Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths. Neuromorphic systems built on spiking neurons excel at processing time series data efficiently through sparse, event-driven computation, consuming energy only upon input events. Quantum computing, on the other hand, operates on state spaces that grow exponentially in dimension with the number of qubits -- as a consequence of tensor-product composition -- with quantum states admitting superposition across basis states and entanglement between subsystems. Hybrid approaches combining these paradigms have begun to show potential, but existing quantum spiking models have important limitations. Notably, they implement classical memory mechanisms on single qubits, requiring repeated measurements to estimate firing probabilities, while relying on conventional backpropagation for training. In this paper, we propose a novel stochastic quantum spiking (SQS) neuron model that addresses these challenges. The SQS neuron uses multi-qubit quantum circuits to realize a spiking unit with internal quantum memory, enabling event-driven probabilistic spike generation in a single shot during inference. Furthermore, we study networks of SQS neurons, dubbed SQS neural networks (SQSNN), and demonstrate that they can be trained via a hardware-friendly local learning rule, eliminating the need for global classical backpropagation. The proposed SQSNN model is shown via experiments with both conventional and neuromorphic datasets to improve over previous quantum spiking neural networks, as well as over classical counterparts, when fixing the overall number of trainable parameters, highlighting its potential for event-driven applications such as neuromorphic integrated sensing and communications (N-ISAC).

Reliable uncertainty quantification is critical for trustworthy AI. Conformal Prediction (CP) provides prediction sets with distribution-free coverage guarantees, but its two main variants face complementary limitations. Split CP (SCP) suffers from data inefficiency due to dataset partitioning, while full CP (FCP) improves data efficiency at the cost of prohibitive retraining complexity. Recent approaches based on meta-learning or in-context learning (ICL) partially mitigate these drawbacks. However, they rely on training procedures not specifically tailored to CP, which may yield large prediction sets. We introduce an efficient FCP framework, termed enhanced ICL-based FCP (E-ICL+FCP), which employs a permutation-invariant Transformer-based ICL model trained with a CP-aware loss. By simulating the multiple retrained models required by FCP without actual retraining, E-ICL+FCP preserves coverage while markedly reducing both inefficiency and computational overhead. Experiments on synthetic and real tasks demonstrate that E-ICL+FCP attains superior efficiency-coverage trade-offs compared to existing SCP and FCP baselines.

Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications, assisting with tasks including troubleshooting, standards interpretation, and network optimization. However, their deployment in practice must balance inference cost, latency, and reliability. In this work, we study an edge-cloud-expert cascaded LLM-based knowledge system that supports decision-making through a question-and-answer pipeline. In it, an efficient edge model handles routine queries, a more capable cloud model addresses complex cases, and human experts are involved only when necessary. We define a misalignment-cost constrained optimization problem, aiming to minimize average processing cost, while guaranteeing alignment of automated answers with expert judgments. We propose a statistically rigorous threshold selection method based on multiple hypothesis testing (MHT) for a query processing mechanism based on knowledge and confidence tests. The approach provides finite-sample guarantees on misalignment risk. Experiments on the TeleQnA dataset –a telecom-specific benchmark – demonstrate that the proposed method achieves superior cost-efficiency compared to conventional cascaded baselines, while ensuring reliability at prescribed confidence levels.

As the dense deployment of access points (APs) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems presents significant challenges, per-AP coverage can be expanded using large-scale antenna arrays (LAAs). However, this approach incurs high implementation costs and substantial fronthaul demands due to the need for dedicated RF chains for all antennas. To address these challenges, we propose a hybrid beamforming framework that integrates wave-domain beamforming via stacked intelligent metasurfaces (SIM) with conventional digital processing. By dynamically manipulating electromagnetic waves, SIM-equipped APs enhance beamforming gains while significantly reducing RF chain requirements. We formulate a joint optimization problem for digital and wave-domain beamforming along with fronthaul compression to maximize the weighted sum-rate for both uplink and downlink transmission under finite-capacity fronthaul constraints. Given the high dimensionality and non-convexity of the problem, we develop alternating optimization-based algorithms that iteratively optimize digital and wave-domain variables. Numerical results demonstrate that the proposed hybrid schemes outperform conventional hybrid schemes, that rely on randomly set wave-domain beamformers or restrict digital beamforming to simple power control. Moreover, the proposed scheme employing sufficiently deep SIMs achieves near fully-digital performance with fewer RF chains in the high signal-to-noise ratios regime.

To support real-world decision-making, it is crucial for models to be well-calibrated, i.e., to assign reliable confidence estimates to their predictions. Uncertainty quantification is particularly important in personalized federated learning (PFL), as participating clients typically have small local datasets, making it difficult to unambiguously determine optimal model parameters. Bayesian PFL (BPFL) methods can potentially enhance calibration, but they often come with considerable computational and memory requirements due to the need to track the variances of all the individual model parameters. Furthermore, different clients may exhibit heterogeneous uncertainty levels owing to varying local dataset sizes and distributions. To address these challenges, we propose LR-BPFL, a novel BPFL method that learns a global deterministic model along with personalized low-rank Bayesian corrections. To tailor the local model to each client’s inherent uncertainty level, LR-BPFL incorporates an adaptive rank selection mechanism. We evaluate LR-BPFL across a variety of datasets, demonstrating its advantages in terms of calibration, accuracy, as well as computational and memory requirements. The code is available at \url{https://github.com/Bernie0115/LR-BPFL.}

Integrating deep neural networks (DNNs) into wireless receivers can enhance reliability in the presence of hard-to-model channels. However, in order to successfully deploy deep receivers, one must address the rapid channel variations while accounting for the limited availability of data and computing resources. This paper presents a novel framework for rapid online learning of deep receivers that builds on continual Bayesian learning. By modeling the channel variations as a dynamic system in the space of DNN model parameters, we enable efficient single-step updates, supporting the rapid training of Bayesian DNNs using limited data. We propose two online learning algorithms based on extended Kalman filtering and on Bayesian gradients. Unlike typical approaches that avoid catastrophic forgetting, our methods prioritize adapting to current channel realization. Numerical results show that the proposed continual Bayesian learning formulation yields deep receivers that can effectively adapt to varying channels with minimal computational overhead.

Over-the-air federated learning (FL), i.e., AirFL, leverages computing primitively over multiple access channels. A long-standing challenge in AirFL is to achieve coherent signal alignment without relying on expensive channel estimation and feedback. This paper proposes NCAirFL, a CSI-free AirFL scheme based on unbiased non-coherent detection at the edge server. By exploiting binary dithering and a longterm memory based error-compensation mechanism, NCAirFL achieves a convergence rate of order$\mathcal{O}(1 / \sqrt{T})$in terms of the average square norm of the gradient for general non-convex and smooth objectives, where$T$is the number of communication rounds. Experiments demonstrate the competitive performance of NCAirFL compared to vanilla FL with ideal communications and to coherent transmission-based benchmarks.

Post-hoc calibration of pre-trained models is critical for ensuring reliable inference, especially in safety-critical domains such as healthcare. Conformal Prediction (CP) offers a robust post-hoc calibration framework, providing distribution-free statistical coverage guarantees for prediction sets by leveraging held-out datasets. In this work, we address a decentralized setting where each device has limited calibration data and can communicate only with its neighbors over an arbitrary graph topology. We propose two message-passing-based approaches for achieving reliable inference via CP: quantile-based distributed conformal prediction (Q-DCP) and histogram-based distributed conformal prediction (H-DCP). Q-DCP employs distributed quantile regression enhanced with tailored smoothing and regularization terms to accelerate convergence, while H-DCP uses a consensus-based histogram estimation approach. Through extensive experiments, we investigate the trade-offs between hyperparameter tuning requirements, communication overhead, coverage guarantees, and prediction set sizes across different network topologies. The code of our work is released on: https://github.com/HaifengWen/Distributed-Conformal-Prediction.

We introduce adaptive learn-then-test (aLTT), an efficient hyperparameter selection procedure that provides finite-sample statistical guarantees on the population risk of AI models. Unlike the existing learn-then-test (LTT) technique, which relies on conventional p-value-based multiple hypothesis testing (MHT), aLTT implements sequential data-dependent MHT with early termination by leveraging e-processes. As a result, aLTT can reduce the number of testing rounds, making it particularly well-suited for scenarios in which testing is costly or presents safety risks. Apart from maintaining statistical validity, in applications such as online policy selection for offline reinforcement learning and prompt engineering, aLTT is shown to achieve the same performance as LTT while requiring only a fraction of the testing rounds.

Deploying artificial intelligence (AI) models on edge devices involves a delicate balance between meeting stringent complexity constraints, such as limited memory and energy resources, and ensuring reliable performance in sensitive decision-making tasks. One way to enhance reliability is through uncertainty quantification via Bayesian inference. This approach, however, typically necessitates maintaining and running multiple models in an ensemble, which may exceed the computational limits of edge devices. This paper introduces a low-complexity methodology to address this challenge by distilling calibration information from a more complex model. In an offline phase, predictive probabilities generated by a high-complexity cloud-based model are leveraged to determine a threshold based on the typical divergence between the cloud and edge models. At run time, this threshold is used to construct credal sets – ranges of predictive probabilities that are guaranteed, with a user-selected confidence level, to include the predictions of the cloud model. The credal sets are obtained through thresholding of a divergence measure in the simplex of predictive probabilities. Experiments on visual and language tasks demonstrate that the proposed approach, termed Conformalized Distillation for Credal Inference (CD-CI), significantly improves calibration performance compared to low-complexity Bayesian methods, such as Laplace approximation, making it a practical and efficient solution for edge AI deployments.

We investigate a lossy source compression problem in which both the encoder and decoder are equipped with a pre-trained sequence predictor. We propose an online lossy compression scheme that, under a$0-1$loss distortion function, ensures a deterministic, per-sequence upper bound on the distortion (outage) level for any time instant. The outage guarantees apply irrespective of any assumption on the distribution of the sequences to be encoded or on the quality of the predictor at the encoder and decoder. The proposed method, referred to as online conformal compression (OCC), is built upon online conformal prediction-a novel method for constructing confidence intervals for arbitrary predictors. Numerical results show that OCC achieves a compression rate comparable to that of an idealized scheme in which the encoder, with hindsight, selects the optimal subset of symbols to describe to the decoder, while satisfying the overall outage constraint.

Predictive models are often required to produce reliable predictions under statistical conditions that are not matched to the training data. A common type of training-testing mismatch is covariate shift, where the conditional distribution of the target variable given the input features remains fixed, while the marginal distribution of the inputs changes. Weighted conformal risk control (W-CRC) uses data collected during the training phase to convert point predictions into prediction sets with valid risk guarantees at test time despite the presence of a covariate shift. However, while W-CRC provides statistical reliability, its efficiency - measured by the size of the prediction sets - can only be assessed at test time. In this work, we relate the generalization properties of the base predictor to the efficiency of W-CRC under covariate shifts. Specifically, we derive a bound on the inefficiency of the W-CRC predictor that depends on algorithmic hyperparameters and task-specific quantities available at training time. This bound offers insights on relationships between the informativeness of the prediction sets, the extent of the covariate shift, and the size of the calibration and training sets. Experiments on fingerprinting-based localization validate the theoretical results.

The selection of hyperparameters, such as prompt templates in large language models (LLMs), must often strike a balance between reliability and cost. In many cases, structural relationships between the expected reliability levels of the hyperparameters can be inferred from prior information and held-out data -- e.g., longer prompt templates may be more detailed and thus more reliable. However, existing hyperparameter selection methods either do not provide formal reliability guarantees or are unable to incorporate structured knowledge in the hyperparameter space. This paper introduces reliability graph-based Pareto testing (RG-PT), a novel multi-objective hyperparameter selection framework that maintains formal reliability guarantees in terms of false discovery rate (FDR), while accounting for known relationships among hyperparameters via a directed acyclic graph. Edges in the graph reflect expected reliability and cost trade-offs among hyperparameters, which are inferred via the Bradley-Terry (BT) ranking model from prior information and held-out data. Experimental evaluations demonstrate that RG-PT significantly outperforms existing methods such as learn-then-test (LTT) and Pareto testing (PT) through a more efficient exploration of the hyperparameter space.

Selecting artificial intelligence (AI) models, such as large language models (LLMs), from multiple candidates requires accurate performance estimation. This is ideally achieved through empirical evaluations involving abundant real-world data. However, such evaluations are costly and impractical at scale. To address this challenge, autoevaluation methods leverage synthetic data produced by automated evaluators, such as LLMs-as-judges, reducing variance but potentially introducing bias. Recent approaches have employed semi-supervised prediction-powered inference ($\texttt{PPI}$) to correct for the bias of autoevaluators. However, the use of autoevaluators may lead in practice to a degradation in sample efficiency compared to conventional methods using only real-world data. In this paper, we propose $\texttt{R-AutoEval+}$, a novel framework that provides finite-sample reliability guarantees on the model evaluation, while also ensuring an enhanced (or at least no worse) sample efficiency compared to conventional methods. The key innovation of $\texttt{R-AutoEval+}$ is an adaptive construction of the model evaluation variable, which dynamically tunes its reliance on synthetic data, reverting to conventional methods when the autoevaluator is insufficiently accurate. Experiments on the use of LLMs-as-judges for the optimization of quantization settings for the weights of an LLM, for prompt design in LLMs, and for test-time reasoning budget allocation in LLMs confirm the reliability and efficiency of $\texttt{R-AutoEval+}$.

Stacked intelligent metasurfaces (SIMs), which are composed of multi-layer programmable metasurfaces, support beamforming in the wave domain, utilizing a limited number of radio frequency (RF) chains. This work investigates the application of SIMs in the downlink of cell-free massive multiple-input multiple-output systems with finite-capacity fronthaul links. Specifically, we address the joint optimization of fronthaul compression and hybrid digital and wave-domain beamforming. To tackle the resulting highly non-convex problem, an alternating optimization algorithm is proposed, which iteratively optimizes digital processing and wave beamforming variables. Numerical results demonstrate that the proposed method outperforms baseline schemes relying solely on digital or wave beamforming, achieving near fully-digital performance with a few RF chains, assuming a sufficiently high signal-to-noise ratio (SNR).

We consider a novel resource allocation framework designed to achieve optimal resource management for edgeassisted inference tasks. This is obtained by a new optimization approach, addressed as conformal Lyapunov optimization, which integrates online conformal risk control (O-CRC) with conventional Lyapunov optimization (LO). Unlike traditional LO, this approach ensures compliance with deterministic long-term reliability constraints. Simulation results, based on an edgeassisted segmentation task, demonstrate the effectiveness of the proposed method in balancing energy consumption and inference performance, while maintaining strict control over deterministic long-term constraints, related to the false negative segmentation rate.

This work investigates a collaborative sensing and data collection system in which multiple uncrewed aerial vehicles (UAVs) sense an area of interest and transmit images to a cloud server (CS) for processing. To accelerate the completion of sensing missions, including data transmission, the sensing task is divided into individual private sensing tasks for each UAV and a common sensing task that is executed by all UAVs to enable cooperative transmission. Unlike existing studies, we explore the use of an advanced cell-free multiple-input-multiple-output (MIMO) network, which effectively manages inter-UAV interference. To further optimize wireless channel utilization, we propose a hybrid transmission strategy that combines time-division multiple access (TDMA), nonorthogonal multiple access (NOMA), and cooperative transmission. The problem of jointly optimizing task splitting ratios and the hybrid TDMA-NOMA-cooperative transmission strategy is formulated with the objective of minimizing mission completion time. Extensive numerical results demonstrate the effectiveness of the proposed task allocation and hybrid transmission scheme in accelerating the completion of sensing missions.

Hybrid beamforming is vital in modern wireless systems, especially for massive MIMO and millimeter-wave (mmWave) deployments, offering efficient directional transmission with reduced hardware complexity. However, effective beamforming in multi-user scenarios relies heavily on accurate channel state information, the acquisition of which often requires significant pilot overhead, degrading system performance. To address this and inspired by the spatial congruence between sub-6GHz (sub-6G) and mmWave channels, we propose a Sub-6G information Aided Multi-User Hybrid Beamforming (SA-MUHBF) framework, avoiding excessive use of pilots at mmWave. SA-MUHBF employs a convolutional neural network to predict mmWave beamspace from sub-6G channel estimate, followed by a novel multi-layer graph neural network for analog beam selection and a linear minimum mean-square error algorithm for digital beamforming. Numerical results demonstrate that SA-MUHBF efficiently predicts the mmWave beamspace representation and achieves superior spectrum efficiency over state-of-the-art benchmarks. Moreover, SA-MUHBF demonstrates robust performance across varied sub-6G system configurations and exhibits strong generalization to unseen scenarios.

The recent advances in quantum information processing, sensing, and communications are surveyed with the objective of identifying the associated knowledge gaps and formulating a roadmap for their future evolution. Since the operation of quantum systems is prone to the deleterious effects of decoherence, which manifests itself in terms of bit-flips, phase-flips, or both, the pivotal subject of quantum error mitigation is reviewed both in the presence and absence of quantum coding. The state of the art, knowledge gaps, and future evolution of quantum machine learning (QML) are also discussed, followed by a discourse on quantum radar systems and briefly hypothesizing about the feasibility of integrated sensing and communications (ISAC) in the quantum domain (QD). Finally, we conclude with a set of promising future research ideas in the field of ultimately secure quantum communications with the objective of harnessing ideas from the classical communications field.

The widespread adoption of artificial intelligence (AI) in next-generation communication systems is challenged by the heterogeneity of traffic and network conditions, which call for the use of highly contextual, site-specific, data. A promising solution is to rely not only on real-world data, but also on synthetic pseudo-data generated by a network digital twin (NDT). However, the effectiveness of this approach hinges on the accuracy of the NDT, which can vary widely across different contexts. To address this problem, this paper introduces contextaware doubly-robust (CDR) learning, a novel semi-supervised scheme that adapts its reliance on the pseudo-data to the different levels of fidelity of the NDT across contexts. CDR is evaluated on the task of downlink beamforming where it outperforms previous state-of-the-art approaches, providing a 24% loss decrease when compared to doubly-robust (DR) semi-supervised learning in regimes with low labeled data availability.

Bayesian Neural Networks (BNNs) generate an ensemble of possible models by treating model weights as random variables. This enables them to provide superior estimates of decision uncertainty. However, implementing Bayesian inference in hardware is resource-intensive, as it requires noise sources to generate the desired model weights. In this work, we introduce Bayes2IMC, an in-memory computing (IMC) architecture designed for binary BNNs that leverages the stochasticity inherent to nanoscale devices. Our novel design, based on Phase-Change Memory (PCM) crossbar arrays eliminates the necessity for Analog-to-Digital Converter (ADC) within the array, significantly improving power and area efficiency. Hardware-software co-optimized corrections are introduced to reduce device-induced accuracy variations across deployments on hardware, as well as to mitigate the effect of conductance drift of PCM devices. We validate the effectiveness of our approach on the CIFAR-10 dataset with a VGGBinaryConnect model containing 14 million parameters, achieving accuracy metrics comparable to ideal software implementations. We also present a complete core architecture, and compare its projected power, performance, and area efficiency against an equivalent SRAM baseline, showing a 3.8 to$9.6 \times $improvement in total efficiency (in GOPS/W/mm2) and a 2.2 to$5.6 \times $improvement in power efficiency (in GOPS/W). In addition, the projected hardware performance of Bayes2IMC surpasses most memristive BNN architectures reported in the literature, achieving up to 20% higher power efficiency compared to the state-of-the-art.

In amembership inference attack(MIA), an attacker exploits the overconfidence exhibited by typical machine learning models to determine whether a specific data point was used to train a target model. In this paper, we analyze the performance of thelikelihood ratio attack(LiRA) within an information-theoretical framework that allows the investigation of the impact of thealeatoric uncertaintyin the true data generation process, of theepistemic uncertaintycaused by a limited training data set, and of thecalibration levelof the target model. We compare three different settings, in which the attacker receives decreasingly informative feedback from the target model:confidence vector(CV) disclosure, in which the output probability vector is released;true label confidence(TLC) disclosure, in which only the probability assigned to the true label is made available by the model; anddecision set(DS) disclosure, in which an adaptive prediction set is produced as in conformal prediction. We derive bounds on the advantage of an MIA adversary with the aim of offering insights into the impact of uncertainty and calibration on the effectiveness of MIAs. Simulation results demonstrate that the derived analytical bounds predict well the effectiveness of MIAs.

