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Content caching has emerged as an effective approach to combat the increasing strains on our current network infrastructure. This method is further improved when combining caching with source coding. However, additional complexity is incurred by creating this hybrid method, as the source coding component comes with associated feasibility constraints and decoding costs. This paper presents an approach to balance this complexity with the coding gains by selecting the best-performing subset of files to compress, while the others are left uncoded. This problem is shown to be NP-hard in general and difficult to solve in an iteration-free manner. To this end, two novel approaches are outlined: an iterative-based solution, which uses the features of the entropy function to derive the most suitable files to compress jointly, and a meta-heuristic version, which is based on the Genetic Algorithm. When compared to an exhaustive search, the proposed solutions are found to be sub-optimal but falling above the 90th percentile of all possible solutions on average. Significantly, the iterative method produces results within one percentile of the meta-heuristic approach yet it finds a solution 2.31 times faster. The iterative approach has an additional benefit, in that it is able to predict the relative gains when adding more files to a compression group. It is thus able to terminate prematurely if the estimated gains are less than a chosen threshold.

Modern media data such as 360° videos and light field (LF) images are typically captured in much higher dimensions than the observers’ visual displays. To efficiently browse high-dimensional media, a navigational streaming model is considered: a client navigates the media space by dictating a navigation path to a server, who in response transmits the corresponding pre-encoded media data units (MDU) to the client one-by-one in sequence. Assuming that the MDU quality is pre-chosen and fixed, the problem resides in selecting and storing redundant representations of MDUs at the server in order to best trade off storage and transmission costs, while enabling adequate user’s random access. We address this problem with a landmark-based MDU optimization framework. The media space is divided into neighborhoods, each containing one landmark (a chosen MDU). MDUs in a neighborhood use the associated landmark as a predictor for inter-coding. Thus, for any MDU transition within the same neighborhood, only one inter-coded MDU transmission is required when the landmark resides in the decoder buffer. It results in lower transmission cost and enables navigational random access. To optimize an MDU structure, we employ tree-structured vector quantizer (TSVQ) to first optimize landmark locations, then iteratively add P-MDUs as refinements using a fast branch-and-bound technique. Taking interactive LF images and viewport adaptive 360° images as illustrative applications, and I-, P- and previously proposed merge frames to intra- and inter-code MDUs, we show experimentally that landmarked MDU structures can noticeably reduce the expected transmission cost compared with MDU structures without landmarks. [ABSTRACT FROM AUTHOR]

Cloud radio-access networks (C-RAN) have been proposed as an enabling technology for keeping up with the requirements of next-generation wireless networks. Most existing works on C-RAN study the uplink or the downlink separately. However, designing the uplink and the downlink jointly may bring additional advantages, especially if message source-destination information is taken into account. In this paper, this idea is demonstrated by considering pairwise message exchange between users in a C-RAN. A multi-pair two-way transmission scheme is proposed which targets maximizing the end-to-end sum rate. In the proposed scheme, a lattice-based computation strategy is used, where the baseband processing unit (BBU) pool decodes integer linear combinations of paired users’ codewords instead of decoding linear combinations of individual codewords. The BBU pool then precodes the computed signals, compresses, and forwards them to the remote radio heads (RRHs), which in turn decompress the signals and send them to the users. Finally, each user decodes its desired message using its own message as side information. The achievable rate of this scheme is derived, optimized, and evaluated numerically. Simulation results reveal that significant end-to-end rate improvement can be achieved using the proposed scheme compared to existing schemes. [ABSTRACT FROM AUTHOR]

Light fields enable increasing the degree of realism and immersion of visual experience by capturing a scene with a higher number of dimensions than conventional 2D imaging. On another side, higher dimensionality entails significant storage and transmission overhead compared to traditional video. Conventional coding schemes achieve high coding gains by employing an asymmetric codec design, where the encoder is significantly more complex than the decoder. However, in the case of light fields, the communication and processing among different cameras could be expensive, and the possibility of trading the complexity between the encoder and the decoder becomes a desirable feature. We leverage the distributed source coding paradigm to effectively reduce the encoder’s complexity at the cost of increased computation at the decoder side. Specifically, we train two deep neural networks to improve the two most critical parts of a distributed source coding scheme: the prediction of side information and the estimation of the uncertainty in the prediction. Experiments show considerable BD-rate gains, above 59% over HEVC-Intra and 17.45% over our previous method DLFC-I.

