channel states, among which there is no degraded order due to the multi-antenna deployment. To guarantee reliable transmissions under the arbitrary unpredictable fading state, we generalize the layered broadcast approach in the channel by inducing the general rate splitting scheme. It assigns the specific sub-message layers for every possible state set and splits the transmission rate by allocating power to each layer. Under this scheme, a generalized broadcast approach is designed to be suitable for multi-antenna transmission. The maximum average rate subject to a transmit power constraint can be determined by a non-convex optimization problem over power allocation and rate tuple. We propose an iterative algorithm based on certain properties that the optimal solution would meet under some necessary conditions. It can output one local optimum for the optimization and thus assist in obtaining a scheme with a decent performance.

We study a class of gossip networks where a source delivers fresh status updates through networks consisting of a set of gossiping nodes. Contrary to previous works, the source delivers updates subject to an age-dependent point process where the rates are related to the age process at each node. Moreover, our work divides the age-dependent gossip networks into two types, i.e., type-A and type-B, where the former allows only one node sampling from source and the latter allows all. We first prove the necessary Markovity and ergodicity of both age-dependent gossip networks and proceed with deriving the general form of unique stationary distributions. With these premises established, closed-form expressions of the stationary age moments for type-A networks with arbitrary topologies are derived with the help of SHSs, with three specified results given for the line, ring and fully-connected networks. Meanwhile, we prove that no closed-form expressions of stationary age moments can be obtained in type-B networks. An approximated model for arbitrary-connected type-B networks is proposed, where we formulate the stationary moment equations for the approximated SHSs under the guarantee of Lagrange stability. Then, we provide a moment closure method to solve two symmetric cases, i.e., fully-connected and ring networks, approximately and verify the effectiveness of our algorithm by comparing to the simulations.

Symmetric multilevel diversity coding (SMDC) is a multi-source coding problem where the independent sources are ordered according to their importance. It was shown that sepa-rately encoding independent sources, referred to as superposition coding, is optimal. In this paper, an (L, s) sliding secure SMDC problem is considered, where$L$is the number of encoders and$s$is the security threshold, which means that each source$X$ais kept perfectly secure if no more than a -$s$encoders are accessible. It is shown that superposition coding is optimal for$s$= 1. The rate region for (L, s) = (3, 2) is characterized, which implies the suboptimality of superposition coding for the general problem. The main idea that joint coding can reduce rates is that we can use the previous source X a -1 as the secret key of X a. Based on this idea, a pseudo-superposition coding scheme is proposed to achieve the minimum sum rate, which uses superposition for the$s$sets of sources Xl, X2,‥ Xs-1, (Xs, Xs+1,”, XL). and joint encoding among Xs, Xs+1,”, XL.

We study a class of gossip networks where a single source delivers fresh status updates through networks consisting of a set of gossiping nodes. Contrary to previous works, the source delivers updates to nodes subject to an age-dependent point process where the update rates are related to the age process at each node. We derive the closed-form expressions of stationary age moments for the disconnected networks, and demonstrate the general procedure of moment analysis in the age-dependent gossip networks based on stochastic hybrid systems (SHSs). Considering the analytical difficulties in solving the infinite-dimensional stationary moment equations in more complex topologies, we provide two numerical methods to solve for the numerical values of stationary age moments approximately, and verify the effectiveness of these methods on the first three age moments in ring networks.

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 paper considers a Gaussian multi-input multi-output (MIMO) wiretap channel with a legitimate transmitter, a legitimate receiver (Bob), an eavesdropper (Eve), and a cooperative jammer. All nodes may be equipped with multiple antennas. Traditionally, the jammer transmits Gaussian noise (GN) to enhance the security. However, using this approach, the jamming signal interferes not only with Eve but also with Bob. In this paper, besides the GN strategy, we assume that the jammer can also choose to use the encoded jammer (EJ) strategy, i.e., instead of GN, it transmits a codeword from an appropriate codebook. In certain conditions, the EJ scheme enables Bob to decode the jamming codeword and thus cancel the interference, while Eve remains unable to do so even if it knows all the codebooks. We first derive an inner bound on the system’s secrecy rate under the strong secrecy metric, and then consider the maximization this bound through precoder design in a computationally efficient manner. In the single-input multi-output (SIMO) case, we prove that although non-convex, the power control problems can be optimally solved for both GN and EJ schemes. In the MIMO case, we propose to solve the problems using the matrix simultaneous diagonalization (SD) technique, which requires quite a low computational complexity. Simulation results show that by introducing a cooperative jammer with coding capability, and allowing it to switch between the GN and EJ schemes, a dramatic increase in the secrecy rate can be achieved. In addition, the proposed algorithms can significantly outperform the current state of the art benchmarks in terms of both secrecy rate and computation time.