The integration of neuromorphic computing and transformers through spiking neural networks (SNNs) offers a promising path to energy-efficient sequence modeling, with the potential to overcome the energy-intensive nature of the artificial neural network (ANN)-based transformers. However, the algorithmic efficiency of SNN-based transformers cannot be fully exploited on GPUs due to architectural incompatibility. This article introduces Xpikeformer, a hybrid analog-digital hardware architecture designed to accelerate SNN-based transformer models. The architecture integrates analog in-memory computing (AIMC) for feedforward and fully connected layers, and a stochastic spiking attention (SSA) engine for efficient attention mechanisms. We detail the design, implementation, and evaluation of Xpikeformer, demonstrating significant improvements in energy consumption and computational efficiency. Through image classification tasks and wireless communication symbol detection tasks, we show that Xpikeformer can achieve inference accuracy comparable to the GPU implementation of ANN-based transformers. Evaluations reveal that Xpikeformer achieves a$13\times $reduction in energy consumption at approximately the same throughput as the state-of-the-art (SOTA) digital accelerator for ANN-based transformers. In addition, Xpikeformer achieves up to$1.9\times $energy reduction compared to the optimal digital ASIC projection of SOTA SNN-based transformers.

In modern wireless network architectures, such as O-RAN, artificial intelligence (AI)-based applications are deployed at intelligent controllers to carry out functionalities like scheduling or power control. The AI “apps” are selected on the basis of contextual information such as network conditions, topology, traffic statistics, and design goals. The mapping between context and AI model parameters is ideally done in a zero-shot fashion via an automatic model selection (AMS) mapping that leverages only contextual information without requiring any current data. This paper introduces a general methodology for the online optimization of AMS mappings. Optimizing an AMS mapping is challenging, as it requires exposure to data collected from many different contexts. Therefore, if carried out online, this initial optimization phase would be extremely time consuming. A possible solution is to leverage a digital twin of the physical system to generate synthetic data from multiple simulated contexts. However, given that the simulator at the digital twin is imperfect, a direct use of simulated data for the optimization of the AMS mapping would yield poor performance when tested in the real system. This paper proposes a novel method for the online optimization of AMS mapping that corrects for the bias of the simulator by means of limited real data collected from the physical system. Experimental results for a graph neural network-based power control app demonstrate the significant advantages of the proposed approach.

Conformal risk control (CRC) is a recently proposed technique that applies post-hoc to a conventional point predictor to provide calibration guarantees. Generalizing conformal prediction (CP), with CRC, calibration is ensured for a set predictor that is extracted from the point predictor to control a risk function such as the probability of miscoverage or the false negative rate. The original CRC requires the available data set to be split between training and validation data sets. This can be problematic when data availability is limited, resulting in inefficient set predictors. In this paper, a novel CRC method is introduced that is based on cross-validation, rather than on validation as the original CRC. The proposed cross-validation CRC (CV-CRC) extends a version of the jackknife-minmax from CP to CRC, allowing for the control of a broader range of risk functions. CV-CRC is proved to offer theoretical guarantees on the average risk of the set predictor. Furthermore, numerical experiments show that CV-CRC can reduce the average set size with respect to CRC when the available data are limited.

In a manner analogous to their classical counterparts, quantum classifiers are vulnerable to adversarial attacks that perturb their inputs. A promising countermeasure is to train the quantum classifier by adopting an attack-aware, or adversarial, loss function. This paper studies the generalization properties of quantum classifiers that are adversarially trained against bounded-norm white-box attacks. Specifically, a quantum adversary maximizes the classifier's loss by transforming an input state$\rho(x)$into a state$\tau$that is$\epsilon$-close to the original state$\rho(x)$in p-Schatten distance. Under suitable assumptions on the quantum embedding$\rho(x)$, we derive novel information-theoretic upper bounds on the generalization error of adversarially trained quantum classifiers for$p=1$and$p=\infty$. The derived upper bounds consist of two terms: the first is an exponential function of the 2-Renyi mutual information between classical data and quantum embedding, while the second term scales linearly with the adversarial perturbation size$\epsilon$. Both terms are shown to decrease as$1/\sqrt{T}$over the training set size$T$. An extension is also considered in which the adversary assumed during training has different parameters$p$and$\epsilon$as compared to the adversary affecting the test inputs. Finally, we validate our theoretical findings with numerical experiments for a synthetic setting.

The safe integration of machine learning modules in decision-making processes hinges on their ability to quantify uncertainty. A popular technique to achieve this goal is conformal prediction (CP), which transforms an arbitrary base predictor into a set predictor with coverage guarantees. While CP certifies the predicted set to contain the target quantity with a user-defined tolerance, it does not provide control over the average size of the predicted sets, i.e., over the informativeness of the prediction. In this work, a theoretical connection is established between the generalization properties of the base predictor and the informativeness of the resulting CP prediction sets. To this end, an upper bound is derived on the expected size of the CP set predictor that builds on generalization error bounds for the base predictor. The derived upper bound provides insights into the dependence of the average size of the CP set predictor on the amount of calibration data, the target reliability, and the generalization performance of the base predictor. The theoretical insights are validated using simple numerical regression and classification tasks.

Adaptive Risk Control (ARC) is an online calibration strategy based on set prediction that offers worst-case deterministic long-term risk control, as well as statistical marginal coverage guarantees. ARC adjusts the size of the prediction set by varying a single scalar threshold based on feedback from past decisions. In this work, we introduce Localized Adaptive Risk Control (L-ARC), an online calibration scheme that targets statistical localized risk guarantees ranging from conditional risk to marginal risk, while preserving the worst-case performance of ARC. L-ARC updates a threshold function within a reproducing kernel Hilbert space (RKHS), with the kernel determining the level of localization of the statistical risk guarantee. The theoretical results highlight a trade-off between localization of the statistical risk and convergence speed to the long-term risk target. Thanks to localization, L-ARC is demonstrated via experiments to produce prediction sets with risk guarantees across different data subpopulations, significantly improving the fairness of the calibrated model for tasks such as image segmentation and beam selection in wireless networks.

The demand for improved satellite communication (SatCom)-based broadband connectivity has led to significant technological advancements, particularly in non-geostationary orbit (NGSO) satellites. The new SatCom systems are expected to have flexible beam footprints with fully adaptable payloads while being energy-efficient. With this in mind, this paper explores using neuromorphic processors (NPs) for the in-orbit receive digital beamforming design. We specifically address the beamsteering challenges of high-speed user mobility by means of beamforming adaptation. Inspired by thinned antenna arrays, the proposed beamforming solutions are based on the least absolute shrinkage and selection operator (LASSO) and are adapted to NPs using spiking locally competitive algorithms, namely S-LCA and S-LCA with graded spikes. The proposed approaches can benefit from the energy efficiency of NPs and further reduce the SatCom payload’s power consumption by turning off as many radio frequency chains as possible without compromising the beamforming performance. Numerical experiments conducted on a real-world aeronautical dataset demonstrate that the proposed NP-oriented solutions offer performance on par with conventional optimization algorithms, with the promise of a lower energy expenditure after future implementation on dedicated hardware.

Addressing the communication bottleneck inherent in federated learning (FL), over-the-air FL (AirFL) has emerged as a promising solution, which is, however, hampered by deep fading conditions. In this paper, we propose AirFL-Mem, a novel scheme designed to mitigate the impact of deep fading by implementing a long-term memory mechanism. Convergence bounds are provided that account for long-term memory, as well as for existing AirFL variants with short-term memory, for general non-convex objectives. The theory demonstrates that AirFL-Mem exhibits the same convergence rate of federated aver-aging (FedAvg) with ideal communication, while the performance of existing schemes is generally limited by error floors. The theoretical results are also leveraged to propose a novel convex optimization strategy for the truncation threshold used for power control in the presence of Rayleigh fading channels. Experimental results validate the analysis, confirming the advantages of a long-term memory mechanism for the mitigation of deep fading.

Conventional frequentist learning is known to yield poorly calibrated models that fail to reliably quantify the uncertainty of their decisions. Bayesian learning can improve calibration, but formal guarantees apply only under restrictive assumptions about correct model specification. Conformal prediction (CP) offers a general framework for the design of set predictors with calibration guarantees that hold regardless of the underlying data generation mechanism. However, when training data are limited, CP tends to produce large, and hence uninformative, predicted sets. This paper introduces a novel meta-learning solution that aims at reducing the set prediction size. Unlike prior work, the proposed meta-learning scheme, referred to as meta-XB, i) builds on cross-validation-based CP, rather than the less efficient validation-based CP; and ii) preserves formal per-task calibration guarantees, rather than less stringent task-marginal guarantees. Finally, meta-XB is extended to adaptive non-conformal scores, which are shown empirically to further enhance marginal per-input calibration.

The increasing adoption of Artificial Intelligence (AI) in engineering problems calls for the development of calibration methods capable of offering robust statistical reliability guarantees. The calibration of black box AI models is carried out via the optimization of hyperparameters dictating architecture, optimization, and/or inference configuration. Prior work has introduced learn-then-test (LTT), a calibration procedure for hyperparameter optimization (HPO) that provides statistical guarantees on average performance measures. Recognizing the importance of controlling risk-aware objectives in engineering contexts, this work introduces a variant of LTT that is designed to provide statistical guarantees on quantiles of a risk measure. We illustrate the practical advantages of this approach by applying the proposed algorithm to a radio access scheduling problem.

Standard Bayesian learning is known to have suboptimal generalization capabilities under misspecification and in the presence of outliers. Probably approximately correct (PAC)-Bayes theory demonstrates that the free energy criterion minimized by Bayesian learning is a bound on the generalization error for Gibbs predictors (i.e., for single models drawn at random from the posterior) under the assumption of sampling distributions uncontaminated by outliers. This viewpoint provides a justification for the limitations of Bayesian learning when the model is misspecified, requiring ensembling, and when data are affected by outliers. In recent work, PAC-Bayes bounds-referred to as PACm-were derived to introduce free energy metrics that account for the performance of ensemble predictors, obtaining enhanced performance under misspecification. This work presents a novel robust free energy criterion that combines the generalized logarithm score function with PACm ensemble bounds. The proposed free energy training criterion produces predictive distributions that are able to concurrently counteract the detrimental effects of misspecification-with respect to both likelihood and prior distribution-and outliers.

Design of mechanisms towards deploying over-the-air federated learning (FL) encounters many key challenges. One of them is to comply with the power and bandwidth constraints of the shared channel, while causing minimum deterioration to the learning performance compared to baseline noiseless implementations. For additive white Gaussian noise (AWGN) channels with instantaneous per-device power constraints, prior work has demonstrated the optimality of a power control mechanism based on norm clipping. This was done through the minimization of an upper bound on the optimality gap for smooth learning objectives satisfying the Polyak-Lojasiewicz (PL) condition. In this paper, we make two contributions to the development of AirFL based on norm clipping, which we refer to as AirFL-Clip. First, we provide a convergence bound for AirFL-Clip that applies to general smooth and non-convex learning objectives. Unlike existing results, the derived bound is free from run-specific parameters, thus supporting an offline evaluation. Second, we extend AirFL-Clip to include Top-k sparsification and linear compression. For this generalized protocol, referred to as AirFL-Clip-Comp, we derive a convergence bound for general smooth and non-convex learning objectives. We argue and demonstrate via experiments, that the only time-varying quantities present in the bound can be efficiently estimated offline by leveraging the well-studied properties of sparse recovery algorithms.

The recent development of scalable Bayesian inference methods has renewed interest in the adoption of Bayesian learning as an alternative to conventional frequentist learning that offers improved model calibration via uncertainty quantification. Recently, federated averaging Langevin dynamics (FALD) was introduced as a variant of federated averaging that can efficiently implement distributed Bayesian learning in the presence of noiseless communications. In this paper, we propose wireless FALD (WFALD), a novel protocol that realizes FALD in wireless systems by integrating over-the-air computation and channel-driven sampling for Monte Carlo updates. Unlike prior work on wireless Bayesian learning, WFALD enables (i) multiple local updates between communication rounds; and (ii) stochastic gradients computed by mini-batch. A convergence analysis is presented in terms of the 2- Wasserstein distance between the samples produced by WFALD and the targeted global posterior distribution. Analysis and experiments show that, when the signal-to-noise ratio is sufficiently large, channel noise can be fully repurposed for Monte Carlo sampling, thus entailing no loss in performance.

Bayesian Federated Learning (FL) offers a principled framework to account for the uncertainty caused by limitations in the data available at the nodes implementing collaborative training. In Bayesian FL, nodes exchange information about local posterior distributions over the model parameters space. This paper focuses on Bayesian FL implemented in a Device-to-Device (D2D) network via Decentralized Stochastic Gradient Langevin Dynamics (DSGLD), a recently introduced gradient-based Markov Chain Monte Carlo (MCMC) method. Based on the observation that DSGLD applies random Gaussian perturbations to the model parameters, we propose to leverage channel noise on the D2D links as a mechanism for MCMC sampling. The proposed approach is compared against a conventional implementation of frequentist FL based on compression and digital transmission, highlighting advantages and limitations.

An important primitive for quantum networking is entanglement distillation, whose goal is to enhance the fidelity of entangled qubits through local operations and classical communication (LOCC). Existing distillation protocols assume the availability of ideal, noiseless, communication channels. In this paper, we study the case in which communication takes place over noisy binary symmetric channels. We propose to implement local processing through parameterized quantum circuits (PQCs) that are optimized to maximize the average fidelity, while accounting for communication errors. The introduced approach, Noise Aware-LOCCNet (NA-LOCCNet), is shown to have significant advantages over existing protocols designed for noiseless communications.

Artificial Intelligent (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.

Unmanned aerial base stations (UABSs) can be deployed in vehicular wireless networks to support applications such as extended sensing via vehicle-to-everything (V2X) services. A key problem in such systems is designing algorithms that can efficiently optimize the trajectory of the UABS in order to maximize coverage. In existing solutions, such optimization is carried out from scratch for any new traffic configuration, often by means of conventional reinforcement learning (RL). In this paper, we propose the use of continual meta-RL as a means to transfer information from previously experienced traffic configurations to new conditions, with the goal of reducing the time needed to optimize the UABS's policy. Adopting the Continual Meta Policy Search (CoMPS) strategy, we demonstrate significant efficiency gains as compared to conventional RL, as well as to naive transfer learning methods.

Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control and monitor software-based, “open”, communication systems, which play the role of the physical twin (PT). In the general framework presented in this work, the DT builds a Bayesian model of the communication system, which is leveraged to enable core DT functionalities such as control via multi-agent reinforcement learning (MARL) and monitoring of the PT for anomaly detection. We specifically investigate the application of the proposed framework to a simple case-study system encompassing multiple sensing devices that report to a common receiver. The Bayesian model trained at the DT has the key advantage of capturing epistemic uncertainty regarding the communication system, e.g., regarding current traffic conditions, which arise from limited PT-to-DT data transfer. Experimental results validate the effectiveness of the proposed Bayesian framework as compared to standard frequentist model-based solutions.

Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler for reliable AI systems. However, conventional hardware realizations of BNNs are resource intensive, requiring the imple-mentation of random number generators for synaptic sampling. Owing to their inherent stochasticity during programming and read operations, nanoscale memristive devices can be directly leveraged for sampling, without the need for additional hardware resources. In this paper, we introduce a novel Phase Change Memory (PCM)-based hardware implementation for BNNs with binary synapses. The proposed architecture consists of separate weight and noise planes, in which PCM cells are configured and operated to represent the nominal values of weights and to generate the required noise for sampling, respectively. Using experimentally observed PCM noise characteristics, for the ex-emplary Breast Cancer Dataset classification problem, we obtain hardware accuracy and expected calibration error matching that of an 8-bit fixed-point (FxP8) implementation, with projected savings of over$9\times$in terms of core area transistor count.

Simulating quantum channels is a fundamental primitive in quantum computing, since quantum channels define general (trace-preserving) quantum operations. An arbitrary quantum channel cannot be exactly simulated using a finite-dimensional programmable quantum processor, making it important to develop optimal approximate simulation techniques. In this paper, we study the challenging setting in which the channel to be simulated varies adversarially with time. We propose the use of matrix exponentiated gradient descent (MEGD), an online convex optimization method, and analytically show that it achieves a sublinear regret in time. Through experiments, we validate the main results for time-varying dephasing channels using a programmable generalized teleportation processor.

A key component of a quantum machine learning model operating on classical inputs is the design of an embedding circuit mapping inputs to a quantum state. This paper studies a transfer learning setting in which classical-to-quantum embedding is carried out by an arbitrary parametric quantum circuit that is pre-trained based on data from a source task. At run time, a binary quantum classifier of the embedding is optimized based on data from the target task of interest. The average excess risk, i.e., the optimality gap, of the resulting classifier depends on how (dis)similar the source and target tasks are. We introduce a new measure of (dis)similarity between the binary quantum classification tasks via the trace distances. An upper bound on the optimality gap is derived in terms of the proposed task (dis)similarity measure, two Rényi mutual information terms between classical input and quantum embedding under source and target tasks, as well as a measure of complexity of the combined space of quantum embeddings and classifiers under the source task. The theoretical results are validated on a simple binary classification example.

Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control, monitor, and analyze software-based, “open”, communication systems that are expected to dominate 6G deployments. Notably, DT platforms provide a sandbox in which to test artificial intelligence (AI) solutions for communication systems, potentially reducing the need to collect data and test algorithms in the field, i.e., on the physical twin (PT). A key challenge in the deployment of DT systems is to ensure that virtual control optimization, monitoring, and analysis at the DT are safe and reliable, avoiding incorrect decisions caused by “model exploitation”. To address this challenge, this paper presents a general Bayesian framework with the aim of quantifying and accounting for model uncertainty at the DT that is caused by limitations in the amount and quality of data available at the DT from the PT. In the proposed framework, the DT builds a Bayesian model of the communication system, which is leveraged to enable core DT functionalities such as control via multi-agent reinforcement learning (MARL), monitoring of the PT for anomaly detection, prediction, data-collection optimization, and counterfactual analysis. To exemplify the application of the proposed framework, we specifically investigate a case-study system encompassing multiple sensing devices that report to a common receiver. Experimental results validate the effectiveness of the proposed Bayesian framework as compared to standard frequentist model-based solutions.

Type-based multiple access (TBMA) is a semantics-aware multiple access protocol for remote inference. In TBMA, codewords are reused across transmitting sensors, with each codeword being assigned to a different observation value. Existing TBMA protocols are based on fixed shared codebooks and on conventional maximum-likelihood or Bayesian decoders, which require knowledge of the distributions of observations and channels. In this letter, we propose a novel design principle for TBMA based on the information bottleneck (IB). In the proposed IB-TBMA protocol, the shared codebook is jointly optimized with a decoder based on artificial neural networks (ANNs), so as to adapt to source, observations, and channel statistics based on data only. We also introduce the Compressed IB-TBMA (CIB-TBMA) protocol, which improves IB-TBMA by enabling a reduction in the number of codewords via an IB-inspired clustering phase. Numerical results demonstrate the importance of a joint design of codebook and neural decoder, and validate the benefits of codebook compression.

