We study distributed hypothesis testing under a covertness constraint in the non-alert situation, which requires that under the null-hypothesis an external warden be unable to detect whether communication between the sensor and the decision center is taking place. We characterize the achievable Stein exponent of this setup when the channel from the sensor to the decision center is a partially-connected discrete memoryless channel (DMC), i.e., when certain output symbols can only be induced by some of the inputs. The Stein-exponent in this case, does not depend on the specific transition law of the DMC and equals Shalaby and Papamarcou's exponent without a warden but where the sensor can send $k$ noise-free bits to the decision center, for $k$ a function that is sublinear in the observation length $n$. For fully-connected DMCs, we propose an achievable Stein-exponent and show that it can improve over the local exponent at the decision center. All our coding schemes do not require that the sensor and decision center share a common secret key, as commonly assumed in covert communication. Moreover, in our schemes the divergence covertness constraint vanishes (almost) exponentially fast in the obervation length $n$, again, an atypical behaviour for covert communication.

The paper presents an improved upper bound (achievability result) on the optimal tradeoff between Normalized Delivery Time (NDT) and computation load for distributed computing MapReduce systems in certain ranges of the parameters. The upper bound is based on interference alignment combined with zero-forcing. The paper further provides a lower bound (converse) on the optimal NDT-computation tradeoff that can be achieved when IVAs are partitioned into sub-IVAs, and these sub-IVAs are then transmitted (in an arbitrary form) by a single node, without cooperation among nodes. For appropriate linear functions (e.g., XORs), such non-cooperative schemes can achieve some of the best NDT-computation tradeoff points so far obtained in the literature. However, as our lower bound shows, any non-cooperative scheme achieves a worse NDT-computation tradeoff than our new proposed scheme for certain parameters, thus proving the necessity of cooperative schemes like zero-forcing to attain the optimal NDT-computation tradeoff.

This paper explores the tradeoff between covert communication capacity and secret key requirements over discrete memoryless channels (DMCs). We focus on settings where under a covertness constraint both communication and key rates are measured as the number of bits per square root of the block-length. While previous work has identified the maximum covert communication rates and the corresponding minimum key rates needed to achieve them, our study characterizes the minimum key rates necessary for all of desired covert communication rates. In equivalent terms, we determine, for any given key rate, the set of achievable covert rates. This relationship defines what we call the covert capacity-key tradeoff.Our analysis reveals several new insights. In scenarios where only small key rates are available and the adversary has a stronger channel than the intended receiver, binary signaling is optimal—regardless of the specific channel characteristics or input alphabets. In these cases, the covert capacity increases linearly with the available key rate. In other cases and for larger key rates, the covert capacity-key tradeoff grows sublinearly.We also extend our findings to multi-access channels (MACs) with binary inputs.

This paper identifies the Stein exponent of two distributed detection (binary hypothesis testing) setups with limited communication over a discrete memoryless channel (DMC). In the first setup, the DMC can only be used k(n) times, where k(n) grows sublinearly in the length of the observations n. In the second setup, the DMC can be used n times, however a block-input cost constraint Cnis imposed and Cngrows sublinearly in n. The optimal Stein exponent coincides for both setups and depends on whether the DMC is partially-connected, i.e., one of the output symbols can only be induced by a strict subset of the input symbols, or fully-connected. For partially-connected DMCs, the optimal Stein exponent of our setups coincides with the optimal Stein exponent (identified by Han and by Shalaby and Papamarcou) for the scenario where the sensor can communicate a sublinear (in n) number of bits to the decision center and communication is over a noiseless link. In contrast, for fully-connected DMCs the optimal Stein exponent collapses and is given by the optimal Stein exponent of the local test at the decision center. In this case, the sensor and the DMC do not help in improving the Stein exponent. Our results hold for general independent and identically distributed sources.

The main objective of this paper is to analyze a closed-loop feedback system where a transmitter probes a discrete memoryless channel (DMC) and can adapt its inputs based on the previous channel outputs. We prove that, regardless of the transmitter’s strategy, the conditional type of the outputs given the inputs remains close to the DMC transition law PY|X. This general result enables the study of fundamental limits in certain adaptive systems.As an application, we establish a converse result for an integrated sensing and communication (ISAC) model. In this setting, the transmitter also functions as a radar receiver, aiming to simultaneously transmit a message over the channel and estimate the channel state from the backscattered feedback signals. We show that the fundamental limits of the closed loop system are the same as of the open-loop system where the transmitter can use the feedback signal to estimate the state but not to produce adaptive channel inputs. This result holds as long as the sum of the admissible-average-decoding-error-probability, denoted ϵ, and the admissible-excess-distortion-probability, denoted δ, is below 1, i.e., δ + ϵ < 1.

Semantic-and task-oriented communication has emerged as a promising approach to reducing the latency and bandwidth requirements of the next-generation mobile networks by transmitting only the most relevant information needed to complete a specific task at the receiver. This is particularly advantageous for machine-oriented communication of high-data-rate content, such as images and videos, where the goal is rapid and accurate inference, rather than perfect signal reconstruction. While semantic-and task-oriented compression can be implemented in conventional communication systems, joint source–channel coding (JSCC) offers an alternative end-to-end approach by optimizing compression and channel coding together, or even directly mapping the source signal to the modulated waveform. Although all digital communication systems today rely on separation, thanks to its modularity, JSCC is known to achieve higher performance in finite blocklength scenarios and to avoidcliffand theleveling-off effectsin time-varying channel scenarios. This article provides an overview of the information theoretic foundations of JSCC, surveys practical JSCC designs over the decades, and discusses the reasons for their limited adoption in practical systems. We then examine the recent resurgence of JSCC, driven by the integration of deep learning techniques, particularly through DeepJSCC, highlighting its many surprising advantages in various scenarios. Finally, we discuss why it may be time to reconsider today’s strictly separate architectures and reintroduce JSCC to enable high-fidelity, low-latency communications in critical applications such as autonomous driving, drone surveillance, or wearable systems.

This paper proposes coding schemes and information-theoretic converse results for the transmission of heterogeneous delay-constrained traffic over interference networks with random user activity and random data arrivals. The heterogeneous delay-constrained traffic is composed of delay-tolerant traffic and delay-sensitive traffic where only the former can benefit from transmitter and receiver cooperation since the latter is subject to stringent delay constraints. Even for the delay-tolerant traffic, the total number of cooperation rounds at transmitter and receiver sides is limited to D rounds. Each transmitter is assumed to be active with probability$\rho \in [{0,1}]$, and we study two different models for traffic arrival, each model reflecting a different application type. In Model 1, each active transmitter sends a delay-tolerant message, and with probability$\rho _{f} \in [{0,1}]$also transmits an additional delay-sensitive message; in Model 2, each active transmitter sends either a delay-sensitive message with probability$\rho _{f}$or a delay-tolerant message with probability$1- \rho _{f}$. For both models, we derive inner and outer bounds on the fundamental per-user multiplexing gain (MG) region of the symmetric Wyner network as well as inner bounds on the fundamental MG region of the hexagonal model. The per-user MG of an interference network describes the logarithmic growth of the largest average per-user rate that can be achieved over the network at high signal-to-noise ratios (SNR). Our inner and outer bounds on the per-user MG are generally close and coincide in special cases. They also show that when both transmitters and receivers can cooperate, then under Model 1, transmitting delay-sensitive messages hardly causes any penalty on the sum per-user MG, and under Model 2, operating at large delay-sensitive per-user MGs incurs no penalty on the delay-tolerant per-user MG and thus even increases the sum per-user MG. However, when only receivers can cooperate, the maximum delay-tolerant per-user MG that our bounds achieve at maximum delay-sensitive per-user MG is significantly decreased.

The fundamental limit of Semantic Communications (joint source-channel coding) is established when the transmission needs to be kept covert from an external warden. We derive information-theoretic achievability and matching converse results and we show that source and channel coding separation holds for this setup. Furthermore, we show through an experimental setup that one can train a deep neural network to achieve covert semantic communication for the classification task. Our numerical experiments confirm our theoretical findings, which indicate that for reliable joint source-channel coding, the number of transmitted source symbols can only scale as the square-root of the number of channel uses.

In this paper, we caracterize the fundamental limits of a communication system with three users (i.e., three transmitters) and a single receiver where communication from two covert users must remain undetectable to an external warden. Our results show a tradeoff between the highest rates that are simultaneously achievable for the three users. They further show that the presence of a non-covert user in the system can enhance the capacities of the covert users under stringent secret-key constraints. To derive our fundamental limits, we provide an information-theoretic converse proof and present a coding scheme that achieves the performance of our converse result. Our coding scheme is based on multiplexing different code phases, which seems to be essential to exhaust the entire tradeoff region between the rates at the covert and the two non-covert users. This property is reminiscent of the setup with multiple non-covert users, where multiplexing is also required to exhaust the entire rate-region.

This paper studies the problem of distributed detection (binary hypothesis testing) over a discrete memoryless channel (DMC) under the constraint that an eavesdropping adversary should not be able to determine whether communication is ongoing or not, i.e., communication over the DMC has to remain covert. The main contribution of the paper is an upper bound on the largest possible Stein exponent, showing that it cannot exceed the largest exponent achievable under zero-rate communication over a noise-free link. In interesting special cases, the upper bound is achieved by a local test at the decision center that completely ig-nores the communication. In these cases, the covertness constraint renders communication useless for improving the Stein exponent.

A point-to-point integrated sensing and communication (ISAC) system is considered where a transmitter conveys a message to a receiver over a discrete memoryless channel (DMC) and simultaneously estimates the state of the channel through the backscattered signals of the emitted waveform. We derive achievability and converse bounds on the rate-distortion-error tradeoff in the finite blocklength regime, and also characterize the second-order rate-distortion-error region for the proposed setup. Numerical analysis shows that our proposed joint ISAC scheme significantly outperforms traditional time-sharing based schemes where the available resources are split between the sensing and communication tasks.

A communication setup is considered where a single transmitter wishes to convey messages to one or two receivers and simultaneously estimate the states of the receivers through the backscattered signals of the emitted waveform. The scenario at hand is motivated by joint radar and communication, which aims to co-design radar sensing and communication over shared spectrum and hardware. In this paper, we model the communication channel as a simple memoryless channel with independent and identically distributed (i.i.d.) time-varying state sequences and we model the backscattered signals by (strictly causal) generalized feedback. For single-receiver systems of this form, we fully characterize the capacity-distortion tradeoff, defined as the largest rate at which a message can reliably be conveyed to the receiver while simultaneously allowing the transmitter to sense the state sequence with a given allowed distortion. Our results show a tradeoff between the achievable rates and distortions, and that this tradeoff only stems from a common choice of the input distribution (the waveform) but not from other properties of the utilized codes. To better illustrate the capacity-distortion tradeoff, we propose a numerical method to compute the optimal inputs (waveforms) that achieve the desired tradeoff. For two-receiver systems with two states, we characterize the capacity-distortion tradeoff region of physically degraded broadcast channels (BC) as a rather straightforward extension of the single receiver case. Here, a tradeoff not only arises between sensing and communication performances but also between the various rates and the distortions of the different states. Similarly to the single-receiver case, the optimal co-design scheme exploits the generalized feedback signals only for sensing but not for improving communication performance. This is different for general two-receiver BCs, where optimal co-design schemes exploit generalized feedback also to improve capacity. However, as we show, also for BCs the optimal sensing performance only depends on the chosen input distribution (waveform) but not on the code construction used to accomplish the communication task. For general BCs, we provide inner and outer bounds on the capacity-distortion region, as well as a sufficient condition when this capacity-distortion region is equal to the product of the capacity region and the set of achievable distortions, in which case no tradeoff between sensing and communication occurs. A number of illustrative examples demonstrate that the optimal co-design schemes outperform conventional schemes that split the resources between sensing and communication, both for single-receiver and BC systems.