The multiple-input multiple-output (MIMO) wiretap channel (WTC) serves as a fundamental model for exploring information-theoretic secrecy in wireless communication systems, involving a transmitter, a legitimate user, and an eavesdropper. This paper investigates the optimality of proper complex signals in complex WTCs. Our primary contribution lies in the derivation of a determinant inequality, which establishes that the secrecy rate of degraded complex MIMO WTCs is maximized when the signal is proper, meaning that its pseudo-covariance matrix is a zero matrix. Remarkably, we extend this result beyond the degraded scenario to the general complex WTC by leveraging a min-max reformulation of the secrecy capacity. Thus, we demonstrate that focusing on proper signals is sufficient when examining the secrecy capacity of the complex WTC. Overall, this work highlights the significance of the determinant inequality we derive and its implications for optimizing secrecy rates in the complex WTC.

In this paper, we consider stegotext reconstruction problem in information embedding. By adding the requirement of restoring the stegotext under certain fidelity criterion, we generalize the concept of reversible/irreversible information embedding. We focus on the stegotext reconstruction in a discrete memoryless host dependent attack channel, which can be regarded as a generalized Gel’fand-Pinsker problem with an input reconstruction constraint. For this problem, we prove an upper bound and a lower bound on its embedding capacity-distortion function, which is defined to describe the tradeoff between embedding information rate, host composition loss, and stegotext reconstruction distortion. In particular, our upper and lower bounds thus obtained match each other for the binary XOR attack channel with Hamming distortion and Costa’s additive Gaussian attack channel with quadratic loss. We further consider a variant of this problem, where host signal is available at the encoder in a causal way. For this case, we completely characterize its capacity-distortion function.

The conventional enhancement-and-perturbation approach to establishing Gaussian extremal inequalities is refined via a novel monotone path argument in the product probability space. This refined approach is illustrated with simplified/corrected proofs of the Liu-Viswanath extremal inequality and a vector generalization of Costa’s entropy power inequality. The power of this refinement is further demonstrated by characterizing two information-theoretic limits, namely, the capacity region of the multiple-input multiple-output (MIMO) Gaussian broadcast channel with private and common messages and the rate-distortion-equivocation function of vector Gaussian secure source coding, which have previously resisted the attack of the conventional approach.

In this paper, we characterize the generalized degrees-of-freedom (GDoF) region of the two-user$(M,N_{1},N_{2})$multiple-input multiple-output (MIMO) broadcast channel with delayed channel state information at the transmitter (CSIT), where there are one transmitter with$M$antennas and two receivers with$N_{1}$and$N_{2}$antennas, respectively. Under delayed CSIT, different from the existing converse approaches in the multiple-input single-output (MISO) GDoF and MIMO degrees-of-freedom (DoF) models, we incorporate new components into traditional approaches for this MIMO GDoF converse. For the achievability, we generalize the existing MISO achievable scheme. Our result reveals how the channel strength and antenna configuration impact the GDoF region of the two-user MIMO broadcast channel with delayed CSIT. Furthermore, the extension of our converse to a GDoF outer region of the$K$-user MIMO broadcast channel with delayed CSIT is also provided.

Ideally, the bi-directional channel in time division duplex (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems exhibits reciprocity. However, the involvement of low-cost and non-ideal radio frequency (RF) chains disrupts this reciprocity. Consequently, prior to fully leveraging the advantage of channel reciprocity, it is essential to implement channel calibration. Despite numerous over-the-air calibration methods, such as Argos, the typical least square (LS) are proposed in the literature, none of their criteria directly focus on the calibration performance. To address this gap, we propose a novel deep learning based approach that aims to optimize the calibration performance and introduce device-level intelligence towards 6G networks. To be specific, two cascaded modules are designed in a model-assisted end-to-end manner. Firstly, we propose the double-CNN-based channel denoising module for joint bi-directional channel estimation by exploiting the characteristics of mmWave channel. Secondly, the deep calibration learning module is meticulously designed to obtain the calibration coefficients with the aid of assisted model. This traceable assisted model is established by leveraging the expert knowledge of calibration process, based on which the MetrNet and the CaliNet are designed. Numerical results demonstrate the superior performance of our proposed method compared to existing calibration methods. Particularly, additional simulations and analysis are conducted to verify the effectiveness of the two properly designed modules.

