We study a unified information-theoretic framework for integrated sensing and communications (ISAC), applicable to both monostatic and bistatic sensing scenarios. Special attention is given to the case where the sensing receiver (Rx) is required to produce a “soft" estimate of the state sequence, with logarithmic loss serving as the performance metric. We derive lower and upper bounds on the capacity-distortion function, which delineates the fundamental tradeoff between communication rate and sensing distortion. These bounds coincide when the channel between the ISAC transmitter (Tx) and the communication Rx is degraded with respect to the channel between the ISAC Tx and the sensing Rx, or vice versa. Furthermore, we provide a complete characterization of the capacity-distortion function for an ISAC system that simultaneously transmits information over a binary-symmetric channel and senses additive Bernoulli states through another binary-symmetric channel. The Gaussian counterpart of this problem is also explored, which, together with a state-splitting trick, fully determines the capacity-distortion-power function under the squared error distortion measure.

This paper investigates the tightness of existing bounds on the quadratic Gaussian distortion-rate-perception functions with limited common randomness and the i.i.d. output constraint, under perception measures based on the Kullback–Leibler divergence and the squared Wasserstein-2 distance. For the squared Wasserstein-2 distance-based perception measure, we improve the best-known lower bound by introducing a tunable parameter. Moreover, via the connection between rate-distortion-perception coding and entropy-constrained scalar quantization, it is revealed that all existing bounds, including the improved one, are generally not tight in the weak perception constraint regime. Our findings shed light on the information-theoretic performance limits of rate-distortion-perception coding and offer guidelines for developing practical schemes.

We consider the problem of simulating a noisy channel using noiseless channels with unlimited shared randomness. This can be interpreted as the reverse problem of Shannon’s noisy channel coding theorem. In contrast to previous works, we employ Rényi divergence (with the parameter α ∈ [0,∞]) to measure the level of approximation, and we obtain the reverse Shannon theorem under this measure, which characterizes the Rényi simulation rate, the minimum communication rate required for the Rényi divergence vanishing asymptotically. Our derivation is done by a precise large-deviation analysis. When the communication rate is above the Rényi simulation rate, we provide a complete characterization of the convergence exponent for the Rényi divergence, called the reliability function. When the communication rate is below the Rényi simulation rate, we determine the linear increasing rate for the Rényi divergence, which implies the strong converse exponent for the order-α fidelity.

The de Bruijn identity states that Fisher information is equal to twice the time-derivative of Shannon differential entropy along heat flow. In the same spirit, a generalized version of Fisher information, termed the Rényi–Fisher information, was introduced by Jizba, Dunningham, and Prokš [Entropy, 2021], which is defined as twice the time-derivative of Rényi differential entropy along heat flow. Based on this Rényi–Fisher information, we establish several sharp Rényi-entropic isoperimetric inequalities, which generalize the classic entropic isoperimetric inequality to the Rényi setting. Utilizing these isoperimetric inequalities, we extend the classical Cramér–Rao inequality from Fisher information to Rényi–Fisher information. We then use these generalized Cramér–Rao inequalities to determine the signs of derivatives of Rényi entropy along heat flow, strengthening existing results on the complete monotonicity of Rényi entropy. We lastly explore applications of our Rényi-entropic isoperimetric inequalities in entropy power inequalities. We establish a sharp Rényi entropy power inequality under the assumption that one of two independent random vectors is Gaussian.

We derive a single-letter characterization of the fundamental distortion-rate-perception tradeoff with limited common randomness under the squared error distortion measure and the squared Wasserstein-2 perception measure. This characterization is further shown to admit an explicit evaluation in the case of Gaussian sources. In addition, we clarify two different notions of universal representation. As a byproduct, soft-covering lemmas with respect to the Wasserstein-2 distance are established.

In this paper, we investigate the completely monotone conjecture along the heat flow for the Rényi entropy. We confirm this conjecture for the order of derivative up to 4, when the order of Rényi entropy is in certain regimes. We also investigate concavity of Rényi entropy power and the complete monotonicity of Tsallis entropy. We recover and slightly extend Hung’s result on the fourth-order derivative of the Tsallis entropy, and observe that the complete monotonicity holds for Tsallis entropy of order 2, which is equivalent to that the noise stability with respect to the heat semigroup is completely monotone. Based on this observation, we conjecture that the complete monotonicity holds for Tsallis entropy of all orders α ∈ (1, 2). Our proofs in this paper are based on the techniques of integration-by-parts, sum-of-squares, and curve-fitting.