A set of low-cost sensors is deployed to infer the network topology of a self-organizing wireless network. The sensors operate in a non-invasive fashion, extracting only the timings of data packets and acknowledgment (ACK) packets from all nodes in a network. The meta-data also reports the source node of each packet, but not the destination nodes or the contents of the packets. A central processor collects the meta-data from the sensors, and the goal is for the processor to infer the network topology based solely on such information. Prior work leveraged causality metrics to identify which links are active. If the data timings and ACK timings of two nodes– say node 1 and node 2, respectively– are causally related, this may be taken as evidence that node 1 is communicating to node 2 (which sends back ACK packets to node 1). This paper starts with the observation that packet losses can weaken the causality relationship between data and ACK timing streams. To obviate this problem, a new Expectation Maximization (EM)-based algorithm is introduced– EM-causality discovery algorithm (EM-CDA)– which treats packet losses as latent variables. EM-CDA iterates between the estimation of packet losses and the evaluation of causality metrics. The method is validated through extensive experiments in wireless sensor networks on the NS-3 simulation platform.

Most works on federated learning (FL) focus on the most common frequentist formulation of learning whereby the goal is minimizing the global empirical loss. Frequentist learning, however, is known to be problematic in the regime of limited data as it fails to quantify epistemic uncertainty in prediction. Bayesian learning provides a principled solution to this problem by shifting the optimization domain to the space of distribution in the model parameters. This paper proposes a novel mechanism for the efficient implementation of Bayesian learning in wireless systems. Specifically, we focus on a standard gradient-based Markov Chain Monte Carlo (MCMC) method, namely Langevin Monte Carlo (LMC), and we introduce a novel protocol, termed Wireless Federated LMC (WFLMC), that is able to repurpose channel noise for the double role of seed randomness for MCMC sampling and of privacy preservation. To this end, based on the analysis of the Wasserstein distance between sample distribution and global posterior distribution under privacy and power constraints, we introduce a power allocation strategy as the solution of a convex program. The analysis identifies distinct operating regimes in which the performance of the system is power-limited, privacy-limited, or limited by the requirement of MCMC sampling. Both analytical and simulation results demonstrate that, if the channel noise is properly accounted for under suitable conditions, it can be fully repurposed for both MCMC sampling and privacy preservation, obtaining the same performance as in an ideal communication setting that is not subject to privacy constraints.

In this paper, we consider the problem of power control for a wireless network with an arbitrarily time-varying topology, including the possible addition or removal of nodes. A data-driven design methodology that leverages graph neural networks (GNNs) is adopted in order to efficiently parametrize the power control policy mapping the channel state information (CSI) to transmit powers. The specific GNN architecture, known as random edge GNN (REGNN), defines a non-linear graph convolutional filter whose spatial weights are tied to the channel coefficients. While prior work assumed a joint training approach whereby the REGNN-based policy is shared across all topologies, this paper targets adaptation of the power control policy based on limited CSI data regarding the current topology. To this end, we propose a novel modular meta-learning technique that enables the efficient optimization of module assignment. While black-box meta-learning optimizes a general-purpose adaptation procedure via (stochastic) gradient descent, modular meta-learning finds a set of reusable modules that can form components of a solution for any new network topology. Numerical results validate the benefits of meta-learning for power control problems over joint training schemes, and demonstrate the advantages of modular meta-learning when data availability is extremely limited.

Recent years have witnessed growing interest in the application of deep neural networks (DNNs) for receiver design, which can potentially be applied in complex environments without relying on knowledge of the channel model. However, the dynamic nature of communication channels often leads to rapid distribution shifts, which may require periodically retraining. This paper formulates a data-efficient two-stage training method that facilitates rapid online adaptation. Our training mechanism uses a predictive meta-learning scheme to train rapidly from data corresponding to both current and past channel realizations. Our method is applicable to any deep neural network (DNN)-based receiver, and does not require transmission of new pilot data for training. To illustrate the proposed approach, we study DNN-aided receivers that utilize an interpretable model-based architecture, and introduce a modular training strategy based on predictive meta-learning. We demonstrate our techniques in simulations on a synthetic linear channel, a synthetic non-linear channel, and a COST 2100 channel. Our results demonstrate that the proposed online training scheme allows receivers to outperform previous techniques based on self-supervision and joint-learning by a margin of up to 2.5 dB in coded bit error rate in rapidly-varying scenarios.

The overall predictive uncertainty of a trained predictor can be decomposed into separate contributions due to epistemic and aleatoric uncertainty. Under a Bayesian formulation, assuming a well-specified model, the two contributions can be exactly expressed (for the log-loss) or bounded (for more general losses) in terms of information-theoretic quantities (Xu and Raginsky [2020]). This paper addresses the study of epistemic uncertainty within an information-theoretic framework in the broader setting of Bayesian meta-learning. A general hierarchical Bayesian model is assumed in which hyperparameters determine the per-task priors of the model parameters. Exact characterizations (for the log-loss) and bounds (for more general losses) are derived for the epistemic uncertainty – quantified by the minimum excess meta-risk (MEMR)– of optimal meta-learning rules. This characterization is leveraged to bring insights into the dependence of the epistemic uncertainty on the number of tasks and on the amount of per-task training data. Experiments are presented that use the proposed information-theoretic bounds, evaluated via neural mutual information estimators, to compare the performance of conventional learning and meta-learning as the number of meta-learning tasks increases.

This paper studies distributed Bayesian learning in a setting encompassing a central server and multiple workers by focusing on the problem of mitigating the impact of stragglers. The standard one-shot, or embarrassingly parallel, Bayesian learning protocol known as consensus Monte Carlo (CMC) is generalized by proposing two straggler-resilient solutions based on grouping and coding. Two main challenges in designing straggler-resilient algorithms for CMC are the need to estimate the statistics of the workers' outputs across multiple shots, and the joint non-linear post-processing of the outputs of the workers carried out at the server. This is in stark contrast to other distributed settings like gradient coding, which only require the per-shot sum of the workers' outputs. The proposed methods, referred to as Group-based CMC (G-CMC) and Coded CMC (C-CMC), leverage redundant computing at the workers in order to enable the estimation of global posterior samples at the server based on partial outputs from the workers. Simulation results show that C-CMC may outperform G-CMC for a small number of workers, while G-CMC is generally preferable for a larger number of workers.

Predicting fading channels is a classical problem with a vast array of applications, including as an enabler of artificial intelligence (AI)-based proactive resource allocation for cellular networks. Under the assumption that the fading channel follows a stationary complex Gaussian process, as for Rayleigh and Rician fading models, the optimal predictor is linear, and it can be directly computed from the Doppler spectrum via standard linear minimum mean squared error (LMMSE) estimation. However, in practice, the Doppler spectrum is unknown, and the predictor has only access to a limited time series of estimated channels. This paper proposes to leverage meta-learning in order to mitigate the requirements in terms of training data for channel fading prediction. Specifically, it first develops an offline low-complexity solution based on linear filtering via a meta-trained quadratic regularization. Then, an online method is proposed based on gradient descent and equilibrium propagation (EP). Numerical results demonstrate the advantages of the proposed approach, showing its capacity to approach the genie-aided LMMSE solution with a small number of training data points.

A broadcast/multicast communication system is studied in which layered division multiplexing (LDM) is applied to support differential quality-of-service (QoS) levels. Focusing on a practical scenario in which the transmitter does not know the fading distribution, layer allocation is optimized based on a dataset sampled during deployment. The optimality gap caused by the availability of limited data is bounded via a generalization analysis, and is shown to be monotonically decreasing as the dataset grows larger. Numerical experiments demonstrate that LDM improves spectral efficiency even for small datasets; and that, for sufficiently large datasets, the proposed mirror-descent-based layer optimization scheme achieves an expected rate close to that achieved when the transmitter knows the fading distribution.

Wall-clock convergence time and communication load are key performance metrics for the distributed implementation of stochastic gradient descent (SGD) in parameter server settings. Communication-adaptive distributed Adam (CADA) has been recently proposed as a way to reduce communication load via the adaptive selection of workers. CADA is subject to performance degradation in terms of wall-clock convergence time in the presence of stragglers. This paper proposes a novel scheme named grouping-based CADA (G-CADA) that retains the advantages of CADA in reducing the communication load, while increasing the robustness to stragglers at the cost of additional storage at the workers. G-CADA partitions the workers into groups of workers that are assigned the same data shards. Groups are scheduled adaptively at each iteration, and the server only waits for the fastest worker in each selected group. We provide analysis and experimental results to elaborate the significant gains on the wall-clock time, as well as communication load and computation load, of G-CADA over other benchmark schemes.

Terrestrial and satellite communication networks often rely on two-hop wireless architectures with an access channel followed by backhaul links. Examples include cloud-radio access networks (C-RAN) and low-Earth orbit (LEO) satellite systems. Furthermore, communication services characterized by the coexistence of heterogeneous requirements are emerging as key use cases. This article studies the performance of critical service (CS) and non-CS (NCS) for Internet-of-Things (IoT) systems sharing a grant-free channel consisting of radio access and backhaul segments. On the radio access segment, IoT devices send packets to a set of noncooperative access points (APs) using slotted ALOHA (SA). The APs then forward correctly received messages to a base station over a shared wireless backhaul segment adopting SA. We study first a simplified erasure channel model, which is well suited for satellite applications. Then, in order to account for terrestrial scenarios, the impact of fading is considered. Among the main conclusions, we show that orthogonal interservice resource allocation is generally preferred for NCS devices, while nonorthogonal protocols can improve the throughput and packet success rate of CS devices for both terrestrial and satellite scenarios.

Conventional frequentist learning, as assumed by existing federated learning protocols, is limited in its ability to quantify uncertainty, incorporate prior knowledge, guide active learning, and enable continual learning. Bayesian learning provides a principled approach to address all these limitations, at the cost of an increase in computational complexity. This paper studies distributed Bayesian learning in a wireless data center setting encompassing a central server and multiple distributed workers. Prior work on wireless distributed learning has focused exclusively on frequentist learning, and has introduced the idea of leveraging uncoded transmission to enable “over-the-air” computing. Unlike frequentist learning, Bayesian learning aims at evaluating approximations or samples from a global posterior distribution in the model parameter space. This work investigates for the first time the design of distributed one-shot, or “embarrassingly parallel”, Bayesian learning protocols in wireless data centers via consensus Monte Carlo (CMC). Uncoded transmission is introduced not only as a way to implement “over-the-air” computing, but also as a mechanism to deploychannel-driven MC sampling: Rather than treating channel noise as a nuisance to be mitigated, channel-driven sampling utilizes channel noise as an integral part of the MC sampling process. A simple wireless CMC scheme is first proposed that is asymptotically optimal under Gaussian local posteriors. Then, for arbitrary local posteriors, a variational optimization strategy is introduced. Simulation results demonstrate that, if properly accounted for, channel noise can indeed contribute to MC sampling and does not necessarily decrease the accuracy level.

Quantum machine learning has emerged as a potential practical application of near-term quantum devices. In this work, we study a two-layer hybrid classical-quantum classifier in which a first layer of quantum stochastic neurons implementing generalized linear models (QGLMs) is followed by a second classical combining layer. The input to the first, hidden, layer is obtained via amplitude encoding in order to leverage the exponential size of the fan-in of the quantum neurons in the number of qubits per neuron. To facilitate implementation of the QGLMs, all weights and activations are binary. While the state of the art on training strategies for this class of models is limited to exhaustive search and single-neuron perceptron-like bit-flip strategies, this letter introduces a stochastic variational optimization approach that enables the joint training of quantum and classical layers via stochastic gradient descent. Experiments show the advantages of the approach for a variety of activation functions implemented by QGLM neurons.

Neuromorphic data carries information in spatio-temporal patterns encoded by spikes. Accordingly, a central problem in neuromorphic computing is training spiking neural networks (SNNs) to reproduce spatio-temporal spiking patterns in response to given spiking stimuli. Most existing approaches model the input-output behavior of an SNN in a deterministic fashion by assigning each input to a specific desired output spiking sequence. In contrast, in order to fully leverage the time-encoding capacity of spikes, this work proposes to train SNNs so as to matchdistributionsof spiking signals rather than individual spiking signals. To this end, the paper introduces a novel hybrid architecture comprising a conditional generator, implemented via an SNN, and a discriminator, implemented by a conventional artificial neural network (ANN). The role of the ANN is to provide feedback during training to the SNN within an adversarial iterative learning strategy that follows the principle of generative adversarial network (GANs). In order to better capture multi-modal spatio-temporal distribution, the proposed approach – termed SpikeGAN – is further extended to support Bayesian learning of the generator's weight. Finally, settings with time-varying statistics are addressed by proposing an online meta-learning variant of SpikeGAN. Experiments bring insights into the merits of the proposed approach as compared to existing solutions based on (static) belief networks and maximum likelihood (or empirical risk minimization). In our experiments, handwritten digit images generated by SpikeGAN are observed to train an ANN classifier with$20\%$higher accuracy than a comparable belief network. Our experiments also demonstrate the use of SpikeGAN to generate neuromorphic data sets from handwritten digits. It is shown that these data can be used to train an SNN classifier that achieves an accuracy level approaching the baseline accuracy of an SNN classifier trained on rate-encoded real data.

Broadcast/multicast communication systems are typically designed to optimize the outage rate criterion, which neglects the performance of the fraction of clients with the worst channel conditions. Targeting ultra-reliable communication scenarios, this paper takes a complementary approach by introducing the conditional value-at-risk (CVaR) rate as the expected rate of a worst-case fraction of clients. To support differential quality-of-service (QoS) levels in this class of clients, layered division multiplexing (LDM) is applied, which enables decoding at different rates. Focusing on a practical scenario in which the transmitter does not know the fading distribution, layer allocation is optimized based on a dataset sampled offline. The optimality gap caused by the availability of limited data is bounded via a generalization analysis, and the sample complexity is shown to increase as the designated fraction of worst-case clients decreases. Considering this theoretical result, meta-learning is introduced as a means to reduce sample complexity by leveraging data from previous deployments. Numerical experiments demonstrate that LDM improves spectral efficiency even for small datasets; that, for sufficiently large datasets, the proposed mirror-descent-based layer optimization scheme achieves a CVaR rate close to that achieved when the transmitter knows the fading distribution; and that meta-learning can significantly reduce data requirements.

Meta-learning automatically infers an inductive bias by observing data from a number of related tasks. The inductive bias is encoded by hyperparameters that determine aspects of the model class or training algorithm, such as initialization or learning rate. Meta-learning assumes that the learning tasks belong to a task environment, and that tasks are drawn from the same task environment both during meta-training and meta-testing. This, however, may not hold true in practice. In this paper, we introduce the problem of transfer meta-learning, in which tasks are drawn from a target task environment during meta-testing that may differ from the source task environment observed during meta-training. Novel information-theoretic upper bounds are obtained on the transfer meta-generalization gap, which measures the difference between the meta-training loss, available at the meta-learner, and the average loss on meta-test data from a new, randomly selected, task in the target task environment. The first bound, on the average transfer meta-generalization gap, captures the meta-environment shift between source and target task environments via the KL divergence between source and target data distributions. The second, PAC-Bayesian bound, and the third, single-draw bound, account for this shift via the log-likelihood ratio between source and target task distributions. Furthermore, two transfer meta-learning solutions are introduced. For the first, termed Empirical Meta-Risk Minimization (EMRM), we derive bounds on the average optimality gap. The second, referred to as Information Meta-Risk Minimization (IMRM), is obtained by minimizing the PAC-Bayesian bound. IMRM is shown via experiments to potentially outperform EMRM.

Spiking neural networks (SNNs) capture some of the efficiency of biological brains for inference and learning via the dynamic, online, and event-driven processing of binary time series. Most existing learning algorithms for SNNs are based on deterministic neuronal models, such as leaky integrate-and-fire, and rely on heuristic approximations of backpropagation through time that enforces constraints such as locality. In contrast, probabilistic SNN models can be trained directly via principled online, local, and update rules that have proven to be particularly effective for resource-constrained systems. This article investigates another advantage of probabilistic SNNs, namely, their capacity to generate independent outputs when queried over the same input. It is shown that the multiple generated output samples can be used during inference to robustify decisions and to quantify uncertainty-a feature that deterministic SNN models cannot provide. Furthermore, they can be leveraged for training in order to obtain more accurate statistical estimates of the log-loss training criterion and its gradient. Specifically, this article introduces an online learning rule based on generalized expectation-maximization (GEM) that follows a three-factor form with global learning signals and is referred to as GEM-SNN. Experimental results on structured output memorization and classification on a standard neuromorphic dataset demonstrate significant improvements in terms of log-likelihood, accuracy, and calibration when increasing the number of samples used for inference and training.

In order to unlock the full advantages of massive multiple-input multiple-output (MIMO) in the downlink, the base station (BS) must leverage information about the downlink fading channels. However, in frequency division duplex (FDD) systems, full channel reciprocity does not hold, and acquiring information about the downlink channels generally requires downlink pilot transmission followed by uplink feedback. Prior work proposed to design pilot transmission, feedback, and channel state information (CSI) estimation, or directly downlink beamforming, via deep learning in an end-to-end manner. While previous work only used downlink pilots in a single slot, in this work, we introduce an enhanced end-to-end design that leverages partial uplink-downlink reciprocity and temporal correlation of the fading processes by utilizing jointly downlink and uplink pilots across multiple time slots. The proposed method is based on a novel deep learning architecture – HyperRNN – that combines hypernetworks and recurrent neural networks (RNNs) to optimize the transfer of long-term invariant channel features from uplink to downlink. Simulation results demonstrate that the HyperRNN achieves a lower normalized mean square error (NMSE) performance in terms of channel estimation, and that it attains a larger achievable sum-rate when applied to multi-user beamforming, as compared to the state of the art.

Spiking Neural Networks (SNNs) offer a novel computational paradigm that captures some of the efficiency of biological brains by processing through binary neural dynamic activations. Probabilistic SNN models are typically trained to maximize the likelihood of the desired outputs by using unbiased estimates of the log-likelihood gradients. While prior work used single-sample estimators obtained from a single run of the network, this paper proposes to leverage multiple compartments that sample independent spiking signals while sharing synaptic weights. The key idea is to use these signals to obtain more accurate statistical estimates of the log-likelihood training criterion, as well as of its gradient. The approach is based on generalized expectation-maximization (GEM), which optimizes a tighter approximation of the log-likelihood using importance sampling. The derived online learning algorithm implements a three-factor rule with global per-compartment learning signals. Experimental results on a classification task on the neuromorphic MNIST-DVS data set demonstrate significant improvements in terms of log-likelihood, accuracy, and calibration when increasing the number of compartments used for training and inference.

Mobile devices can offload deep neural network (DNN)-based inference to the cloud, overcoming local hardware and energy limitations. However, offloading adds communication delay, thus increasing the overall inference time, and hence it should be used only when needed. An approach to address this problem consists of the use of adaptive model partitioning based on early-exit DNNs. Accordingly, the inference starts at the mobile device, and an intermediate layer estimates the accuracy: If the estimated accuracy is sufficient, the device takes the inference decision; Otherwise, the remaining layers of the DNN run at the cloud. Thus, the device offloads the inference to the cloud only if it cannot classify a sample with high confidence. This offloading requires a correct accuracy prediction at the device. Nevertheless, DNNs are typically miscalibrated, providing overconfident decisions. This work shows that the employment of a miscalibrated early-exit DNN for offloading via model partitioning can significantly decrease inference accuracy. In contrast, we argue that implementing a calibration algorithm prior to deployment can solve this problem, allowing for more reliable offloading decisions.

Meta-learning aims at optimizing the hyperparameters of a model class or training algorithm from the observation of data from a number of related tasks. Following the setting of Baxter [1], the tasks are assumed to belong to the same task environment, which is defined by a distribution over the space of tasks and by per-task data distributions. The statistical properties of the task environment thus dictate the similarity of the tasks. The goal of the meta-learner is to ensure that the hyperparameters obtain a small loss when applied for training of a new task sampled from the task environment. The difference between the resulting average loss, known as meta-population loss, and the corresponding empirical loss measured on the available data from related tasks, known as meta-generalization gap, is a measure of the generalization capability of the meta-learner. In this paper, we present novel information-theoretic bounds on the average absolute value of the meta-generalization gap. Unlike prior work [2], our bounds explicitly capture the impact of task relatedness, the number of tasks, and the number of data samples per task on the meta-generalization gap. Task similarity is gauged via the Kullback-Leibler (KL) and Jensen-Shannon (JS) divergences. We illustrate the proposed bounds on the example of ridge regression with meta-learned bias.