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.

Federated learning (FL) is an emerging machine learning setting designed to preserve privacy. However, constantly updating model parameters on uplink channels results in huge communication overload, which is a major challenge for FL. In this paper, we consider an adaptive gradient quantization approach based on rate-distortion optimization in FL, which consists of a non-stationary random walk model on the true global optimal model parameters. Unlike traditional quantization methods, our goal is to minimize the total communication costs when the global server reconstructs model parameters under distortion constraints. Furthermore, when considering the iterative process, we utilize the Kalman filter to reduce computational complexity. And in each iteration, a generalized water-filling algorithm is used to calculate the optimal quantization levels for each local client. Numerical results show that the proposed method outperforms conventional quantization methods in terms of reducing communication costs.

In this paper, we study the optimality of proper signals for wiretap channels (WTC). We characterize the secrecy capacity of WTC in both separate and augmented forms, and show that proper Gaussian signals can achieve the secrecy capacity for both single-input multiple-output (SIMO) and single-input single-output (SISO) WTC. We also use an accelerated difference of convex functions algorithm (ADCA) to verify our results.

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 consider a general point-to-point fading multiple-input multiple-output (MIMO) Gaussian channel. The channel suffers block fading and with finite channel states, among which there is no degraded order on state information due to multi-antenna deployment. To guarantee reliable transmissions under arbitrary unpredictable fading states, we generalize the layered broadcast approach in the channel by inducing a rate splitting scheme. It assigns a specific sub-message layer for every possible state set and splits the transmission rate by allocating power to each layer. Under this scheme, a generalized broadcast approach is designed to be suitable for multi-antenna transmission. The maximum average rate under total power constraint can be characterized by an optimization problem. Numerical examples are provided to show the optimality of our transmission scheme within degradedness among different channel state information. Meanwhile, such generalization can also provide a decent performance under the general non-degraded case.

The extremal inequality approach plays a key role in network information theory problems. In this paper, we propose a novel monotone path construction in product probability space. The optimality of Gaussian distribution is then established by standard perturbation arguments.

The secrecy capacity of the multiple-input multiple-output (MIMO) Gaussian cognitive interference channel with common, private and confidential messages is characterized in this paper. To show the achievability, we use jointly Gaussian auxiliary random variables to evaluate the existing single-letter description of the capacity region for the discrete memoryless channel. The converse part is established by invoking linear estimation theory and a new version of the extremal inequality. To resolve the Gaussian optimality problem in the extremal inequality, the perturbation framework is employed. Our results explore the connections between the MIMO Gaussian broadcast channel and its distributive antennas counterpart, i.e., the MIMO Gaussian interference channel.

In this paper, we investigate the generalized degrees-of-freedom (GDoF) of the asymmetric interference channel with delayed channel state information at the transmitter (CSIT), where each transmitter has two antennas, each receiver has one antenna, and the strength for each interfering link can vary. The optimal sum-GDoF is characterized by matched converse and achievability proof. Through our results, we also reveal that in our antenna setting, the symmetric GDoF lower bound in [Mohanty et. al, TIT 2019] can be elevated, and the symmetric GDoF upper bound in [Mohanty et. al, TIT 2019] is tight in fact.

This paper considers a new extremal inequality and proves that a Gaussian distribution is one of the solutions to hold this extremal inequality. The method of factorization is applied in the proof. We also exploit the chain rule in two separate ways to establish the subadditivity of factorization. As an application, the converse part of the rate-distortion problem of the quadratic Gaussian Gray-Wyner system with single-letter characterization is solved using this new extremal entropy inequality.

This paper studies the secure source coding for distributed function computation with a functional privacy constraint. In particular, two distributed encoders, named Alice and Bob, observe two correlated sources and independently compress and transmit their observations to a fusion center who wants to compute a function of the two sources. A passive eavesdropper named Eve, with access to the side information, is able to listen the link between Alice and the fusion center. Two privacy constraints are considered. The first one is a functional privacy constraint for Eve, who is interested in a given function of the two sources rather than original sources. The second requirement is that the fusion center is not allowed to learn too much information about the original sources. For the proposed problem, an inner bound on the achievable rate-distortion-leakage region is characterized for the discrete memoryless setting. When the function of interest at Eve degenerates to the source X and the fusion center has access to uncoded source Y , our inner bound is tight.

