arXiv subjects

Stefano Rini

Publications and source records attributed to Stefano Rini.

At least 73 records · Page 4Linked to original sources

arXiv2017

On the Capacity of the Carbon Copy onto Dirty Paper Channel

The "Carbon Copy onto Dirty Paper" (CCDP) channel is the compound "writing on dirty paper" channel in which the channel output is obtained as the sum of the channel input, white Gaussian noise and a Gaussian state sequence randomly selected among a set possible realizations. The transmitter has non-causal knowledge of the set of possible state sequences but does not know which sequence is selected to produce the channel output. We study the capacity of the CCDP channel for two scenarios: (i) the state sequences are independent and identically distributed, and (ii) the state sequences are scaled versions of the same sequence. In the first scenario, we show that a combination of superposition coding, time-sharing and Gel'fand-Pinsker binning is sufficient to approach the capacity to within three bits per channel use for any number of possible state realizations. In the second scenario, we derive capacity to within four bits-per-channel-use for the case of two possible state sequences. This result is extended to the CCDP channel with any number of possible state sequences under certain conditions on the scaling parameters which we denote as "strong fading" regime. We conclude by providing some remarks on the capacity of the CCDP channel in which the state sequences have any jointly Gaussian distribution.

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arXiv2017

On Capacity of the Writing onto Fast Fading Dirt Channel

The "Writing onto Fast Fading Dirt" (WFFD) channel is investigated to study the effects of partial channel knowledge on the capacity of the "writing on dirty paper" channel. The WFFD channel is the Gel'fand-Pinsker channel in which the output is obtained as the sum of the input, white Gaussian noise and a fading-times-state term. The fading-times-state term is equal to the element-wise product of the channel state sequence, known only at the transmitter, and a fast fading process, known only at the receiver. We consider the case of Gaussian distributed channel states and derive an approximate characterization of capacity for different classes of fading distributions, both continuous and discrete. In particular, we prove that if the fading distribution concentrates in a sufficiently small interval, then capacity is approximately equal to the AWGN capacity times the probability of this interval. We also show that there exists a class of fading distributions for which having the transmitter treat the fading-times-state term as additional noise closely approaches capacity. Although a closed-form expression of the capacity of the general WFFD channel remains unknown, our results show that the presence of fading can severely reduce the usefulness of channel state knowledge at the transmitter.

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arXiv2017

A General Framework for Low-Resolution Receivers for MIMO Channels

The capacity of a discrete-time multi-input multi-output (MIMO) Gaussian channel with output quantization is investigated for different receiver architectures. A general formulation of this problem is proposed in which the antenna outputs are processed by analog combiners while sign quantizers are used for analog-to-digital conversion. To exemplify this approach, four analog receiver architectures of varying generality and complexity are considered: (a) multiple antenna selection and sign quantization of the antenna outputs, (b) single antenna selection and multilevel quantization, (c) multiple antenna selection and multilevel quantization, and (d) linear combining of the antenna outputs and multilevel quantization. Achievable rates are studied as a function of the number of available sign quantizers and compared among different architectures. In particular, it is shown that architecture (a) is sufficient to attain the optimal high signal-to-noise ratio performance for a MIMO receiver in which the number of antennas is larger than the number of sign quantizers. Numerical evaluations of the average performance are presented for the case in which the channel gains are i.i.d. Gaussian.

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arXiv2017

Compress-and-Estimate Source Coding for a Vector Gaussian Source

We consider the remote vector source coding problem in which a vector Gaussian source is to be estimated from noisy linear measurements. For this problem, we derive the performance of the compress-and-estimate (CE) coding scheme and compare it to the optimal performance. In the CE coding scheme, the remote encoder compresses the noisy source observations so as to minimize the local distortion measure, independent from the joint distribution between the source and the observations. In reconstruction, the decoder estimates the original source realization from the lossy-compressed noisy observations. For the CE coding in the Gaussian vector case, we show that, if the code rate is less than a threshold, then the CE coding scheme attains the same performance as the optimal coding scheme. We also introduce lower and upper bounds for the performance gap above this threshold. In addition, an example with two observations and two sources is studied to illustrate the behavior of the performance gap.

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arXiv2017

Capacity Outer Bound and Degrees of Freedom of Wiener Phase Noise Channels with Oversampling

The discrete-time Wiener phase noise channel with an integrate-and-dump multi-sample receiver is studied. A novel outer bound on the capacity with an average input power constraint is derived as a function of the oversampling factor. This outer bound yields the degrees of freedom for the scenario in which the oversampling factor grows with the transmit power $P$ as $P^α$. The result shows, perhaps surprisingly, that the largest pre-log that can be attained with phase modulation at high signal-to-noise ratio is at most $1/4$.