This paper addresses the optimization of distributed compression in a sensor network. A direct communication among the sensors is not possible so that noisy measurements of a single relevant signal have to be locally compressed in order to meet the rate constraints of the communication links to a common receiver. This scenario is widely known as the Chief Executive Officer (CEO) problem and represents a long-standing problem in information theory. In recent years significant progress has been achieved and the rate region has been completely characterized for specific distributions of involved processes and distortion measures. While algorithmic solutions of the CEO problem are principally known, their practical implementation quickly becomes challenging due to complexity reasons. In this contribution, an efficient greedy algorithm to determine feasible solutions of the CEO problem is derived using the information bottleneck (IB) approach. Following the Wyner-Ziv coding principle, the quantizers are successively designed using already optimized quantizer mappings as side-information. However, processing this side-information in the optimization algorithm becomes a major bottleneck because the memory complexity grows exponentially with number of sensors. Therefore, a sequential compression scheme leading to a compact representation of the side-information and ensuring moderate memory requirements even for larger networks is introduced. This internal compression is optimized again by means of the IB method. Numerical results demonstrate that the overall loss in terms of relevant mutual information can be made sufficiently small even with a significant compression of the side-information. The performance is compared to separately optimized quantizers and a centralized quantization. Moreover, the influence of the optimization order for asymmetric scenarios is discussed.

An $n$ -dimensional source with memory is observed by $K$ isolated encoders via parallel channels, who compress their observations to transmit to the decoder via noiseless rate-constrained links while leveraging their memory of the past. At each time instant, the decoder receives $K$ new codewords from the observers, combines them with the past received codewords, and produces a minimum-distortion estimate of the latest block of $n$ source symbols. This scenario extends the classical one-shot CEO problem to multiple rounds of communication with communicators maintaining the memory of the past. We extend the Berger-Tung inner and outer bounds to the scenario with inter-block memory, showing that the minimum asymptotically (as $n \to \infty $) achievable sum rate required to achieve a target distortion is bounded by minimal directed mutual information problems. For the Gauss-Markov source observed via $K$ parallel AWGN channels, we show that the inner bound is tight and solve the corresponding minimal directed mutual information problem, thereby establishing the minimum asymptotically achievable sum rate. Finally, we explicitly bound the rate loss due to a lack of communication among the observers; that bound is attained with equality in the case of identical observation channels. The general coding theorem is proved via a new nonasymptotic bound that uses stochastic likelihood coders and whose asymptotic analysis yields an extension of the Berger-Tung inner bound to the causal setting. The analysis of the Gaussian case is facilitated by reversing the channels of the observers. [ABSTRACT FROM AUTHOR]

Distributed Arithmetic Coding (DAC) is a practical realization of Slepian-Wolf coding that partitions source space into cosets. Coset Cardinality Spectrum (CCS) is an important property of DAC that was defined in our previous work. In this paper, we give two applications of CCS. First, we find that DAC bitstream is not compact. The rate loss of DAC bitstream is caused by two factors: unequal coset partitioning and bit indivisibility. It is proved that as code length goes to infinity, the expected value of bit-indivisibility rate loss will tend to 0.5 for any irrational Rate Change Step (RCS), where the RCS refers to the rate change when one source bit is flipped. With the help of CCS, the bit-indivisibility rate loss can be compensated to some extend. Especially, for any irrational RCS, as code length goes to infinity, the expected value of the remaining bit-indivisibility rate loss after compensation will tend to about 0.47. The second application of CCS is DAC decoder design. We derive the formula of path metric and find that in the original paper on DAC, the intuitive formula of path metric is not correct. The backward-replacing algorithm is proposed to make full use of memory. Experimental results confirm the correctness of theoretical analyses. [ABSTRACT FROM AUTHOR]