In this paper, for discrete time status updating system with infinite size, we derive the explicit expression of average age of information (AoI) and its stationary distribution. Notice that there is no packet loss in an infinite size system, we successfully characterize the random dynamics of discrete AoI using a two-dimensional state vector, which simultaneously tracks the value of AoI and the age of packet currently under service. A two-dimensional stochastic process is constituted and we completely solve all the stationary probabilities. Then, as one of the marginal distributions the stationary distribution of AoI is obtained, with which the average AoI is also determined.

In this paper, we investigate a two-way lossy source coding problem, where both users have access to a common message from a helper encoder. When the correlated sources are a product of two reversely degraded components, we characterize the rate-distortion region. The converse part for this mismatched case is proved by suitably applying the enhancement argument. We combine the Heegard-Berger scheme and the Wyner-Ziv encoding to obtain the achievability part.

In this paper, we consider the age of information (AoI) of a discrete time status updating system, focusing on finding the stationary AoI distribution assuming that the Ber/G/1/1 queue is used. Following the standard queueing theory, we show that by invoking a two-dimensional state vector which tracks the AoI and packet age in system simultaneously, the stationary AoI distribution can be derived by analyzing the steady state of the constituted two-dimensional stochastic process. We give the general formula of the AoI distribution and calculate the explicit expression when the service time is also geometrically distributed. The discrete and continuous AoI are compared, we depict the mean of discrete AoI and that of continuous time AoI for system with M/M/1/1 queue. Although the stationary AoI distribution of some continuous time single-server system has been determined before, in this paper, we shall prove that the standard queueing theory is still appliable to analyze the discrete AoI, which is even stronger than the proposed methods handling the continuous AoI.

This article studies the problem of Sum-secure Degrees of Freedom (SDoF) of the$(M,M,N,N)$multiple-input–multiple-output (MIMO) interference channel with local output feedback, so as to build an information-theoretic foundation and provide practical transmission schemes for 6G-enabled Vehicles-to-Vehicles (V2V). For this problem, we propose two novel transmission schemes, i.e., the interference decoding scheme and the interference alignment scheme, and thus establish a sum-SDoF lower bound. In particular, to optimize the phase duration, we analyze the security and decoding constraints and formulate a linear-fractional optimization problem. Furthermore, we show that the derived sum-SDoF lower bound is the sum-SDoF for$M \le N/2$,$N=M$, and$2N \le M$antenna configurations, and reveal that for a fixed$N$, the optimal$M$to maximize the sum-SDoF is not less than$2N$. Through simulations, we examine the secure sum-rate performance of proposed transmission schemes and reveal that using local output feedback can lead to a higher secure sum-rate than that by using delayed channel state information at the transmitter (CSIT).

In this article, we consider a secure one-helper distributed source coding system, with action-dependent side information. Specifically, the main encoder observes an independent and identically distributed (i.i.d.) source Xnand wishes to compress this source to the decoder. The helper compresses the correlated source Ynto the decoder. The eavesdropper, having access to the information bits sent by the main encoder, can observe side information Znas well. Besides, side information Znand correlated source Yncan be influenced by a cost-constrained action sequence taken by the decoder. This class of problems can be seen as an nontrivial extension of the secure one-helper source coding with additional cost-constrained actions. The purpose of this article is to study the impact of the helper on achievable rate-distortion-cost-leakage. An inner bound of the achievable rate-distortion-cost-leakage is provided for the discrete memoryless setting. This inner bound is further shown to be tight when Ynis required to be reconstructed losslessly, and equivalent to the inner bound of Villard et al. when there is no action taken at the decoder. Numerical examples on binary symmetric sources are also discussed in this article.

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.

We investigate the problem of the successive refinement for Wyner-Ziv coding with degraded side information and obtain a complete characterization of the rate region for the quadratic vector Gaussian case. The achievability part is based on the evaluation of the Tian-Diggavi inner bound that involves Gaussian auxiliary random vectors. For the converse part, a matching outer bound is obtained with the aid of a new extremal inequality. Herein, the proof of this extremal inequality depends on the integration of the monotone path argument and the doubling trick as well as information-estimation relations.