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arXiv2017

Capacity of Discrete-Time Wiener Phase Noise Channels to Within a Constant Gap

The capacity of the discrete-time channel affected by both additive Gaussian noise and Wiener phase noise is studied. Novel inner and outer bounds are presented, which differ of at most $6.65$ bits per channel use for all channel parameters. The capacity of this model can be subdivided in three regimes: (i) for large values of the frequency noise variance, the channel behaves similarly to a channel with circularly uniform iid phase noise; (ii) when the frequency noise variance is small, the effects of the additive noise dominate over those of the phase noise, while (iii) for intermediate values of the frequency noise variance, the transmission rate over the phase modulation channel has to be reduced due to the presence of phase noise.

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arXiv2016

A unified graphical approach to random coding for multi-terminal networks

A unified graphical approach to random coding for any memoryless, single-hop, K-user channel with or without common information is defined through two steps. The first step is user virtualization: each user is divided into multiple virtual sub-users according to a chosen rate-splitting strategy. This results in an enhanced channel with a possibly larger number of users for which more coding possibilities are available and for which common messages to any subset of users can be encoded. Following user virtualization, the message of each user in the enhanced model is coded using a chosen combination of coded time-sharing, superposition coding and joint binning. A graph is used to represent the chosen coding strategies: nodes in the graph represent codewords while edges represent coding operations. This graph is used to construct a graphical Markov model which illustrates the statistical dependency among codewords that can be introduced by the superposition coding or joint binning. Using this statistical representation of the overall codebook distribution, the error probability of the code is shown to vanish via a unified analysis. The rate bounds that define the achievable rate region are obtained by linking the error analysis to the properties of the graphical Markov model. This proposed framework makes it possible to numerically obtain an achievable rate region by specifying a user virtualization strategy and describing a set of coding operations. The union of these rate regions defines the maximum achievable rate region of our unified coding strategy.

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arXiv2016

Optimal Rate Allocation in Mismatched Multiterminal Source Coding

We consider a multiterminal source coding problem in which a source is estimated at a central processing unit from lossy-compressed remote observations. Each lossy-encoded observation is produced by a remote sensor which obtains a noisy version of the source and compresses this observation minimizing a local distortion measure which depends only on the marginal distribution of its observation. The central node, on the other hand, has knowledge of the joint distribution of the source and all the observations and produces the source estimate which minimizes a different distortion measure between the source and its reconstruction. In this correspondence, we investigate the problem of optimally choosing the rate of each lossy-compressed remote estimate so as to minimize the distortion at the central processing unit subject to a bound on the overall communication rate between the remote sensors and the central unit. We focus, in particular, on two models of practical relevance: the case of a Gaussian source observed in additive Gaussian noise and reconstructed under quadratic distortion, and the case of a binary source observed in bit-flipping noise and reconstructed under Hamming distortion. In both scenarios we show that there exist regimes under which having more remote encoders does reduce the source distortion: in other words, having fewer, high-quality remote estimates provides a smaller distortion than having more, lower-quality estimates.

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arXiv2016

On the Capacity of the Dirty Paper Channel with Fast Fading and Discrete Channel States

The "writing dirty paper" capacity result crucially dependents on the perfect channel knowledge at the transmitter as the presence of even a small uncertainty in the channel realization gravely hampers the ability of the transmitter to pre-code its transmission against the channel state. This is particularly disappointing as it implies that interference pre-coding in practical systems is effective only when the channel estimates at the users have very high precision, a condition which is generally unattainable in wireless environments. In this paper we show that substantial improvements are possible when the state sequence is drawn from a discrete distribution, such as a constrained input constellation, for which state decoding can be approximately optimal. We consider the "writing on dirty paper" channel in which the state sequence is multiplied by a fast fading process and derive conditions on the fading and state distributions for which state decoding closely approaches capacity. These conditions intuitively relate to the ability of the receiver to correctly identify both the input and the state realization despite of the uncertainty introduced by fading.