This paper investigates three closely related topics—Rényi resolvability, noise stability, and anti-contractivity. The Rényi resolvability problem refers to approximating a target output distribution of a given channel in the Rényi divergence when the input is set to a function of a given uniform random variable. This problem for the Rényi parameter in (0, 2] ∪ {∞} was first studied by the present author and Tan in 2019. In the present paper, we provide a complete solution to this problem for the Rényi parameter in the entire range R∪{±∞}. We then connect the Rényi resolvability problem to the noise stability problem, by observing that maximizing or minimizing theq-stability of a set is equivalent to a variant of the Rényi resolvability problem. By such a connection, we provide sharp dimension-free bounds on theq-stability. We lastly relate the noise stability problem to the anti-contractivity of a Markov operator (i.e., conditional expectation operator), where the terminology “anti-contractivity” introduced by us refers to as the opposite property of the well-known contractivity/hyercontractivity. We derive sharp dimension-free anti-contractivity inequalities. All of the results in this paper are evaluated for binary distributions. Our proofs in this paper are mainly based on the method of types, especially strengthened versions of packing-covering lemmas.

In this paper, we derive variational formulas for the asymptotic exponents (i.e., convergence rates) of the concentration and isoperimetric functions in the product Polish probability space under certain mild assumptions. These formulas are expressed in terms of relative entropies (which are from information theory) and optimal transport cost functionals (which are from optimal transport theory). Hence, our results verify an intimate connection among information theory, optimal transport, and concentration of measure or isoperimetric inequalities. In the concentration regime, the corresponding variational formula is in fact a dimension-free bound in the sense that this bound is valid for any dimension. A cardinality bound for the alphabet of the auxiliary random variable in the expression of the asymptotic isoperimetric exponent is provided, which makes the expression computable by a finite-dimensional program for the finite alphabet case. We lastly apply our results to obtain an isoperimetric inequality in the classic isoperimetric setting, which is asymptotically sharp under certain conditions. The proofs in this paper are based on information-theoretic and optimal transport techniques.

In this paper, we study the type graph, namely, a bipartite graph induced by a joint type. We investigate the maximum edge density of induced bipartite subgraphs of this graph having a number of vertices on each side on an exponential scale in the length$n$of the type. This can be seen as an isoperimetric problem. We provide asymptotically sharp bounds for the exponent of the maximum edge density as the length of the type goes to infinity. We also study the biclique rate region of the type graph, which is defined as the set of$(R_{1},R_{2})$such that there exists a biclique of the type graph which has respectively$2^{nR_{1}}$and$2^{nR_{2}}$vertices on the two sides. We provide asymptotically sharp bounds for the biclique rate region as well. We then discuss the connections of these results to noninteractive simulation and hypercontractivity inequalities. Furthermore, as an application of our results, a new outer bound for the zero-error capacity region of the binary adder channel is provided, which improves the previously best known bound, due to Austrin, Kaski, Koivisto, and Nederlof. Our proofs in this paper are based on the method of types and linear algebra.

In this paper, we generalize the log-Sobolev inequalities to Rényi–Sobolev inequalities by replacing the entropy with the two-parameter entropy, which is a generalized version of entropy and closely related to Rényi divergences. We derive the sharp nonlinear dimension-free version of this kind of inequalities. Interestingly, the resultant inequalities show a transition phenomenon depending on the parameters. We then connect Rényi–Sobolev inequalities to contractive and data-processing inequalities, concentration inequalities, and spectral graph theory. Our proofs in this paper are based on the information-theoretic characterization of the Rényi–Sobolev inequalities, as well as the method of types.