We consider a full-duplex wireless Distributed Computing (DC) system under the MapReduce framework. New upper and lower bounds on the optimal tradeoff between Normalized Delivery Time (NDT) and computation load are presented. The upper bound strictly improves over the previous reported upper bounds and is based on two novel interference alignment (IA) schemes tailored to the interference cancellation capabilities of the nodes. Our second IA scheme additionally applies a zero-forcing strategy that allows to accumulate all interference at any of the nodes on the same (small) subspace, leaving the remaining space for useful signals. The lower bound is proved through information-theoretic converse arguments based on carefully chosen multi-access channel (MAC) type arguments and by finding solutions to the optimization problems resulting from these arguments. The lower bound matches an existing upper bound based on zero-forcing and interference cancellation (but no IA) in the regime where each node can store at least half of the files. While optimal in this regime, zero-forcing and interference cancellation are not sufficient to obtain the optimal NDT in scenarios where each node cannot store half of the files. This follows from the previously established optimal NDT under zero-forcing and interference cancellation and our new IA-schemes.

The paper presents exponentially-strong converses for source-coding, channel coding, and hypothesis testing problems. More specifically, it presents alternative proofs for the well-known exponentially-strong converse for almost lossless source-coding with side-information and for channel coding over a discrete memoryless channel (DMC). These alternative proofs are solely based on a change of measure argument on the sets of conditionally or jointly-typical sequences that result in a correct decision, and on the analysis of these measures in the asymptotic regime of infinite blocklengths. The paper also presents new exponentially-strong converses for the$K$-hop hypothesis testing against independence problem with certain Markov chains and for the two-terminal$L$-round interactive compression problem with$J\geq 1$distortion constraints that depend on both sources and both reconstructions. For this latter problem, the exponentially-strong converse result states that whenever the rates lie outside the vanishing-excess-distortion-probability rate-region, then the sum of the$J$excess distortion probabilities asymptotically exceeds 1 or tends to 1 exponentially fast in the blocklength. (When the sum of the excess distortion probabilities exceeds 1, then a larger rate-distortion region is shown to be achievable.) The considered$L$-round$J$-distortion interactive source coding problem includes as special cases the Wyner-Ziv problem, the interactive function computation problem, and the compression with lossy common reconstruction problem. The new strong converse proofs for lossy compression and distributed hypothesis testing are derived using similar change of measure arguments as mentioned earlier and by additionally proving that certain Markov chains involving auxiliary random variables hold in the asymptotic regime of infinite blocklengths.

We consider a full-duplex wireless Distributed Computing (DC) system under the MapReduce framework. New upper and lower bounds on the optimal tradeoff between Normalized Delivery Time (NDT) and computation load are presented. The upper bound strictly improves over the previous reported upper bounds and is based on a novel interference alignment (IA) scheme tailored to the interference cancellation capabilities of MapReduce nodes. The lower bound is proved through information-theoretic converse arguments.

A point-to-point communication is considered where a roadside unite (RSU) wishes to simultaneously send messages of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services to a vehicle. The eMBB message arrives at the beginning of a block and its transmission lasts over the entire block. During each eMBB transmission block, random arrivals of URLLC messages are assumed. To improve the reliability of the URLLC transmissions, the RSU reinforces their transmissions by mitigating the interference of eMBB transmission by means of dirty paper coding (DPC). In the proposed coding scheme, the eMBB messages are decoded based on two approaches: treating interference as noise, and successive interference cancellation. Rigorous bounds are derived for the error probabilities of eMBB and URLLC transmissions achieved by our scheme. Numerical results illustrate that they are lower than bounds for standard time-sharing.

The paper characterizes the fundamental limits of integrated sensing and communication (ISAC) systems with a bi-static radar, where the radar receiver is located close to the transmitter and estimates or detects the state based on the transmitter’s channel inputs and the backscattered signals. Two models are considered. In the first model, the memoryless state sequence is distributed according to a fixed distribution and the goal of the radar receiver is to reconstruct this state-sequence with smallest possible distortion. In the second model, the memoryless state is distributed either according to PSor to QSand the radar’s goal is to detect this underlying distribution so that the missed-detection error probability has maximum exponential decay-rate (maximum Stein exponent). Similarly to previous results, our fundamental limits show that the tradeoff between sensing and communication solely stems from the empirical statistics of the transmitted codewords which influences both performances. The main technical contribution are two strong converse proofs that hold for all probabilities of communication error ϵ and excess-distortion probability or false-alarm probability δ summing to less than 1, ϵ+δ < 1. These proofs are based on two parallel change-of-measure arguments on the sets of typical sequences, one change-of-measure to obtain the desired bound on the communication rate, and the second to bound the sensing performance.

We study the state-dependent wiretap channel with non-causal channel state informations at the encoder in an integrated sensing and communications (ISAC) scenario. In this scenario, the transmitter communicates a message and a state sequence to a legitimate receiver while keeping the message and state-information secret from an external eavesdropper. This paper presents a new achievability result for this doubly-secret scenario, which recovers as special cases the best-known achievability results for the setups without security constraints or with only a security constraint on the message. The impact of the secrecy constraint (no secrecy-constraint, secrecy constraint only on the message, or on the message and the state) is analyzed at hand of a Gaussian-state and Gaussian-channel example.

This paper establishes the fundamental limits of a two-user single-receiver system where communication from User 1 (but not from User 2) needs to be undetectable to an external warden. Our fundamental limits show a tradeoff between the highest rates (or square-root rates) that are simultaneously achievable for the two users. Moreover, coded time-sharing for both users is fundamentally required on most channels, which distinguishes this setup from the more classical setups with either only covert users or only non-covert users. Interestingly, the presence of a non-covert user can be beneficial for improving the covert capacity of the other user.

We study a distributed binary hypothesis testing (HT) problem with communication and security constraints, involving three parties: a remote sensor called Alice, a legitimate decision center called Bob, and an eavesdropper called Eve, all having their own source observations. In this system, Alice conveys a rate-R description of her observations to Bob, and Bob performs a binary hypothesis test on the joint distribution underlying his and Alice’s observations. The goal of Alice and Bob is to maximize the exponential decay of Bob’s miss-detection (type-II error) probability under two constraints: Bob’s false-alarm (type-I error) probability has to stay below a given threshold and Eve’s uncertainty (equivocation) about Alice’s observations should stay above a given security threshold even when Eve learns Alice’s message. For the special case of testing against independence, we characterize the largest possible type-II error exponent under the described type-I error probability and security constraints.

We consider a multi-hop distributed hypothesis testing problem with multiple decision centers (DCs) for testing against independence and where the observations obey some Markov chain. For this system, we characterize the fundamental type-II error exponents region, i.e., the type-II error exponents that the various DCs can achieve simultaneously, under expected rate-constraints. Our results show that this fundamental exponents region is boosted compared to the region under maximum-rate constraints, and that it depends on the permissible type-I error probabilities. When all DCs have equal permissible type-I error probabilities, the exponents region is rectangular and all DCs can simultaneously achieve their optimal type-II error exponents. When the DCs have different permissible type-I error probabilities, a tradeoff between the type-II error exponents at the different DCs arises. New achievability and converse proofs are presented. For the achievability, a new multiplexing and rate-sharing strategy is proposed. The converse proof is based on applying different change of measure arguments in parallel and on proving asymptotic Markov chains. For the special casesK∈ {2, 3}, and for arbitraryK≥ 2 when all permissible type-I error probabilities at the various DCs are equal, we provide simplified expressions for the exponents region; a similar simplification is conjectured for the general case.

Wyner's soft-handoff network is considered where transmitters simultaneously send messages of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. Due to the low-latency requirements, the URLLC messages are transmitted over fewer channel uses compared to the eMBB messages. To improve the reliability of the URLLC transmissions, we propose a coding scheme with finite blocklength codewords that exploits dirty-paper coding (DPC) to precancel the interference from eMBB transmissions. Rigorous bounds are derived for the error probabilities of eMBB and URLLC transmissions achieved by our scheme. Numerical results illustrate that they are lower than for standard time-sharing.

A memoryless state-dependent multiple-access channel (MAC) is considered, where two transmitters wish to convey messages to a single receiver while simultaneously sensing (estimating) the respective states via generalized feedbacks. This scenario is motivated by joint radar and communication, which aims to co-design radar sensing and communication over shared spectrum and hardware. An improved inner bound is provided on the fundamental rate-distortions tradeoff which characterizes the communication rates the transmitters can achieve while simultaneously ensuring that their state-estimates satisfy desired distortion criteria. The new inner bound is based on a scheme where each transmitter codes over the generalized feedback so as to improve the state estimation at the other transmitter. We demonstrate the advantage of the proposed scheme other than previous ones through some examples. This is in contrast to the previously proposed schemes where coding is only used to convey data but not sensing information.

We consider a cooperative X-channel with K transmitters (TXs) and K receivers (Rxs) where Txs and Rxs are gathered into groups of size r respectively. Txs belonging to the same group cooperate to jointly transmit a message to each of the K − r Rxs in all other groups, and each Rx individually decodes all its intended messages. By introducing a new interference alignment (IA) scheme, we prove that when K/r is an integer the Sum Degrees of Freedom (Sum-DoF) of this channel is lower bounded by 2r if K/r ∈ {2, 3} and by $\frac{{K(K - r) - {r^2}}}{{2K - 3r}}$ if K/r ≥ 4. We also prove that the Sum-DoF is upper bounded by $\frac{{{\text{K}}({\text{K}} - {\text{r}})}}{{2{\text{K}} - 3{\text{r}}}}$. The proposed IA scheme finds application in a wireless distributed MapReduce framework, where it improves the normalized data delivery time (NDT) compared to the state of the art.

We study distributed binary hypothesis testing with a single sensor and two remote decision centers that are also equipped with local sensors. The communication between the sensor and the two decision centers takes place over three links: a shared link to both centers and an individual link to each of the two centers. All communication links are subject to expected rate constraints. This paper characterizes the optimal exponents region of the type-II error for given type-I error thresholds at the two decision centers and further simplifies the expressions in the special case of having only the single shared link. The exponents region illustrates a gain under expected rate constraints compared to equivalent maximum rate constraints. Moreover, it exhibits a tradeoff between the exponents achieved at the two centers.

Consider a multiple-input single-output system, where the nonnegative, peak-limited inputs ${X_1}, \ldots ,{X_{{n_{\text{T}}}}} \in [0,\mathcal{A}]$ are subject to first- and second-moment sum-constraints on all antennas. The paper characterizes all probability distributions that can be induced for the "channel image," which is given by the inner product of the input vector with a given channel vector. Key to this result is the description of input vectors that achieve a given deterministic channel image with the smallest energy, where "energy" of an input vector refers to a weighted sum of its one- and two-norms. Minimum-energy input vectors have an interesting structure: depending on the desired channel image, some of the weakest antennas are silenced, and the remaining antennas are chosen according to a shifted and amplitude-constrained beamforming rule.