Reconfigurable intelligent surfaces (RISs) are typically used in multi-user systems to mitigate interference among active transmitters. In contrast, this paper studies a setting with a conventional active encoder as well as a passive encoder that modulates the reflection pattern of the RIS. The RIS hence serves the dual purpose of improving the rate of the active encoder and of enabling communication from the second encoder. The capacity region is characterized, and information-theoretic insights regarding the trade-offs between the rates of the two encoders are derived by focusing on the high- and low-power regimes.

Meta-learning optimizes an inductive bias—typically in the form of the hyperparameters of a base-learning algorithm—by observing data from a finite number of related tasks. This paper presents an information-theoretic bound on the generalization performance of any given meta-learner, which builds on the conditional mutual information (CMI) framework of Steinke and Zakynthinou (2020). In the proposed extension to meta-learning, the CMI bound involves a training meta-supersample obtained by first sampling 2N independent tasks from the task environment, and then drawing 2M independent training samples for each sampled task. The meta-training data fed to the meta-learner is modelled as being obtained by randomly selecting N tasks from the available 2N tasks and M training samples per task from the available 2M training samples per task. The resulting bound is explicit in two CMI terms, which measure the information that the meta-learner output and the base-learner output provide about which training data are selected, given the entire meta-supersample. Finally, we present a numerical example that illustrates the merits of the proposed bound in comparison to prior information-theoretic bounds for meta-learning.

One of the key challenges in training Spiking Neural Networks (SNNs) is that target outputs typically come in the form of natural signals, such as labels for classification or images for generative models, and need to be encoded into spikes. This is done by handcrafting target spiking signals, which in turn implicitly fixes the mechanisms used to decode spikes into natural signals, e.g., rate decoding. The arbitrary choice of target signals and decoding rule generally impairs the capacity of the SNN to encode and process information in the timing of spikes. To address this problem, this work introduces a hybrid variational autoencoder architecture, consisting of an encoding SNN and a decoding Artificial Neural Network (ANN). The role of the decoding ANN is to learn how to best convert the spiking signals output by the SNN into the target natural signal. A novel end-to-end learning rule is introduced that optimizes a directed information bottleneck training criterion via surrogate gradients. We demonstrate the applicability of the technique in an experimental settings on various tasks, including real-life datasets.

Federated Learning (FL) refers to distributed protocols that avoid direct raw data exchange among the participating devices while training for a common learning task. This way, FL can potentially reduce the information on the local data sets that is leaked via communications. In order to provide formal privacy guarantees, however, it is generally necessary to put in place additional masking mechanisms. When FL is implemented in wireless systems via uncoded transmission, the channel noise can directly act as a privacy-inducing mechanism. This paper demonstrates that, as long as the privacy constraint level, measured via differential privacy (DP), is below a threshold that decreases with the signal-to-noise ratio (SNR), uncoded transmission achieves privacy “for free”, i.e., without affecting the learning performance. More generally, this work studies adaptive power allocation (PA) for distributed gradient descent in wireless FL with the aim of minimizing the learning optimality gap under privacy and power constraints. Both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) transmission with “over-the-air-computing” are studied, and solutions are obtained in closed form for an offline optimization setting. Furthermore, heuristic online methods are proposed that leverage iterative one-step-ahead optimization. The importance of dynamic PA and the potential benefits of NOMA versus OMA are demonstrated through extensive simulations.

The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a shared model by multiple individual clients via federated learning (FL). To improve the communication efficiency of FL implementations in wireless systems, recent works have proposed compression and dimension reduction mechanisms, along with digital and analog transmission schemes that account for channel noise, fading, and interference. The prior art has mainly focused on star topologies consisting of distributed clients and a central server. In contrast, this paper studies FL over wireless device-to-device (D2D) networks by providing theoretical insights into the performance of digital and analog implementations of decentralized stochastic gradient descent (DSGD). First, we introduce generic digital and analog wireless implementations of communication-efficient DSGD algorithms, leveraging random linear coding (RLC) for compression and over-the-air computation (AirComp) for simultaneous analog transmissions. Next, under the assumptions of convexity and connectivity, we provide convergence bounds for both implementations. The results demonstrate the dependence of the optimality gap on the connectivity and on the signal-to-noise ratio (SNR) levels in the network. The analysis is corroborated by experiments on an image-classification task.

A fog-radio access network (F-RAN) architecture is studied for an Internet-of-Things (IoT) system in which wireless sensors monitor a number of multi-valued events and transmit in the uplink using grant-free random access to multiple edge nodes (ENs). Each EN is connected to a central processor (CP) via a finite-capacity fronthaul link. In contrast to conventional information-agnostic protocols based on separate source-channel (SSC) coding, where each device uses a separate codebook, this paper considers an information-centric approach based on joint source-channel (JSC) coding via a non-orthogonal generalization of type-based multiple access (TBMA). By leveraging the semantics of the observed signals, all sensors measuring the same event share the same codebook (with non-orthogonal codewords), and all such sensors making the same local estimate of the event transmit the same codeword. The F-RAN architecture directly detects the events’ values without first performing individual decoding for each device. Cloud and edge detection schemes based on Bayesian message passing are designed and trade-offs between cloud and edge processing are assessed.

A communication link aided by a reconfigurable intelligent surface (RIS) is studied in which the transmitter can control the state of the RIS via a finite-rate control link. Channel state information (CSI) is acquired at the receiver based on pilot-assisted channel estimation, and it may or may not be shared with the transmitter. Considering quasi-static fading channels with imperfect CSI, capacity-achieving signalling is shown to implement joint encoding of the transmitted signal and of the response of the RIS. This demonstrates the information-theoretic optimality of RIS-based modulation, or “single-RF MIMO” systems. In addition, a novel signalling strategy based on separate layered encoding that enables practical successive cancellation-type decoding at the receiver is proposed. Numerical experiments show that the conventional scheme that fixes the reflection pattern of the RIS, irrespective of the transmitted information, as to maximize the achievable rate is strictly suboptimal, and is outperformed by the proposed adaptive coding strategies at all practical signal-to-noise ratio (SNR) levels.

Consider a multi-cell mobile edge computing network, in which each user wishes to compute the product of a user-generated data matrix with a network-stored matrix. This is done through task offloading by means of input uploading, distributed computing at edge nodes (ENs), and output downloading. Task offloading may suffer long delay since servers at some ENs may be straggling due to random computation time, and wireless channels may experience severe fading and interference. This paper aims to investigate the interplay among upload, computation, and download latencies during the offloading process in the high signal-to-noise ratio regime from an information-theoretic perspective. A policy based on cascaded coded computing and on coordinated and cooperative interference management in uplink and downlink is proposed and proved to be approximately optimal for a sufficiently large upload time. By investing more time in uplink transmission, the policy creates data redundancy at the ENs, which can reduce the computation time, by enabling the use of coded computing, as well as the download time via transmitter cooperation. Moreover, the policy allows computation time to be traded for download time. Numerical examples demonstrate that the proposed policy can improve over existing schemes by significantly reducing the end-to-end execution time.

Gradient-based distributed learning in parameter server (PS) computing architectures is subject to random delays due to straggling worker nodes and to possible communication bottlenecks between PS and workers. Solutions have been recently proposed to separately address these impairments based on the ideas of gradient coding (GC), worker grouping, and adaptive worker selection. This article provides a unified analysis of these techniques in terms of wall-clock time, communication, and computation complexity measures. Furthermore, in order to combine the benefits of GC and grouping in terms of robustness to stragglers with the communication and computation load gains of adaptive selection, novel strategies, named lazily aggregated GC (LAGC) and grouped-LAG (G-LAG), are introduced. Analysis and results show that G-LAG provides the best wall-clock time and communication performance while maintaining a low computational cost, for two representative distributions of the computing times of the worker nodes.

Cooperative training methods for distributed machine learning are typically based on the exchange of local gradients or local model parameters. The latter approach is known as Federated Learning (FL). An alternative solution with reduced communication overhead, referred to as Federated Distillation (FD), was recently proposed that exchanges only averaged model outputs. While prior work studied implementations of FL over wireless fading channels, here we propose wireless protocols for FD and for an enhanced version thereof that leverages an offline communication phase to communicate "mixed-up" covariate vectors. The proposed implementations consist of different combinations of digital schemes based on separate source-channel coding and of over-the-air computing strategies based on analog joint source-channel coding. It is shown that the enhanced version FD has the potential to significantly outperform FL in the presence of limited spectral resources.

Consider an Internet-of-Things (IoT) system that monitors a number of multi-valued events through multiple sensors sharing the same bandwidth. Each sensor measures data correlated to one or more events, and communicates to the fusion center at a base station using grant-free random access whenever the corresponding event is active. The base station aims at detecting the active events, and, for each active event, to determine a scalar value describing each active event's state. A conventional solution based on Separate Source-Channel (SSC) coding would use a separate codebook for each sensor and decode the sensors' transmitted packets at the base station in order to subsequently carry out events' detection. In contrast, this paper considers a potentially more efficient solution based on Joint Source-Channel (JSC) coding via a non-orthogonal generalization of Type-Based Multiple Access (TBMA). Accordingly, all sensors measuring the same event share the same codebook (with non-orthogonal codewords), and the base station directly detects the events' values without first performing individual decoding for each sensor. A novel Bayesian message-passing detection scheme is developed for the proposed TBMA-based protocol, and its performance is compared to conventional solutions.

When a channel model is available, learning how to communicate on fading noisy channels can be formulated as the (unsupervised) training of an autoencoder consisting of the cascade of encoder, channel, and decoder. An important limitation of the approach is that training should be generally carried out from scratch for each new channel. To cope with this problem, prior works considered joint training over multiple channels with the aim of finding a single pair of encoder and decoder that works well on a class of channels. As a result, joint training ideally mimics the operation of non-coherent transmission schemes. In this paper, we propose to obviate the limitations of joint training via meta-learning: Rather than training a common model for all channels, meta-learning finds a common initialization vector that enables fast training on any channel. The approach is validated via numerical results, demonstrating significant training speed-ups, with effective encoders and decoders obtained with as little as one iteration of Stochastic Gradient Descent.

Spiking Neural Networks (SNNs) offer a promising alternative to conventional Artificial Neural Networks (ANNs) for the implementation of on-device low-power online learning and inference. On-device training is, however, constrained by the limited amount of data available at each device. In this paper, we propose to mitigate this problem via cooperative training through Federated Learning (FL). To this end, we introduce an online FL-based learning rule for networked on-device SNNs, which we refer to as FL-SNN. FL-SNN leverages local feedback signals within each SNN, in lieu of back-propagation, and global feedback through communication via a base station. The scheme demonstrates significant advantages over separate training and features a flexible trade-off between communication load and accuracy via the selective exchange of synaptic weights.

In this paper, we study the coexistence of critical and non-critical Internet of Things (IoT) services on a grant-free channel consisting of radio access and backhaul segments. On the radio access segment, IoT devices send packets to access points (APs) over an erasure collision channel using the slotted ALOHA protocol. Then, the APs forward correctly received messages to a base station (BS) over a shared wireless backhaul segment, modeled as an erasure collision channel. The APs hence play the role of uncoordinated relays that provide space diversity and may reduce performance losses caused by collisions. Both non-orthogonal and inter-service orthogonal resource sharing are considered and compared. Throughput and reliability metrics are analyzed, and numerical results are provided to assess the performance trade-offs between critical and non-critical IoT services.

This work studies the uplink of a multi-tenant cloud radio access network (C-RAN) system with spectrum pooling. In the system, each operator has a cloud processor (CP) connected to a set of proprietary radio units (RUs) through finite-capacity fronthaul links. The uplink spectrum is divided into private and shared subbands, and all the user equipments (UEs) of the participating operators can simultaneously transmit signals on the shared subband. To mitigate inter-operator interference on the shared subband, the CPs of the participating operators can exchange compressed uplink baseband signals on finite-capacity backhaul links. This work tackles the problem of jointly optimizing bandwidth allocation, transmit power control and fronthaul compression strategies. In the optimization, we impose that the inter-operator privacy loss be limited by a given threshold value. An iterative algorithm is proposed to find a suboptimal solution based on the matrix fractional programming approach. Numerical results validate the advantages of the proposed optimized spectrum pooling scheme.

Networks of spiking neurons and Winner- Take-All spiking circuits (WTA -SNNs) can detect information encoded in spatio-temporal multi-valued events. These are described by the timing of events of interest, e.g., clicks, as well as by categorical numerical values assigned to each event, e.g., like or dislike. Other use cases include object recognition from data collected by neuromorphic cameras, which produce, for each pixel, signed bits at the times of sufficiently large brightness variations. Existing schemes for training WTA -SNNs are limited to rate-encoding solutions, and are hence able to detect only spatial patterns. Developing more general training algorithms for arbitrary WTA -SNNs inherits the challenges of training (binary) Spiking Neural Networks (SNNs). These amount, most notably, to the non-differentiability of threshold functions, to the recurrent behavior of spiking neural models, and to the difficulty of implementing backpropagation in neuromorphic hardware. In this paper, we develop a variational online local training rule for WTA-SNNs, referred to as VOWEL, that leverages only local pre- and post-synaptic information for visible circuits, and an additional common reward signal for hidden circuits. The method is based on probabilistic generalized linear neural models, control variates, and variational regularization. Experimental results on real-world neuromorphic datasets with multi-valued events demonstrate the advantages of WTA-SNNs over conventional binary SNNs trained with state-of-the-art methods, especially in the presence of limited computing resources.

A communication link aided by a Reconfigurable Intelligent Surface (RIS) is studied, in which the transmitter can control the state of the RIS via a finite-rate control link. Prior work mostly assumed a fixed RIS configuration irrespective of the transmitted information. In contrast, this work derives information-theoretic limits, and demonstrates that the capacity is achieved by a scheme that jointly encodes information in the transmitted signal as well as in the RIS configuration. In addition, a novel signaling strategy based on layered encoding is proposed that enables practical successive cancellation-type decoding at the receiver. Numerical experiments demonstrate that the standard max-SNR scheme that fixes the configuration of the RIS as to maximize the Signal-to-Noise Ratio (SNR) at the receiver is strictly suboptimal, and is outperformed by the proposed strategies at all practical SNR levels.

A multi-cell mobile edge computing network is studied, in which each user wishes to compute the product of a user-generated data matrix with a network-stored matrix through data uploading, distributed edge computing, and output downloading. Assuming randomly straggling edge servers, this paper investigates the interplay among upload, compute, and download times in high signal-to-noise ratio regimes. A policy based on cascaded coded computing and on coordinated and cooperative interference management in uplink and downlink is proposed and proved to be approximately optimal for sufficiently large upload times. By investing more time in uplink transmission, the policy creates data redundancy at the edge nodes to reduce both computation times by coded computing, and download times via transmitter cooperation. Moreover, it allows computing times to be traded for download times.

The uplink of a Cloud Radio Access Network (C-RAN) architecture is studied, where decoding in the cloud takes place at distributed decoding processors. To mitigate the impact of straggling decoders in the cloud, the cloud re-encodes the received frames via a linear code before distributing them to the decoding processors, which estimate linear combinations of the codewords. Focusing on Gaussian channels, and assuming the use of lattice codes at the users, we derive the computational rates and frame error probabilities at the cloud. The approach differs from Compute-and-Forward in that the combination of codewords is not caused by the channel but purposefully created in the cloud by encoding the received signals to reduce the decoding delay.

A Fog-Radio Access Network (F-RAN) is studied in which cache-enabled Edge Nodes (ENs) with dedicated fronthaul connections to the cloud aim at delivering contents to mobile users. Using an information-theoretic approach, this work tackles the problem of quantifying the potential latency reduction that can be obtained by enabling Device-to-Device (D2D) communication over out-of-band broadcast links. Following prior work, the Normalized Delivery Time (NDT) - a metric that captures the high signal-to-noise ratio worst-case latency - is adopted as the performance criterion of interest. Joint edge caching, downlink transmission, and D2D communication policies based on compress-and-forward are proposed that are shown to be information-theoretically optimal to within a constant multiplicative factor of two for all values of the problem parameters, and to achieve the minimum NDT for a number of special cases. The analysis provides insights on the role of D2D cooperation in improving the delivery latency.

Large matrix multiplications are central to large-scale machine learning applications. These operations are often carried out on a distributed computing platform with a master server and multiple workers in the cloud operating in parallel. For such distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a trade-off between recovery threshold, i.e., the number of workers required to recover the matrix product, and communication load, i.e., the total amount of data to be downloaded from the workers. In this paper, in addition to exact recovery requirements, we impose security and privacy constraints on the data matrices, and study the recovery threshold as a function of the communication load. We first assume that both matrices contain private information and that workers can collude to eavesdrop on the content of these data matrices. For this problem, we introduce a novel class of secure codes, referred to as secure generalized PolyDot (SGPD) codes, that generalize state-of-the-art non-secure codes for matrix multiplication. SGPD codes allow a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers while providing perfect secrecy for the two data matrices. We then study a connection between secure matrix multiplication and private information retrieval. We specifically assume that one of the data matrices is taken from a public set known to all the workers. In this setup, the identity of the matrix of interest should be kept private from the workers. For this model, we present a variant of generalized PolyDot codes that can guarantee both secrecy of one matrix and privacy for the identity of the other matrix for the case of no colluding servers.

A multi-cell Fog-Radio Access Network (F-RAN) architecture is considered in which Internet of Things (IoT) devices periodically make noisy observations of a Quantity of Interest (QoI) and transmit using grant-free access in the uplink. The devices in each cell are connected to an Edge Node (EN), which may also have a finite-capacity fronthaul link to a central processor. In contrast to conventional information-agnostic protocols, the devices transmit using a Type-Based Multiple Access (TBMA) protocol that is tailored to enable the estimate of the field of correlated QoIs in each cell based on the measurements received from IoT devices. In this paper, this form of information-centric radio access is studied for the first time in a multi-cell F-RAN model with edge or cloud detection. Edge and cloud detection are designed and compared for a multi-cell system. Optimal model-based detectors are introduced and the resulting asymptotic behavior of the probability of error at cloud and edge is derived. Then, for the scenario in which a statistical model is not available, data-driven edge and cloud detectors are discussed and evaluated in numerical results.

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resources allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, the problem is to determine, in an online fashion at each Transmission Time Interval (TTI), the configurations that maximizes the long-term average number of IoT devices that are able to both access and deliver data. Given the complexity of optimal algorithms, a Cooperative Multi-Agent Deep Neural Network based Q-learning (CMA-DQN) approach is developed, whereby each DQN agent independently control a configuration variable for each group. The DQN agents are cooperatively trained in the same environment based on feedback regarding transmission outcomes. CMA-DQN is seen to considerably outperform conventional heuristic approaches based on load estimation.

Neuromorphic hardware platforms, such as Intel's Loihi chip, support the implementation of Spiking Neural Networks (SNNs) as an energy-efficient alternative to Artificial Neural Networks (ANNs). SNNs are networks of neurons with internal analogue dynamics that communicate by means of binary time series. In this work, a probabilistic model is introduced for a generalized set-up in which the synaptic time series can take values in an arbitrary alphabet and are characterized by both causal and instantaneous statistical dependencies. The model, which can be considered as an extension of exponential family harmoniums to time series, is introduced by means of a hybrid directed-undirected graphical representation. Furthermore, distributed learning rules are derived for Maximum Likelihood and Bayesian criteria under the assumption of fully observed time series in the training set.

In a distributed computing system operating according to the map-shuffle-reduce framework, coding data prior to storage can be useful both to reduce the latency caused by straggling servers and to decrease the inter-server communication load in the shuffle phase. In prior work, a concatenated coding scheme was proposed for a matrix multiplication task. In this scheme, the outer Maximum Distance Separable (MDS) code is leveraged to correct erasures caused by stragglers, while the inner repetition code is used to improve the communication efficiency in the shuffle phase by means of coded multi-casting. In this work, it is demonstrated that it is possible to leverage the redundancy created by repetition coding in order to increase the rate of the outer MDS code and hence to increase the multicasting opportunities in the shuffle phase. As a result, the proposed approach is shown to improve over the best known latency-communication overhead trade-off.