Identifying the urban community structure is important for promoting smart city. In contrast to community detection in social network, the urban community construction involves both the information of population flow a nd t he distribution of public services. However, few methods take into account the two factors simultaneously in a reasonable manner. This causes several problems such as isolated nodes and geographical fragmentation in urban planning. To address the challenges of concerning the population flow a nd p ublic s ervices distribution in an unified framework, we formulate the identification problem of urban community structure as graph modularity optimization with constraints on the public services distribution. The combination of hierarchical clustering and node level adjustment strategies is applied, to capture the required information of community structure. In our framework, The attributed graph regards the geographical zones as nodes, the interaction between geographical zones as edges, the distribution of public services as attributes. Moreover, We evaluate our approach by using both the synthetic data set and the real-world data set. Results show the efficacy of the proposed method on identifying the urban community structure.

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.

The change point detection theory seeks to capture the changes as soon as possible after they occur, with low false alarm. We investigate this problem under the condition that the post-change distribution defined on a finite sample space is not available. We introduce a sequential version of universal hypothesis test across the curved boundary, and prove that this sequential test asymptotically achieves smaller average sample size than any other sequential test. Based on this sequential test, we propose two types of change point detection procedures, one is Lorden procedure and another is Shiryaev-Roberts procedur. Both of them are asymptotically optimal with their corresponding criterion. The substantial statistic properties are presented by simulations. Our results shed some light on optimal analysis in nonparametric change point detection procedures.

This paper studies the secure multiterminal source coding problem with actions. In particular, one main encoder observes an independent and identically distributed (i.i.d.) source Xnand wishes to compress this source lossyly to the decoder. Another encoder observes the source Ynand wants to compress this source losslessly to the decoder. A passive eavesdropper having access to the side information Zncan observe the information bits sent by the main encoder. In this scenario, the decoder is allowed to choose actions affecting the correlated source Ynand the side information Zn. For this problem, we characterize the optimal rate-distortion-cost-leakage region for a discrete memoryless setting.

In this paper, we consider Successive refinement for the Wyner-Ziv source coding problem, in which sources are vector Gaussian distributed and side information follows a degraded order assumption. We fully characterize its ratedistortion region with covariance mean square error distortions. We use jointly Gaussian auxiliary random variables to evaluate the existing single-letter description of the rate region by Tian and Diggavi, which gives the achievability. The converse relies on the information-estimation relationship, with which the tight outer bound is obtained.

The problem of joint information transmission and input signalling estimation over a state-dependent channel is considered. The general single-letter upper and lower bounds of the optimal capacity-distortion trade-offs are provided. For the Gaussian dirty paper channel, we calculate the bounds by choosing parameters carefully, and obtain a computable form eventually. In such a way, we obtain a complete characterization for this problem setting.

Degrees of freedom (DoF) of the three-user multiple-input multiple-output (MIMO) broadcast channel (BC) with delayed CSIT was derived for most antenna configurations except for the case of , where transmitter has M antennas and each receiver has N antennas. In this paper, for that problem, we propose an effective scheme for acquiring a higher achievable DoF than the value via existing methods. In the initial transmission phase, we transmit more data symbols than the amount that the receivers can instantaneously decode. Then, we generate auxiliary symbols for decoding the data symbols. Specifically, our scheme introduces an integrated design for the generation of auxiliary symbols. As a result, a higher achievable DoF, i.e., [12MN/(7M+2N)], can be achieved for specific antenna configurations, where .2N <; M <; 2.5N.

A single-letter lower bound on the sum rate of multiple description coding with tree-structured distortion constraints is established by generalizing Ozarow's celebrated converse argument through the introduction of auxiliary random variables that form a Markov tree. For the quadratic vector Gaussian case, this lower bound is shown to be achievable by an extended El Gamal-Cover scheme, yielding a complete characterization of the minimum sum rate.

We establish a new extremal inequality, which is further leveraged to give a complete characterization of the rate region of the vector Gaussian CEO problem with the trace distortion constraint. The proof of this extremal inequality hinges on a careful analysis of the Karush-Kuhn-Tucker necessary conditions for the non-convex optimization problem associated with the Berger-Tung scheme, which enables us to integrate the perturbation argument by Wang and Chen and the distortion projection method by Rahman and Wagner.