The main contribution of this paper is a strong converse result for K-hop distributed hypothesis testing against independence with multiple (intermediate) decision centers under a Markov condition. Our result shows that the set of type-II error exponents that can simultaneously be achieved at all the terminals does not depend on the maximum permissible type-I error probabilities. Our strong converse proof is based on a change of measure argument and on the asymptotic proof of specific Markov chains. This proof method seems to be useful also in other applications, and is appealing because it does not require resorting to variational characterizations or blowing-up methods as in previous related proofs.

Distributed computing has become one of the most important frameworks in dealing with large computation tasks. In this paper, we propose a systematic construction of coded computing schemes for MapReduce-type distributed systems. The construction builds upon placement delivery arrays (PDA), originally proposed by Yanet al.for coded caching schemes. The main contributions of our work are three-fold. First, we identify a class of PDAs, calledComp-PDAs, and show how to obtain a coded computing scheme from any Comp-PDA. We also characterize the normalized number of stored files (storage load), computed intermediate values (computation load), and communicated bits (communication load), of the obtained schemes in terms of the Comp-PDA parameters. Then, we show that the performance achieved by Comp-PDAs describing Maddah-Ali and Niesen’s coded caching schemes matches a new information-theoretic converse, thus establishing the fundamental region of all achievable performance triples. In particular, we characterizeallthe Comp-PDAs achieving the pareto-optimal storage, computation, and communication (SCC) loads of the fundamental region. Finally, we investigate the file complexity of the proposed schemes, i.e., the smallest number of files required for implementation. In particular, we describe Comp-PDAs that achieve pareto-optimal SCC triples with significantly lower file complexity than the originally proposed Comp-PDAs.

The paper studies distributed binary hypothesis testing over a two-hop relay network where both the relay and the receiver decide on the hypothesis. Both communication links are subject to expected rate constraints, which differs from the classical assumption of maximum rate constraints. We exactly characterize the set of type-II error exponent pairs at the relay and the receiver when both type-I error probabilities are constrained by the same value$\epsilon > 0$. No tradeoff is observed between the two exponents, i.e., one can simultaneously attain maximum type-II error exponents both at the relay and at the receiver. For$\epsilon_{1}\neq\epsilon_{2}$, we present an achievable exponents region, which we obtain with a scheme that applies different versions of a basic two-hop scheme that is optimal under maximum rate constraints. We use the basic two-hop scheme with two choices of parameters and rates, depending on the transmitter's observed sequence. For$\epsilon_{1}=\epsilon_{2}$, a single choice is shown to be sufficient. Numerical simulations indicate that extending to three or more parameter choices is never beneficial.

This paper studies the channel capacity of intensity-modulation direct-detection (IM/DD) visible light communication (VLC) systems under both optical and electrical power constraints. Specifically, it derives the asymptotic capacities in the high and low signal-to-noise ratio (SNR) regimes under peak, first-moment, and second-moment constraints. The results show that first- and second-moment constraints are never simultaneously active in the asymptotic low-SNR regime, and only in few cases in the asymptotic high-SNR regime. Moreover, the second-moment constraint is more stringent in the asymptotic low-SNR regime than in the high-SNR regime.

The capacity region of several multiuser models in information theory can be enlarged by utilizing feedback of the received symbols. This is in contradiction to the discrete memoryless case, where feedback is known not to change the capacity. In this paper, we consider two broadcast models with noisy feedback from both the receivers. The models are derived from a standard memoryless scalar GBC, where two intermediate passive nodes are assumed to be observing the transmissions via separate noisy links corrupted by independent AWGN. In our first model, the scalar output from each intermediate node is passed through two additional independent AWGN links, called feedback and forward links. The output of the feedback link is observed by the transmitter as feedback, whereas only the forward link is observed by the corresponding decoder. We derive conditions that are both necessary and sufficient for feedback to enlarge the capacity region. In the second model, the two outputs of a standard GBC are observed by the respective decoders, but the transmitter observes the sum of the symbols at the receivers using causal feedback. We show that such a feedback has no effect on the capacity region.

Cascaded binary hypothesis testing is studied in this paper with two decision centers at the relay and the receiver. All terminals have their own observations, where we assume that the observations at the transmitter, the relay, and the receiver form a Markov chain in this order. The communication occurs over two hops, from the transmitter to the relay, and from the relay to the receiver. Expected rate constraints are imposed on both communication links. In this work, we characterize the optimal type-II error exponents at the two decision centers under constraints on the allowed type-I error probabilities. Our recent work characterized the optimal type-II error exponents in the special case when the two decision centers have same type-I error constraints and provided an achievability scheme for the general setup. To obtain the exact characterization for the general case, in this paper we provide a new converse proof as well as a new matching achievability scheme. Our results indicate that under unequal type-I error constraints at the relay and the receiver, a tradeoff arises between the maximum type-II error probabilities at these two terminals. Previous results showed that such a tradeoff does not exist under equal type-I error constraints or under general type-I error constraints when a maximum rate constraint is imposed on the communication links.

This paper analyses the multiplexing gain (MG) achievable over Wyner’s symmetric network with random user activity and random arrival of mixed-delay traffic. The mixed-delay traffic is composed of delay-tolerant traffic and delay-sensitive traffic where only the former can benefit from transmitter and receiver cooperation since the latter is subject to stringent decoding delays. The total number of cooperation rounds at transmitter and receiver sides is limited to D rounds. We derive inner and outer bounds on the MG region. In the limit as D$\rightarrow\infty$, the bounds coincide and the results show that transmitting delaysensitive messages does not cause any penalty on the sum MG. For finite D our bounds are still close and prove that the penalty caused by delay-sensitive transmissions is small.

This paper analyzes the multiplexing gains (MG) for simultaneous transmission of delay-sensitive and delay-tolerant data over interference networks. In the considered model, only delay-tolerant data can profit from coordinated multipoint (CoMP) transmission or reception techniques, because delay-sensitive data has to be transmitted without further delay. Transmission of delay-tolerant data is also subject to a delay constraint, which is however less stringent than the one on delay-sensitive data. Different coding schemes are proposed, and the corresponding MG pairs for delay-sensitive and delay-tolerant data characterized for Wyner’s linear symmetric network and for Wyner’s two-dimensional hexagonal network with and without sectorization. Information-theoretic converses are established for these models. For Wyners linear symmetric network the bounds match whenever the cooperation rates are sufficiently large or the delay-sensitive MG is small or moderate. These results show that on Wyner’s symmetric linear network and for sufficiently large cooperation rates, the largest MG for delay-sensitive data can be achieved without penalizing the maximum sum-MG of both delay-sensitive and delay-tolerant data. Our achievable schemes show that a similar conclusion holds for Wyner’s hexagonal network only for the model with sectorization. In the model without sectorization, a penalty in sum-MG is incurred whenever one insists on a positive delay-sensitive MG.

It is well known that the capacity region of an average transmit power constrained Gaussian Broadcast Channel (GBC) with independent noise realizations at the receivers is enlarged by the presence of causal noiseless feedback. When the noise variances at the receivers are identical, even passive feedback via independent memoryless Gaussian links can lead to a capacity region enlargement. The last fact remains true even when the feedback noise variance is very high, and available only from one of the receivers. While such capacity enlargements are feasible for several other feedback models in the Gaussian BC setting, it is also known that feedback does not change the capacity region for physically degraded broadcast channels. In this paper, we consider a two user GBC with independent noise realizations at the receivers, where the feedback links from the receivers are corrupted by independent additive Gaussian noise processes. We investigate the set of four noise variances, two forward and two feedback, for which no capacity enlargement is possible. A sharp characterization of this region is derived, i.e., any quadruple outside the presented region will lead to a capacity enlargement, whereas quadruples inside will leave the capacity region unchanged. Our results lead to the conclusion that when the forward noise variances are different, too noisy a feedback from one of the receivers alone is not always beneficial for enlarging the capacity region, be it from the stronger user or the weaker one, in sharp contrast to the case of equal forward noise variances.

The paper presents upper and lower bounds on the per-sector degrees of freedom (DoF) of a sectorized hexagonal cellular model when neighboring base stations (BSs) can cooperate during at most Δ interaction rounds over rate-limited backhaul links. The lower bound is based on practically implementable beamforming and adapts the way BSs cooperate to the sectorization of the cells. It improves over the naive approach that ignores this sectorization in terms of the sum-rate, both at finite signal-to-noise ratio (SNR) and in the high-SNR limit. For moderate SNR, the new scheme improves also over an opportunistic cooperation strategy where each message is decoded based on the signals received at the three adjacent sectors with the best SNR. The upper bound is information-theoretic and holds for all possible coding schemes, including for example interference alignment with unlimited symbol extensions whose practical implementation currently seems out of reach. Lower and upper bounds show that the complexity constraint, imposed by limiting the number of interaction rounds Δ, indeed limits the largest achievable sum-rate and DoF. In particular, irrespective of the backhaul capacity μ, the per-sector DoF cannot exceed a threshold which depends on Δ.

This paper studies the problem of discriminating two multivariate Gaussian distributions in a distributed manner. Specifically, it characterizes in a special case the optimal type- II error exponent as a function of the available communication rate. As a side-result, the paper also presents the optimal type-II error exponent of a slight generalization of the hypothesis testing against conditional independence problem where the marginal distributions under the two hypotheses can be different.

Feedback is known to enlarge the capacity region of a Gaussian Broadcast Channel (GBC) with independent noise realizations at the receivers, and an average power constraint at the transmitter. The capacity enlargement may occur even when there is noisy feedback from only one of the two receivers. However, recent results show the existence of a feedback noise threshold, beyond which one-sided feedback from only the stronger receiver is futile in enlarging the capacity region. The current paper presents a tight characterization of the feedback noise threshold, which separates the regimes where feedback from only the stronger receiver enlarges the capacity or leaves it unchanged. The scheme used to prove this result also leads to some interesting observations on noisy feedback from only the weak receiver.

A memoryless state-dependent broadcast channel (BC) is considered, where the transmitter wishes to convey two private messages to two receivers while simultaneously estimating the respective states via generalized feedback. The model at hand is motivated by a joint radar and communication system where radar and data applications share the same frequency band. For physically degraded BCs with i.i.d. state sequences, we characterize the capacity-distortion region tradeoff. For general BCs, we provide inner and outer bounds on the capacity-distortion region, as well as a sufficient condition when it is equal to the product of the capacity region and the set of achievable distortion. Interestingly, the proposed synergetic design significantly outperforms a conventional approach that splits the resource either for sensing or communication.

We investigate the testing-against-independence problem over a cooperative MAC with two sensors and a single detector under an average rate constraint on the sensors-detector links. For this setup, we design a variable-length coding scheme that maximizes the achievable type-II error exponent when the type-I error probability is limited to ϵ. Similarly to the single-link result, we show here that the optimal error exponent depends on ϵ and that variable-length coding allows to increase the rates over the optimal fixed-length coding scheme by the factor (1 − ϵ)−1.