Distributed Source Coding (DSC) schemes rely on separate encoding but joint decoding of statistically dependent sources, which exhibit correlation. More specifically, Distributed Joint Source-Channel coding (DJSC) is associated with the scenario, where the correlated source signals are transmitted through a noisy channel. On one hand, employing DSC or DJSC schemes exploits the existing correlation between sensors resulting in minimising the transmission energy required by the sources, while maintaining reliable communication. On the other hand, Network Coding (NC) is an efficient data transport technique leveraging network efficiency, by allowing Relay Nodes (RNs) in a communication network to combine multiple data packets received via the incoming links before transmitting them to the Destination Node (DN). In this paper, the bandwidth-efficient Distributed Joint Turbo Trellis-Coded Modulation (DJTTCM) aided by both Dynamic Network Coding (DNC) and Adaptive DNC (ADNC)-based cooperative transmission schemes are proposed. Both systems are proposed for supporting correlated source transmissions over hostile channels experiencing both small-scale and large-scale fading in which the RNs dynamically transmit its non-binary linear combinations to the DN. A substantial gain of 19.5 dB was attained at a correlation coefficient of ρ = 0.8 over its counterpart dispensing with NC.

In this work, lossy distributed compression of a pair of correlated sources is considered. Conventionally, Shannon’s random coding arguments — using randomly generated unstructured codebooks whose blocklength is taken to be asymptotically large — are used to derive achievability results. However, in some multi-terminal communications scenarios, using random codes with constant finite blocklength in certain coding architectures leads to improved achievable regions compared to the conventional approach. In other words, in some network communication scenarios, there is a finite optimal value in the blocklength of the randomly generated code used for distributed processing of information sources. Motivated by this, a coding scheme is proposed which consists of two codebook layers: i) the primary codebook which has constant finite blocklength, and ii) the secondary codebook whose blocklength is taken to be asymptotically large. The achievable performance is analyzed in two steps. In the first step, a characterization of an inner bound to the achievable region is derived in terms information measures which are functions of multi-letter probability distributions. In the next step, a computable single-letter inner-bound to the achievable region is extracted. It is shown through an example that the resulting rate-distortion region is strictly larger than the Berger-Tung achievable region. [ABSTRACT FROM AUTHOR]

It was demonstrated that, as a nonlinear implementation of Slepian-Wolf Coding, Distributed Arithmetic Coding (DAC) outperforms traditional Low-Density Parity-Check (LPDC) codes for short code length and biased sources. This fact triggers research efforts into theoretical analysis of DAC. In our previous work, we proposed two analytical tools, Codebook Cardinality Spectrum (CCS) and Hamming Distance Spectrum, to analyze DAC for independent and identically-distributed (i.i.d.) binary sources with uniform distribution. This article extends our work on CCS from uniform i.i.d. binary sources to biased i.i.d. binary sources. We begin with the final CCS and then deduce each level of CCS backwards by recursion. The main finding of this article is that the final CCS of biased i.i.d. binary sources is not uniformly distributed over [0, 1). This article derives the final CCS of biased i.i.d. binary sources and proposes a numerical algorithm for calculating CCS effectively in practice. All theoretical analyses are well verified by experimental results. [ABSTRACT FROM AUTHOR]

The problem of robust distributed source coding for three correlated sources is studied in this work. A lattice-based scheme is proposed and the analysis of its performance is provided in the high resolution regime. Special attention is paid to the degenerate case where the three sources are Gaussian and identical. In this case, our scheme is shown to achieve within an asymptotic gap of 0.069 bits in terms of rate per description from the information-theoretic limit of quadratic symmetric Gaussian multiple description coding with central and individual decoders, when the side distortion and the ratio between the central and side distortions both approach 0. [ABSTRACT FROM AUTHOR]

