arXiv subjects

Shlomo Shamai

Publications and source records attributed to Shlomo Shamai.

At least 37 records · Page 2Linked to original sources

arXiv2024

Achieving Gaussian Vector Broadcast Channel Capacity with Scalar Lattices

A coding scheme with scalar lattices is applied to K-receiver, Gaussian, vector broadcast channels with K independent messages, one for each receiver. The method decomposes each receiver channel into parallel scalar channels with known interference and applies dirty paper coding with a modulo interval, amplitude shift keying (ASK), and probabilistic shaping to each scalar channel. The achievable rate tuples include all points inside the capacity region by choosing truncated Gaussian shaping, large ASK alphabets, and large modulo intervals.

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arXiv2024

Feedback Capacity of the Continuous-Time ARMA(1,1) Gaussian Channel

We consider the continuous-time ARMA(1,1) Gaussian channel and derive its feedback capacity in closed form. More specifically, the channel is given by $\boldsymbol{y}(t) =\boldsymbol{x}(t) +\boldsymbol{z}(t)$, where the channel input $\{\boldsymbol{x}(t) \}$ satisfies average power constraint $P$ and the noise $\{\boldsymbol{z}(t)\}$ is a first-order {\em autoregressive moving average} (ARMA(1,1)) Gaussian process satisfying $$ \boldsymbol{z}^\prime(t)+κ\boldsymbol{z}(t)=(κ+λ)\boldsymbol{w}(t)+\boldsymbol{w}^\prime(t), $$ where $κ>0,~λ\in\mathbb{R}$ and $\{\boldsymbol{w}(t) \}$ is a white Gaussian process with unit double-sided spectral density. We show that the feedback capacity of this channel is equal to the unique positive root of the equation $$ P(x+κ)^2 = 2x(x+\vert κ+λ\vert)^2 $$ when $-2κ<λ<0$ and is equal to $P/2$ otherwise. Among many others, this result shows that, as opposed to a discrete-time additive Gaussian channel, feedback may not increase the capacity of a continuous-time additive Gaussian channel even if the noise process is colored. The formula enables us to conduct a thorough analysis of the effect of feedback on the capacity for such a channel. We characterize when the feedback capacity equals or doubles the non-feedback capacity; moreover, we disprove continuous-time analogues of the half-bit bound and Cover's $2P$ conjecture for discrete-time additive Gaussian channels.

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arXiv2024

Cell-Free MIMO Perceptive Mobile Networks: Cloud vs. Edge Processing

Perceptive mobile networks implement sensing and communication by reusing existing cellular infrastructure. Cell-free multiple-input multiple-output, thanks to the cooperation among distributed access points, supports the deployment of multistatic radar sensing, while providing high spectral efficiency for data communication services. To this end, the distributed access points communicate over fronthaul links with a central processing unit acting as a cloud processor. This work explores four different types of PMN uplink solutions based on Cell-free multiple-input multiple-output, in which the sensing and decoding functionalities are carried out at either cloud or edge. Accordingly, we investigate and compare joint cloud-based decoding and sensing (CDCS), hybrid cloud-based decoding and edge-based sensing (CDES), hybrid edge-based decoding and cloud-based sensing (EDCS) and edge-based decoding and sensing (EDES). In all cases, we target a unified design problem formulation whereby the fronthaul quantization of signals received in the training and data phases are jointly designed to maximize the achievable rate under sensing requirements and fronthaul capacity constraints. Via numerical results, the four implementation scenarios are compared as a function of the available fronthaul resources by highlighting the relative merits of edge- and cloud-based sensing and communications. This study provides guidelines on the optimal functional allocation in fronthaul-constrained networks implementing integrated sensing and communications.

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arXiv2024

Successive Refinement in Large-Scale Computation: Advancing Model Inference Applications

Modern computationally-intensive applications often operate under time constraints, necessitating acceleration methods and distribution of computational workloads across multiple entities. However, the outcome is either achieved within the desired timeline or not, and in the latter case, valuable resources are wasted. In this paper, we introduce solutions for layered-resolution computation. These solutions allow lower-resolution results to be obtained at an earlier stage than the final result. This innovation notably enhances the deadline-based systems, as if a computational job is terminated due to time constraints, an approximate version of the final result can still be generated. Moreover, in certain operational regimes, a high-resolution result might be unnecessary, because the low-resolution result may already deviate significantly from the decision threshold, for example in AI-based decision-making systems. Therefore, operators can decide whether higher resolution is needed or not based on intermediate results, enabling computations with adaptive resolution. We present our framework for two critical and computationally demanding jobs: distributed matrix multiplication (linear) and model inference in machine learning (nonlinear). Our theoretical and empirical results demonstrate that the execution delay for the first resolution is significantly shorter than that for the final resolution, while maintaining overall complexity comparable to the conventional one-shot approach. Our experiments further illustrate how the layering feature increases the likelihood of meeting deadlines and enables adaptability and transparency in massive, large-scale computations.