This paper analyses the multiplexing gain (MG) achievable over a general interference network with random user activity and random arrival of mixed-delay traffic. The mixed-delay traffic is composed of delay-tolerant traffic and delay-sensitive traffic where only the former can benefit from receiver cooperation since the latter is subject to stringent decoding delays. Two setups are considered. In the first setup, each active transmitter always has delay-tolerant data to send and delay-sensitive data arrival is random. In the second setup, both delay-tolerant and delay-sensitive data arrivals are random, and only one of them is present at any given transmitter. The MG regions of both setups are completely characterized for Wyner’s soft-handoff network. For Wyner’s symmetric linear and hexagonal networks inner bounds on the MG region are presented.

We consider updating strategies for a local cache which downloads time-sensitive files from a remote server through a bandwidth-constrained link. The files are requested randomly from the cache by local users according to a popularity distribution which varies over time according to a Markov chain structure. We measure the freshness of the requested time-sensitive files through their Age of Information (AoI). The goal is then to minimize the average AoI of all requested files by appropriately designing the local cache’s downloading strategy. To achieve this goal, the original problem is relaxed and cast into a Constrained Markov Decision Problem (CMDP), which we solve using a Lagrangian approach and Linear Programming. Inspired by this solution for the relaxed problem, we propose a practical cache updating strategy that meets all the constraints of the original problem. Under certain assumptions, the practical updating strategy is shown to be optimal for the original problem in the asymptotic regime of a large number of files. For a finite number of files, we show the gain of our practical updating strategy over the traditional square-root-law strategy (which is optimal for fixed non time-varying file popularities) through numerical simulations.

Placement delivery arrays for distributed computing (Comp-PDAs) have recently been proposed as a framework to construct universal computing schemes for MapReduce-like systems. In this work, we extend this concept to systems with straggling nodes, i.e., to systems where a subset of the nodes cannot accomplish the assigned map computations in due time. Unlike most previous works that focused on computing linear functions, our results are universal and apply for arbitrary map and reduce functions. Our contributions are as follows. Firstly, we show how to construct a universal coded computing scheme for MapReduce-like systems with straggling nodes from any given Comp-PDA. We also characterize the storage and communication loads of the resulting scheme in terms of the Comp-PDA parameters. Then, we prove an information-theoretic converse bound on the storage-communication (SC) tradeoff achieved by universal computing schemes with straggling nodes. We show that the information-theoretic bound matches the performance achieved by the coded computing schemes with straggling nodes corresponding to the Maddah-Ali and Niesen (MAN) PDAs, i.e., to the Comp-PDAs describing Maddah-Ali and Niesen's coded caching scheme. Interestingly, the MAN-PDAs are optimal for any number of straggling nodes. This implies that the map phase of optimal coded computing schemes does not need to be adapted to the number of stragglers in the system. We show that the points that lie exactly on the fundamental SC tradeoff cannot be achieved with Comp-PDAs that require smaller number of files than the MAN-PDAs. This is however possible for some of the points that lie close to the SC tradeoff. For these latter points, the decrease in the requested number of files can be exponential in the number of nodes of the system. We also model the total execution time, and numerically show that the active set size should be chosen to balance the duration of the map phase and the durations of the shuffle and reduce phases.

A single-sensor two-detectors system is considered where the sensor communicates with both detectors and Detector 1 communicates with Detector 2, all over noise-free rate-limited links. The sensor and both detectors observe discrete memoryless source sequences whose joint probability mass function depends on a binary hypothesis. The goal at each detector is to guess the binary hypothesis in a way that, for increasing observation lengths, the probability of error under one of the hypotheses decays to zero with largest possible exponential decay, whereas the probability of error under the other hypothesis can decay to zero or to a small positive number arbitrarily slow. For the setting with positive communication rates from the sensor to the detectors and when both detectors are interested in maximizing the error exponent under the same hypothesis, we characterize the set of all possible exponents in a special case of testing against independence. In this case the cooperation link allows Detector 2 to increase its Type-II error exponent by an amount that is equal to the exponent attained at Detector 1. We also provide a general inner bound on the set of achievable error exponents that shows a tradeoff between the exponents at the two detectors in most cases. When the two detectors aim at maximizing the error exponent under different hypotheses and the distribution at the Sensor is different under the two hypotheses, then we show that such a tradeoff does not exist. We propose a general scheme that allows each detector to attain the same exponent as if it was the only detector in the system. For the setting with zero-rate communication on both links, we exactly characterize the set of possible exponents and the gain brought up by cooperation, in function of the number of bits that are sent over the two links. Notice that, for this setting, tradeoffs between the exponents achieved at the two detectors arise only in few particular cases. In all other cases, each detector achieves the same performance as if it were the only detector in the system.

This paper studies the capacity of a general multiple-input multiple-output (MIMO) free-space optical intensity channel under a per-input-antenna peak-power constraint and a total average-power constraint over all input antennas. The focus is on the scenario with more transmit than receive antennas. In this scenario, different input vectors can yield identical distributions at the output, when they result in the same image vector under multiplication by the channel matrix. We first determine the most energy-efficient input vectors that attain each of these image vectors. Based on this, we derive an equivalent capacity expression in terms of the image vector, and establish new lower and upper bounds on the capacity of this channel. The bounds match when the signal-to-noise ratio (SNR) tends to infinity, establishing the high-SNR asymptotic capacity. We also characterize the low-SNR slope of the capacity of this channel.

Coding and testing schemes for binary hypothesis testing over noisy networks are proposed and their corresponding type-II error exponents are derived. When communication is over a discrete memoryless channel (DMC), our scheme combines Shimokawa-Han-Amari's hypothesis testing scheme with Borade-Nakiboglu-Zheng's unequal error protection (UEP) for channel coding where source and channel codewords are simultaneously decoded. The resulting exponent is optimal for the newly introduced class of generalized testing against conditional independence. When communication is over a multi-access channel (MAC), our scheme combines hybrid coding with UEP. The resulting error exponent over the MAC is optimal in the case of generalized testing against conditional independence with independent observations at the two sensors when the MAC decomposes into two individual DMCs. In this case, separate source-channel coding is sufficient and no UEP is required. This same conclusion holds also under arbitrarily correlated sensor observations when testing is against independence.

This paper investigates the capacity of block fading optical intensity channels with more transmit than receive antennas under different assumptions on the transmitter's channel state information (CSI). Lower and upper bounds on the capacities are derived using the entropy power inequality (EP!) and a dual expression for capacity. Our lower bounds for perfect and partial CSI utilize a transmit-antenna cooperation strategy based on minimum-energy signaling, which we proposed recently. For perfect CSI, this lower bound matches the upper bound asymptotically in the high signal-to-noise ratio (SNR) regime. For imperfect CSI, our lower bound is close to its perfect-CSI counterpart.

The distributed hypothesis testing (DHT) problem is considered, in which the joint distribution of a pair of sequences present at separated terminals, is governed by one of two possible hypotheses. The decision needs to be made by one of the terminals (the "decoder"). The other terminal (the "encoder") uses a noisy channel in order to help the decoder with the decision. This problem can be seen as a generalization of the side-information variant of the DHT problem, where the rate-limited link is replaced by a noisy channel. A recent work by Salehkalaibar and Wigger has derived an achievable Stein exponent for this problem, by employing concepts from the DHT scheme of Shimokawa et al., and from unequal error protection coding for a single special message. In this work we extend the view to a trade-off between the two error exponents, additionally building on multiple codebooks and two special messages with unequal error protection. As a by product, we also present an achievable exponent trade-off for a rate-limited link, which generalizes Shimokawa et al..

The optimal storage-computation tradeoff is characterized for a MapReduce-like distributed computing system with straggling nodes, where only a part of the nodes can be utilized to compute the desired output functions. The result holds for arbitrary output functions and thus generalizes previous results that restricted to linear functions. Specifically, in this work, we propose a new information-theoretical converse and a new matching coded computing scheme, that we call coded computing for straggling systems (CCS).

A cloud radio access network (C-RAN) is considered where the first hop from the user equipments (UEs) to the basestations (BSs) is modeled by the fading Wyner soft-handoff model. The focus is on mixed-delay constraints where a set of messages (so called “slow” messages) are jointly decoded in the cloud unit (CU), whereas the remaining messages (called “fast” messages) have to be decoded immediately at the BSs. This paper presents inner and outer bounds on the capacity region for such a setup. Moreover, the multiplexing gain region is characterized exactly. The presented results show that for small fronthaul capacity it is beneficial to send both “fast” and “slow” messages. However, when the rate of “fast” messages is already large, then increasing it further, deteriorates the sum-rate of the system. In this regime, the stringent decoding delay on the “fast” messages penalizes the overall performance. Our results indicate that this penalty is larger at moderate SNR than at high SNR and it is also larger for random time-varying fading coefficients than for static ones.

The paper considers a coded caching setup with two libraries and where only one of them needs to be kept secret from an external eavesdropper. We provide upper and lower bounds on the secrecy rate-memory tradeoff for systems with K = 2 or K = 3 receivers. Our bounds are tight in some regimes and show that the standard (non-secure) coded caching upper bound can be approached for a wide range of parameters. In some cases, the proposed upper bound on the secrecy rate-memory tradeoff is even lower than the lower bound for standard coded caching. The reason is that in our setup the ratio of receivers requesting secure files over those requesting nonsecure files is fixed and known to everyone in advance. The transmitter can thus adjust the contents stored in the cache memories to this ratio.

International audience

This paper derives upper and lower bounds on the secrecy capacity-memory tradeoff of a wiretap erasure broadcast channel (BC) with Kw weak receivers and Ks strong receivers, where weak receivers and strong receivers have the same erasure probabilities and cache sizes, respectively. The lower bounds are achieved by the schemes that meticulously combine joint cache-channel coding with wiretap coding and key-aided onetime pads. The presented upper bound holds more generally for arbitrary degraded BCs and arbitrary cache sizes. When only weak receivers have cache memories, upper and lower bounds coincide for small and large cache memories, thus providing the exact secrecy capacity-memory tradeoff for this setup. The derived bounds further allow us to conclude that the secrecy capacity is positive even when the eavesdropper is stronger than all the legitimate receivers with cache memories. Moreover, they show that the secrecy capacity-memory tradeoff can be significantly smaller than its non-secure counterpart, but it grows much faster when cache memories are small. This paper also presents a lower bound on the global secrecy capacity-memory tradeoff where one is allowed to optimize the cache assignment subject to a total cache budget. It is close to the best known lower bound without secrecy constraint. For small total cache budget, the global secrecy capacity-memory tradeoff is achieved by assigning all the available cache memory uniformly over all the receivers if the eavesdropper is stronger than all the legitimate receivers, and it is achieved by assigning the cache memory uniformly only over the weak receivers if the eavesdropper is weaker than the strong receivers.

Coding and testing schemes and the corresponding achievable type-II error exponents are presented for binary hypothesis testing over two-hop relay networks. The schemes are based on cascade source coding techniques and unanimous decision-forwarding, the latter meaning that a terminal decides on the null hypothesis only if all previous terminals have decided on the null hypothesis. If the observations at the transmitter, the relay, and the receiver form a Markov chain in this order, then, without loss in performance, the proposed cascade source code can be replaced by two independent point-to-point source codes, one for each hop. The decoupled scheme (combined with decision-forwarding) is shown to attain the optimal type-II error exponents for various instances of “testing against conditional independence.” The same decoupling is shown to be optimal also for some instances of “testing against independence,” when the observations at the transmitter, the receiver, and the relay form a Markov chain in this order and when the relay-to-receiver link is of sufficiently high rate. For completeness, this paper also presents an analysis of the Shimokawa-Han-Amari binning scheme for the point-to-point hypothesis testing setup.