In this paper, we study the vector Gaussian Chief Executive Officer (CEO) problem under logarithmic loss distortion measure. Specifically, ${K} \geq 2$ agents observe independently corrupted Gaussian noisy versions of a remote vector Gaussian source, and communicate independently with a decoder or CEO over rate-constrained noise-free links. The CEO also has its own Gaussian noisy observation of the source and wants to reconstruct the remote source to within some prescribed distortion level where the incurred distortion is measured under the logarithmic loss penalty criterion. We find an explicit characterization of the rate-distortion region of this model. The result can be seen as the counterpart to the vector Gaussian setting of that by Courtade-Weissman which provides the rate-distortion region of the model in the discrete memoryless setting. For the proof of this result, we obtain an outer bound by means of a technique that relies on the de Bruijn identity and the properties of Fisher information. The approach is similar to Ekrem-Ulukus outer bounding technique for the vector Gaussian CEO problem under quadratic distortion measure, for which it was there found generally non-tight; but it is shown here to yield a complete characterization of the region for the case of logarithmic loss measure. Also, we show that Gaussian test channels with time-sharing exhaust the Berger-Tung inner bound, which is optimal. Furthermore, application of our results allows us to find the complete solutions of two related problems: a quadratic vector Gaussian CEO problem with determinant constraint and the vector Gaussian distributed Information Bottleneck problem. Finally, we develop Blahut-Arimoto type algorithms that allow to compute numerically the regions provided in this paper, for both discrete and Gaussian models. With the known relevance of the logarithmic loss fidelity measure in the context of learning and prediction, the proposed algorithms may find usefulness in a variety of applications where learning is performed distributively. We illustrate the efficiency of our algorithms through some numerical examples. [ABSTRACT FROM AUTHOR]

Signal processing applications dealing with secure transmission are enjoying increasing attention lately. This paper provides some theoretical insights as well as a practical solution for transmitting a hash of an image to a central server to be compared with a reference image. The proposed solution employs a rigid image registration technique viewed in a distributed source coding perspective. In essence, it embodies a phase encoding framework to let the decoder estimate the transformation parameters using a very modest amount of information about the original image. The problem is first cast in an ideal setting and then it is solved in a realistic scenario, giving more prominence to low computational complexity in both the transmitter and receiver, minimal hash size, and hash security. Satisfactory experimental results are reported on a standard images set. [ABSTRACT FROM AUTHOR]

Caching is an efficient way to reduce network traffic congestion during peak hours, by storing some content at the user’s local cache memory, even without knowledge of user’s later demands. Maddah-Ali and Niesen proposed a two-phase (placement phase and delivery phase) coded caching strategy for broadcast channels with cache-aided users. This paper investigates the same model under the constraint that content is placed uncoded within the caches, that is, when bits of the files are simply copied within the caches. When the cache contents are uncoded and the users’ demands are revealed, the caching problem can be connected to an index coding problem. This paper focuses on deriving fundamental performance limits for the caching problem by using tools for the index coding problem that were either known or are newly developed in this work. First, a converse bound for the caching problem under the constraint of uncoded cache placement is proposed based on the “acyclic index coding converse bound.” This converse bound is proved to be achievable by the Maddah-Ali and Niesen’s scheme when the number of files is not less than the number of users, and by a newly derived index coding achievable scheme otherwise. The proposed index coding achievable scheme is based on distributed source coding and strictly improves on the widely used “composite (index) coding” achievable bound and its improvements, and is of independent interest. An important consequence of the findings of this paper is that advancements on the coded caching problem posed by Maddah-Ali and Niesen are thus only possible by considering strategies with coded placement phase. A recent work by Yu et al has however shown that coded cache placement can at most half the network load compared to the results presented in this paper. [ABSTRACT FROM AUTHOR]