Tensor operations, such as matrix multiplication, are central to large-scale machine learning applications. These operations can be carried out on a distributed computing platform with a master server at the user side and multiple workers in the cloud operating in parallel. For distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a tradeoff between recovery threshold and communication load. In this work, we impose an additional security constraint on the data matrices and assume that workers can collude to eavesdrop on the content of these data matrices. Specifically, we introduce a novel class of secure codes, referred to as secure generalized PolyDot codes, that generalizes previously published non-secure versions of these codes for matrix multiplication. These codes extend the state-of-the-art by allowing a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers.

Distributed computing platforms typically assume the availability of reliable and dedicated connections among the processors. This work considers an alternative scenario, relevant for wireless data centers and federated learning, in which the distributed processors, operating on generally distinct coded data, are connected via shared wireless channels accessed via full-duplex transmission. The study accounts for both wireless and computing impairments, including interference, imperfect Channel State Information, and straggling processors, and it assumes a Map-Shuffle-Reduce coded computing paradigm. The total latency of the system, obtained as the sum of computing and communication delays, is studied for different shuffling strategies revealing the interplay between distributed computing, coding, and cooperative or coordinated transmission.

A Fog-Radio Access Network (F-RAN) with arbitrary number of edge nodes and users is studied in which the users are able to cooperate by communicating over out-of-band broadcast Device-to-Device (D2D) links. Placement and delivery strategies are proposed with the aim of minimizing the Normalized Delivery Time (NDT) - a metric that captures the high signal-to-noise ratio worst-case latency for delivering any subset of requested contents to the users. The proposed strategies, based on compress-and-forward, are shown to be optimal to within a constant multiplicative factor of two for all values of the problem parameters. The analysis provides insights on the role of D2D cooperation in improving the delivery latency.

This paper considers a cloud-RAN architecture with cache-enabled multi-antenna Edge Nodes (ENs) that deliver content to cache-enabled end-users. The ENs are connected to a central server via limited-capacity fronthaul links, and, based on the information received from the central server and the cached contents, they transmit on the shared wireless medium to satisfy users' requests. By leveraging cooperative transmission as enabled by ENs' caches and fronthaul links, as well as multicasting opportunities provided by users' caches, a close-to-optimal caching and delivery scheme is proposed. As a result, the minimum Normalized Delivery Time (NDT), a high-SNR measure of delivery latency, is characterized to within a multiplicative constant gap of 3/2 under the assumption of uncoded caching and fronthaul transmission, and of one-shot linear precoding. This result demonstrates the interplay among fronthaul links capacity, ENs' caches, and end-users' caches in minimizing the content delivery time.

Cooperative training methods for distributed machine learning typically assume noiseless and ideal communication channels. This work studies some of the opportunities and challenges arising from the presence of wireless communication links. We specifically consider wireless implementations of Federated Learning (FL) and Federated Distillation (FD), as well as of a novel Hybrid Federated Distillation (HFD) scheme. Both digital implementations based on separate source-channel coding and over-the-air computing implementations based on joint source-channel coding are proposed and evaluated over Gaussian multiple-access channels.

Consider a collaborative task carried out by two autonomous agents that can communicate over a noisy channel. Each agent is only aware of its own state, while the accomplishment of the task depends on the value of the joint state of both agents. As an example, both agents must simultaneously reach a certain location of the environment, while only being aware of their own positions. Assuming the presence of feedback in the form of a common reward to the agents, a conventional approach would apply separately: (i) an off-the-shelf coding and decoding scheme in order to enhance the reliability of the communication of the state of one agent to the other; and (ii) a standard multiagent reinforcement learning strategy to learn how to act in the resulting environment. In this work, it is argued that the performance of the collaborative task can be improved if the agents learn how to jointly communicate and act. In particular, numerical results for a baseline grid world example demonstrate that the jointly learned policy carries out compression and unequal error protection by leveraging information about the action policy.

This paper investigates the probability that the delay and the peak-age of information exceed a desired threshold in a point-to-point communication system with short information packets. The packets are generated according to a stationary memoryless Bernoulli process, placed in a single-server queue and then transmitted over a wireless channel. A variable-length stop-feedback coding scheme-a general strategy that encompasses simple automatic repetition request (ARQ) and more sophisticated hybrid ARQ techniques as special cases-is used by the transmitter to convey the information packets to the receiver. By leveraging finite-blocklength results, the delay violation and the peak-age violation probabilities are characterized without resorting to approximations based on larg-deviation theory as in previous literature. Numerical results illuminate the dependence of delay and peak-age violation probability on system parameters such as the frame size and the undetected error probability, and on the chosen packet-management policy. The guidelines provided by our analysis are particularly useful for the design of low-latency ultra-reliable communication systems.

We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at delivering contents on demand with minimal average latency from a time-varying library of popular contents. Uncached requested files can be transferred from the cloud to the eRRHs by following either backhaul or fronthaul modes. The backhaul mode transfers fractions of the requested files, while the fronthaul mode transmits quantized baseband samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the eRRHs to be updated, which may lower future delivery latencies. In contrast, the fronthaul mode enables cooperative C-RAN transmissions that may reduce the current delivery latency. Taking into account the trade-off between current and future delivery performance, this letter proposes an adaptive selection method between the two delivery modes to minimize the long-term delivery latency. Assuming an unknown and time-varying popularity model, the method is based on model-free Reinforcement Learning (RL). Numerical results confirm the effectiveness of the proposed RL.

The uplink of a cloud radio access network architecture is studied in which decoding at the cloud takes place via network function virtualization on commercial off-the-shelf servers. In order to mitigate the impact of straggling decoders in this platform, a novel coding strategy is proposed, whereby the cloud re-encodes the received frames via a linear code before distributing them to the decoding processors. Transmission of a single frame is considered first, and upper bounds on the resulting frame unavailability probability as a function of the decoding latency are derived by assuming a binary symmetric channel for uplink communications. Then, the analysis is extended to account for random frame arrival times. In this case, the tradeoff between an average decoding latency and the frame error rate is studied for two different queuing policies, whereby the servers carry out per-frame decoding or continuous decoding, respectively. Numerical examples demonstrate that the bounds are useful tools for code design and that coding is instrumental in obtaining a desirable compromise between decoding latency and reliability.

Hybrid beamforming is known to be a cost-effective and wide-spread solution for a system with large-scale antenna arrays. This paper studies the optimization of the analog and digital components of the hybrid beamforming solution for remote radio heads (RRHs) in a downlink cloud radio access network architecture. Digital processing is carried out at a baseband processing unit (BBU) in the “cloud,” and the precoded baseband signals are quantized prior to transmission to the RRHs via finite-capacity fronthaul links. In this system, we consider two different channel state information (CSI) scenarios: 1) ideal CSI at the BBU and 2) imperfect effective CSI. The optimization of digital beamforming and fronthaul quantization strategies at the BBU as well as analog radio-frequency (RF) beamforming at the RRHs is a coupled problem since the effect of the quantization noise at the receiver depends on the precoding matrices. The resulting joint optimization problem is examined with the goal of maximizing the weighted downlink sum-rate and the network energy efficiency. Fronthaul capacity and per-RRH power constraints are enforced along with constant modulus constraint on the RF beamforming matrices. For the case of perfect CSI, a block coordinate descent scheme is proposed based on the weighted minimum-mean-square-error approach by relaxing the constant modulus constraint of the analog beamformer. Also, we present the impact of imperfect CSI on the weighted sum-rate and network energy efficiency performance, and the algorithm is extended by applying the sample average approximation. The numerical results confirm the effectiveness of the proposed scheme and show that the proposed algorithm is robust to estimation errors.

In fog-aided cellular systems, content delivery latency can be minimized by jointly optimizing edge caching and transmission strategies. In order to account for the cache capacity limitations at the edge nodes (ENs), transmission generally involves both fronthaul transfer from a cloud processor with access to the content library to the ENs and wireless delivery from the ENs to the users. In this paper, the resulting problem is studied from an information-theoretic viewpoint by making the following practically relevant assumptions: 1) the ENs have multiple antennas; 2) only uncoded fractional caching is allowed; 3) the fronthaul links are used to send fractions of contents; and 4) the ENs are constrained to use one-shot linear zero-forcing precoding on the wireless channel. Assuming off-line proactive caching and focusing on a high signal-to-noise ratio (SNR) latency metric, the optimal information-theoretic performance is investigated under both serial and pipelined fronthaul-edge transmission modes. The analysis characterizes the minimum high-SNR latency in terms of normalized delivery time (NDT) for worst case users' demands. The characterization is exact for a subset of system parameters and is generally optimal within a multiplicative factor of 3/2 for the serial case and 2 for the pipelined case. The results bring insights into the optimal interplay between edge and cloud processing in fog-aided wireless networks as a function of system resources, including the number of antennas at the ENs, the ENs' cache capacity, and the fronthaul capacity.

The performance of orthogonal and non-orthogonal multiple access is studied for the multiplexing of enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) users in the uplink of a multi-cell Cloud Radio Access Network (C-RAN) architecture. While eMBB users can operate over long codewords spread in time and frequency, URLLC users' transmissions are random and localized in time due to their low-latency requirements. These requirements also call for decoding of URLLC packets to be carried out at the edge nodes (ENs), whereas eMBB traffic can leverage the interference management capabilities of centralized decoding at the cloud. Using information-theoretic arguments, the performance tradeoffs between eMBB and URLLC traffic types are investigated in terms of rate for the former, and rate, access latency, and reliability for the latter. The analysis includes non-orthogonal multiple access (NOMA) with different decoding architectures, such as puncturing and successive interference cancellation (SIC). The main results bring insight on effective design choices as a function of inter-cell interference, signal-to-noise ratio levels, and fronthaul capacity constraints.

Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes. In this paper, the problem of training a two-layer SNN is studied for the purpose of classification, under a Generalized Linear Model (GLM) probabilistic neural model that was previously considered within the computational neuroscience literature. Conventional classification rules for SNNs operate offline based on the number of output spikes at each output neuron. In contrast, a novel training method is proposed here for a first-to-spike decoding rule, whereby the SNN can perform an early classification decision once spike firing is detected at an output neuron. Numerical results bring insights into the optimal parameter selection for the GLM neuron and on the accuracy-complexity trade-off performance of conventional and first-to-spike decoding.

The uplink of a cloud radio access network architecture is studied in which decoding at the cloud takes place via network function virtualization (NFV) on commercial off-the-shelf (COTS) servers. In order to mitigate the impact of straggling decoders in the cloud computing platform, a novel coding strategy is proposed, whereby the cloud re-encodes the received frames via a linear code before distributing them to the decoding processors. Upper bounds on the resulting frame unavailability probability (FUP) as a function of the decoding latency are derived by assuming a binary symmetric channel for uplink communications. The bounds leverage large deviation results for correlated variables, and depend on the properties of both the uplink linear channel code adopted at the user and the NFV linear code applied at the cloud. Numerical examples demonstrate that the bounds are useful tools for code design, and that coding is instrumental in obtaining a desirable tradeoff between FUP and decoding latency.

This paper investigates the distribution of delay and peak age of information in a communication system where packets, generated according to an independent and identically distributed Bernoulli process, are placed in a single-server queue with first-come first-served discipline and transmitted over an additive white Gaussian noise (AWGN) channel. When a packet is correctly decoded, the sender receives an instantaneous error-free positive acknowledgment, upon which it removes the packet from the buffer. In the case of negative acknowledgment, the packet is retransmitted. By leveraging finite-blocklength results for the AWGN channel, we characterize the delay violation and the peak-age violation probability without resorting to approximations based on large deviation theory as in previous literature. Our analysis reveals that there exists an optimum blocklength that minimizes the delay violation and the peak-age violation probabilities. We also show that one can find two blocklength values that result in very similar average delay but significantly different delay violation probabilities. This highlights the importance of focusing on violation probabilities rather than on averages.

Device-to-Device (D2D) communication can support the operation of cellular systems by reducing the traffic in the network infrastructure. In this paper, the benefits of D2D communication are investigated in the context of a Fog-Radio Access Network (F-RAN) that leverages edge caching and fron-thaul connectivity for the purpose of content delivery. Assuming offline caching, out-of-band D2D communication, and an F-RAN with two edge nodes and two user equipments, an information-theoretically optimal caching and delivery strategy is presented that minimizes the delivery time in the high signal- to- noise ratio regime. The delivery time accounts for the latency caused by fronthaul, downlink, and D2D transmissions. The proposed optimal strategy is based on a novel scheme for an X -channel with receiver cooperation that leverages tools from real interference alignment. Insights are provided on the regimes in which D2D communication is beneficial.

In cellular systems, content delivery latency can be minimized by jointly optimizing edge caching, fronthaul transmission from a cloud processor (CP) with access to the content library, and wireless transmission. In this paper, this problem is studied from an information-theoretic viewpoint by making the following practically relevant assumptions: 1) the ENs have multiple antennas; 2) only uncoded fractional caching is allowed; 3) the fronthau llinks are used to send fractions of contents; and 4) the ENs are constrained to use one-shot linear precoding on the wireless channel. Assuming offline caching and focusing on a high signal-to-noise ratio (SNR) latency performance metric, the proposed caching and delivery policy is shown to be either exactly optimal or optimal within a multiplicative factor of 3/2. The results bring insights into the optimal interplay between edge and cloud processing in fog-aided wireless networks as a function of system resources, including the number of antennas at the ENs, the ENs' cache capacity and the fronthaul capacity.

In a Fog Radio Access Network (F-RAN), the cloud processor (CP) collects channel state information (CSI) from the edge nodes (ENs) over fronthaul links. As a result, the CSI at the cloud is generally affected by an error due to outdating. In this work, the problem of content delivery based on fronthaul transmission and edge caching is studied from an information-theoretic perspective in the high signal-to-noise ratio (SNR) regime. For the set-up under study, under the assumption of perfect CSI, prior work has shown the (approximate or exact) optimality of a scheme in which the ENs transmit information received from the cloud and cached contents over orthogonal resources. In this work, it is demonstrated that a non-orthogonal transmission scheme is able to substantially improve the latency performance in the presence of imperfect CSI at the cloud.

Fog radio access network (F-RAN) architectures can leverage both cloud processing and edge caching for content delivery to the users. To this end, F-RAN utilizes caches at the edge nodes (ENs) and fronthaul links connecting a cloud processor to ENs. Assuming time-invariant content popularity, existing information-theoretic analyses of content delivery in F-RANs rely on offline caching with separate content placement and delivery phases. In contrast, this paper focuses on the scenario in which the set of popular content is time-varying, hence necessitating the online replenishment of the ENs' caches along with the delivery of the requested files. The analysis is centered on the characterization of the long-term normalized delivery time (NDT), which captures the temporal dependence of the coding latencies accrued across multiple time slots in the high signal-to-noise ratio regime. Online edge caching and delivery schemes are investigated for both serial and pipelined transmission modes across fronthaul and edge segments. Analytical results demonstrate that, in the presence of a time-varying content popularity, the rate of fronthaul links sets a fundamental limit on the long-term NDT of F-RAN system. Analytical results are further verified by numerical simulation, yielding important design insights.

With the emergence of Cloud-RAN as one of the dominant architectural solutions for the next-generation mobile networks, the reliability and latency on the fronthaul (FH) segment become critical performance metrics for applications such as the Tactile Internet. Ensuring FH performance is further complicated by the switch from point-to-point dedicated FH links to packet-based multi-hop FH networks. This change is largely justified by the fact that packet-based fronthauling allows the deployment of FH networks on the existing Ethernet infrastructure. This paper proposes to improve the reliability and latency of packet-based fronthauling by means of multi-path diversity and erasure coding of the MAC frames transported by the FH network. Under a probabilistic model that assumes a single service, the average latency required to obtain reliable FH transport and the reliability-latency tradeoff is first investigated. The analytical results are then validated and complemented by a numerical study that accounts for the coexistence of the enhanced mobile broadband and ultra-reliable low-latency services in fifth-generation networks by comparing orthogonal and non-orthogonal sharing of FH resources.

Motivated by the practical constraints arising in emerging sensor network and Internet-of-Things (IoT) applications, the zero-delay transmission of a Gaussian measurement over a real single-input multiple-output (SIMO) additive white Gaussian noise (AWGN) channel is studied with a low-resolution analog-to-digital converter (ADC) front end. Joint optimization of the encoder and the decoder mapping is tackled under both the mean squared error (MSE) distortion and the distortion outage probability (DOP) criteria, with an average power constraint on the channel input. Optimal encoder and decoder mappings are identified for a one-bit ADC front end under both criteria. For the MSE distortion, the optimal encoder mapping is shown to be non-linear in general, while it tends to a linear encoder in the low signal-to-noise ratio (SNR) regime, and to an antipodal digital encoder in the high SNR regime. This is in contrast to the optimality of linear encoding at all SNR values in the presence of a full-precision front end. For the DOP criterion, it is shown that the optimal encoder mapping is piecewise constant and can take only two opposite values when it is non-zero. For both the MSE distortion and the DOP criteria, necessary optimality conditions are then derived for $K$ -level ADC front ends as well as front ends with multiple one-bit ADCs. These conditions are used to obtain numerically optimized solutions. Extensive numerical results are also provided in order to gain insights into the structure of the optimal encoding and decoding mappings.

Massive full-dimensional multiple-input multiple-output (FD-MIMO) base stations (BSs) have the potential to bring multiplexing and coverage gains by means of three-dimensional (3D) beamforming. The key technical challenges for their deployment include the presence of limited-resolution front ends and the acquisition of channel state information (CSI) at the BSs. This paper investigates the use of FD-MIMO BSs to provide simultaneously high-rate data communication and mobile 3D positioning in the downlink. The analysis concentrates on the problem of beamforming design by accounting for imperfect CSI acquisition via time division duplex-based training and for the finite resolution of analog-to-digital converter and digital-to-analog converter at BSs. Both unstructured beamforming and a low-complexity Kronecker beamforming solution are considered, where for the latter the beamforming vectors are decomposed into separate azimuth and elevation components. The proposed algorithmic solutions are based on the Bussgang theorem, rank-relaxation, and successive convex approximation (SCA) methods. Comprehensive numerical results demonstrate that the proposed schemes can effectively cater to both data communication and positioning services, providing only minor performance degradations compared to the conventional cases in which either only the data communication or only positioning is implemented. Moreover, the proposed low-complexity Kronecker beamforming is seen to guarantee a limited performance loss in the presence of a large number of BS antennas.

Fog-aided network architectures for 5G systems encompass wireless edge nodes, referred to as remote radio systems (RRSs), as well as remote cloud center (RCC) processors, which are connected to the RRSs via a fronthaul access network. RRSs and RCC are operated via network functions virtualization, enabling a flexible split of network functionalities that adapts to network parameters such as fronthaul latency and capacity. This paper focuses on uplink communications and investigates the cloud-edge allocation of two important network functions, namely, the control functionality of rate selection and the data-plane function of decoding. Three functional splits are considered: 1) distributed radio access network, in which both functions are implemented in a decentralized way at the RRSs; 2) cloud RAN, in which instead both functions are carried out centrally at the RCC; and 3) a new functional split, referred to as fog RAN (F-RAN), with separate decentralized edge control and centralized cloud data processing. The model under study consists of a time-varying uplink channel with fixed scheduling and cell association in which the RCC has global but delayed channel state information due to fronthaul latency, while the RRSs have local but more timely CSI. Using the adaptive sum-rate as the performance criterion, it is concluded that the F-RAN architecture can provide significant gains in the presence of user mobility.

