We consider the problem of reliable communication over a discrete memoryless channel (DMC) with the help of a relay, termed the information bottleneck (IB) channel. There is no direct link between the source and the destination, and the information flows in two hops. The first hop is a noisy channel from the source to the relay. The second hop is a noiseless but limited-capacity backhaul link from the relay to the decoder. We further assume that the relay is oblivious to the transmission codebook. We examine two mismatch scenarios. In the first setting, we assume the decoder is restricted to use some fixed decoding rule, which is mismatched to the actual channel. In the second setting, we assume that the relay is restricted to use some fixed compression metric, which is again mismatched to the statistics of the relay input. We establish bounds on the random-coding capacity of both settings, some of which are shown to be ensemble tight.

Motivated by the emerging technology of oblivious processing in remote radio heads with universal decoders, we formulate and analyze in this paper a compound version of the information bottleneck problem. In this problem, a Markov chain X→Y→ Z is assumed, and the marginals P X and P Y are set. The mutual information between X and Z is sought to be maximized over the choice of the conditional probability of Z given Y from a given class, under the worst choice of the joint probability of the pair (X,Y) from a different class. We provide values, bounds, and various characterizations for specific instances of this problem: the binary symmetric case, the scalar Gaussian case, the vector Gaussian case, the symmetric modulo-additive case, and the total variation constraints case. Finally, for the general case, we propose a Blahut-Arimoto type of alternating iterations algorithm to find a consistent solution to this problem.

We consider a two-terminal variant (double-sided) of the information bottleneck problem, which is related to biclus-tering. In our setup, $x$ and Y are dependent random variables and the problem is to find two independent channels $\mathrm{P}_{\cup 1\times}$ and $\mathrm{p}_{\vee 1!}$ (setting the Markovian structure $\cup\rightarrow\times\rightarrow \mathrm{Y}\rightarrow$ V) that maximize $I(\cup;\mathrm{V})$ subject to constraints on the relevant mutual information expressions: $I(\cup;\mathrm{X})$ and $I(\mathrm{V};\mathrm{Y})$. For jointly Gaussian X and Y, we show that Gaussian channels are optimal in the low-SNR regime, but not for general SNR. Similarly, it is shown that for a doubly symmetric binary source, binary symmetric channels are optimal when the correlation is low, and are suboptimal for high correlation. We conjecture that Z and S channels are optimal when the correlation is 1 (i.e., $\mathrm{X}=\mathrm{Y})$, and provide supporting numerical evidence.

The information bottleneck (IB) problem of jointly stationary Gaussian sources is considered. A water-filling solution for the IB rate is given in terms of its SNR spectrum and whose rate is attained via frequency domain test-channel realization. A time-domain realization of the IB rate, based on linear prediction, is also proposed, which lends itself to an efficient implementation of the corresponding remote source-coding problem. A compound version of the problem is addressed, in which the joint distribution of the source is not precisely specified but rather in terms of a lower bound on the guaranteed mutual information. It is proved that a white SNR spectrum is optimal for this setting.

A diamond network is considered in which the central processor is connected, via backhaul noiseless links, to multiple conferencing base stations that communicate with a single user over a multiple access channel. We propose coding techniques along with lower and upper bounds on the capacity. Our achievability scheme uses a common cloud coding strategy based on the technique proposed by Wand, Wigger, and Zaidi (2018) and extends it beyond two relays. Our upper bounds generalize the method proposed by Bidokhti and Kramer (2016) and lead to new bounds for the multiple conferencing relay setting. Specializing our upper bounds to the two relay scenario, we provide new bounds and improve the state-of-the-art.

High precision indoor position estimation enables new opportunities for a variety of commercial, industrial and consumer applications. In this paper, we consider a phase-based method to calculate range from noisy measurements of a Frequency Comb in a multi-fading environment. It can be used to determine the range between devices for the next-generation High Accuracy Distance Measurement (HADM) protocol. We have conducted a quantitative analysis of various estimation approaches, considering both Monte-Carlo simulations of synthetic data in a variety of ranges. Moreover, we have proposed evaluation schemes for situations in which only a subset of data is available and some information may be missing, which extends existing approaches.