We consider a K-receiver wiretap broadcast channel where Kw receivers are weak and have cache memories and Ks receivers are strong and have no cache memories. We derive an upper bound on the secrecy rate-memory tradeoff under a joint secrecy constraint and under decentralized caching. In contrast to previous works, prefetching in our scheme is purely decentralized and receivers randomly sample from a random key stream available at the transmitter and from the files in a library. For small cache sizes, the performance of our scheme improves with increasing length of the random key stream. For moderate and large cache sizes, a small key stream suffices to perform close to the information-theoretic limit of the system.

Consider distributed hypothesis testing over multiple-access channels (MACs), where the receiver wishes to maximize the type-II error exponent under a constrained type-I error probability. For this setup, we propose a scheme that combines hybrid coding with a MAC-version of Borades unequal error protection. It achieves the optimal type-II error exponent for a generalization of testing against independence over an orthogonal MAC when the transmitters' sources are independent. In this case, hybrid coding can be replaced by the simpler separate source-channel coding. The paper also presents upper and lower bounds on the optimal type-II error exponent for generalized testing against independence of Gaussian sources over a Gaussian MAC. The bounds are close and significantly larger than a type-II error exponent that is achievable using separate source-channel coding.

A detection system with a single sensor and K detectors is considered, where each of the terminals observes a memoryless source sequence and the sensor sends a common message to all the detectors. The communication of this message is assumed error-free but rate-limited. The joint probability mass function (pmf) of the source sequences observed at the terminals depends on an M-ary hypothesis (M ≥ K), and the goal of the communication is that each detector can guess the underlying hypothesis. Each detector k aims to maximize the error exponent under hypothesis k, while ensuring a small probability of error under all other hypotheses. This paper presents an achievable exponents region for the case of positive communication rate, and characterizes the optimal exponents region for the case of zero communication rate. All results extend also to a composite hypothesis testing scenario.

Wyner's soft-handoff network with mixed delay constraints is considered when neighbouring receivers can cooperate over rate-limited links. Each source message is a combination of independent “fast” and “slow” bits, where the former are subject to a stringent decoding delay. Inner and outer bounds on the capacity region are derived, and the multiplexing gain region is characterized when only transmitters or only receivers cooperate.

A major practical limitation of the Maddah-Ali-Niesen coded caching techniques is their high subpacketization level. For the simple network with a single server and multiple users, Yan et al. proposed an alternative scheme with the so-called placement delivery arrays (PDA). Such a scheme requires slightly higher transmission rates but significantly reduces the subpack-etization level. In this paper, we extend the PDA framework and propose three low-subpacketization schemes for combination networks, i.e., networks with a single server, multiple relays, and multiple cache-aided users that are connected to subsets of relays. One of the schemes achieves the cutset lower bound on the link rate when the cache memories are sufficiently large. Our other two schemes apply only to resolvable combination networks. For these network and for a wide range of cache sizes, the new schemes perform closely to the coded caching schemes that directly apply Maddah-Ali-Niesen scheme while having significantly reduced subpacketization levels.

This paper investigates the capacity of the multiple- input multiple-output free-space optical intensity channel under a per-input-antenna peak-power constraint and a total average-power constraint over all input antennas. Our work considers the setup with more transmit than receive antennas, and characterizes capacity as an alternative optimization problem over the distribution of the input vector times the channel matrix. This alternative capacity expression is then used to obtain upper and lower bounds on the capacity, which match asymptotically in the high signal-to-noise ratio regime.

Coding schemes are proposed for Wyner's soft-handoff model and for the sectorized hexagonal model when some of the messages are delay-sensitive and cannot profit from transmitter or receiver cooperation. For the soft-handoff network we also provide a converse. It matches the multiplexing-gain achieved by our scheme when the multiplexing gain of the delay-sensitive messages is low or moderate or when the cooperation links have high capacities. In these cases, the sum-multiplexing gain is the same as if only delay-tolerant messages (which can profit from cooperation) were sent. A similar conclusion holds for the sectorized hexagonal model, when the capacities of the cooperation links are large.

We consider a MapReduce-like distributed computing system. We derive a lower bound on the communication cost for any given storage and computation costs. This lower bound matches the achievable bound we proposed recently. As a result, we completely characterize the optimal tradeoff between the storage, the computation, and the communication. Our result generalizes the previous one by Li et at. to also account for the number of computed intermediate values.

A distributed binary hypothesis testing problem is studied with one observer and two decision centers. Achievable type-II error exponents are derived for testing against conditional independence when the observer communicates with the two decision centers over one common and two individual noise-free bit pipes and when it communicates with them over a noisy broadcast channel. The results are based on a coding and testing scheme that splits the observations into subblocks, so that transmitter and receivers can independently apply to each subblock either Gray-Wyner coordination coding with side-information or hybrid joint source-channel coding with side-information, followed by a Neyman-Pearson test over the subblocks at the receivers. This approach allows to avoid introducing further error exponents that one would expect from the receivers' decoding operations related to binning or the noisy transmission channel. The derived exponents are shown to be optimal in some special cases when communication is over noise-free links. The results reveal a tradeoff between the type-II error exponents at the two decision centers.

An erasure broadcast network is considered with two disjoint sets of receivers: a set of weak receivers with all-equal erasure probabilities and equal cache sizes and a set of strong receivers with all-equal erasure probabilities and no cache memories. Lower and upper bounds are presented on the capacity-memory tradeoff of this network (the largest rate at which messages can be reliably communicated for given cache sizes). The lower bound is achieved by means of a joint cache-channel coding scheme and significantly improves over traditional schemes based on the separate cache-channel coding. In particular, it is shown that the joint cache-channel coding offers new global caching gains that scale with the number of strong receivers in the network. The upper bound uses bounding techniques from degraded broadcast channels and introduces an averaging argument to capture the fact that the contents of the cache memories are designed before knowing users' demands. The derived upper bound is valid for all stochastically degraded broadcast channels. The lower and upper bounds match for a single weak receiver (and any number of strong receivers) when the cache size does not exceed a certain threshold. Improved bounds are presented for the special case of a single weak and a single strong receiver with two files and the bounds are shown to match over a large range of cache sizes.

This paper derives upper and lower bounds on the capacity of the multiple-input single-output free-space optical intensity channel with signal-independent additive Gaussian noise subject to both an average-intensity and a peak-intensity constraint. In the limit where the signal-to-noise ratio (SNR) tends to infinity, the asymptotic capacity is specified, while in the limit where the SNR tends to zero, the exact slope of the capacity is given.

In this paper, we consider a lossy single-user caching problem with correlated sources. We first describe the fundamental interplay between the source correlations, the capacity of the user's cache, the user's reconstruction distortion requirements, and the final delivery-phase (compression) rate. We then illustrate this interplay using a multivariate Gaussian source example and a binary symmetric source example. To fully explore the effect of the user's distortion requirements, we formulate the caching problem using f-separable distortion functions recently introduce by Shkel and Verdú. The class of f-separable distortion functions includes separable distortion functions as a special case, and our analysis covers both the expected- and excess-distortion settings in detail. We also determine what “common information” should be placed in the cache, and what information should be transmitted during the delivery phase. To this end, two new common-information measures are introduced for caching, and their relationship to the common-information measures of Wyner, Gács, and Körner is discussed in detail.

Improved lower bounds are derived on the average and worst case rate-memory tradeoffs of the Maddah-Ali and Niesen-coded caching scenario. For any number of users and files and for arbitrary cache sizes, the multiplicative gap between the exact rate-memory tradeoff and the new lower bound is shown to be less than 2.315 in the worst case scenario and 2.507 in the average-case scenario.

This paper investigates the downlink of a cloud radio access network (C-RAN) in which a central processor communicates with two mobile users through two base stations (BSs). The BSs act as relay nodes and cooperate with each other through error-free rate-limited links. We develop and analyze two coding schemes for this scenario. The first coding scheme modifies the Liu-Kang scheme (to make it amenable to a rigorous analysis) and extends it to introduce common codewords and to apply for downlink C-RAN with BS-to-BS cooperation. This first coding scheme enables arbitrary correlation among the auxiliary codewords that are recovered by the BSs. We show that this scheme improves over previous schemes for various instances of Gaussian C-RAN channels. In particular, in many scenarios, the scheme can better exploit the possibility of BS-to-BS cooperation than other schemes. The second coding scheme extends the distributed decode and forward (DDF) scheme by means of Gray-Wyner compression and by exploiting the cooperation links between BSs. In addition and as a separate extension, we provide an improved capacity approximation for the DDF strategy for the capacity of a general N-BS L-user C-RAN model in the memoryless Gaussian case.

This paper considers a K-user Gaussian broadcast channel (BC) where receivers are equipped with cache memories. Lower and upper bounds are established on the capacity-memory tradeoff, i.e., the largest rate achievable for given cache-memories. The lower bound is based on a joint cache-channel coding scheme which generalizes the recently proposed piggyback coding to Gaussian BCs with unequal cache sizes. This paper also establishes lower and upper bounds on the global capacity-memory tradeoff, i.e., the maximum capacity-memory tradeoff over all possible cache assignments subject to a total cache memory constraint. The bounds match when the total cache memory is sufficiently large. It is shown that significantly larger rates can be achieved by carefully assigning larger cache memories to weaker receivers. In particular, cache allocation allows communication at rates that are (fundamentally) impossible to achieve with equal cache assignment. This shows the merit in carefully designing the cache size allocation in conjunction with channel qualities.

We derive upper and lower bounds on the secure capacity-memory tradeoff of the K-user (K > 2) wiretap erasure broadcast channel where Kwreceivers are weak and have cache memories of equal size, and Ksreceivers are strong and have no cache. The bounds coincide for small and large cache memories. The lower bound also exhibits that cache memories provide larger gains under a secrecy constraint than without such a constraint. The lower bound is based on a joint cache-channel coding scheme that simultaneously exploits the cache contents and the channel statistics. Moreover, we show for the two-user scenario that in the regime of small cache memories, the capacity-memory tradeoff is larger when only the weaker receiver has cache memory than when this cache memory is split equally among the two receivers.

The degraded K-receiver broadcast channel (BC) is studied when receivers are aided with cache memories. Lower and upper bounds are derived on the capacity-memory tradeoff, i.e., on the largest rate that can be achieved as a function of the receivers' cache sizes. The lower bounds are achieved by two new coding schemes that benefit from non-uniform cache assignment. The paper also provides lower and upper bounds on the global capacity-memory tradeoff of degraded BCs, i.e., on the largest capacity-memory tradeoff that can be attained by optimizing the receivers cache-assignment subject to a total cache memory budget. The bounds coincide when the total cache memory budget is sufficiently small or sufficiently large, with the thresholds depending on the BC statistics. For a small total cache budget M, it is optimal to assign all the cache memory to the weakest receiver. In this regime, the global capacity-memory tradeoff grows as M/D, where D denotes the total number of files in the system. For a large total cache budget, it is optimal to assign a positive cache memory to every receiver, where weaker receivers are assigned larger cache memories than stronger receivers. When the total cache budget M exceeds a threshold, then the global capacity-memory tradeoff grows as 1/K.M/D.A uniform cache-assignment policy is suboptimal.