Nonconvex optimization plays a key role in multi-user information theory and related fields, but it is usually difficult to solve. The rate region of the Gray–Wyner source coding system (or almost equivalently, the mutual information region) is a typical example in nonconvex optimization, whose single-letter expression was given by Gray and Wyner. However, due to the nonconvexity of the optimization involved in this expression, previously, there was none nontrivial discrete source for which the analytic expression is known. In this paper, we propose a new strategy to solve nonconvex optimization problems. By this strategy, we provide the analytic expression for the doubly symmetric binary source (DSBS), which confirms positively a conjecture of Gray and Wyner in 1974. We also provide the analytic expression of the mutual information region for the Gaussian source, and provide (or recover) the analytic expressions of the lossy Gray–Wyner region for both the DSBS and Gaussian source. Our proof strategy relies on an auxiliary measure technique and the analytical expression of the optimal-transport divergence region.

The channel synthesis problem has been widely investigated over the last decade. In this paper, we consider the sequential version in which the encoder and the decoder work in a sequential way. Under a mild assumption on the target joint distribution we provide a complete (single-letter) characterization of the solution for the point-to-point case, which shows that the canonical symbol-by-symbol mapping is not optimal in general, but is indeed optimal if we make some additional assumptions on the encoder and decoder. We also extend this result to the broadcast scenario and the interactive communication scenario. We provide bounds in the broadcast setting and a complete characterization of the solution under a mild condition on the target joint distribution in the interactive communication case. Our proofs are based on a Rényi entropy method.

The channel synthesis problem has been widely investigated over the last decade. In this paper, we consider the sequential version in which the encoder and the decoder work in a sequential way. Under a mild assumption on the target joint distribution we provide a complete (single-letter) characterization of the solution for the point-to-point case, which shows that the canonical symbol-by-symbol mapping is not optimal in general, but is indeed optimal if we make some additional assumptions on the encoder and decoder. We also extend this result to the broadcast scenario and the interactive communication scenario. We provide bounds in the broadcast setting and a complete characterization of the solution under a mild condition on the target joint distribution in the interactive communication case.

In this paper, we study the type graph, namely a bipartite graph induced by a joint type. We study the maximum edge density of induced bipartite subgraphs of this graph having a number of vertices on each side on an exponential scale. This can be seen as an isoperimetric problem. We provide asymptotically sharp bounds for the exponent of the maximum edge density as the blocklength goes to infinity. We also study the biclique rate region of the type graph, which is defined as the set of ($R_{1}, R_{2}$) such that there exists a biclique of the type graph which has respectively$e^{nR_{1}}$and$e^{nR_{2}}$vertices on the two sides. We provide asymptotically sharp bounds for the biclique rate region as well. We also apply similar techniques to strengthen small-set expansion theorems.

We leverage proof techniques from discrete Fourier analysis and an existing result in coding theory to derive new bounds for the problem of non-interactive simulation of binary random variables. Previous bounds in the literature were derived by applying data processing inequalities concerning maximal correlation or hypercontractivity. We show that our bounds are sharp in some regimes. Indeed, for a specific instance of the problem parameters, our main result resolves an open problem posed by E. Mossel in 2017. As by-products of our analyses, various new properties of the average distance and distance enumerator of binary block codes are established.

This paper investigates basic trade-offs between energy and delay in wireless communication systems using finite blocklength theory. We first assume that data arrive in constant stream of bits, which are put into packets and transmitted over a communications link. Our results show that depending on exactly how energy is measured, in general energy depends √d-1or √Vd-1log d, where d is the delay. This means on that the energy decreases quite slowly with increasing delay. Furthermore, to approach the absolute minimum of -1.59 dB on energy, bandwidth has to increase very rapidly, much more than what is predicted by infinite blocklength theory. We then consider the scenario when data arrive stochastically in packets and can be queued. We devise a scheduling algorithm based on finite blocklength theory and develop bounds for the energy-delay performance. Our results again show that the energy decreases quite slowly with increasing delay.

In this correspondence, we correct an erroneous result on the achievability part of the Rényi common information with order 1 + s E (1, 2] in (L. Yu and V. Y. F. Tan, “Wyner's common information under Rényi divergence measures,” IEEE Trans. Inf. Theory, vol. 64, no. 5, pp. 3616-3632, May 2018). The new achievability result (upper bound) of the Rényi common information no longer coincides with Wyner's common information. We also provide a new converse result (lower bound) in this correspondence for the Rényi common information with order 1 + s ϵ (1, ∞]. Numerical results show that for doubly symmetric binary sources, the new upper and lower bounds coincide for the order 1 + s ϵ (1, 2] and they are both strictly larger than Wyner's common information for this case.