A new class of structured codes called quasi group codes (QGCs) is introduced. A QGC is a subset of a group code. In contrast with the group codes, QGCs are not closed under group addition. The parameters of the QGC can be chosen, such that the size of $\mathcal {C}+\mathcal {C}$ is equal to any number between $|\mathcal {C}|$ and $|\mathcal {C}|^{2}$. We analyze the performance of a specific class of QGCs. This class of QGCs is constructed by assigning single-letter distributions to the indices of the codewords in a group code. Then, the QGC is defined as the set of codewords whose index is in the typical set corresponding to these single-letter distributions. The asymptotic performance limits of this class of QGCs are characterized using single-letter information quantities. Corresponding covering and packing bounds are derived. It is shown that the point-to-point channel capacity and optimal rate-distortion function are achievable using QGCs. Coding strategies based on QGCs are introduced for three fundamental multi-terminal problems: the Körner-Marton problem for modulo prime-power sums, computation over the multiple access channel (MAC), and MAC with distributed states. For each problem, a single-letter achievable rate-region is derived. It is shown, through examples, that the coding strategies improve upon the previous strategies based on the unstructured codes, linear codes, and group codes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a lattice-based robust distributed source coding system for two correlated sources and provide a detailed performance analysis under the high resolution assumption. It is shown, among other things, that, in the asymptotic regime where: 1) the side distortion approaches 0 and 2) the ratio between the central and side distortions approaches 0, our scheme is capable of achieving the information-theoretic limit of quadratic multiple description coding when the two sources are identical, whereas a variant of the random coding scheme by Chen and Berger with Gaussian codes has a performance loss of 0.5 bits relative to this limit. [ABSTRACT FROM AUTHOR]

We consider a function computation problem in a three-node wireless network. Nodes A and B observe two correlated sources $X$ and $Y$ , respectively, and want to compute a function $f(X,Y)$. To achieve this, nodes A and B send messages to a relay node C at rates $R_{A}$ and $R_{B}$ , respectively. The relay C then broadcasts a message to A and B at rate $R_{C}$. We allow block coding and study the achievable region of rate triples under both zero-error and $\epsilon $ -error. As a preparation, we first consider a broadcast network from the relay to A and B. A and B have side information $X$ and $Y$ , respectively. The relay node C observes both $X$ and $Y$ and broadcasts an encoded message to A and B. We want to obtain the optimal broadcast rate such that A and B can recover the function $f(X,Y)$ from the received message and their individual side information $X$ and $Y$ , respectively. For this problem, we show equivalence between $\epsilon $ -error and zero-error computations–this gives a rate characterization for zero-error computation. As a corollary, this also gives a rate characterization for the relay network under zero error for a class of functions called component-wise one-to-one functions when the support set of $p_{XY}$ is full. For the relay network, the zero-error rate region for arbitrary functions is characterized in terms of graph coloring of some suitably defined probabilistic graphs. We then give a single-letter inner bound to this rate region. Furthermore, we extend the graph theoretic ideas to address the $\epsilon $ -error problem and obtain a single-letter inner bound. [ABSTRACT FROM AUTHOR]

This paper investigates a multi-terminal source coding problem under a logarithmic loss fidelity which does not necessarily lead to an additive distortion measure. The problem is motivated by an extension of the information bottleneck method to a multi-source scenario where several encoders have to build cooperatively rate-limited descriptions of their sources in order to maximize information with respect to other unobserved (hidden) sources. More precisely, we study fundamental information-theoretic limits of the so-called: 1) two-way collaborative information bottleneck (TW-CIB) and 2) the collaborative distributed information bottleneck (CDIB) problems. The TW-CIB problem consists of two distant encoders that separately observe marginal (dependent) components $X_{1}$ and $X_{2}$ and can cooperate through multiple exchanges of limited information with the aim of extracting information about hidden variables $(Y_{1},Y_{2})$ , which can be arbitrarily dependent on $(X_{1},X_{2})$. On the other hand, in CDIB, there are two cooperating encoders which separately observe $X_{1}$ and $X_{2}$ and a third node which can listen to the exchanges between the two encoders in order to obtain information about a hidden variable $Y$. The relevance (figure-of-merit) is measured in terms of a normalized (per-sample) multi-letter mutual information metric (log-loss fidelity), and an interesting tradeoff arises by constraining the complexity of descriptions, measured in terms of the rates needed for the exchanges between the encoders and decoders involved. Inner and outer bounds to the complexity-relevance region of these problems are derived from which optimality is characterized for several cases of interest. Our resulting theoretical complexity-relevance regions are finally evaluated for binary symmetric and Gaussian statistical models, showing theoretical tradeoffs between the complexity-constrained descriptions and their relevance with respect to the hidden variables. [ABSTRACT FROM AUTHOR]