A Fog Radio Access Network (F-RAN) is a cellular wireless system that enables content delivery via the caching of popular content at edge nodes (ENs) and cloud processing. The existing information-theoretic analyses of F-RAN systems, and special cases thereof, make the assumption that all requests should be guaranteed the same delivery latency, which results in identical latency for all files in the content library. In practice, however, contents may have heterogeneous timeliness requirements depending on the applications that operate on them. Given per-EN cache capacity constraint, there exists a fundamental trade-off among the delivery latencies of different users' requests, since contents that are allocated more cache space generally enjoy lower delivery latencies. For the case with two ENs and two users, the optimal latency trade-off is characterized in the high-SNR regime in terms of the Normalized Delivery Time (NDT) metric. The main results are illustrated by numerical examples.

In a Fog Radio Access Network (F-RAN) architecture, edge nodes (ENs), such as base stations, are equipped with limited-capacity caches, as well as with fronthaul links that can support given transmission rates from a cloud processor. Existing information-theoretic analyses of content delivery in F-RANs have focused on offline caching with separate content placement and delivery phases. In contrast, this work considers an online caching set-up, in which the set of popular files is time-varying and both cache replenishment and content delivery can take place in each time slot. The analysis is centered on the characterization of the long-term Normalized Delivery Time (NDT), which captures the temporal dependence of the coding latencies accrued across multiple time slots in the high signal-to-noise ratio regime. Online caching and delivery schemes based on reactive and proactive caching are investigated, and their performance is compared to optimal offline caching schemes both analytically and via numerical results.

This study addresses the achievable sum-rate for the uplink of a cloud radio access network (C-RAN) operating in a linear Wyner-type topology, i.e., with partial channel connectivity. In the system, the radio units (RUs) communicate with a central, or cloud, unit (CU) by means of digital finite-capacity fronthaul links. The messages sent by the user equipments (UEs) are jointly decoded by the CU based on the compressed baseband signals received on the fronthaul links. Unlike prior works, each RU is assumed to be also connected to its neighboring RUs via finite-capacity fronthaul links. Under the standard assumption that the RUs do not perform channel decoding (i.e., oblivious RUs), each RU performs in-network processing of the uplink received signal and of the compressed baseband signal received from the adjacent RU, with the CU carrying out channel decoding. A closed-form expression of the achievable sum-rate is derived assuming point-to-point compression, and then analytical expressions are provided for more advanced fronthaul compression schemes that leverage side information. Numerical examples provide insights into the advantages of inter-RU communications and into the performance gap to existing sum-rate upper bounds.

Fronthaul limitations in terms of capacity and latency motivate the growing interest in the wireless industry for the study of alternative functional splits between cloud and edge nodes in Cloud Radio Access Network (C-RAN). This work contributes to this line of work by investigating the optimal functional split of control and data plane functionalities at the edge nodes and at the Remote Cloud Center (RCC) as a function of the fronthaul latency. The model under study consists of a two-user time-varying uplink channel in which the RCC has global but delayed channel state information (CSI) due to fronthaul latency, while edge nodes have local but timely CSI. Adopting the adaptive sum-rate as the performance criterion, functional splits whereby the control functionality of rate selection and the decoding of the data-plane frames are carried out at either the edge nodes or at the RCC are compared, demonstrating the potential advantages of implementing control functions at the edge.

In this paper, we investigate the total delivery latency across the fronthaul and wireless segments of a Fog Radio Access Network (F-RAN) under the assumption that cloud processor and edge nodes (ENs) are connected by a multicast fronthaul link. The total delivery latency is assessed via the Normalized Delivery Time (NDT) metric which provides a high signal-to- noise ratio (SNR) measure of the relative delivery worst-case latency with respect to an interference-free system. We derive upper and lower bounds on the achievable NDT for a F-RAN with two ENs and two users as a function of cache and fronthaul resources. The upper bound is obtained by studying the NDT achieved by delivery strategies that encompass both coded and uncoded multicast strategies on the fronthaul. The lower bound is instead derived by leveraging information theoretic converse arguments. Upper and lower bounds are shown to coincide, hence characterizing the minimum NDT, for a large range of problem parameters. Among the conclusions of this study, we demonstrate that coded multicasting is not useful for reducing the NDT for the mentioned range of parameters.

Mobile cloud computing relieves the tension between computation‐intensive mobile applications and battery‐constrained mobile devices by enabling the offloading of computing tasks from mobiles to a remote processors. This study considers a mobile cloud computing scenario in which the ‘cloudlet’ processor that provides offloading opportunities to mobile devices is mounted on unmanned aerial vehicles (UAVs) to enhance coverage. Focusing on a slotted communication system with frequency division multiplexing between mobile and UAV, the joint optimisation of the number of input bits transmitted in the uplink by the mobile to the UAV, the number of input bits processed by the cloudlet at the UAV and the number of output bits returned by the cloudlet to the mobile in the downlink in each slot is carried out by means of dual decomposition under maximum latency constraints with the aim of minimising the mobile energy consumption. Numerical results reveal the critical importance of an optimised bit allocation in order to enable significant energy savings as compared with local mobile execution for stringent latency constraints.

Cloud-radio access network (C-RAN) is characterized by a hierarchical structure, in which the baseband-processing functionalities of remote radio heads (RRHs) are implemented by means of cloud computing at a central unit (CU). A key limitation of C-RANs is given by the capacity constraints of the fronthaul links connecting RRHs to the CU. In this letter, the impact of this architectural constraint is investigated for the fundamental functions of random access and active user equipment (UE) identification in the presence of a potentially massive number of UEs. In particular, the standard C-RAN approach based on quantize-and-forward and centralized detection is compared to a scheme based on an alternative CU-RRH functional split that enables local detection. Both techniques leverage Bayesian sparse detection. Numerical results illustrate the relative merits of the two schemes as a function of the system parameters.

Zero-delay transmission of a Gaussian source over an additive white Gaussian noise (AWGN) channel is considered with a 1-bit analog-to-digital converter (ADC) front end and correlated side information at the receiver. The design of the optimal encoder and decoder is studied for two different performance criteria, namely the mean squared error (MSE) distortion and the distortion outage probability (DOP), under an average power constraint on the channel input. For both criteria, necessary optimality conditions for the encoder and the decoder are derived, which are then used to numerically obtain encoder and decoder mappings that satisfy these conditions. Using these conditions, it is observed that the numerically optimized encoder (NOE) under the MSE distortion criterion is periodic, and its period increases with the correlation between the source and the receiver side information. For the DOP, it is instead seen that the NOE mappings periodically acquire positive and negative values, which decay to zero with increasing source magnitude, and the interval over which the mapping takes non-zero values becomes wider with the correlation between the source and the side information. Finally, inspired by the mentioned properties of the NOE mappings, parameterized encoder mappings with a small number of degrees of freedom are proposed for both distortion criteria, and their performance is compared with that of the NOE mappings.

A fog-aided wireless network architecture is studied in which edge nodes (ENs), such as base stations, are connected to a cloud processor via dedicated fronthaul links while also being endowed with caches. Cloud processing enables the centralized implementation of cooperative transmission strategies at the ENs, albeit at the cost of an increased latency due to fronthaul transfer. In contrast, the proactive caching of popular content at the ENs allows for the low-latency delivery of the cached files, but with generally limited opportunities for cooperative transmission among the ENs. The interplay between cloud processing and edge caching is addressed from an information-theoretic viewpoint by investigating the fundamental limits of a high signal-to-noise-ratio metric, termed normalized delivery time (NDT), which captures the worst case coding latency for delivering any requested content to the users. The NDT is defined under the assumptions of either serial or pipelined fronthaul-edge transmission, and is studied as a function of fronthaul and cache capacity constraints. Placement and delivery strategies across both fronthaul and wireless, or edge, segments are proposed with the aim of minimizing the NDT. Information-theoretic lower bounds on the NDT are also derived. Achievability arguments and lower bounds are leveraged to characterize the minimal NDT in a number of important special cases, including systems with no caching capabilities, as well as to prove that the proposed schemes achieve optimality within a constant multiplicative factor of 2 for all values of the problem parameters.

In a fast-fading environment, e.g., high-speed railway communications, channel estimation and tracking require the availability of a number of pilot symbols that is at least as large as the number of independent channel parameters. Aiming at reducing the number of necessary pilot symbols, this work proposes a novel technique for joint channel tracking and decoding, which is based on the following three ideas. 1) Sparsity: While the total number of channel parameters to be estimated is large, the actual number of independent multipath components is generally small; 2) Long-term versus short-term channel parameters: Each multipath component is typically characterized by long-term parameters that slowly change with respect to the duration of a transmission time slot, such as delays or average power values, and by fast-varying fading amplitudes; and 3) Code-aided methods: Decision-feedback techniques can optimally leverage past, and partially reliable, decisions on the data symbols to obtain “virtual” pilots via the expectation-maximization (EM) algorithm. Numerical results show that the proposed code-aided EM algorithm is effective in performing joint channel tracking and decoding even for velocities as high as 350 km/h, as in high-speed railway communications, and with as few as four pilots per orthogonal frequency-division multiplexing data symbol, as in the IEEE 802.11a/n/p standards, outperforming existing schemes at the cost of larger computational complexity.

Caching of popular multimedia content at small-cell base stations (BSs) is a promising solution to reduce the traffic load of macro-BSs without relying on a high-speed backhaul architecture. While most prior work analyzed the effect of smallcell caching, or femto-caching, under the assumption of negligible interference between macro-BS and small-cell BS, this paper contributes to a more recent line of work in which the benefits of caching are reconsidered in the presence of interference on the downlink channel. In particular, a binary fading one-sided interference channel is considered in which the small-cell BS, whose transmission is interfered by the macro-BS, has a limitedcapacity cache. An information-theoretic metric that captures the delivery latency is defined and fully characterized through information-theoretic achievability and converse arguments as a function of the cache capacity, as well as of the capacity of the backhaul link connecting cloud and small-cell BS.

Research efforts to incorporate multicast and broadcast transmission into the cellular network architecture are gaining momentum, particularly for multimedia streaming applications. Layered division multiplexing (LDM), a form of nonorthogonal multiple access (NOMA), can potentially improve unicast throughput and broadcast coverage with respect to traditional orthogonal frequency division multiplexing (FDM) or time division multiplexing (TDM), by simultaneously using the same frequency and time resources for multiple unicast or broadcast transmissions. In this paper, the performance of LDM-based unicast and broadcast transmission in a cellular network is studied by assuming a single frequency network (SFN) operation for the broadcast layer, while allowing for arbitrarily clustered cooperation for the transmission of unicast data streams. Beamforming and power allocation between unicast and broadcast layers, and hence the so-called injection level in the LDM literature, are optimized with the aim of minimizing the sum-power under constraints on the user-specific unicast rates and on the common broadcast rate. The problem is tackled by means of successive convex approximation (SCA) techniques, as well as through the calculation of performance upper bounds by means of semidefinite relaxation (SDR). Numerical results are provided to compare the orthogonal and non-orthogonal multiplexing of broadcast and unicast traffic.

In the downlink of the Cloud-Radio Access Network (C-RAN) cellular architecture, complex baseband signals are transmitted from a central unit (CU) in the “cloud” over digital fronthaul links to distributed radio units (RUs). The standard design of digital fronthauling is based on quantization that operates separately over each fronthaul link. In this paper, a fronthaul quantization scheme is proposed that, unlike conventional schemes, implements a joint quantization mapping across all fronthaul links that is adapted to the current channel conditions. As compared to the current standard approach, the proposed multivariate quantization (MQ) scheme only requires additional processing at the CU, while no modification is needed at the RUs. The algorithm is extended to enable variable-length compression, and is compared via numerical results to a related approach based on the information-theoretic technique of multivariate compression.

This paper studies the joint design of cloud and edge processing for the downlink of a fog radio access network (F-RAN). In an F-RAN, as in cloud-RAN (C-RAN), a baseband processing unit (BBU) can perform joint baseband processing on behalf of the remote radio heads (RRHs) that are connected to the BBU by means of the fronthaul links. In addition to the minimal functionalities of conventional RRHs in C-RAN, the RRHs in an F-RAN may be equipped with local caches, in which frequently requested contents can be stored, as well as with baseband processing capabilities. They are hence referred to as enhanced RRH (eRRH). This paper focuses on the design of the delivery phase for an arbitrary pre-fetching strategy used to populate the caches of the eRRHs. Two fronthauling modes are considered, namely, a hard-transfer mode, whereby non-cached files are communicated over the fronthaul links to a subset of eRRHs, and a soft-transfer mode, whereby the fronthaul links are used to convey quantized baseband signals as in a C-RAN. Unlike the hard-transfer mode in which baseband processing is traditionally carried out only at the eRRHs, the soft-transfer mode enables both centralized precoding at the BBU and local precoding at the eRRHs based on the cached contents, by means of a novel superposition coding approach. To attain the advantages of both approaches, a hybrid design of soft- and hard-transfer modes is also proposed. The problem of maximizing the delivery rate is tackled under fronthaul capacity and per-eRRH power constraints. Numerical results are provided to compare the performance of hard- and soft-transfer fronthauling modes, as well as of the hybrid scheme, for different baseline pre-fetching strategies.

Fog Radio Access Network (F-RAN) is an emerging wireless network architecture that leverages caching capabilities at the wireless edge nodes, as well as edge connectivity to the cloud via fronthaul links. This paper aims at providing a latency-centric analysis of the degrees of freedom of an F-RAN by accounting for the total content delivery delay across the fronthaul and wireless segments of the network. The main goal of the analysis is the identification of optimal caching, fronthaul and edge transmission policies. The study is based on the introduction of a novel performance metric, referred to as the Normalized Delivery Time (NDT), which measures the total delivery latency as compared to an ideal interference-free system. An information-theoretically optimal characterization of the trade-off between NDT, on the one hand, and fronthaul and caching resources, on the other, is derived for a class of F-RANs with two edge nodes and two users. Using these results, the interplay between caching and cloud connectivity is highlighted, as well as the impact of both caching and fronthaul resources on the delivery latency.

This paper considers the zero-delay transmission of a Gaussian source over an additive white Gaussian noise (AWGN) channel with a one-bit analog-to-digital converter (ADC) front end. The optimization of the encoder and decoder is tackled under both the mean squared error (MSE) distortion and the outage distortion criteria with an average power constraint. For MSE distortion, the optimal transceiver is identified over the space of symmetric encoders. This result demonstrates that the linear encoder, which is optimal with a full-precision front end, approaches optimality only in the low signal-to-noise ratio (SNR) regime; while, digital transmission is optimal in the high SNR regime. For the outage distortion criterion, the structure of the optimal encoder and decoder are obtained. In particular, it is shown that the encoder mapping is piecewise constant and can take only two opposite values when it is non-zero.

Zero-delay transmission of a Gaussian source over an additive white Gaussian noise (AWGN) channel with a 1-bit analog-to-digital converter (ADC) front end is investigated in the presence of correlated side information at the receiver. The design of the optimal encoder is considered for the mean squared error (MSE) distortion criterion under an average power constraint on the channel input. A necessary condition for the optimality of the encoder is derived. A numerically optimized encoder (NOE) is then obtained that aims that enforcing the necessary condition. It is observed that, due to the availability of receiver side information, the optimal encoder mapping is periodic, with its period depending on the correlation coefficient between the source and the side information. We then propose two parameterized encoder mappings, referred to as periodic linear transmission (PLT) and periodic BPSK transmission (PBT), which trade-off optimality for reduced complexity as compared to the NOE solution. We observe via numerical results that PBT performs close to the NOE in the high signal-to-noise ratio (SNR) regime, while PLT approaches the NOE performance in the low SNR regime.

Cloud radio access network (C‐RAN) is a prominent architecture for fifth generation wireless cellular system that is based on the centralisation of baseband processing for multiple distributed radio units (RUs) at a control unit (CU). In this study, it is proposed to leverage the C‐RAN architecture to enable the implementation of direct localisation of the position of mobile devices from the received signals at distributed RUs. With ideal connections between the CU and the RUs, direct localisation is known to outperform traditional indirect localisation , whereby the location of a source is estimated from intermediary parameters estimated at the RUs. However, in a C‐RAN system with capacity limited fronthaul links, the advantage of direct localisation may be offset by the distortion caused by the quantisation of the received signal at the RUs. In this study, the performance of direct localisation is studied by accounting for the effect of fronthaul quantisation with or without dithering. An approximate maximum likelihood localisation is developed. Then, the Cramér–Rao bound on the squared position error of direct localisation with quantised observations is derived. Finally, the performance of indirect localisation and direct localisation with or without dithering is compared via numerical results.

This letter studies the robust design of downlink precoding for cloud radio access network (C-RAN) in the presence of asynchronism among remote radio heads (RRHs). Specifically, a C-RAN downlink system is considered in which nonideal fronthaul links connecting two RRHs to a baseband unit (BBU) may cause a time offset, as well as a phase offset, between the transmissions of the two RRHs. The offsets are a priori not known to the BBU. With the aim of counteracting the unknown time offset, a robust precoding scheme is considered that is based on the idea of correlating the signal transmitted by one RRH with a number of delayed versions of the signal transmitted by the other RRH. For this transmission strategy, the problem of maximizing the worst-case minimum rate is tackled while satisfying per-RRH transmit power constraints. Numerical results are reported that verify the advantages of the proposed robust scheme as compared to the conventional nonrobust design criteria as well as noncooperative transmission.

A train backbone network consists of a sequence of nodes arranged in a linear topology. A key step that enables communication in such a network is that of topology discovery (TD), or train inauguration, whereby nodes learn in a distributed fashion the physical topology of the backbone network. While the current standard for train inauguration assumes wired links between adjacent backbone nodes, this work investigates the more challenging scenario in which the nodes communicate wirelessly. The key motivations for this desired switch from wired TD to wireless one are the flexibility and capability for expansion and upgrading of a wireless backbone. The implementation of TD over wireless channels is made difficult by the broadcast nature of the wireless medium, as well as by fading and interference. A novel TD protocol is proposed, which overcomes these issues and requires relatively minor changes to the wired standard. The protocol is shown via analysis and numerical results to be robust to the impairments caused by the wireless channel, including interference from other trains.

By means of the emerging technique of dynamic Time Division Duplex (TDD), the switching point between uplink and downlink transmissions can be optimized across a multi-cell system in order to reduce the impact of inter-cell interference. It has been recently recognized that optimizing also the order in which uplink and downlink transmissions, or more generally the two directions of a two-way link, are scheduled can lead to significant benefits in terms of interference reduction. In this work, the optimization of bi-directional scheduling is investigated in conjunction with the design of linear precoding and equalization for a general multi-link MIMO two-way system. A simple algorithm is proposed that performs the joint optimization of the ordering of the transmissions in the two directions of the two-way links and of the linear transceivers, with the aim of minimizing the interference leakage power. Numerical results demonstrate the effectiveness of the proposed strategy.

A key practical constraint on the design of Hybrid automatic repeat request (HARQ) schemes is the modem chip area that needs to be allocated to store previously received packets. The fact that, in modern wireless standards, this area can amount to a large fraction of the overall chip has recently highlighted the importance of HARQ buffer management, that is, of the use of advanced compression policies for storage of received data. This work tackles the analysis of the throughput of standard HARQ schemes, namely Type-I, Chase Combining and Incremental Redundancy, under the assumption of a finite-capacity HARQ buffer by taking an information-theoretic standpoint based on random coding. Both coded modulation, via Gaussian signaling, and Bit Interleaved Coded Modulation (BICM) are considered. The analysis sheds light on questions of practical relevance for HARQ buffer management such as on the type of information to be extracted from the received packets and on how to store it.

The papers in this special issue presents cutting-edge research results in the emerging area of energy harvesting wireless communications and wireless energy transfer. This first issue starts with a review article coauthored by the guest editors that summarizes recent results in the broad area of energy harvesting communications, in particular, in information-theoretic, offline and online schedulingtheoretic, medium access, networking approaches to energy harvesting communications, as well as in energy cooperation and simultaneous wireless energy and information transfer.

This paper summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access, and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed, as well as models for energy consumption at the nodes.