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arXiv2024

Integrated Sensing and Communication in the Finite Blocklength Regime

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.

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arXiv2024

Lower Bounds on Mutual Information for Linear Codes Transmitted over Binary Input Channels, and for Information Combining

It has been known for a long time that the mutual information between the input sequence and output of a binary symmetric channel (BSC) is upper bounded by the mutual information between the same input sequence and the output of a binary erasure channel (BEC) with the same capacity. Recently, Samorodintsky discovered that one may also lower bound the BSC mutual information in terms of the mutual information between the same input sequence and a more capable BEC. In this paper, we strengthen Samordnitsky's bound for the special case where the input to the channel is distributed uniformly over a linear code. Furthermore, for a general (not necessarily binary) input distribution $P_X$ and channel $W_{Y|X}$, we derive a new lower bound on the mutual information $I(X;Y^n)$ for $n$ transmissions of $X\sim P_X$ through the channel $W_{Y|X}$.

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arXiv2024

Improved Bounds on the Number of Support Points of the Capacity-Achieving Input for Amplitude Constrained Poisson Channels

This work considers a discrete-time Poisson noise channel with an input amplitude constraint $\mathsf{A}$ and a dark current parameter $λ$. It is known that the capacity-achieving distribution for this channel is discrete with finitely many points. Recently, for $λ=0$, a lower bound of order $\sqrt{\mathsf{A}}$ and an upper bound of order $\mathsf{A} \log^2(\mathsf{A})$ have been demonstrated on the cardinality of the support of the optimal input distribution. In this work, we improve these results in several ways. First, we provide upper and lower bounds that hold for non-zero dark current. Second, we produce a sharper upper bound with a far simpler technique. In particular, for $λ=0$, we sharpen the upper bound from the order of $\mathsf{A} \log^2(\mathsf{A})$ to the order of $\mathsf{A}$. Finally, some other additional information about the location of the support is provided.

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arXiv2023

Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications

This paper is focused on multiuser load modulation arrays (MU-LMAs) which are attractive due to their low system complexity and reduced cost for millimeter wave (mmWave) multi-input multi-output (MIMO) systems. The existing precoding algorithm for downlink MU-LMA relies on a sub-array structured (SAS) transmitter which may suffer from decreased degrees of freedom and complex system configuration. Furthermore, a conventional LMA codebook with codewords uniformly distributed on a hypersphere may not be channel-adaptive and may lead to increased signal detection complexity. In this paper, we conceive an MU-LMA system employing a full-array structured (FAS) transmitter and propose two algorithms accordingly. The proposed FAS-based system addresses the SAS structural problems and can support larger numbers of users. For LMA-imposed constant-power downlink precoding, we propose an FAS-based normalized block diagonalization (FAS-NBD) algorithm. However, the forced normalization may result in performance degradation. This degradation, together with the aforementioned codebook design problems, is difficult to solve analytically. This motivates us to propose a Deep Learning-enhanced (FAS-DL-NBD) algorithm for adaptive codebook design and codebook-independent decoding. It is shown that the proposed algorithms are robust to imperfect knowledge of channel state information and yield excellent error performance. Moreover, the FAS-DL-NBD algorithm enables signal detection with low complexity as the number of bits per codeword increases.

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arXiv2023

Interference Networks with Random User Activity and Heterogeneous Delay Constraints

To answer the call for a new theoretical framework to simultaneously accommodate random user activity and heterogeneous delay traffic in Internet of Things (IoT) systems, in this paper we propose coding schemes and information-theoretic converse results for the transmission of heterogeneous delay traffic over interference networks with random user activity and random data arrivals. The heterogeneous 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. Each transmitter is active with probability $ρ\in [0,1]$. We consider two different models for the arrival of the mixed-delay traffic: in Model~$1$, each active transmitter sends a delay-tolerant message, and with probability $ρ_f \in [0,1]$ also transmits an additional delay-sensitive message; in Model~$2$, each active transmitter sends either a delay-sensitive message with probability $ρ_f$ or a delay-tolerant message with probability $1-ρ_f$. 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. Our inner and outer bounds are generally very 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 increases the sum per-user MG.

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arXiv2023

Joint Coding of eMBB and URLLC in Vehicle-to-Everything (V2X) Communications

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.