Compute-and-forward (CF) harnesses interference in wireless communications by exploiting structured coding. The key idea of CF is to compute integer combinations of code words from multiple source nodes, rather than to decode individual code words by treating others as noise. Compute-compress-and-forward (CCF) can further enhance the network performance by introducing compression operations at receivers. In this paper, we develop a more general compression framework, termed generalized CCF (GCCF), where the compression function involves the selection of message segments over finite fields. We show that GCCF achieves a broader compression rate region than CCF. We also compare our compression rate region with the fundamental Slepian–Wolf (SW) region. We show that GCCF is optimal in the sense of achieving the minimum total compression rate. We also establish the criteria under which GCCF achieves the SW region. In addition, we consider a two-hop relay network employing the GCCF scheme. We formulate a sum-rate maximization problem and develop an approximate algorithm to solve the problem. Numerical results are presented to demonstrate the performance superiority of GCCF over CCF and other schemes. [ABSTRACT FROM AUTHOR]

In the context of distributed joint source-channel coding, we conceive a joint decoding and estimation scheme for binary Markov sources exhibiting spatio-temporal correlation. The proposed scheme is designed based on the serial concatenation of a trellis coded modulation (TCM) scheme and a unity-rate code. The symbol-based maximum a posteriori algorithm employed for TCM decoding is modified in order to exploit the source correlation. The estimation of both the spatial and temporal correlation parameters is performed jointly with the iterative decoding, hence allowing the estimated parameters to be updated after each iteration. Our simulation results reveal that when both the spatial and temporal correlation parameters are unknown, the proposed joint decoding and estimation scheme approaches the performance to the ideal system relying on perfectly known correlation parameters, therefore, demonstrating the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

A distributed joint source-channel coding scheme based on a unity-rate code (URC) assisted trellis coded modulation (TCM) is proposed, which exploits the spatiotemporal correlation of symbol-based sources. More specifically, asymmetric distributed source coding of two spatially correlated Markov sources is considered, where one of the sources is assumed to be perfectly decoded and to be available at the receiver of the other source as side information. In order to exploit the temporal correlation statistics, an iterative decoding process exchanging extrinsic information between the amalgamated URC-assisted TCM and a soft-symbol source decoder employing a modified symbol-based maximum a posteriori algorithm is invoked. Furthermore, the Slepian–Wolf (SW) bound of symbol-based sources having spatiotemporal correlation is derived and the benefits of exploiting the spatiotemporal correlation using the proposed coding scheme are demonstrated by our extrinsic information transfer chart analysis. It is shown from our simulation results that upon exploiting the spatiotemporal correlation of the sources, the proposed coding scheme is capable of operating within 0.02 b of the SW bound. [ABSTRACT FROM PUBLISHER]

The so-called chief executive officer problem suggests that the source message can be recovered at the destination by merging a set of corrupted replicas forwarded by multiple relays, as long as these replicas are sufficiently correlated with the original message. In this paper, we build on Slepian-Wolf’s correlated source coding theorem to design a simple, yet efficient power allocation scheme for a multirelay system, in which the direct link is unavailable to convey information. In such a system, the replicas forwarded by the relays are allowed to contain intra-link errors due to previous unreliable hops, and the destination is supposed to retrieve the source message by jointly decoding all received replicas. Importantly, the proposed power allocation is asymptotically optimal at high signal-to-noise ratio. [ABSTRACT FROM PUBLISHER]