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arXiv2016

The Carbon Copy onto Dirty Paper Channel with Statistically Equivalent States

Costa's "writing on dirty paper" capacity result establishes that full state pre-cancellation can be attained in Gelfand-Pinsker channel with additive state and additive Gaussian noise. The "carbon copy onto dirty paper" channel is the extension of Costa's model to the compound setting: M receivers each observe the sum of the channel input, Gaussian noise and one of M Gaussian state sequences and attempt to decode the same common message. The state sequences are all non-causally known at the transmitter which attempts to simultaneously pre-code its transmission against the channel state affecting each output. In this correspondence we derive the capacity to within 2.25 bits-per-channel-use of the carbon copying onto dirty paper channel in which the state sequences are statistically equivalent, having the same variance and the same pairwise correlation. For this channel capacity is approached by letting the channel input be the superposition of two codewords: a base codeword, simultaneously decoded at each user, and a top codeword which is pre-coded against the state realization at each user for a portion 1/M of the time. The outer bound relies on a recursive bounding in which incremental side information is provided at each receiver. This result represents a significant first step toward determining the capacity of the most general "carbon copy onto dirty paper" channel in which state sequences appearing in the different channel outputs have any jointly Gaussian distribution.

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arXiv2015

A unified graphical approach to random coding for multi-terminal networks

A unified approach to the derivation of rate regions for single-hop memoryless networks is presented. A general transmission scheme for any memoryless, single-hop, k-user channel with or without common information, is defined through two steps. The first step is user virtualization: each user is divided into multiple virtual sub-users according to a chosen rate-splitting strategy which preserves the rates of the original messages. This results in an enhanced channel with a possibly larger number of users for which more coding possibilities are available. Moreover, user virtualization provides a simple mechanism to encode common messages to any subset of users. Following user virtualization, the message of each user in the enhanced model is coded using a chosen combination of coded time-sharing, superposition coding and joint binning. A graph is used to represent the chosen coding strategies: nodes in the graph represent codewords while edges represent coding operations. This graph is used to construct a graphical Markov model which illustrates the statistical dependency among codewords that can be introduced by the superposition coding or joint binning. Using this statistical representation of the overall codebook distribution, the error probability of the code is shown to vanish via a unified analysis. The rate bounds that define the achievable rate region are obtained by linking the error analysis to the properties of the graphical Markov model. This proposed framework makes it possible to numerically obtain an achievable rate region by specifying a user virtualization strategy and describing a set of coding operations. The largest achievable rate region can be obtained by considering all the possible rate-splitting strategies and taking the union over all the possible ways to superimpose or bin codewords.

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arXiv2015

On the Dirty Paper Channel with Fast Fading Dirt

Costa`s "writing on dirty paper" result establishes that full state pre-cancellation can be attained in the Gel`fand-Pinsker problem with additive state and additive white Gaussian noise. This result holds under the assumptions that full channel knowledge is available at both the transmitter and the receiver. In this work we consider the scenario in which the state is multiplied by an ergodic fading process which is not known at the encoder. We study both the case in which the receiver has knowledge of the fading and the case in which it does not: for both models we derive inner and outer bounds to capacity and determine the distance between the two bounds when possible. For the channel without fading knowledge at either the transmitter or the receiver, the gap between inner and outer bounds is finite for a class of fading distributions which includes a number of canonical fading models. In the capacity approaching strategy for this class, the transmitter performs Costa`s pre-coding against the mean value of the fading times the state while the receiver treats the remaining signal as noise. For the case in which only the receiver has knowledge of the fading, we determine a finite gap between inner and outer bounds for two classes of discrete fading distribution. The first class of distributions is the one in which there exists a probability mass larger than one half while the second class is the one in which the fading is uniformly distributed over values that are exponentially spaced apart. Unfortunately, the capacity in the case of a continuous fading distribution remains very hard to characterize.

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arXiv2015

Indirect Rate-Distortion Function of a Binary i.i.d Source

The indirect source-coding problem in which a Bernoulli process is compressed in a lossy manner from its noisy observations is considered. These noisy observations are obtained by passing the source sequence through a The indirect source-coding problem in which a Bernoulli process is compressed in a lossy manner from its noisy observations is considered. These noisy observations are obtained by passing the source sequence through a binary symmetric channel so that the channel crossover probability controls the amount of information available about the source realization at the encoder. We use classic results in rate-distortion theory to compute an expression of the rate-distortion function for this model, where the Bernoulli source is not necessarily symmetric. The indirect rate-distortion function is given in terms of a solution to a simple equation. In addition, we derive an upper bound on the indirect rate-distortion function which is given in a closed. These expressions capture precisely the expected behavior that the noisier the observations, the smaller the return from increasing bit-rate to reduce distortion.