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arXiv2023

Distributed Information Bottleneck for a Primitive Gaussian Diamond MIMO Channel

This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and MIMO Rayleigh fading. The channel state is an independent and identically distributed (i.i.d.) process known at the relays but unknown to the destination. The relays are oblivious, i.e., they are unaware of the codebook and treat the transmitted signal as a random process with known statistics. The bottleneck constraints prevent the relays to communicate the channel state information (CSI) perfectly to the destination. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node knows the CSI and the relays can cooperate with each other, and also two achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters.

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arXiv2023

On Mismatched Oblivious Relaying

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.

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arXiv2023

Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction

The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services, such as enhanced mobile broadband (eMBB) devices, by only allocating resources when necessary. The main challenge is posed by the uncertainty in the process of URLLC packet generation, which mandates the use of predictors for URLLC traffic in the coming frames. In practice, such prediction may overestimate or underestimate the amount of URLLC data to be generated, yielding either an excessive or an insufficient amount of resources to be pre-emptively allocated for URLLC packets. In this paper, we introduce a novel scheduler for URLLC packets that provides formal guarantees on reliability and latency irrespective of the quality of the URLLC traffic predictor. The proposed method leverages recent advances in online conformal prediction (CP), and follows the principle of dynamically adjusting the amount of allocated resources so as to meet reliability and latency requirements set by the designer.

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arXiv2023

Strong Converses for Memoryless Bi-Static ISAC

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 $P_S$ or to $Q_S$ and 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.

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arXiv2023

Integrated Communication and Receiver Sensing with Security Constraints on Message and State

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.

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arXiv2022

Calibrating AI Models for Wireless Communications via Conformal Prediction

When used in complex engineered systems, such as communication networks, artificial intelligence (AI) models should be not only as accurate as possible, but also well calibrated. A well-calibrated AI model is one that can reliably quantify the uncertainty of its decisions, assigning high confidence levels to decisions that are likely to be correct and low confidence levels to decisions that are likely to be erroneous. This paper investigates the application of conformal prediction as a general framework to obtain AI models that produce decisions with formal calibration guarantees. Conformal prediction transforms probabilistic predictors into set predictors that are guaranteed to contain the correct answer with a probability chosen by the designer. Such formal calibration guarantees hold irrespective of the true, unknown, distribution underlying the generation of the variables of interest, and can be defined in terms of ensemble or time-averaged probabilities. In this paper, conformal prediction is applied for the first time to the design of AI for communication systems in conjunction to both frequentist and Bayesian learning, focusing on demodulation, modulation classification, and channel prediction.

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arXiv2022

Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization

Two of the main principles underlying the life cycle of an artificial intelligence (AI) module in communication networks are adaptation and monitoring. Adaptation refers to the need to adjust the operation of an AI module depending on the current conditions; while monitoring requires measures of the reliability of an AI module's decisions. Classical frequentist learning methods for the design of AI modules fall short on both counts of adaptation and monitoring, catering to one-off training and providing overconfident decisions. This paper proposes a solution to address both challenges by integrating meta-learning with Bayesian learning. As a specific use case, the problems of demodulation and equalization over a fading channel based on the availability of few pilots are studied. Meta-learning processes pilot information from multiple frames in order to extract useful shared properties of effective demodulators across frames. The resulting trained demodulators are demonstrated, via experiments, to offer better calibrated soft decisions, at the computational cost of running an ensemble of networks at run time. The capacity to quantify uncertainty in the model parameter space is further leveraged by extending Bayesian meta-learning to an active setting. In it, the designer can select in a sequential fashion channel conditions under which to generate data for meta-learning from a channel simulator. Bayesian active meta-learning is seen in experiments to significantly reduce the number of frames required to obtain efficient adaptation procedure for new frames.

cs.LG↗

arXiv2022

Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction

AI tools can be useful to address model deficits in the design of communication systems. However, conventional learning-based AI algorithms yield poorly calibrated decisions, unabling to quantify their outputs uncertainty. While Bayesian learning can enhance calibration by capturing epistemic uncertainty caused by limited data availability, formal calibration guarantees only hold under strong assumptions about the ground-truth, unknown, data generation mechanism. We propose to leverage the conformal prediction framework to obtain data-driven set predictions whose calibration properties hold irrespective of the data distribution. Specifically, we investigate the design of baseband demodulators in the presence of hard-to-model nonlinearities such as hardware imperfections, and propose set-based demodulators based on conformal prediction. Numerical results confirm the theoretical validity of the proposed demodulators, and bring insights into their average prediction set size efficiency.

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