We consider the exact channel synthesis problem. This problem concerns the determination of the minimum amount of information required to create exact correlation remotely when there is a certain rate of randomness shared by two terminals. This problem generalizes an existing approximate version, in which the generated joint distribution is required to be close to a target distribution under the total variation (TV) distance measure (instead being exactly equal to the target distribution). We provide single-letter inner and outer bounds on the admissible region of the shared randomness rate and the communication rate for the exact channel synthesis problem. These two bounds coincide for doubly symmetric binary sources. We observe that for such sources, the admissible rate region for exact channel synthesis is strictly included in that for the TV-approximate version. We also extend the exact and TV-approximate channel synthesis problems to sources with countably infinite alphabets and continuous sources; the latter includes Gaussian sources. As by-products, lemmas concerning soft-covering under Rényi divergence measures are derived.

Recently, two extensions of Wyner's common information-exact and Rényi common informations-were introduced respectively by Kumar, Li, and El Gamal (KLE), and the present authors. The class of common information problems involves determining the minimum rate of the common input to two independent processors needed to exactly or approximately generate a target joint distribution. For the exact common information problem, exact generation of the target distribution is required, while for Wyner's and α-Rényi common informations, the relative entropy and Rényi divergence with order α were respectively used to quantify the discrepancy between the synthesized and target distributions. The exact common information is larger than or equal to Wyner's common information. However, it was hitherto unknown whether the former is strictly larger than the latter for some joint distributions. In this paper, we first establish the equivalence between the exact and ∞-Rényi common informations, and then provide single-letter upper and lower bounds for these two quantities. For doubly symmetric binary sources, we show that the upper and lower bounds coincide, which implies that for such sources, the exact and ∞-Rényi common informations are completely characterized. Interestingly, we observe that for such sources, these two common informations are strictly larger than Wyner's. This answers an open problem posed by KLE. Furthermore, we extend Wyner's, ∞-Rényi, and exact common informations to sources with countably infinite or continuous alphabets, including Gaussian sources.

Recently, two extensions of Wyner's common information-exact and Rényi common informations-were introduced respectively by Kumar, Li, and El Gamal (KLE), and the present authors. The class of common information problems refers to determining the minimum rate of the common input to two independent processors needed to generate an exact or approximate joint distribution. For the exact common information problem, an exact generation of the target distribution is required, while for Wyner's and α-Rényi common informations, the relative entropy and Rényi divergence with order α were respectively used to quantify the discrepancy between the synthesized and target distributions. The exact common information is larger than or equal to Wyner's common information. However, it was hitherto unknown whether the former is strictly larger than the latter. In this paper, we first establish the equivalence between the exact and ∞-Rényi common informations, and then provide single-letter upper and lower bounds for these two quantities. For doubly symmetric binary sources, we show that the upper and lower bounds coincide, which implies that for such sources, the exact and ∞-Rényi common informations are completely characterized. Interestingly, we observe that for such sources, these two common informations are strictly larger than Wyner's. This answers an open problem posed by KLE.

We consider the exact channel synthesis problem. This problem concerns the determination of the amount of information required to create exact correlation remotely when there is a certain rate of randomness shared by two terminals. This problem generalizes an existing approximate version, in which the generated joint distribution is restricted to be close to a target distribution under the total variation (TV) distance measure, instead being exactly equal to the target distribution. We provide single-letter inner and outer bounds on the admissible region of the shared randomness rate and the communication rate for the exact channel synthesis problem. These two bounds coincide for doubly symmetric binary sources, which implies that for such sources, the admissible rate region is completely characterized. We observe that for such sources, the admissible rate region for exact channel synthesis is strictly included in that for TV-approximate version.