A multistatic radar set-up is considered in which distributed receive antennas are connected to a Fusion Center (FC) via limited-capacity backhaul links. Similar to cloud radio access networks in communications, the receive antennas quantize the received baseband signal before transmitting it to the FC. The problem of maximizing the detection performance at the FC jointly over the code vector used by the transmitting antenna and over the statistics of the noise introduced by backhaul quantization is investigated. Specifically, adopting the information-theoretic criterion of the Bhattacharyya distance to evaluate the detection performance at the FC and information-theoretic measures of the quantization rate, the problem at hand is addressed via a Block Coordinate Descent (BCD) method coupled with Majorization-Minimization (MM). Numerical results demonstrate the advantages of the proposed joint optimization approach over more conventional solutions that perform separate optimization.

A key practical constraint on the design of hybrid automatic repeat request (HARQ) schemes is the size of the on-chip buffer that is available at the receiver to store previously received packets. In fact, in modern wireless standards such as LTE and LTE-A, the HARQ buffer size is one of the main drivers of the modem area and power consumption. This has recently highlighted the importance of HARQ buffer management, that is, of the use of buffer-aware transmission schemes and of advanced compression policies for the storage of received data. This work investigates HARQ buffer management by leveraging information-theoretic achievability arguments based on random coding. Specifically, standard HARQ schemes, namely Type-I, Chase Combining, and Incremental Redundancy, are first studied under the assumption of a finite-capacity HARQ buffer by considering both coded modulation, via Gaussian signaling, and Bit Interleaved Coded Modulation (BICM). The analysis sheds light on the impact of different compression strategies, namely the conventional compression log-likelihood ratios and the direct digitization of baseband signals, on the throughput. The optimization of coding blocklength is also investigated, highlighting the benefits of HARQ buffer-aware transmission scheme.

In various wireless systems, such as sensor RFID networks and body area networks with implantable devices, the transmitted signals are simultaneously used both for information transmission and for energy transfer. In order to satisfy the conflicting requirements on information and energy transfer, this paper proposes the use of constrained run-length limited (RLL) codes in lieu of conventional unconstrained (i.e, random-like) capacity-achieving codes. The receiver's energy utilization requirements are modeled stochastically, and constraints are imposed on the probabilities of battery underflow and overflow at the receiver. It is demonstrated that the codewords' structure afforded by the use of constrained codes enables the transmission strategy to be better adjusted to the receiver's energy utilization pattern, as compared to classical unstructured codes. As a result, constrained codes allow a wider range of trade-offs between the rate of information transmission and the performance of energy transfer to be achieved.

In the downlink of cloud radio access networks, a central encoder is connected to multiple multi-antenna base stations (BSs) via finite-capacity backhaul links. At the central encoder, precoding is followed by compression in order to produce the rate-limited bit streams delivered to each BS over the corresponding backhaul link. In current state-of-the-art schemes, the signals intended for different BSs are compressed independently. In contrast, this work proposes to leverage joint compression, also referred to as multivariate compression, of the signals for different BSs in order to better control the effect of the additive quantization noises at the mobile stations (MSs). The problem of maximizing the weighted sum-rate over precoding and compression strategies is formulated subject to power and backhaul capacity constraints. An iterative algorithm is proposed that achieves a stationary point of the problem. From numerical results, it is confirmed that the proposed joint precoding and compression strategy outperforms conventional approaches based on independent compression across the BSs.

This work investigates efficient backhaul compression strategies for the uplink of cloud radio access networks with a general multihop backhaul topology. In these systems, each radio unit (RU) communicates with the managing control unit (CU) through a set of intermediate RUs. A baseline multiplex-and-forward (MF) scheme is first studied in which each RU forwards the bit streams received from the connected RUs without any processing. It is observed that this strategy may cause significant performance degradation in the presence of a dense deployment of RUs. To obviate this problem, a scheme is proposed in which each RU decompresses the received bit streams and performs linear in-network processing of the decompressed signals. For both the MF and the decompress-process-and-recompress (DPR) backhaul schemes, the optimal design is addressed with the aim of maximizing the sum-rate under the backhaul capacity constraints. Based on the analysis, numerical results are provided to compare the performance of the MF and DPR schemes, highlighting the potential advantage of in-network processing.

A novel Bayesian modulation classification scheme is proposed for a single-antenna system over frequency-selective fading channels. The method is based on Gibbs sampling as applied to a latent Dirichlet Bayesian network (BN). The use of the proposed latent Dirichlet BN provides a systematic solution to the convergence problem encountered by the conventional Gibbs sampling approach for modulation classification. The method generalizes, and is shown to improve upon, the state of the art.

In various wireless systems, such as sensor RFID networks and body area networks with implantable devices, the transmitted signals are simultaneously used both for information transmission and for energy transfer. To satisfy the conflicting requirements on information and energy transfer, this paper proposes the use of constrained run-length limited (RLL) codes in lieu of conventional unconstrained (i.e., random-like) capacity-achieving codes. The receiver's energy utilization requirements are modeled stochastically, and constraints are imposed on the probabilities of battery underflow and overflow at the receiver. It is demonstrated that the codewords' structure afforded by the use of constrained codes enables the transmission strategy to be better adjusted to the receiver's energy utilization pattern, as compared to classical unstructured codes. As a result, constrained codes allow a wider range of trade-offs between the rate of information transmission and the performance of energy transfer to be achieved.

The problem of optimal actuation for channel and source coding was recently formulated and solved in a number of relevant scenarios. In this class of models, actions are taken at encoders or decoders, either to acquire side information in an efficient way or to control or probe effectively the channel state. In this paper, the problem of embedding information on the actions is studied for both the source and the channel coding setups. In both cases, a decoder is present that observes only a function of the actions taken by an encoder or a decoder of an action-dependent point-to-point link. For the source coding model, this decoder wishes to reconstruct a lossy version of the source being transmitted over the point-to-point link, while for the channel coding problem, the decoder wishes to retrieve a portion of the message conveyed over the link. For the problem of source coding with actions taken at the decoder, a single letter characterization of the set of all achievable tuples of rate, distortions at the two decoders, and action cost is derived, under the assumption that the mentioned decoder observes a function of the actions noncausally, strictly causally or causally. A special case of the problem in which the actions are taken by the encoder is also solved. A single-letter characterization of the achievable capacity-cost region is then obtained for the channel coding setup with actions. Examples are provided that shed light into the effect of information embedding on the actions for the action-dependent source and channel coding problems.

The model of a side information vending machine (VM) accounts for scenarios in which the measurement of side information sequences can be controlled via the selection of cost-constrained actions. In this paper, the three-node cascade source coding problem is studied under the assumption that a side information VM is available at the intermediate and/or end node of the cascade. A single-letter characterization of the achievable tradeoff among the transmission rates, distortions in the reconstructions at the intermediate and end node, and cost for acquiring the side information is derived for a number of relevant special cases. It is shown that a joint design of the description, source, and control signals used to guide the selection of the actions at downstream nodes is generally necessary for an efficient use of the available communication links. In particular, for all the considered models, layered coding strategies prove to be optimal, whereby the base layer fulfills two network objectives: 1) determining the actions of downstream nodes and 2) simultaneously providing a coarse description of the source. Design of the optimal coding strategy is shown via examples to depend on both the network topology and action costs. Examples also illustrate the involved performance tradeoffs across the network.

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.

In network MIMO cellular systems, subsets of base stations (BSs), or remote radio heads, are connected via backhaul links to central units (CUs) that perform joint encoding in the downlink and joint decoding in the uplink. Focusing on the uplink, an effective solution for the communication between BSs and the corresponding CU on the backhaul links is based on compressing and forwarding the baseband received signal from each BS. In the presence of ergodic fading, communicating the channel state information (CSI) from the BSs to the CU may require a sizable part of the backhaul capacity. In a prior work, this aspect was studied by assuming a Compress-Forward-Estimate (CFE) approach, whereby the BSs compress the training signal and CSI estimation takes place at the CU. In this work, instead, an Estimate-Compress-Forward (ECF) approach is investigated, whereby the BSs perform CSI estimation and forward a compressed version of the CSI to the CU. This choice is motivated by the information theoretic optimality of separate estimation and compression. Various ECF strategies are proposed that perform either separate or joint compression of estimated CSI and received signal. Moreover, the proposed strategies are combined with distributed source coding when considering multiple BSs. "Semi-coherent" strategies are also proposed that do not convey any CSI or training information on the backhaul links. Via numerical results, it is shown that a proper design of ECF strategies based on joint received signal and estimated CSI compression or of semi-coherent schemes leads to substantial performance gains compared to more conventional approaches based on non-coherent transmission or the CFE approach.

The problem of optimal actuation for channel and source coding was recently formulated and solved in a number of relevant scenarios. In this class of models, actions are taken either to acquire side information in an efficient way for source coding, or to control or probe effectively the channel state for channel coding. In this paper, the problem of embedding information on the actions is introduced by considering the presence of an additional decoder that observes only a function of the actions. For the source coding model, this decoder wishes to reconstruct a lossy version of the source being transmitted over the point-to-point link, while, for the channel coding problem, the decoder wishes to retrieve a portion of the message conveyed over the link. In both cases, single-letter performance characterizations are provided for various special cases, along with specific examples.

A bidirectional link between two nodes, Node 1 and Node 2, is studied in which Node 2 is able to acquire information from the environment, e.g., via access to a remote data base or via sensing. Information acquisition is expensive in terms of system resources, e.g., time, bandwidth and energy, and thus should be done efficiently. As a result of the forward communication from Node 1 to Node 2, the latter wishes to obtain information about the the data available at Node 1 and of the data obtained from the environment. The forward link is, however, also used by Node 1 to query Node 2 with the aim of retrieving information from the environment on the backward link. The problem is formulated using the concept of side information “vending machine”, and the optimal trade-off between communication rates, distortions of the estimates produced at the two nodes and costs for information acquisition at Node 2 is derived.

Two mobile users communicate with a central decoder via two base stations. Communication between the mobile users and the base stations takes place over a Gaussian interference channel with constant channel gains or quasi-static fading. Instead, the base stations are connected to the central decoder through orthogonal finite-capacity links, whose connectivity is subject to random fluctuations. There is only receive-side channel state information, and hence the mobile users are unaware of the channel state and of the backhaul connectivity state, while the base stations know the fading coefficients but are uncertain about the backhaul links' state. The base stations are oblivious to the mobile users' codebooks and employ compress-and-forward to relay information to the central decoder. Lower bounds on average achievable throughput are obtained by proposing strategies that combine the broadcast coding approach and layered distributed compression techniques. Numerical results confirm the advantages of the proposed approach with respect to conventional non-robust strategies in both scenarios with and without fading.

The recently proposed set-up of source coding with a side information “vending machine” allows the decoder to select actions in order to control the quality of the side information. The actions can depend on the message received from the encoder and on the previously measured samples of the side information, and are cost constrained. Moreover, the final estimate of the source by the decoder is a function of the encoder's message and depends causally on the side information sequence. Previous work by Permuter and Weissman has characterized the rate-distortion-cost function in the special case in which the source and the “vending machine” are memoryless. In this work, motivated by the related channel coding model introduced by Kramer, the rate-distortion-cost function characterization is extended to a model with in-block memory. Various special cases are studied including block-feedforward and side information repeat request models.

The source coding problem with action-dependent side information at the decoder has recently been introduced to model data acquisition in resource-constrained systems. In this paper, an efficient Blahut-Arimoto-type algorithm for the numerical computation of the rate-distortion-cost function for this problem is proposed. Moreover, a simplified two-stage code structure based on multiplexing is put forth, whereby the first stage encodes the actions and the second stage is composed of an array of classical Wyner-Ziv codes, one for each action. Leveraging this structure, specific coding/decoding strategies are designed based on LDGM codes and message passing. Through numerical examples, the proposed code design is shown to achieve performance close to the rate-distortion-cost function.

This work considers a setting in which an encoder wishes to communicate with a decoder through a relay that is connected to the decoder via a finite-capacity link. Motivated by communication on the uplink of a cloud radio access cellular network, it is assumed that the relay compresses and forwards the received signal; moreover, the decoder has side information about the transmitted signal that is subject to fading whose realization is unknown to encoder and relay. A robust transmission and compression strategy is proposed that aims at minimizing the transmitted power under competitive rate optimality constraints. This contrasts with more conventional worst-case or average performance criteria. The transmission strategy is based on a broadcast coding and is parameterized by the maximum tolerable delay in terms of number of fading coherence blocks. Numerical results demonstrate the role of delay and the advantages of broadcast coding over the conventional single-layer transmission.

This paper addresses robust communication on a fading relay channel in which the relay is connected to the decoder via an out-of-band digital link of limited capacity. Both the source-to-relay and the source-to-destination links are subject to fading gains, which are generally unknown to the encoder prior to transmission. To overcome this impairment, a hybrid automatic retransmission request (HARQ) protocol is combined with multi-layer broadcast transmission, thus allowing for variable-rate decoding. Moreover, motivated by cloud radio access network applications, the relay operation is limited to compress-and-forward. The aim is maximizing the throughput performance as measured by the average number of successfully received bits per channel use, under either long-term static channel (LTSC) or short-term static channel (STSC) models. In order to opportunistically leverage better channel states based on the HARQ feedback from the decoder, an adaptive compression strategy at the relay is also proposed. Numerical results confirm the effectiveness of the proposed strategies.

This paper introduces a novel technique for access by a cognitive Secondary User (SU) using best-effort transmission to a spectrum with an incumbent Primary User (PU), which uses Type-I Hybrid ARQ. The technique leverages the primary ARQ protocol to perform Interference Cancellation (IC) at the SU receiver (SUrx). Two IC mechanisms that work in concert are introduced: Forward IC, where SUrx, after decoding the PU message, cancels its interference in the (possible) following PU retransmissions of the same message, to improve the SU throughput; Backward IC, where SUrx performs IC on previous SU transmissions, whose decoding failed due to severe PU interference. Secondary access policies are designed that determine the secondary access probability in each state of the network so as to maximize the average long-term SU throughput by opportunistically leveraging IC, while causing bounded average long-term PU throughput degradation and SU power expenditure. It is proved that the optimal policy prescribes that the SU prioritizes its access in the states where SUrx knows the PU message, thus enabling IC. An algorithm is provided to optimally allocate additional secondary access opportunities in the states where the PU message is unknown. Numerical results are shown to assess the throughput gain provided by the proposed techniques.

In this work, joint decompression and decoding is studied for the uplink of multi-antenna cloud radio access networks. In this system, a set of multi-antenna mobile stations (MSs) wish to communicate with a “cloud” decoder through a set of multi-antenna base stations (BSs), which are connected to the cloud decoder through digital backhaul links of limited capacity. The BSs compress the received signal and send it to the cloud decoder, which performs joint decoding of the signals from all MSs. While the conventional solution prescribes that the cloud decoder performs first decompression and then decoding, recent work has shown that potentially larger rates can be achieved with joint decompression and decoding (JDD) at the cloud decoder. The sum-rate maximization problem with JDD, under the assumption of Gaussian test channels, is shown here to be an instance of a class of non-convex problems known as Difference of Convex (DC) problems. Based on this observation, an iterative algorithm based on the Majorization Minimization (MM) approach is proposed that guarantees convergence to a stationary point of the sum-rate maximization problem. Numerical results demonstrate the advantage of the proposed algorithm compared to the conventional approach based on separate decompression and decoding.

A relay channel with orthogonal components in which the destination is affected by an interference signal that is non-causally available only at the source is studied. The interference signal has structure in that it is produced by another transmitter communicating with its own destination. Moreover, the interferer is not willing to adjust its communication strategy to minimize the interference. Knowledge of the interferer's signal may be acquired by the source, for instance, by exploiting HARQ retransmissions on the interferer's link. The source can then utilize the relay not only for communicating its own message, but also for cooperative interference mitigation at the destination by informing the relay about the interference signal. Proposed transmission strategies are based on partial decode-and-forward (PDF) relaying and leverage the interference structure. Achievable schemes are derived for discrete memoryless models, Gaussian and Ricean fading channels. Furthermore, optimal strategies are identified in some special cases. Finally, numerical results bring insight into the advantages of utilizing the interference structure at the source, relay or destination.

This paper considers the problem of lossy compression for the computation of a function of two correlated sources, both of which are observed at the encoder. Due to presence of observation costs, the encoder is allowed to observe only subsets of the samples from both sources, with a fraction of such sample pairs possibly overlapping. The rate-distortion function is characterized for memoryless sources, and then specialized to Gaussian and binary sources for selected functions and with quadratic and Hamming distortion metrics, respectively. The optimal measurement overlap fraction is shown to depend on the function to be computed by the decoder, on the source statistics, including the correlation, and on the link rate. Special cases are discussed in which the optimal overlap fraction is the maximum or minimum possible value given the sampling budget, illustrating non-trivial performance trade-offs in the design of the sampling strategy. Finally, the analysis is extended to the multi-hop set-up with jointly Gaussian sources, where each encoder can observe only one of the sources.

In some communication networks, such as passive RFID systems, the energy used to transfer information between a sender and a recipient can be reused for successive communication tasks. In fact, from known results in physics, any system that exchanges information via the transfer of given physical resources, such as radio waves, particles and qubits, can conceivably reuse, at least part, of the received resources. This paper aims at illustrating some of the new challenges that arise in the design of communication networks in which the signals exchanged by the nodes carry both information and energy. To this end, a baseline two-way communication system is considered in which two nodes communicate in an interactive fashion. In the system, a node can either send an "onquotedblright symbol (or "1quotedblright), which costs one unit of energy, or an "offquotedblright signal (or "0quotedblright), which does not require any energy expenditure. Upon reception of a "1quotedblright signal, the recipient node "harvestsquotedblright, with some probability, the energy contained in the signal and stores it for future communication tasks. Inner and outer bounds on the achievable rates are derived. Numerical results demonstrate the effectiveness of the proposed strategies and illustrate some key design insights.

Source coding with a side information “vending machine” is a recently proposed framework in which the statistical relationship between the side information and the source, instead of being given and fixed as in the classical Wyner–Ziv problem, can be controlled by the decoder. This control action is selected by the decoder based on the message encoded by the source node. Unlike conventional settings, the message can thus carry not only information about the source to be reproduced at the decoder, but also control information aimed at improving the quality of the side information. In this paper, the analysis of the tradeoffs between rate, distortion, and cost associated with the control actions is extended from the previously studied point-to-point setup to two basic multiterminal models. First, a distributed source coding model is studied, in which two encoders communicate over rate-limited links to a decoder, whose side information can be controlled. The control actions are selected by the decoder based on the messages encoded by both source nodes. For this setup, inner bounds are derived on the rate-distortion-cost region for both cases in which the side information is available causally and noncausally at the decoder. These bounds are shown to be tight under specific assumptions, including the scenario in which the sequence observed by one of the nodes is a function of the source observed by the other and the side information is available causally at the decoder. Then, a cascade scenario in which three nodes are connected in a cascade and the last node has controllable side information is also investigated. For this model, the rate-distortion-cost region is derived for general distortion requirements and under the assumption of causal availability of side information at the last node.

In lossy source coding with side information at the decoder (i.e., the Wyner–Ziv problem), the estimate of the source obtained at the decoder cannot be generally reproduced at the encoder, due to its dependence on the side information. In some applications, this may be undesirable, and a common reconstruction (CR) requirement, whereby one imposes that the encoder and decoder be able to agree on the decoder's estimate, may be instead in order. The rate-distortion function under the CR constraint has been derived recently for a point-to-point (Wyner–Ziv) problem. In this paper, this result is extended to three multiterminal settings with three nodes, namely the Heegard–Berger (HB) problem, its variant with cooperating decoders, and the cascade source coding problem. The HB problem consists of an encoder broadcasting to two decoders with respective side information. The cascade source coding problem is characterized by a two-hop system with side information available at the intermediate and final nodes. For the HB problem with the CR constraint, the rate-distortion function is derived under the assumption that the side information sequences are (stochastically) degraded. The rate-distortion function is also calculated explicitly for three examples, namely Gaussian source and side information with quadratic distortion metric, and binary source and side information with erasure and Hamming distortion metrics. The rate-distortion function is then characterized for the HB problem with cooperating decoders and (physically) degraded side information. For the cascade problem with the CR constraint, the rate-distortion region is obtained under the assumption that side information at the final node is physically degraded with respect to that at the intermediate node. For the latter two cases, it is worth emphasizing that the corresponding problem without the CR constraint is still open. Outer and inner bounds on the rate-distortion region are also obtained for the cascade problem under the assumption that the side information at the intermediate node is physically degraded with respect to that at the final node. For the three examples mentioned above, the bounds are shown to coincide. Finally, for the HB problem, the rate-distortion function is obtained under the more general requirement of constrained reconstruction, whereby the decoder's estimate must be recovered at the encoder only within some distortion.