A necessary condition is established for the lossy transmission of correlated sources over a memoryless multiple-access channel (MAC). It is used to derive lower bounds on the symmetric distortions that are achievable over Gaussian and binary adder MACs. When specialized to symmetric Gaussian MACs and Gaussian sources, the new lower bound recovers Lapidoth and Tinguely's max-correlation lower bound (2010) when the channel bandwidth is equal to the source bandwidth, and it improves on it when the channel bandwidth is higher. An analogous condition is also derived for the MAC with correlated sources and feedback.

This paper provides several asymptotic capacity results for the multiple-input multiple-output free-space optical intensity channel in the regime of high signal-to-noise ratio (SNR). For the case where the channel matrix has full column rank, the asymptotic capacity is derived assuming a peak-power constraint on each transmit antenna, or an average-power constraint on the total power across all transmit antennas, or both. For multiple-input and single-output channels, the asymptotic high-SNR capacity is derived when either only the total average power is constrained, or only the per-antenna peak power is constrained, or both but with the average-power constraint being sufficiently loose.

Improved lower bounds on the worst-case and the average-case rate-memory tradeoffs for the Maddah-Ali&Niesen coded-caching scenario are presented. For any number of users and files and for arbitrary cache sizes, the multiplicative gap between the exact rate-memory tradeoff and the new lower bound is less than 2.315 in the worst-case scenario and less than 2.507 in the average-case scenario.

We consider a system where a local cache maintains a collection of N dynamic content items that are randomly requested by local users. A capacity-constrained link to a remote network server limits the ability of the cache to hold the latest version of each item at all times, making it necessary to design an update policy. Using an age of information metric, we show under a relaxed problem formulation that an asymptotically optimal policy updates a cached item in proportion to the square root of the item's popularity. We then show experimentally that a physically realizable policy closely approximates the asymptotic optimal policy.

The paper studies the wiretap erasure broadcast channel (BC) with an external eavesdropper when the legitimate receivers have cache memories. Various secure coding schemes are proposed for a scenario where Kwweak receivers have same erasure probabilities and Ksstrong receivers have same erasure probabilities. The coding schemes achieve the cache-aided secrecy capacity when only weak receivers have cache memories and this cache memory is either small or large. They also allow to conclude the following: 1) Under a total cache budget it is often beneficial to assign the cache memories unequally between strong and weak receivers. 2.) Joint cache-channel coding is necessary to attain the optimal performance. 3.) The secrecy capacity can be positive even when the eavesdropper is stronger than the legitimate receivers.

This paper derives the asymptotic capacity for the multiple-input single-output free-space optical intensity channel in the regime of high signal-to-noise ratio (SNR). The asymptotic result is proven via upper and lower bounds on capacity at finite SNR.

Binary hypothesis testing over single and parallel cascade channels is considered where sensors communicate with dedicated relays, and these relays with a single final receiver. All relays as well as the final receiver decide on the binary hypothesis governing the joint probability distribution of the observations at the sensors, relays, and final receiver. The quantity of interest is the set of feasible type-II error exponents that allow for the type-I error probabilities to vanish asymptotically as the observation length increases. A coding scheme is proposed and the corresponding set of feasible type-II error exponents is analyzed by means of a modified Han-type analysis that can account for distributed decisions based on different codebooks and for nodes forwarding their decisions to other nodes. The obtained exponent region is optimal in some special cases.

We derive upper and lower bounds on the secure capacity-memory tradeoff of the two-user wiretap erasure BC with cache memory at the weaker receiver. The bounds coincide when the cache memory exceeds a given threshold. The lower bound also exhibits that cache memories provide larger gains under a secrecy constraint than without such a constraint. Moreover, for a large set of parameters the capacity-memory tradeoff is larger if only the weaker receiver has cache memory than when this cache memory is split equally among the receivers. The lower bound is based on a joint cache-channel coding scheme that simultaneously exploits the cache contents and the channel statistics. Such a joint design yields significant gains over a separation-based design.

This work investigates the downlink of cloud radio access networks (C-RANs), assuming digital cooperation links among the base stations (BSs). A generalization of the data-sharing scheme is proposed for the case of two BSs and two mobile users. The generalized data-sharing scheme includes a common part and allows full exploitation of correlation among auxiliary codewords. The cooperation links between the BSs are used to exchange and to redirect indices precomputed at the central processor. On the other hand, by simplifying the achievable rate region of the distributed decode-forward (DDF) scheme, it is shown that the DDF scheme for broadcast achieves the capacity region of a downlink $N$-BS $L$-user C- RAN with BS cooperation under the memoryless Gaussian model to within a gap of $\frac{L}{2}#x002B;\frac{\min\ {N,L\log_2 N\}}{2}$ bits per dimension. Numerical evaluations for the memoryless Gaussian model indicate that the generalized data-sharing scheme 1) outperforms the DDF scheme in the low-power regime and when the channel gain matrix is ill-conditioned and 2) benefits more from BS cooperation.

We study the capacity of a broadcast packet-erasure network with receiver caching. The receivers in the network are divided into two groups: A group of strong receivers with small packet erasure probabilities, and a group of weak receivers with large packet erasure probabilities. The weak receivers are provided with local cache memories as compensation for their poor channels. Achievable (lower) and converse (upper) bounds for the optimal capacity-memory tradeoff are derived. The lower bounds are proved using new joint cache-channel coding schemes that significantly outperform naive separate cache-channel coding schemes. For the case of two receivers, the capacity-memory tradeoff is completely characterized for a range of useful cache memory sizes.

A necessary condition for the transmissibility of correlated sources over a multi-access channel (MAC) is presented. The condition is related to Wyner's common information and to the Slepian-Wolf capacity region of the MAC with private and common messages. An analogous condition for the transmissibility of remote sources over a MAC is also derived. Here the transmitters only observe noisy versions of the sources.

We present upper and lower bounds on the per-user multiplexing gain (MG) of Wyner's circular soft-handoff model and Wyner's circular full model with cognitive transmitters and receivers with cache memories. The bounds are tight for cache memories with prelog μ that exceeds 2/3D in the soft-handoff model and exceeds D in the full model, where D denotes the number of possibly demanded files. In these cases the per-user MG of the two models is 1 + μ/D, the same as for non-interfering point-to-point links with caches at the receivers. Large receiver cache-memories thus allow to completely mitigate interference in these networks.

We propose two coding schemes for the two-receiver discrete memoryless broadcast channel (BC) with rate-limited feedback from one or both receivers. They improve over the no feedback capacity region for a large class of channels, including the class of strictly essentially less-noisy BCs that we introduce in this paper. Examples of strictly essentially less-noisy BCs are the binary symmetric BC or the binary erasure BC with unequal crossover or erasure probabilities at the two receivers. When the feedback rates are sufficiently large, our schemes recover all previously known capacity results for discrete memoryless BCs with feedback. In both our schemes, we let the receivers feedback quantization messages about their receive signals. In the first scheme, the transmitter simply relays the quantization information obtained from Receiver 1 to Receiver 2, and vice versa. This provides each receiver with a second observation of the input signal and can thus improve its decoding performance unless the BC is physically degraded. Moreover, each receiver uses its knowledge of the quantization message describing its own outputs so as to attain the same performance as if this message had not been transmitted at all. In our second scheme, the transmitter first reconstructs and processes the quantized output signals, and then sends the outcome as a common update information to both receivers. A special case of our second scheme also applies to memoryless BCs without feedback but with strictly causal state-information at the transmitter and causal state-information at the receivers. It recovers all previous achievable regions also for this setup with state-information.

The capacity of the semideterministic discrete memoryless broadcast channel (SD-BC) with partial message side-information (P-MSI) at the receivers is established. In the setting without a common message, it is shown that P-MSI to the stochastic receiver alone can increase capacity, whereas P-MSI to the deterministic receiver can only increase capacity if also the stochastic receiver has P-MSI. The latter holds only for the setting without a common message: if the encoder also conveys a common message, then P-MSI to the deterministic receiver alone can increase capacity. These capacity results are used to show that feedback from the stochastic receiver can increase the capacity of the SD-BC without P-MSI and the sum-rate capacity of the SD-BC with P-MSI at the deterministic receiver. The link between P-MSI and feedback is a feedback code, which-roughly speaking-turns feedback into P-MSI at the stochastic receiver, and hence helps the stochastic receiver mitigate experienced interference. For the case, where the stochastic receiver has full MSI (F-MSI) and can thus fully mitigate experienced interference also in the absence of feedback, it is shown that feedback cannot increase capacity.

This paper addresses a coordination problem between two agents (Agents 1 and 2) in the presence of a noisy communication channel which depends on an external system state {x0,t}. The channel takes as inputs both agents' actions, {x1,t} and {x2,t} and produces outputs that are observed strictly causally at Agent 2 but not at Agent 1. The system state is available either causally or non-causally at Agent 1 but unknown at Agent 2. Necessary and sufficient conditions on a joint distribution Q̅(x0, x1, x2) to be implementable asymptotically (i.e, when the number of taken actions grows large) are provided for both causal and non-causal state information at Agent 1. Since the coordination degree between the agents' actions, x1,tand x2,t, and the system state x0,tis measured in terms of an average payoff function, feasible payoffs are fully characterized by implementable joint distributions. In this sense, our results allow us e.g., to derive the performance of optimal power control policies on an interference channel and to assess the gain provided by non-causal knowledge of the system state at Agent 1. The derived proofs readily yield new results also for the problem of state-communication under a causality constraint at the decoder.

We propose a base-station (BS) cooperation model for broadcasting a discrete memoryless source in a cellular or heterogeneous network. The model allows the receivers to use helper BSs to improve network performance, and it permits the receivers to have prior side information about the source. We establish the model's information-theoretic limits in two operational modes: In Mode 1, the helper BSs are given information about the channel codeword transmitted by the main BS, and in Mode 2 they are provided information about the source. Optimal codes for Mode 1 use hash-and-forward coding at the helper BSs; while, in Mode 2, optimal codes use source codes from Wyner's helper side-information problem at the helper BSs. We prove the optimality of both approaches by way of a new list-decoding generalisation of [8, Thm. 6], and, in doing so, show an operational duality between Modes 1 and 2.

We consider a multi-agent scenario with K ≥ 2 agents that have partial information about some random nature state, and that take actions in a repeated manner. Each agent also has imperfect observations of the other agents' past actions and the nature state realization. Our goal is to characterize the set of asymptotically implementable distributions on the agents' actions and the nature state. We solve this problem for general K when all agents have only causal nature state information (NSI) and for K = 2 when: one agent has causal NSI and the other agent has non-causal NSI; or in some special cases when both agents have non-causal NSI.

In this paper, we study how the number of conferencing rounds effects the capacity of large interference networks. We take Wyner's asymmetric linear (soft-handoff) model, include conferencing links between closely located transmitters and receivers, and we consider the per-user asymptotic multiplexing gain. Our results show, for example, that when the capacities of the conferencing links scale at most (1/4) log P with the power P and when one can choose which transmitters and receivers cooperate, then there is no loss in terms of asymptotic multiplexing gain in having only one round of conferencing. In contrast, when the capacities of the conferencing links grow faster than (1/4) log P, then the asymptotic multiplexing gain with one round of conferencing is strictly smaller than that achieved with multiple rounds.