A coupling of two distributions PXand PYis a joint distribution PXYwith marginal distributions equal to PXand PY. Given marginals PXand PYand a real-valued function f of the joint distribution PXY, what is its minimum over all couplings PXYof PXand PY? We study the asymptotics of such coupling problems with different f's and with X and Y replaced by Xn= (X1, . . . , Xn) and Yn= (Y1, . . . , Yn) where Xiand Yiare i.i.d. copies of random variables X and Y with distributions PXand PY, respectively. These include the maximal coupling, minimum distance coupling, maximal guessing coupling, and minimum entropy coupling problems. We characterize the limiting values of these coupling problems as n tends to infinity. We show that they typically converge at least exponentially fast to their limits. Moreover, for the problems of maximal coupling and minimum excess-distance probability coupling, we also characterize (or bound) the optimal convergence rates (exponents). Furthermore, for the maximal guessing coupling problem, we show that it is equivalent to the distribution approximation problem. Therefore, some existing results for the latter problem can be used to derive the asymptotics of the maximal guessing coupling problem. We also study the asymptotics of the maximal guessing coupling problem for two general sources and a generalization of this problem, named the maximal guessing coupling through a channel problem. We apply the preceding results to several new information-theoretic problems, including exact intrinsic randomness, exact resolvability, channel capacity with input distribution constraint, and perfect stealth and secrecy communication.

The conventional channel resolvability problem refers to the determination of the minimum rate required for an input process so that the output distribution approximates a target distribution in either the total variation distance or the relative entropy. In contrast to previous works, in this paper, we use the (normalized or unnormalized) Rényi divergence (with the Rényi parameter in[0, 2] U {∞}) to measure the level of approximation. We also provide asymptotic expressions for normalized Rényi divergence when the Rényi parameter is larger than or equal to 1 as well as (lower and upper) bounds for the case when the same parameter is smaller than 1. We characterize the Rényi resolvability, which is defined as the minimum rate required to ensure that the Rényi divergence vanishes asymptotically. The Rényi resolvabilities are the same for both the normalized and unnormalized divergence cases. In addition, when the Rényi parameter smaller than 1, consistent with the traditional case where the Rényi parameter is equal to 1, the Rényi resolvability equals the minimum mutual information over all input distributions that induce the target output distribution. When the Rényi parameter is larger than 1 the Rényi resolvability is, in general, larger than the mutual information. The optimal Rényi divergence is proven to vanish at least exponentially fast for both of these two cases, as long as the code rate is larger than the Rényi resolvability. The optimal exponential rate of decay for i.i.d. random codes is also characterized exactly. We apply these results to the wiretap channel, and completely characterize the optimal tradeoff between the rates of the secret and non-secret messages when the leakage measure is given by the (unnormalized) Rényi divergence. This tradeoff differs from the conventional setting when the leakage is measured by the traditional mutual information.

The random variable simulation problem consists in using a k-dimensional i.i.d. random vector Xkwith distribution PXkto simulate an n-dimensional i.i.d. random vector Ynso that its distribution is approximately QYn. In contrast to previous works, in this paper, we consider the standard Rényi divergence and two variants of all orders to measure the level of approximation. These two variants are the max-Rényi divergence Dαmax(P, Q) and the sum-Rényi divergence Du (P, Q). When α = ∞, these two measures are strong because for any ϵ ≥ 0, D∞max(P, Q) ≤ ϵ or D∞+(P, Q) ≤ ϵ implies e-ϵ≤ (P(x)/Q(x))ϵfor all x. Under these Rényi divergence measures, we characterize the asymptotics of normalized divergences as well as the Rényi conversion rates. The latter is defined as the supremum of n/k such that the Rényi divergences vanish asymptotically. Our results show that, when the Rényi parameter is in the interval (0, 1), the Rényi conversion rates equal the ratio of the Shannon entropies H (PX)/H (QY), which is consistent with traditional results in which the total variation measure was adopted. When the Rényi parameter is in the interval (1, ∞), the Rényi conversion rates are, in general, smaller than H (PX)/H (QY). When specialized to the case in which either PXor QYis uniform, the simulation problem reduces to the source resolvability and intrinsic randomness problems. The preceding results are used to characterize the asymptotics of Rényi divergences and the Rényi conversion rates for these two cases.