Distributed arithmetic coding (DAC) is a variant of AC that can realize Slepian-Wolf coding in a nonlinear way. In our previous work, we defined codebook cardinality spectrum (CCS) and Hamming distance spectrum (HDS) for DAC. In this paper, we make use of CCS and HDS to analyze tailed DAC, which is a form of DAC that, as traditional AC, maps the last few symbols of each source block onto non-overlapped intervals. First, we derive the exact HDS formula for tailless DAC, a form of DAC that maps all the symbols of each source block onto overlapped intervals, and show that the HDS formula previously given is in fact approximation. Then, the HDS formula is extended to tailed DAC. Using CCS, we also deduce the average codebook cardinality, which is closely related to decoding complexity, and rate loss of tailed DAC. The effects of tail length are extensively analyzed. It is revealed that by increasing tail length to a value not close to the bitstream length, closely spaced codewords within the same codebook can be removed at the cost of a higher decoding complexity and a larger rate loss. Finally, theoretical analyses are verified by experiments. [ABSTRACT FROM PUBLISHER]

In the context of distributed joint source–channel coding, we conceive reduced-complexity turbo trellis coded modulation (TTCM)-aided syndrome-based block decoding for estimating the cross-over probability pe of the binary symmetric channel, which models the correlation between a pair of sources. Our joint decoder achieves an accurate correlation estimation for varying correlation coefficients at 3 dB lower SNR, than conventional TTCM decoder, despite its considerable complexity reduction. [ABSTRACT FROM PUBLISHER]

It is well known that distributed source coding can improve the reliability and energy efficiency of wireless sensor networks by exploiting the correlation among sensing data. In this letter, we strengthen the benefits of such an approach by proposing a simple, general, efficient power allocation scheme for an arbitrary amount of sensors. The proposed power allocation is asymptotically optimal at high signal-to-noise ratio. [ABSTRACT FROM PUBLISHER]

This paper considers noisy Wyner–Ziv coding (WZC), in which a remote noisy source is compressed with side information at the decoder only. The decoder side information is not limited to being Gaussian, thus this noisy WZC problem cannot be transformed into the conventional one tailored for noiseless sources. A new coding structure, named the residual-quantization (RQ) based noisy WZC, is proposed to solve the problem. This scheme explicitly constructs the theoretical auxiliary random variable to facilitate optimal reconstruction of the noisy source. In this two-stage encoder, the noisy source is quantized twice and the quantization error (residue) of the first stage is the input of the second stage. By sending only the quantization index of the second stage to the decoder, the corresponding code rate can theoretically approach the noisy WZC bound. Moreover, the RQ-based noisy WZC is implemented using graph-based codes. The main challenge is that it is necessary to design a codebook that is simultaneously good for source and channel coding for the first stage quantization, since this quantization code also acts as a channel code at the decoder. This problem is solved by constructing a low-density parity check (LDPC) code with edge degrees optimized for channel coding, and enhancing its performance for source coding by using a modified reinforced belief-propagation quantization algorithm. Simulation results show that the noisy Wyner-Ziv bounds can be practically approached by our implementation. In addition, the proposed implementation offers more flexibility in the code rates compared with the existing practical designs, making it more suitable for emerging applications such as fronthaul compression. [ABSTRACT FROM AUTHOR]

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

Distributed arithmetic coding (DAC) is an effective technique for implementing Slepian–Wolf coding (SWC). It has been shown that a DAC code partitions source space into unequal-size codebooks, so that the overall performance of DAC codes depends on the cardinality and structure of these codebooks. The problem of DAC codebook cardinality has been solved by the so-called codebook cardinality spectrum (CCS). This paper extends the previous work on CCS by studying the problem of DAC codebook structure. We define Hamming distance spectrum (HDS) to describe DAC codebook structure and propose a mathematical method to calculate the HDS of DAC codes. The theoretical analyses are verified by experimental results. [ABSTRACT FROM AUTHOR]

Independent component analysis (ICA) is a statistical method for transforming an observable multi-dimensional random vector into components that are as statistically independent as possible from each other. Usually, the ICA framework assumes a model according to which the observations are generated (such as a linear transformation with additive noise). ICA over finite fields is a special case of ICA in which both the observations and the independent components are over a finite alphabet. In this paper, we consider a generalization of this framework in which an observation vector is decomposed to its independent components (as much as possible) with no prior assumption on the way it was generated. This generalization is also known as Barlow’s minimal redundancy representation problem and is considered an open problem. We propose several theorems and show that this hard problem can be accurately solved with a branch and bound search tree algorithm, or tightly approximated with a series of linear problems. Our contribution provides the first efficient set of solutions to Barlow’s problem. The minimal redundancy representation (also known as factorial code) has many applications, mainly in the fields of neural networks and deep learning. The binary ICA is also shown to have applications in several domains, including medical diagnosis, multi-cluster assignment, network tomography, and internet resource management. In this paper, we show that this formulation further applies to multiple disciplines in source coding, such as predictive coding, distributed source coding, and coding of large alphabet sources. [ABSTRACT FROM AUTHOR]