We propose a base-station (BS) cooperation model for broadcasting a discrete memoryless source in a cellular or heterogeneous network. The model allows the receivers to use helper BSs to improve network performance, and it permits the receivers to have prior side information about the source. We establish the model's information-theoretic limits in two operational modes: In Mode 1, the helper BSs are given information about the channel codeword transmitted by the main BS, and in Mode 2 they are provided correlated side information about the source. Optimal codes for Mode 1 use hash-and-forward coding at the helper BSs; while, in Mode 2, optimal codes use source codes from Wyner's helper source-coding problem at the helper BSs. We prove the optimality of both approaches by way of a new list-decoding generalisation used in Theorem 6 of Tuncel (2006), and in doing so, show an operational duality between Modes 1 and 2.

We show that the rate regions achieved by linear-feedback coding schemes over dual multi-antenna Gaussian multi-access channels (MACs) and broadcast channels (BCs) with independent noises coincide. By dual here we mean: (1) the channel matrices of the MAC and the BC are transposes of each other and (2) the same total input-power constraint P is imposed on both the channels. We also present multi-letter expressions for the linear-feedback capacity regions of the two channels, i.e., for the set of all rates that are achievable with the linear-feedback coding schemes. We identify a sub-class of MAC and BC linear-feedback coding schemes that achieve the respective linear-feedback capacity regions, and within these subclasses, we identify pairs of MAC and BC coding schemes that achieve the same rate regions. In the two-user case, when the transmitters or the receiver are single-antenna, the capacity region for the Gaussian MAC is known [20], [15] and the capacity-achieving scheme is a linear-feedback coding scheme. With our results, we can thus determine the linear-feedback capacity region of the two-user Gaussian BC when either transmitter or receivers are single-antenna and we can identify the corresponding linear-feedback capacity-achieving coding schemes. Our results show that the control-theory inspired linear-feedback coding scheme by Elia [11], Wu et al. [30], and Ardestanizadeh et al. [1] is sumrate optimal among all the linear-feedback coding schemes for the symmetric single-antenna Gaussian BC with equal channel gains. More generally, we show that the linear-feedback sum-capacity of the scalar Gaussian BC with independent noises is achieved using a simple rearrangement of Ozarow's MAC encodings and decodings. In the K 3-user case, Kramer [16] and Ardestanizadeh et al. [2] determined the linear-feedback sum-capacity for the symmetric single-antenna Gaussian MAC with equal channel gains. Using our duality result, in this paper, we identify the linear-feedback sum-capacity for the K 3-user single-antenna Gaussian BC with equal channel gains. It is equal to the sum-rate achieved by Ardestanizadeh et al.'s linear-feedback coding scheme [1].

This paper considers the problem of variable-length coding over a discrete memoryless channel with noiseless feedback. This paper provides a stochastic control view of the problem whose solution is analyzed via a newly proposed symmetrized divergence, termed extrinsic Jensen-Shannon (EJS) divergence. It is shown that strictly positive lower bounds on EJS divergence provide nonasymptotic upper bounds on the expected code length. This paper presents strictly positive lower bounds on EJS divergence, and hence nonasymptotic upper bounds on the expected code length, for the following two coding schemes: 1) variable-length posterior matching and 2) MaxEJS coding scheme that is based on a greedy maximization of the EJS divergence. As an asymptotic corollary of the main results, this paper also provides a rate-reliability test. Variable-length coding schemes that satisfy the condition(s) of the test for parameters R and E are guaranteed to achieve a rate R and an error exponent E. The results are specialized for posterior matching and MaxEJS to obtain deterministic one-phase coding schemes achieving capacity and optimal error exponent. For the special case of symmetric binary-input channels, simpler deterministic schemes of optimal performance are proposed and analyzed.

We show that for the multi-antenna Gaussian MAC and BC with perfect feedback, the largest achievable regions with linear-feedback schemes (called linear-feedback capacity regions) coincide when the same total input-power constraint is imposed on both channels and when the MAC channel matrices are the transposes of the BC channel matrices.

We propose two coding schemes for discrete memoryless broadcast channels (DMBCs) with rate-limited feedback. In our first scheme, the encoder does not process the feedback information that it receives, but simply relays it to the other receiver. This first scheme shows that arbitrary small, but positive, feedback rate suffices to improve over the nofeedback capacity for many DMBCs such as: any binary erasure BC (BEBC) with unequal erasure probability at the two receivers, any binary symmetric BC (BSBC) with unequal crossover probability at the receivers, and any binary erasure/binary symmetric BC (BEC/BSC-BC) with nonequal single-user capacity to the receivers. The scheme also improves the entire nofeedback capacity region for any strictly essentially less-noisy BC-a new class of BCs introduced in this paper-that is not physically degraded. In our second scheme, the encoder decodes all the feedback information and processes it with some local information before sending the result to the receivers. For some setups, this second scheme performs better than our first scheme. In the limit, as the available feedback-rates tend to infinity, our second scheme coincides with a special case of the Shayevitz and Wigger (SW) scheme for DMBCs with generalized feedback. The mentioned special case of the SW-scheme includes several other schemes as further special cases, e.g, the schemes by Dueck and by MaddahAli and Tse which achieve capacity or the degrees of freedom on the respectively studied channels. All our results hold also with noisy feedback when the receivers can code over the feedback links.

A coding scheme is proposed for the memoryless Gaussian broadcast channel with correlated noises and feedback. For all noise correlations other than ±1, the gap between the sum-rate that the scheme achieves and the full-cooperation bound vanishes as the signal-to-noise ratio tends to infinity. When the correlation coefficient is -1, the gains afforded by feedback are unbounded and the prelog is doubled. When the correlation coefficient is +1, we demonstrate a dichotomy that if the noise variances are equal, then feedback is useless, and otherwise, feedback affords unbounded rate gains and doubles the prelog. The unbounded feedback gains, however, require perfect (noiseless) feedback. When the feedback links are noisy, the feedback gains are bounded, unless the feedback noise decays to zero sufficiently fast with the signal-to-noise ratio. Extensions to more receivers are also discussed as is the memoryless Gaussian interference channel with feedback.

We study an interference network where equally numbered transmitters and receivers lie on two parallel lines, with each transmitter opposite its intended receiver. We consider two short-range interference models: the asymmetric network, where the signal sent by each transmitter is interfered only by the signal sent by its left neighbor (if present), and a symmetric network, where it is interfered by both its left and its right neighbors. Each transmitter is cognizant of its own message, the messages of the tℓtransmitters to its left, and the messages of the trtransmitters to its right. Each receiver decodes its message based on the signals received at its own antenna, at the rrreceive antennas to its left, and at the rrreceive antennas to its right. For such networks, we provide upper and lower bounds on the multiplexing gain, i.e., on the high signal-to-noise ratio asymptotic logarithmic growth of the sum-rate capacity. In some cases, our bounds coincide, e.g., for the asymmetric network. Our results exhibit an equivalence between the transmitter sideinformation parameters tℓ, tr and the receiver side-information parameters rℓ, rrin the sense that increasing/decreasing tℓor trby a positive integer δ has the same effect on the multiplexing gain as increasing/decreasing rℓor rrby δ. Moreover-even in asymmetric networks-there is an equivalence between the left side-information parameters (tℓ, rℓ) and the right sideinformation parameters (tr, rr).

The source-coding problem with side information at the decoder is studied subject to a constraint that the encoder-to whom the side information is unavailable-be able to compute the decoder's reconstruction sequence to within some distortion. For discrete memoryless sources and finite single-letter distortion measures, an expression is given for the minimal description rate as a function of the joint law of the source and side information and of the allowed distortions at the encoder and at the decoder. The minimal description rate is also computed for a memoryless Gaussian source with squared-error distortion measures. A solution is also provided to a more general problem where there are more than two distortion constraints and each distortion measure may be a function of three arguments: the source symbol, the encoder's reconstruction symbol, and the decoder's reconstruction symbol.

A computable expression for Heegard and Berger's rate-distortion function has eluded information theory for nearly three decades. Heegard and Berger's single-letter achievability bound is well known to be optimal for physically degraded side information; however, it is not known whether the bound is optimal for arbitrarily correlated side information (general discrete memoryless sources). In this paper, we consider a new setup where the side information at one receiver is conditionally less noisy than that at the other. The new setup includes degraded side information as a special case, and it is motivated by the literature on degraded and less noisy broadcast channels. Our key contribution is a converse proving the optimality of Heegard and Berger's achievability bound in a new setting, where the side information is conditionally less noisy and one distortion function is deterministic. The less noisy setup is also generalized to two different successive-refinement problems.

We consider the\(K\geq 2\)-user memoryless Gaussian broadcast channel (BC) with feedback and common message only. We show that linear-feedback schemes with a message point, in the spirit of Schalkwijk and Kailath’s scheme for point-to-point channels or Ozarow and Leung’s scheme for BCs with private messages, are strictly suboptimal for this setup. Even with perfect feedback, the largest rate achieved by these schemes is strictly smaller than capacity\(C\)(which is the same with and without feedback). In the extreme case where the number of receivers\(K\to \infty \), the largest rate achieved by linear-feedback schemes with a message point tends to 0. To contrast this negative result, we describe a scheme for rate-limited feedback that uses the feedback in an intermittent way, i.e., the receivers send feedback signals only in few channel uses. This scheme achieves all rates\(R\)up to capacity\(C\)with an\(L\)th order exponential decay of the probability of error if the feedback rate\(R_{\text {fb}}\)is at least\((L-1)R\)for some positive integer\(L\).

We consider the lossless Kaspi/Heegard-Berger source coding problem where an encoder communicates a common description of two sources to two decoders, and each decoder wants to reconstruct one of the sources with the help of side information. We present new results on the utility of encoder side information for this scenario. We show that for some sources and side informations - e.g., for some instances of conditionally less noisy side information - the minimum rate that is required to describe the sources is strictly reduced when the side information is also known at the encoder. On the other hand, we identify classes of sources and side informations - e.g., physically degraded side information - where encoder side information does not change the minimum description rate. We show similar results for a scenario where one decoder has to reconstruct both sources and for a scenario where the encoder is informed only about one of the decoder's side information.

We consider the successive refinement of information problem with decoder side information. The rate-distortion region is unknown in general; Steinberg & Merhav and Tian & Diggavi solved it in the special case of degraded side information. We extend this special case to a new setup, conditionally less noisy side information, and we give a single-letter solution when one distortion function is deterministic.

We propose two coding schemes for discrete memoryless broadcast channels (DMBCs) with rate-limited feedback from only one receiver. For any positive feedback rate and for the class of strictly less-noisy DMBCs, our schemes strictly improve over the no-feedback capacity region.

A coding scheme for the discrete memoryless broadcast channel with {noiseless, noisy, generalized} feedback is proposed, and the associated achievable region derived. The scheme is based on a block-Markov strategy combining the Marton scheme and a lossy version of the Gray–Wyner scheme with side information. In each block, the transmitter sends fresh data and update information that allows the receivers to improve the channel outputs observed in the previous block. For a generalization of Dueck's broadcast channel, our scheme achieves the noiseless-feedback capacity, which is strictly larger than the no-feedback capacity. For a generalization of Blackwell's channel and when the feedback is noiseless, our new scheme achieves rate points that are outside the no-feedback capacity region. It follows by a simple continuity argument that for both these channels and when the feedback noise is sufficiently low, our scheme improves on the no-feedback capacity even when the feedback is noisy.