We study a generalized version of Wyner's common information problem (also coined the distributed sources simulation problem). The original common information problem is to characterize the minimum rate of the common input to independent processors to generate an approximation of a joint distribution when the distance measure used to quantify the discrepancy between the synthesized and target distributions is the normalized relative entropy. Our generalization involves changing the distance measure to the unnormalized and normalized Renyi divergences of order α = 1+s ∈ [0,2]. We show that the minimum rate needed to ensure the Renyi divergences between the distribution induced by a code and the target distribution vanishes remains the same as the one in Wyner's setting, except when the order α = 1+s=0. This implies that Wyner's common information is rather robust to the choice of distance measure employed. As a byproduct of the proofs used to establish the above results, the exponentially strong converse for the common information problem under the total variation distance measure is established.

A coupling of two distributions PXand PYis a joint distribution PXYwith marginal distributions equal to PXand PY. Given marginals PXand PYand the maximal guessing probability function g(PXY):=maxf:X→YPP{Y=f(X)} of the joint distribution PXY, what is its maximum over all couplings PXYof PXand PY? This is the maximal guessing coupling problem. We study this problem and show that it is equivalent to the probability distribution approximation problem. Therefore, some existing results on the latter problem can be used to derive the asymptotics of the maximal guessing coupling problem. We apply these results to two new information-theoretic problems: channel capacity with input distribution constraint, and perfect stealth-secrecy communication.

This paper investigates the joint source-channel coding problem of sending a memoryless source over a memoryless broadcast channel. An inner bound and several outer bounds on the admissible distortion region are derived, which, respectively, generalize and unify several existing bounds. As a consequence, we also obtain an inner bound and an outer bound for the degraded broadcast channel case. When specialized to the Gaussian or binary source broadcast, the inner bound and outer bound not only recover the best known inner bound and outer bound in the literature but also generate some new results. Besides, we also extend the inner bound and outer bounds to the Wyner-Ziv source broadcast problem, i.e., source broadcast with side information available at decoders. Some new bounds are obtained when specialized to the Wyner-Ziv Gaussian and Wyner-Ziv binary cases.

We study a generalized version of Wyner's common information problem (also coined the distributed source simulation problem). The original common information problem consists in understanding the minimum rate of the common input to independent processors to generate an approximation of a joint distribution when the distance measure used to quantify the discrepancy between the synthesized and target distributions is the normalized relative entropy. Our generalization involves changing the distance measure to the unnormalized and normalized Rényi divergences of order α = 1 + s ∈ [0, 2]. We show that the minimum rate needed to ensure the Rényi divergences between the distribution induced by a code and the target distribution vanishes remains the same as the one in Wyner's setting, except when the order α = 1+s = 0. This implies that Wyner's common information is rather robust to the choice of distance measure employed. As a byproduct of the proofs used to the establish the above results, the exponential strong converse for the common information problem under the total variation distance measure is established.

We revisit the high-dimensional content identification with lossy recovery problem (Tuncel and Gündüz, 2014) and establish an exponential strong converse theorem. As a corollary of the exponential strong converse theorem, we derive an upper bound on the joint identification-error and excess-distortion exponent for the problem. Our main results can be specialized to the biometrical identification problem (Willems, 2003) and the content identification problem (Tuncel, 2009) since these two problems are both special cases of the content identification with lossy recovery problem. We leverage the information spectrum method introduced by Oohama and adapt the strong converse techniques therein to be applicable to the problem at hand.

In image compression and transmission, two basic data redundancies can be identified and exploited: statistical redundancy and perceptual redundancy. Statistical redundancy has been studied for a very long time and has been exploited in most image coding schemes. However, perceptual redundancy has not yet been completely exploited. Furthermore, perceptual redundancy is difficult to exploit by using digital coding techniques because power allocation is performed at the bit level for digital coding. However, as one of one-size-fits-all technique, soft transmission is suitable to exploit the perceptual redundancy because its power allocation is directly applied at the pixel level instead of the bit level. In this paper, we propose a novel image transmission scheme, named FoveaCast , for single-input single-output or multi-input multi-output broadcast systems that effectively utilizes the foveation characteristic of human vision and analog coding techniques to achieve higher visual perceptual quality. Hence, our scheme possesses not only high visual perceptual quality but also graceful quality adaptation. Experimental evaluations show that compared with three existing digital schemes and two existing analog schemes, SoftCast and ParCast, FoveaCast achieves better visual perceptual performance. Meanwhile, it achieves a graceful quality variation along with channel variation, behaving just like SoftCast and ParCast.