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arXiv2014

On the Capacity Region of the Two-user Interference Channel with a Cognitive Relay

This paper considers a variation of the classical two-user interference channel where the communication of two interfering source-destination pairs is aided by an additional node that has a priori knowledge of the messages to be transmitted, which is referred to as the it cognitive relay. For this Interference Channel with a Cognitive Relay (ICCR) In particular, for the class of injective semi-deterministic ICCRs, a sum-rate upper bound is derived for the general memoryless ICCR and further tightened for the Linear Deterministic Approximation (LDA) of the Gaussian noise channel at high SNR, which disregards the noise and focuses on the interaction among the users' signals. The capacity region of the symmetric LDA is completely characterized except for the regime of moderately weak interference and weak links from the CR to the destinations. The insights gained from the analysis of the LDA are then translated back to the symmetric Gaussian noise channel (GICCR). For the symmetric GICCR, an approximate characterization (to within a constant gap) of the capacity region is provided for a parameter regime where capacity was previously unknown. The approximately optimal scheme suggests that message cognition at a relay is beneficial for interference management as it enables simultaneous over the air neutralization of the interference at both destinations.

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arXiv2014

On Capacity of the Dirty Paper Channel with Fading Dirt in the Strong Fading Regime

The classical writing on dirty paper capacity result establishes that full interference pre-cancellation can be attained in Gelfand-Pinsker problem with additive state and additive white Gaussian noise. This result holds under the idealized assumption that perfect channel knowledge is available at both transmitter and receiver. While channel knowledge at the receiver can be obtained through pilot tones, transmitter channel knowledge is harder to acquire. For this reason, we are interested in characterizing the capacity under the more realistic assumption that only partial channel knowledge is available at the transmitter. We study, more specifically, the dirty paper channel in which the interference sequence in multiplied by fading value unknown to the transmitter but known at the receiver. For this model, we establish an approximate characterization of capacity for the case in which fading values vary greatly in between channel realizations. In this regime, which we term the strong fading regime, the capacity pre-log factor is equal to the inverse of the number of possible fading realizations.

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arXiv2014

The Impact of Phase Fading on the Dirty Paper Channel

The impact of phase fading on the classical Costa dirty paper coding channel is studied. We consider a variation of this channel model in which the amplitude of the interference sequence is known at the transmitter while its phase is known at the receiver. Although the capacity of this channel has already been established, it is expressed using an auxiliary random variable and as the solution of a maximization problem. To circumvent the difficulty evaluating capacity, we derive alternative inner and outer bounds and show that the two expressions are to within a finite distance. This provide an approximate characterization of the capacity which depends only on the channel parameters. We consider, in particular, two distributions of the phase fading: circular binomial and circular uniform. The first distribution models the scenario in which the transmitter has a minimal uncertainty over the phase of the interference while the second distribution models complete uncertainty. For circular binomial fading, we show that binning with Gaussian signaling still approaches capacity, as in the channel without phase fading. In the case of circular uniform fading, instead, binning with Gaussian signaling is no longer effective and novel interference avoidance strategies are developed to approach capacity.

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arXiv2013

Energy Efficient Cooperative Strategies for Relay-Assisted Downlink Cellular Systems, Part I: Theoretical Framework

The impact of cognition on the energy efficiency of a downlink cellular system in which multiple relays assist the transmission of the base station is considered. The problem is motivated by the practical importance of relay-assisted solutions in mobile networks, such as LTE-A, in which cooperation among relays holds the promise of greatly improving the energy efficiency of the system. We study the fundamental tradeoff between the power consumption at the base station and the level of cooperation and cognition at the relay nodes. By distributing the same message to multiple relays, the base station consumes more power but it enables cooperation among the relays, thus making the transmission between relays to destination a multiuser cognitive channel. Cooperation among the relays allows for a reduction of the power used to transmit from the relays to the end users due to interference management and the coherent combining gains. These gain are present even in the case of partial or unidirectional transmitter cooperation, which is the case in cognitive channels such as the cognitive interference channel and the interference channel with a cognitive relay. We therefore address the problem of determining the optimal level of cooperation at the relays which results in the smallest total power consumption when accounting for the power reduction due to cognition. A practical design examples and numerical simulation are presented in a companion paper (part II).

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arXiv2012

On the Capacity of the Cognitive Interference Channel with a Common Cognitive Message

In this paper the cognitive interference channel with a common message, a variation of the classical cognitive interference channel in which the cognitive message is decoded at both receivers, is studied. For this channel model new outer and inner bounds are developed as well as new capacity results for both the discrete memoryless and the Gaussian case. The outer bounds are derived using bounding techniques originally developed by Sato for the classical interference channel and Nair and El Gamal for the broadcast channel. A general inner bound is obtained combining rate-splitting, superposition coding and binning. Inner and outer bounds are shown to coincide in the "very strong interference" and the "primary decodes cognitive" regimes. The first regime consists of channels in which there is no loss of optimality in having both receivers decode both messages while in the latter regime interference pre-cancellation at the cognitive receiver achieves capacity. Capacity for the Gaussian channel is shown to within a constant additive gap and a constant multiplicative factor.

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