In the context of spectrum reusing in Cognitive Radio (CR) networks, spectrum availability should be considered with respect to both space and frequency in making the Power Spectral Density (PSD) map concept. For this reason, the sensed PSDs by the distributed sensors in the area are collected and fused by a fusion center (FC). But, for a given zone, the sensed PSD by neighbor CR sensors may contain a shared common component for a while, and this component can be exploited in the theory of the distributed source coding to compress as much as possible. For this reason, distributed compressive sensing (DCS) methods are used here to exploit the correlation between PSDs and compress them more and reduce this type of overhead traffic. The proposed method can be used to develop a framework when the holding time of the users is large in comparison with the rate of the spectrum sensing. [ABSTRACT FROM PUBLISHER]

Joint source-channel coding of multiple correlated sources transmitted over orthogonal channels is considered. Two optimization problems related to coding schemes are formulated: maximal common rate, and maximal sum rate. These two problems model different power saving situations in energy-constrained wireless sensor networks. Solutions of these problems for an arbitrary number of sources are described, and some specific examples are given. [ABSTRACT FROM PUBLISHER]

We consider the distributed source coding system of L correlated Gaussian sources Y_{l},l=1,2,\ldots ,L , which are noisy observations of correlated Gaussian remote sources X_{k}, k=1,2,\ldots ,K . We assume that Y^L=^t(Y1,Y2,\ldots , Y_L) is an observation of the source vector X^{K}=^t(X1,X2,\ldots , XK) , having the form Y^L=AX^K+N^L , where A is a L\times K matrix and N^L=^t(N1,N2,\ldots ,N_L) is a vector of L -independent Gaussian random variables also independent of X^K . In this system, $L$ correlated Gaussian observations are separately compressed by L$ encoders and sent to the information processing center. We study the remote source coding problem, where the decoder at the center attempts to reconstruct the remote source K=L$ and , we study the multiterminal source coding problem, where the decoder wishes to reconstruct the observation Y^{L}=X^{L}+N^{L} . To investigate this problem, we shall establish a result that provides a strong connection between the remote source coding problem and multiterminal source coding problem. Using this result, we derive several new partial solutions to the multiterminal source coding problem. [ABSTRACT FROM AUTHOR]

Energy-harvesting wireless sensor networking is an emerging technology with applications to various fields such as environmental and structural health monitoring. A distinguishing feature of wireless sensors is the need to perform both source coding tasks, such as measurement and compression, and transmission tasks. It is known that the overall energy consumption for source coding is generally comparable to that of transmission, and that a joint design of the two classes of tasks can lead to relevant performance gains. Moreover, the efficiency of source coding in a sensor network can be potentially improved via distributed techniques by leveraging the fact that signals measured by different nodes are correlated. In this paper, a data-gathering protocol for multihop wireless sensor networks with energy-harvesting capabilities is studied whereby the sources measured by the sensors are correlated. Both the energy consumptions of source coding and transmission are modeled, and distributed source coding is assumed. The problem of dynamically and jointly optimizing the source coding and transmission strategies is formulated for time-varying channels and sources. The problem consists in the minimization of a cost function of the distortions in the source reconstructions at the sink under queue stability constraints. By adopting perturbation-based Lyapunov techniques, a close-to-optimal online scheme is proposed that has an explicit and controllable tradeoff between optimality gap and queue sizes. The role of side information available at the sink is also discussed under the assumption that acquiring the side information entails an energy cost. [ABSTRACT FROM PUBLISHER]