Recently, a secrecy measure based on list-reconstruction has been proposed, in which a wiretapper is allowed to produce a list of 2mRLreconstruction sequences and the secrecy is measured by the minimum distortion over the entire list. In this paper, we show that this list secrecy problem is equivalent to the one with secrecy measured by a new quantity lossy equivocation, which is proved to be the minimum optimistic one-achievable source coding rate (the minimum coding rate needed to reconstruct the source within target distortion with positive probability for infinitely many blocklengths) of the source with the wiretapped signal as two-sided information, and also can be seen as a lossy extension of conventional equivocation. Upon this (or list) secrecy measure, we study source-channel secrecy problem in the discrete memoryless Shannon cipher system with noisy wiretap channel. Two inner bounds and an outer bound on the achievable region of secret key rate, list rate, wiretapper distortion, and distortion of legitimate user are given. The inner bounds are derived by using uncoded scheme and (operationally) separate scheme, respectively. Thanks to the equivalence between lossy-equivocation secrecy and list secrecy, information spectrum method is leveraged to prove the outer bound. As special cases, the admissible region for the case of degraded wiretap channel or lossless communication for legitimate user has been characterized completely. For both these two cases, separate scheme is proved to be optimal. Interestingly, however, separation indeed suffers performance loss for other certain cases. Besides, we also extend our results to characterize the achievable region for Gaussian communication case. As a side product, optimistic lossy source coding has also been addressed.

This paper investigates a joint source-channel secrecy problem for the Shannon cipher broadcast system. We suppose list secrecy is applied, i.e., a wiretapper is allowed to produce a list of reconstruction sequences and the secrecy is measured by the minimum distortion over the entire list. For discrete communication cases, we propose a permutation-based uncoded scheme, which cascades a random permutation with a symbol-by-symbol mapping. Using this scheme, we derive an inner bound for the admissible region of secret key rate, list rate, wiretapper distortion, and distortions of legitimate users. For the converse part, we easily obtain an outer bound for the admissible region from an existing result. Comparing the outer bound with the inner bound shows that the proposed scheme is optimal under certain conditions. Besides, we extend the proposed scheme to the scalar and vector Gaussian communication scenarios, and characterize the corresponding performance as well. For these two cases, we also propose another uncoded scheme, orthogonal-transform-based scheme, which achieves the same performance as the permutation-based scheme. Interestingly, by introducing the random permutation or the random orthogonal transform into the traditional uncoded scheme, the proposed uncoded schemes, on one hand, provide a certain level of secrecy, and on the other hand, do not lose any performance in terms of the distortions for legitimate users.

In this paper, we consider video communication over fading channel, where the perfect instantaneous channel state information (CSI) is available at both sender and receiver. Most of existing coding schemes are inefficient in this communication scenario. The reason is that for digital coding scheme, it has high coding efficiency but unavoidably leads to the cliff effect; while for analog scheme, it has graceful video quality variation with channel varying, but has low coding efficiency. Hence, to integrate the advantages of digital coding and analog coding, we propose a hybrid digital-analog (HDA) scheme. In our scheme, we have adopted adaptive power allocation and adaptive forward error coding (FEC) in digital part to accommodate instantaneous channel quality. The evaluation results show that the proposed HDA scheme outperforms ParCast (a state-of-the-art analog scheme) 0.3~2.2dB under the channel Signal-to-Noise Ratio (SNR) from 3dB to 20dB.

This paper investigates the joint source-channel coding problem of sending a memoryless source over a memoryless degraded broadcast channel. An inner bound and an outer bound on the achievable distortion region are derived, which respectively generalize and unify several existing bounds. Moreover, when specialized to Gaussian source broadcast or binary source broadcast, the inner bound and outer bound could recover the best known inner bound and outer bound in the literature. Besides, the inner bound and outer bound are also extended to Wyner-Ziv source broadcast problem, i.e., source broadcast with degraded side information available at decoders. Some new bounds are obtained when specialized to Wyner-Ziv Gaussian case and Wyner-Ziv binary case.