We study two dual settings of information processing. Let $ \mathsf{Y} \rightarrow \mathsf{X} \rightarrow \mathsf{W} $ be a Markov chain with fixed joint probability mass function $ \mathsf{P}_{\mathsf{X}\mathsf{Y}} $ and a mutual information constraint on the pair $ (\mathsf{W},\mathsf{X}) $. For the first problem, known as Information Bottleneck, we aim to maximize the mutual information between the random variables $ \mathsf{Y} $ and $ \mathsf{W} $, while for the second problem, termed as Privacy Funnel, our goal is to minimize it. In particular, we analyze the scenario for which $ \mathsf{X} $ is the input, and $ \mathsf{Y} $ is the output of modulo-additive noise channel. We provide analytical characterization of the optimal information rates and the achieving distributions.

Doppler ambiguity in the range-Doppler domain of linear frequency modulated (LFM) continuous wave radars occurs when a target’s Doppler frequency exceeds half of the chirp repetition frequency. Doppler ambiguity is common in a variety of civil and military radar applications, and an extension of the maximal unambiguous Doppler shift is critical for their practical use. This article utilizes previously neglected high-order phase terms in the received LFM radar echo for extension of maximum unambiguous velocity. A computationally feasible velocity estimation algorithm using hypotheses testing with local maximum likelihood is derived. The ability of the proposed low-complexity algorithm to estimate previously ambiguous target velocity is investigated using recorded automotive radar measurements and via simulations. The performance of the proposed algorithm is evaluated using a novel model of the Doppler estimation errors that accounts for Doppler ambiguity. The ability of the derived model to predict the threshold phenomenon is demonstrated via simulations.

We consider the problem of channel coding over multiterminal state-dependent channels in which neither transmitters nor receivers but only a helper node has a non-causal knowledge of the state. Such channel models arise in many emerging communication schemes. We start by investigating the parallel state-dependent channel with the same but differently scaled state corrupting the receivers. A cognitive helper knows the state in a non-causal manner and wishes to mitigate the interference that impacts the transmission between two transmit–receive pairs. Outer and inner bounds are derived. In our analysis, the channel parameters are partitioned into various cases, and segments on the capacity region boundary are characterized for each case. Furthermore, we show that for a particular set of channel parameters, the capacity region is entirely characterized. In the second part of this work, we address a similar scenario, but now each channel is corrupted by an independent state. We derive an inner bound using a coding scheme that integrates single-bin Gel’fand–Pinsker coding and Marton’s coding for the broadcast channel. We also derive an outer bound and further partition the channel parameters into several cases for which parts of the capacity region boundary are characterized.

The problem of channel coding over the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC) with additive independent Gaussian states is considered. The states are known in a noncausal manner to the encoder, and it wishes to minimize the amount of information that the receivers can learn from the channel outputs about the state sequence. The state leakage rate is measured as a normalized blockwise mutual information between the state sequence and the channel outputs' sequences. We employ a new version of a state-dependent extremal inequality and show that Gaussian input maximizes the state-dependent version of Marton's outer bound. Further, we show that our inner bound coincides with the outer bound. Our result generalizes previously studied scalar Gaussian BC with state and MIMO BC without the state.

We consider a state-dependent parallel Gaussian channel with independent states and a common cognitive helper, in which two transmitters wish to send independent information to their corresponding receivers over two parallel subchannels. Each channel is corrupted by independent additive Gaussian state. The states are not known to the transmitters nor to the receivers, but known to a helper in a noncausal manner. The helper's goal is to assist a reliable communication by mitigating the state. Outer and inner bounds are derived and segments of the capacity region is characterized for various channel parameters.

We study the problem of coding over a general discrete memoryless broadcast channel controlled by random parameters. The parameters are available at the transmitter in a non-causal manner and are subject to a state masking constraint on the receivers. We derive inner and outer bounds on the achievable region and show that for the special case of Gaussian broadcast channel with private messages, these bounds are tight.