This paper investigates the compress-and-forward scheme for an uplink cloud radio access network (C-RAN) model, where multi-antenna base stations (BSs) are connected to a cloud-computing-based central processor (CP) via capacity-limited fronthaul links. The BSs compress the received signals with Wyner-Ziv coding and send the representation bits to the CP; the CP performs the decoding of all the users' messages. Under this setup, this paper makes progress toward the optimal structure of the fronthaul compression and CP decoding strategies for the compress-and-forward scheme in the C-RAN. On the CP decoding strategy design, this paper shows that under a sum fronthaul capacity constraint, a generalized successive decoding strategy of the quantization and user message codewords that allows arbitrary interleaved order at the CP achieves the same rate region as the optimal joint decoding. Furthermore, it is shown that a practical strategy of successively decoding the quantization codewords first, then the user messages, achieves the same maximum sum rate as joint decoding under individual fronthaul constraints. On the joint optimization of user transmission and BS quantization strategies, this paper shows that if the input distributions are assumed to be Gaussian, then under joint decoding, the optimal quantization scheme for maximizing the achievable rate region is Gaussian. Moreover, Gaussian input and Gaussian quantization with joint decoding achieve to within a constant gap of the capacity region of the Gaussian multiple-input multiple-output (MIMO) uplink C-RAN model. Finally, this paper addresses the computational aspect of optimizing uplink MIMO C-RAN by showing that under fixed Gaussian input, the sum rate maximization problem over the Gaussian quantization noise covariance matrices can be formulated as convex optimization problems, thereby facilitating its efficient solution.

A single-letter lower bound on the sum rate of multiple description coding with tree-structured distortion constraints is established and is shown to be tight in the quadratic Gaussian case.

This paper investigates the compress-and-forward scheme for an uplink cloud radio access network (C-RAN) model, where multi-antenna base-stations (BSs) are connected to a cloudcomputing based central processor (CP) via capacity-limited fronthaul links. The BSs perform Wyner-Ziv coding to compress and send the received signals to the CP; the CP performs either joint decoding of both the quantization codewords and the user messages at the same time, or the more practical successive decoding of the quantization codewords first, then the user messages. Under this setup, this paper makes progress toward the optimization of the fronthaul compression scheme by proving two results. First, it is shown that if the input distributions are assumed to be Gaussian, then under joint decoding, the optimal Wyner-Ziv quantization scheme for maximizing the achievable rate region is Gaussian. Second, for fixed Gaussian input, under a sum fronthaul capacity constraint and assuming Gaussian quantization, this paper shows that successive decoding and joint decoding achieve the same maximum sum rate. In this case, the optimization of Gaussian quantization noise covariance matrices for maximizing sum rate can be formulated as a convex optimization problem, therefore can be solved efficiently.

This paper characterizes the rate region of the vector Gaussian CEO problem with the trace distortion constraint. We develop a new analysis technique based on spectral decomposition of mean square error in Berger-Tung scheme. In order to prove the converse part of the rate distortion region, the perturbation method of Wang and Chen is utilized through combining with detailed analysis of Karush-Kuhn-Tucker necessary conditions of the non-convex optimization problem. Finally, we show that Berger-Tung inner bound can achieve the entire rate region of the vector Gaussian CEO problem with the trace distortion constraint, via deriving a novel extremal inequality.

In this letter, we introduce a sequential version of universal hypothesis testing, where the goal of the test is not only to decide between the known null hypothesis and some other unknown alternative hypothesis, but also to use a stopping time to stop the test as soon as rejecting the null hypothesis. Motivated by the method of types in information theory, we establish the sequential test by Höeffding's universal test associated with a curved stopping boundary. It is proved that this sequential test uniformly asymptotically minimizes average sample size for any other sequential test.

In this paper, we give a perturbation proof to vector Gaussian one-help-one problem for characterizing the rate distortion region, in which the challenge is that the conventional entropy power inequality used in scalar Gaussian case is not necessarily tight in vector case. Different from enhancement technique, we take the Fisher information matrix to present the entropy, and then derive a new extremal inequality based on the method of integration along a path of a continuous Gaussian perturbation. This new extremal inequality enables us to give a perturbation proof of Rahman and Wagner's theorem.

The anti-collision mechanism plays a kernel role in random access systems such as RFID, which is usually implemented by MAC protocols such as Dynamic Framed Slotted ALOHA (DFSA). In some standards, there used to define an instruction to cancel the frame in the current interrogation round, such as the QUERYADJUST command in EPCglobal HF Gen 2 [2]. In this paper, we study how to optimally cancel the current frame to maximizing the system throughput, according to optimal stopping time principle, and then propose a suboptimal cancellation strategy, which is easily implemented in practical applications. We further put forward the algorithm of the cancellation strategy in some well-known DFSA protocols to compare the performance via simulations.