Recently, Tianet al.[1]considered joint source-channel coding of transmitting a Gaussian source over$K$-user Gaussian broadcast channel, and derived an outer bound on the admissible distortion region. In[1], they stated “due to its nonlinear form, it appears difficult to determine whether it is always looser than the trivial outer bound in all distortion regimes with bandwidth compression”. However, in this correspondence we solve this problem and prove that for the bandwidth expansion case (with$K\geq 2$), this outer bound is strictly tighter than the trivial outer bound with each user being optimal in the point-to-point setting; while for the bandwidth compression or bandwidth match case, this outer bound actually degenerates to the trivial outer bound. Therefore, our results imply that on one hand, the outer bound given in[1]is nontrivial only for Gaussian broadcast communication ($K\geq 2$) with bandwidth expansion; on the other hand, unfortunately, no nontrivial outer bound exists so far for Gaussian broadcast communication ($K\geq 2$) with bandwidth compression.

Wireless video broadcast/multicast and mobile video communication pose a challenge to the conventional video transmission strategy (which applies separate digital source coding and digital channel coding in a point-to-point communication system) and, therefore, cooperative communication has been proposed to improve the received quality of the receivers with bad channels and the robustness to fading (mobility). In this paper we propose a novel wireless cooperative video coding (WCVC) framework. Specifically, we present a cooperative joint source-channel coding scheme that is based on hybrid digital-analog coding and integrates the advantages of digital coding and analog coding. Compared with most state-of-the-art cooperative video delivery methods, no matter for cooperation scenario or noncooperation scenario, the proposed scheme can avoid the staircase effect and realize continuous quality scalability on condition that the channel quality is within the expected range, and it has strong adaptability to channel variation with higher coding efficiency and better fairness among all receivers. Therefore, it is very suitable for wireless cooperative/noncooperative video broadcast/multicast transmission and cooperative/noncooperative mobile video applications. The experimental results show that the proposed WCVC outperforms SoftCast (which is a new analog scheme) and SVC + hierarchical modulation (HM) (which combines H.264/SVC codec and HM technique) no matter the noncooperative scenario or for cooperative scenario, which verify the effectiveness of our proposed WCVC framework.

Wireless video broadcast/multicast and mobile video communication pose a challenge to the conventional video transmission scheme (which consists of separate digital source coding and digital channel coding). The reason is that the separate coding scheme is based on nonscalable coding design, and this unavoidably leads to cliff effect as well as limits the ability to support multiple users with diverse channel conditions. In this paper, we propose a novel wireless scalable video coding (WSVC) framework. Specifically, we present a hybrid digital-analog (HDA) joint source-channel coding (JSCC) scheme that integrates the advantages of digital coding and analog coding. Moreover, the proposed JSCC is able to broadcast one video with different resolutions to fit various devices with different display resolutions. Compared to most state-of-the-art video delivery methods, it avoids the staircase effect and realizes continuous quality scalability (CQS) on condition that the channel quality is within the expected range, and it has strong adaptability to channel variation with higher coding efficiency and better fairness among all receivers. Therefore, it is very suitable for wireless video broadcast/multicast transmission and mobile video applications. The experimental results show that for broadcasting/multicasting the videos with CIF and QCIF resolutions the proposed WSVC outperforms SoftCast (which is a new analog scheme) average 0.60-5.90 dB and 3.39-9.97 dB respectively, outperforms SVC+HM (which combines H.264/SVC codec and hierarchical modulation technique) average 3.87-9.13 dB and 0.27-10.47 dB respectively, and outperforms DCast (which is an up-to-date video delivery scheme) about 0.2-3.3 dB for the video with CIF resolution. The experimental results verify the effectiveness of our proposed WSVC framework.

In this paper, we propose a novel wireless video transmission scheme named HDA-Cast, which is a hybrid digital-analog (HDA) coding scheme that integrates the advantages of digital coding and analog coding. Relative to most state-of-the-art video transmission methods, it avoids the “cliff effect” provided that the channel quality is within the expected range, gives better fairness among all receivers for multicast, and has strong adaption to channel variation. The evaluation results show that our HDA-Cast is 3.5-9.6 dB better than the SoftCast which is an up-to-date analog scheme. Owing to its strong adaption to channel variation, it can be regarded as a kind of wireless scalable video coding (WSVC).