We consider the capacity region of a three receiver broadcast channel with some message cognition at two receivers. The problem generalizes the bi-directional broadcast channel to include a third receiver, a common message, and (partial) message cognition. We characterize the capacity region for several classes of less noisy, more capable, and deterministic broadcast channels.

This paper considers the problem of variable-length coding over a binary-input channel with noiseless feedback. A deterministic sequential coding scheme is proposed and shown to attain the optimal error exponent for any binary-input channel whose capacity is achieved by the uniform input distribution. The proposed scheme is deterministic and has only one phase of operation, in contrast to all previous coding schemes that achieve the optimal error exponent.

We consider a lossless multi-terminal source coding problem with one transmitter, two receivers and side information. The achievable rate region of the problem is not well understood. In this paper, we characterise the rate region when the side information at one receiver is conditionally less noisy than the side information at the other, given this other receiver's desired source. The conditionally less noisy definition includes degraded side information and a common message as special cases, and it is motivated by the concept of less noisy broadcast channels. The key contribution of the paper is a new converse theorem employing a telescoping identity and the Csiszár sum identity.

The capacity region of the -sender Gaussian multiple access channel with feedback is not known in general. This paper studies the class of linear-feedback codes that includes (nonlinear) nonfeedback codes at one extreme and the linear-feedback codes by Schalkwijk and Kailath, Ozarow, and Kramer at the other extreme. The linear-feedback sum-capacity under symmetric power constraints is characterized, the maximum sum-rate achieved by linear-feedback codes when each sender has the equal block power constraint . In particular, it is shown that Kramer's code achieves this linear-feedback sum-capacity. The proof involves the dependence balance condition introduced by Hekstra and Willems and extended by Kramer and Gastpar, and the analysis of the resulting nonconvex optimization problem via a Lagrange dual formulation. Finally, an observation is presented based on the properties of the conditional maximal correlation-an extension of the Hirschfeld-Gebelein-Rényi maximal correlation-which reinforces the conjecture that Kramer's code achieves not only the linear-feedback sum-capacity, but also the sum-capacity itself (the maximum sum-rate achieved by arbitrary feedback codes).

We derive the capacity region of the two-user dirty-paper Gaussian multiaccess channel (MAC) with conferencing encoders. In this MAC, prior to each transmission block, the transmitters can hold a conference in which they can communicate with each other over error-free bit pipes of given capacities. The received signal suffers not only from additive Gaussian noise but also from additive interference, which is known noncausally to the transmitters but not to the receiver. The additive interference is modeled as Gaussian or uniform over a sphere. We show that the interference can be perfectly mitigated, i.e., that the capacity region without interference can also be achieved in its presence. This holds irrespective of whether the transmitters learn the interference before or after the conference. It follows as a corollary that also for the MAC with degraded message sets, the interference can be perfectly mitigated if it is known noncausally to the transmitters. To derive our results, we generalize Costa's single-user writing-on-dirty-paper achievability result to channels with dependent interference and not-necessarily Gaussian noise.

We consider a variation on the Wyner-Ziv source coding problem with side-information at the decoder where the encoder is required to be able to compute the decoder's reconstruction sequence with some fidelity. This requirement limits the extent to which the reconstruction sequence can depend on the side-information, which is not available to the encoder. For finite-alphabet memoryless sources and single-letter distortion measures we compute the minimal description rate as a function of the joint law of the source and side-information and of the allowed distortions at the encoder and decoder. We also treat memoryless Gaussian sources with mean squared-error distortion measures.

We study Wyner's asymmetric interference network (soft-handoff model) when each transmitter has local, rate-limited side-information about the messages of the J transmitters to its left and the J transmitters to its right. We distinguish two scenarios. In Scenario A the neighbors of Transmitter k can have different, individual, side-information about Message Mk. In Scenario B they all have the same side-information about Mk. For both scenarios we derive the asymptotic multiplexing gain per-user, that is, the limiting ratio of the multiplexing gain divided by the number of users K when K → ∞.

We explore the interplay between interference, cooperation and connectivity in heterogeneous wireless interference networks. Specifically, we consider a 4-user locally-connected interference network with pairwise clustered decoding and show that its degrees of freedom (DoF) are bounded above by 12/5. Interestingly, when compared to the corresponding fully connected setting which is known to have 8/3 DoF, the locally connected network is only missing interference-carrying links, but still has lower DoF, i.e., eliminating these interference-carrying links reduces the DoF. The 12/5 DoF outer bound is obtained through a novel approach that translates insights from interference alignment over linear vector spaces into corresponding sub-modularity relationships between entropy functions.

This paper studies the class of generalized linear feedback codes for additive white Gaussian noise multiple access channel. This class includes (nonlinear) nonfeedback codes at one extreme and linear feedback codes by Schalkwijk and Kailath, Ozarow, and Kramer at the other extreme. The linear sum capacity CL(P), the maximum sum-rate achieved by the generalized linear feedback codes, is characterized under symmetric block power constraints P for all the senders. In particular, it is shown that the Kramer linear code achieves CL(P). Based on the properties of the conditional maximal correlation, an extension of the Hirschfeld-Gebelein-Renyi maximal correlation, it is conjectured that Kramer's linear code achieves not only the linear sum capacity, but also the general sum capacity, i.e., the maximum sum-rate achieved by arbitrary feedback codes.

A coding scheme for the discrete memoryless broadcast channel with (possible noisy) feedback is proposed, and the corresponding achievable region derived. The scheme is based on a block-Markov strategy where in each block the transmitter sends fresh data and update information that allows the receivers to improve the channel outputs observed in the previous block. The region is analyzed for two specific broadcast channels: 1) A generalization of Dueck's channel, where it is shown that for noiseless output-feedback the region coincides with the capacity region; 2) A noisy version of Blackwell's channel, where it is shown that for noiseless - and in some cases noisy - output-feedback, the region improves upon the no-feedback capacity region.

New achievable rate regions are derived for the two-user additive white Gaussian multiple-access channel with noisy feedback. The regions exhibit the following two properties. Irrespective of the (finite) Gaussian feedback-noise variances, the regions include rate points that lie outside the no-feedback capacity region, and when the feedback-noise variances tend to zero the regions converge to the perfect-feedback capacity region.

We study the uplink of a linear cellular model featuring short range inter-cell interference. Specifically, we consider a K-transmitter/K-receiver interference network where the signal transmitted by a given transmitter is interfered by the signal sent by the transmitter to its left. We assume that each transmitter has side-information consisting of the messages of the Jlusers to its left and the Jrusers to its right, and that each receiver can decode its message using the signals received at its own antenna, at the ilantennas to its left, and at the irantennas to its right. For this setting, we characterize the multiplexing gain, i.e., the asymptotic logarithmic growth of the sum-rate capacity at high SNR, and point out interesting duality aspects. We also present results on the multiplexing gain of a symmetric version of this network where the signal sent by a given transmitter is interfered by the signals sent by the transmitter to its left and the transmitter to its right.

We study the three-user multi-antenna Gaussian multiple-access channel (MAC) where prior to the transmission over the MAC the transmitters can communicate with each other over noise-free broadcast pipes of given capacities. We present the capacity region of this channel. Additionally, we also study the three-user multi-antenna Gaussian MAC with common messages and present its capacity region. The main step in deriving these two capacity results consists in proving that Gaussian distributions maximize certain mutual information expressions under multiple Markov constraints. Towards this end, a tool previously used is extended to the vector case and to multiple Markov conditions.

An achievable rate for the discrete memoryless relay channel with receiver-transmitter feedback is proposed based on block-Markov superposition encoding. The achievable rate can also be extended to Gaussian channels. A second achievable rate for the Gaussian relay channel based on a Schalkwijk-Kailath type scheme is presented. For some channels both achievable rates strictly improve upon all previously known achievable rates. For the discrete memoryless relay channel also a converse result is provided.

Upper and lower bounds are derived on the capacity of the free-space optical intensity channel. This channel has a nonnegative input (representing the transmitted optical intensity), which is corrupted by additive white Gaussian noise. To preserve the battery and for safety reasons, the input is constrained in both its average and its peak power. For a fixed ratio of the allowed average power to the allowed peak power, the difference between the upper and the lower bound tends to zero as the average power tends to infinity and their ratio tends to one as the average power tends to zero. When only an average power constraint is imposed on the input, the difference between the bounds tends to zero as the allowed average power tends to infinity, and their ratio tends to a constant as the allowed average power tends to zero.

We derive the capacity region of the Gaussian version of Willemspsilas two-user MAC with conferencing encoders. This setting differs from the classical MAC in that, prior to each transmission block, the two transmitters can communicate with each other over noise-free bit-pipes of given capacities. The derivation requires a new technique for proving the optimality of Gaussian input distributions in certain mutual information maximizations under a Markov constraint. We also consider a Costa-type extension of the Gaussian MAC with conferencing encoders. In this extension, the channel can be described as a two-user MAC with Gaussian noise and Gaussian interference where the interference is known non-causally to the encoders but not to the decoder. We show that as in Costa's setting the interference sequence can be perfectly canceled, i.e., that the capacity region without interference can be achieved.

New upper and lower bounds are presented on the capacity of the free-space optical intensity channel. This channel is characterized by inputs that are nonnegative (representing the transmitted optical intensity) and by outputs that are corrupted by additive white Gaussian noise (because in free space the disturbances arise from many independent sources). Due to battery and safety reasons the inputs are simultaneously constrained in both their average and peak power. For a fixed ratio of the average power to the peak power the difference between the upper and the lower bounds tends to zero as the average power tends to infinity, and the ratio of the upper and lower bounds tends to one as the average power tends to zero. The case where only an average-power constraint is imposed on the input is treated separately. In this case, the difference of the upper and lower bound tends to 0 as the average power tends to infinity, and their ratio tends to a constant as the power tends to zero.

A generic intuition says that the pre-log, or multiplexing gain, cannot be larger than the minimum of the number of transmit and receive dimensions. This suggests that for the scalar broadcast channel, the pre-log cannot exceed one. By contrast, in this note, we show that when the noises are anti-correlated and feedback is present, then a pre-log of two can be attained. In other words, in this special case, in the limit of high SNR, the scalar Gaussian broadcast channel turns into two parallel AWGN channels. Achievability is established via a coding strategy due to Schalkwijk, Kailath, and Ozarow.

We propose an encoding scheme for the Gaussian relay channel with receiver-transmitter feedback based on the Schalkwijk-Kailath coding strategy for the memoryless Gaussian single-user channel with feedback. The scheme has the advantage over previous schemes for the relay channel of being of very low complexity and, for certain channel parameters, achieving much higher rates.

For an interference network where receiver k receives the sum of the signal transmitted by Transmitter k and a scaled version of the signal transmitted by Transmitter k - 1 corrupted by Gaussian noise we compute the pre-log of the sum-rate capacity for the case where each transmitter has side- information consisting of the messages to be sent by its J predecessors.