It has been recently shown by Lapidoth and Steinberg that strictly causal state information can be beneficial in multiple access channels (MACs). Specifically, it was proved that the capacity region of a two-user MAC with independent states, each known strictly causally to one encoder, can be enlarged by letting the encoders send compressed past state information to the decoder. In this study, a generalization of the said strategy is proposed whereby the encoders compress also the past transmitted codewords along with the past state sequences. The proposed scheme uses a combination of long-message encoding, compression of the past state sequences and codewords without binning, and joint decoding over all transmission blocks. The proposed strategy has been recently shown by Lapidoth and Steinberg to strictly improve upon the original one. Capacity results are then derived for a class of channels that include two-user modulo-additive state-dependent MACs. Moreover, the proposed scheme is extended to state-dependent MACs with an arbitrary number of users. Finally, output feedback is introduced and an example is provided to illustrate the interplay between feedback and availability of strictly causal state information in enlarging the capacity region.

A state-dependent degraded broadcast diamond channel is studied where the source-to-relays cut is modeled with two noiseless, finite-capacity digital links with a degraded broadcasting structure, while the relays-to-destination cut is a general multiple access channel controlled by a random state. It is assumed that the source has noncausal channel state information and the relays have no state information. Under this model, first, the capacity is characterized for the case where the destination has state information, i.e., has access to the state sequence. It is demonstrated that in this case, a joint message and state transmission scheme via binning is optimal. Next, the case where the destination does not have state information, i.e., the case with state information at the source only, is considered. For this scenario, lower and upper bounds on the capacity are derived for the general discrete memoryless model. Achievable rates are then computed for the case in which the relays-to-destination cut is affected by an additive Gaussian state. Numerical results are provided that illuminate the performance advantages that can be accrued by leveraging noncausal state information at the source.

For memoryless sources, delayed side information at the decoder does not improve the rate-distortion function. However, this is not the case for sources with memory, as demonstrated by a number of works focusing on the special case of (delayed) feedforward. In this paper, a setting is studied in which the encoder is potentially uncertain about the delay with which measurements of the side information, which is available at the encoder, are acquired at the decoder. Assuming a hidden Markov model for the source sequences, at first, a single-letter characterization is given for the setup where the side information delay is arbitrary and known at the encoder, and the reconstruction at the destination is required to be asymptotically lossless. Then, with delay equal to zero or one source symbol, a single-letter characterization of the rate-distortion region is given for the case where, unbeknownst to the encoder, the side information may be delayed or not. Finally, examples for binary and Gaussian sources are provided.

Two mobile users communicate with a central decoder via two base stations. Communication between the mobile users and the base stations takes place over a Gaussian interference channel with constant channel gains or quasi-static fading. Instead, the base stations are connected to the central decoder through orthogonal finite-capacity links, whose connectivity is subject to random fluctuations. There is only receive-side channel state information, and hence the mobile users are unaware of the channel state and of the backhaul connectivity state, while the base stations know the fading coefficients but are uncertain about the backhaul links' state. The base stations are oblivious to the mobile users' codebooks and employ compress-and-forward to relay information to the central decoder. Upper and lower bounds are derived on average achievable throughput with respect to the prior distribution of the fading coefficients and of the backhaul links' states. The lower bounds are obtained by proposing strategies that combine the broadcast coding approach and layered distributed compression techniques. The upper bound is obtained by assuming that all the nodes know the channel state. Numerical results confirm the advantages of the proposed approach with respect to conventional non-robust strategies in both scenarios with and without fading.

Source coding with a side information "vending machine" is a recently proposed framework in which the statistical relationship between the side information available at the decoder and the source sequence can be controlled by the decoder based on the message received from the encoder. In this paper, the characterization of the optimal rate-distortion performance as a function of the cost associated with the control actions is extended from the previously studied point-to-point set-up to two multiterminal models. First, a distributed source coding model is studied, in which two encoders communicate over rate-limited links to a decoder, whose side information can be controlled based on the control actions selected by one of the encoders. The rate-distortion-cost region is characterized under the assumption of lossless reconstruction of the source encoded by the node that does not control the side information. Then, a three-node cascade scenario is investigated, in which the last node has controllable side information. The rate-distortion-cost region is derived for general distortion requirements and under the assumption of "causal" availability of side information at the last node.

In lossy source coding with side information at the decoder (i.e., the Wyner-Ziv problem), the estimate of the source obtained at the decoder cannot be generally reproduced at the encoder, due to its dependence on the side information. In some applications this may be undesirable, and a Common Reconstruction (CR) requirement, whereby one imposes that encoder and decoder be able to agree on the decoder's estimate, may be instead in order. The rate-distortion function under the CR constraint has been recently derived for the point-to-point (Wyner-Ziv) problem. In this paper, this result is extended to the Heegard-Berger (HB) problem and to its variant with cooperating decoders. Specifically, for the HB problem, the ratedistortion function is derived under the assumption that the side information sequences at the two decoders are stochastically degraded. The rate-distortion function is also calculated explicitly for the special case of binary source and erased side information with Hamming distortion metric. The rate-distortion function is then characterized also for the HB problem with cooperating decoders and physically degraded side information.

Energy efficiency is a key requirement in the design of wireless sensor networks. While most theoretical studies only account for the energy requirements of communication, the sensing process, which includes measurements and compression, can also consume comparable energy. In this paper, the problem of sensing and communicating parallel sources is studied by accounting for the cost of both communication and sensing. In the first formulation of the problem, the sensor has a separate energy budget for sensing and a rate budget for communication, while, in the second, it has a single energy budget for both tasks. Furthermore, in the second problem, each source has its own associated channel. Assuming that sources with larger variances have lower sensing costs, the optimal allocation of sensing energy and rate that minimizes the overall distortion is derived for the first problem. Moreover, structural results on the solution of the second problem are derived under the assumption that the sources with larger variances are transmitted on channels with lower noise.

For memoryless sources, delayed side information at the decoder does not improve the rate-distortion function. However, this is not the case for more general sources with memory, as demonstrated by a number of works focusing on the special case of (delayed) feedforward. In this paper, a setting is studied in which the side information is delayed and the encoder is informed about the side information sequence. Assuming a hidden Markov model for the sources, at first, a single-letter characterization is given for the set-up where the side information delay is arbitrary and known at the encoder, and the reconstruction at the destination is required to be (near) lossless. Then, with delay equal to zero or one source symbol, a single-letter characterization is given of the rate-distortion function for the case where side information may be delayed or not, unbeknownst to the encoder. Finally, an example for a binary source is provided.

The problem of multiple descriptions for a Gaussian source is considered, in which all the decoders have access to correlated Gaussian side-information. Two variations of this problem are studied. First, the rate-distortion tradeoff is characterized under the assumption of a common reconstruction constraint, in which the estimate produced at each of the decoders is also exactly recoverable at the encoder. Secondly, a generalization of this setup is studied in which possibly different distortions are tolerable between the estimates at the encoder and the respective estimates at each of the decoders.

Action-dependent channels model scenarios in which transmission takes place in two successive phases. In the first phase, the encoder selects an “action” sequence, with the twofold aim of conveying information to the receiver and of affecting in a desired way the state of the channel to be used in the second phase. In the second phase, communication takes place in the presence the mentioned action-dependent state. In this work, two extensions of the original action-dependent channel are studied. In the first, the decoder is interested in estimating not only the message, but also the state sequence within an average per-letter distortion. Under the constraint of common reconstruction (i.e., the decoder's estimate of the state must be recoverable also at the encoder) and assuming non-causal state knowledge at the encoder in the second phase, we obtain a single-letter characterization of the achievable rate-distortion-cost trade-off. In the second extension, we study an action-dependent degraded broadcast channel. Under the assumption that the encoder knows the state sequence causally in the second phase, the capacity-cost region is identified. Various examples, including Gaussian channels and a model with a “probing” encoder, are also provided to show the advantage of a proper joint design of the two communication phases.

The model of a side information “vending machine” accounts for scenarios in which acquiring side information is costly and thus should be done efficiently. In this paper, the three-node cascade source coding problem is studied under the assumption that a side information vending machine is available either at the intermediate or at the end node. In both cases, a single-letter characterization of the available trade-offs among the rate, the distortions in the reconstructions at the intermediate and at the end node, and the cost in acquiring the side information are derived under given conditions.

This paper considers the problem of lossy compression for the computation of a function of two correlated sources, both of which are observed at the encoder. Due to presence of observation costs, the encoder is allowed to observe only subsets of the samples from both sources, with a fraction of such sample pairs possibly overlapping. For both Gaussian and binary sources, the distortion-rate function, or rate-distortion function, is characterized for selected functions and with quadratic and Hamming distortion metrics, respectively. Based on these results, for both examples, the optimal measurement overlap fraction is shown to depend on the function to be computed by the decoder, on the source correlation and on the link rate. Special cases are discussed in which the optimal overlap fraction is the maximum or minimum possible value given the sampling budget, illustrating non-trivial performance trade-offs in the design of the sampling strategy.

This work studies distributed compression for the uplink of a cloud radio access network, where multiple multi-antenna base stations (BSs) communicate with a central unit, also referred to as cloud decoder, via capacity-constrained back-haul links. Distributed source coding strategies are potentially beneficial since the signals received at different BSs are correlated. However, they require each BS to have information about the joint statistics of the received signals across the BSs, and are generally sensitive to uncertainties regarding such information. Motivated by this observation, a robust compression method is proposed to cope with uncertainties on the correlation of the received signals. The problem is formulated using a deterministic worst-case approach, and an algorithm is proposed that achieves a stationary point for the problem. From numerical results, it is observed that the proposed robust compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information.

The conventional assumption made in the design of communication systems is that the energy used to transfer information between a sender and a recipient cannot be reused for future communication tasks. A notable exception to this norm is given by passive RFID systems, in which a reader can transfer both information and energy via the transmitted radio signal. Conceivably, any system that exchanges information via the transfer of given physical resources (radio waves, particles, qubits) can potentially reuse, at least part, of the received resources for communication later on. In this paper, a two-way communication system is considered that operates with a given initial number of physical resources, referred to as energy units. The energy units are not replenished from outside the system, and are assumed, for simplicity, to be constant over time. A node can either send an “on” symbol (or “1”), which costs one unit of energy, or an “off” signal (or “0”), which does not require any energy expenditure. Upon reception of a “1” signal, the recipient node “harvests” the energy contained in the signal and stores it for future communication tasks. Inner and outer bounds on the achievable rates are derived, and shown via numerical results to coincide if the number of energy units is large enough.

In cyber-physical systems where sensors measure the temporal evolution of a given phenomenon of interest and radio communication takes place over short distances, the energy spent for source acquisition and compression may be comparable with that used for transmission. Additionally, in order to avoid limited lifetime issues, sensors may be powered via energy harvesting and thus collect all the energy they need from the environment. This work addresses the problem of energy allocation over source acquisition/compression and transmission for energy-harvesting sensors. At first, focusing on a single-sensor, energy management policies are identified that guarantee a minimum average distortion while at the same time ensuring the stability of the queue connecting source and channel encoders. It is shown that the identified class of policies is optimal in the sense that it stabilizes the queue whenever this is feasible by any other technique that satisfies the same average distortion constraint. Moreover, this class of policies performs an independent resource optimization for the source and channel encoders. Suboptimal strategies that do not use the energy buffer (battery) or use it only for adapting either source or channel encoder energy allocation are also studied for performance comparison. The problem of optimizing the desired trade-off between average distortion and backlog size is then formulated and solved via dynamic programming tools. Finally, a system with multiple sensors is considered and time-division scheduling strategies are derived that are able to maintain the stability of all data queues and to meet the average distortion constraints at all sensors whenever it is feasible.

The design of Medium Access Control (MAC) protocols for wireless sensor networks (WSNs) has been conventionally tackled by assuming battery-powered devices and by adopting the network lifetime as the main performance criterion. While WSNs operated by energy-harvesting (EH) devices are not limited by network lifetime, they pose new design challenges due to the uncertain amount of energy that can be harvested from the environment. Novel design criteria are thus required to capture the trade-offs between the potentially infinite network lifetime and the uncertain energy availability. This paper addresses the analysis and design of WSNs with EH devices by focusing on conventional MAC protocols, namely TDMA, framed-ALOHA (FA) and dynamic-FA (DFA), and by accounting for the performance trade-offs and design issues arising due to EH. A novel metric, referred to as delivery probability, is introduced to measure the capability of a MAC protocol to deliver the measurement of any sensor in the network to the intended destination (or fusion center, FC). The interplay between delivery efficiency and time efficiency (i.e., the data collection rate at the FC), is investigated analytically using Markov models. Numerical results validate the analysis and emphasize the critical importance of accounting for both delivery probability and time efficiency in the design of EH-WSNs.

A common model for transmission over wireless links is that of a multiantenna system affected by an additive interfering signal. In some scenarios of interest, such as when the interferer is located close to the transmitter and performs retransmission, interference may be learned by the transmitter, but remain unknown at the receiver. In this case, it is well known that, if transmitter and receiver have perfect channel state information (CSI), then a technique called Dirty Paper Coding (DPC) is able to fully mitigate the interference. This paper studies the impact of imperfect CSI on a multiple-input multiple-output (MIMO) system with interference and compares the performance of DPC with that of a scheme where interference is decoded at the receiver, which we refer to as beamforming with joint decoding (BF-JD). Unlike DPC, which models the interference as an "unstructured" random process, BF-JD exploits the fact that the interfering signal is a codeword of the interferer's codebook. It is demonstrated by analysis and numerical results that BF-JD provides advantages over DPC when CSI is imperfect at the transmitter but perfect at the receiver, whereas this is not true for the case of imperfect CSI at both transmitter and receiver.

Energy efficiency is a key requirement in the design of wireless sensor networks. While most theoretical studies only account for the energy requirements of communication, the sensing process, which includes measurements and compression, can also consume comparable energy. In this paper, the problem of sensing and communicating parallel sources is studied by accounting for the cost of both communication and sensing. In the first formulation of the problem, the sensor has a separate energy budget for sensing and a rate budget for communication, while, in the second, it has a single energy budget for both tasks. Furthermore, in the second problem, each source has its own associated channel. Assuming that sources with larger variances have lower sensing costs, the optimal allocation of sensing energy and rate that minimizes the overall distortion is derived for the first problem. Moreover, structural results on the solution of the second problem are derived under the assumption that the sources with larger variances are transmitted on channels with lower noise. Closed-form solutions are also obtained for the case where the energy budget is sufficiently large. For an arbitrary order on the variances and costs, the optimal solution to the first problem is also obtained numerically and compared with several suboptimal strategies.

A cellular multiple-input multiple-output (MIMO) downlink system is studied in which each base station (BS) transmits to some of the users, so that each user receives its intended signal from a subset of the BSs. This scenario is referred to as network MIMO with partial cooperation, since only a subset of the BSs are able to coordinate their transmission towards any user. The focus of this paper is on the optimization of linear beamforming strategies at the BSs and at the users for network MIMO with partial cooperation. Individual power constraints at the BSs are enforced. It is first shown that the system is equivalent to a MIMO interference channel with generalized linear constraints (MIMO-IFC-GC). The problem of minimizing the weighted sum mean square error (WSMSE) of the data estimates is non-convex, and suboptimal solutions with reasonable complexity need to be devised. Novel designs that aim at minimizing the WSMSE are then proposed. Extensive numerical simulations are provided to compare the performance of the considered schemes for realistic cellular systems.

Utility (e.g., sum-rate) maximization for multiantenna broadcast and interference channels (with one antenna at the receivers) is known to be in general a non-convex problem, if one limits the scope to linear (beamforming) strategies at transmitter and receivers. In this paper, it is shown that, under some standard assumptions, most notably that the utility function is decreasing with the interference levels at the receivers, a global optimal solution can be found with reduced complexity via a suitably designed branch-and-bound method. Although infeasible for real-time implementation, this procedure enables a non-heuristic and systematic assessment of suboptimal techniques. In addition to the global optimal scheme, a real-time suboptimal algorithm, which generalizes the well-known distributed pricing techniques, is also proposed. Finally, numerical results are provided that compare global optimal solutions with suboptimal (pricing) techniques for sum-rate maximization problems, affording insight into issues such as the robustness against bad initializations in real-time suboptimal strategies.

Spectrum leasing via cooperation refers to the possibility for primary users to lease part of the spectral resources to secondary users in exchange for cooperation. This paper proposes a novel implementation of this concept in which secondary cooperation aims at improving the secrecy of the primary link. In particular, a secondary transmission with multiple antennas creates interference on both primary and eavesdropping receivers but an appropriately designed beamformer may impair more the eavesdropper's reception and thus enhance primary secrecy. Design of the secondary beamformer that maximizes the primary secrecy rate while guaranteeing a minimal secondary rate is studied. It is proved that the problem can be solved in a domain that includes only two real numbers irrespective of the number of antennas. Numerical results show that the proposed spectrum leasing strategy increases the primary secrecy rate compared to the case of no spectrum leasing for a wide range of secondary minimum rate constraints.

1An encoder wishes to minimize the bit rate necessary to guarantee that a decoder is able to calculate a symbol-wise function of a sequence available only at the encoder and a sequence that can be measured only at the decoder. This classical problem, first studied by Yamamoto, is addressed here by including two new aspects: (i) The decoder obtains noisy measurements of its sequence, where the quality of such measurements can be controlled via a cost-constrained “action” sequence; (ii) Measurement at the decoder may fail in a way that is unpredictable to the encoder, thus requiring robust encoding. The considered scenario generalizes known settings such as the Heegard-Berger-Kaspi and the “source coding with a vending machine” problems. The rate-distortion-cost function is derived in relevant special cases, along with general upper and lower bounds. Numerical examples are also worked out to obtain further insight into the optimal system design.

The state-dependent multiple access channel (MAC) is considered where the state sequences are known strictly causally to the encoders. First, a two-user MAC with two independent states each known strictly causally to one encoder is revisited, and a new achievable scheme inspired by the recently proposed noisy network coding is presented. This scheme is shown to achieve a rate region that is potentially larger than that provided by recent work for the same model. Next, capacity results are presented for a class of channels that include modulo-additive state-dependent MACs. It is shown that the proposed scheme can be easily extended to an arbitrary number of users. Finally, a similar scheme is proposed for a MAC with common state known strictly causally to all encoders. The corresponding achievable rate region is shown to reduce to the one given in the previous work as a special case for two users.

This work addresses the problem of energy allocation over source compression and transmission for a single energy-harvesting sensor. An optimal class of policies is identified that simultaneously guarantees a maximal average distortion and the stability of the queue connecting source and channel encoders, whenever this is feasible by any other strategy. This class of policies performs an independent resource optimization for the source and channel encoders. Analog transmission techniques as well as suboptimal strategies that do not use the energy buffer (battery) or use it only for adapting either source or channel encoder energy allocation are also studied.

A Gaussian Interference Channel (IC) is investigated in which a relay assists two source-destination pairs. The relay is assumed to receive over dedicated orthogonal channels from the sources (e.g., over orthogonal bands or time slots, or over wired links), while it transmits in the same band as the sources. This scenario is referred to as IC assisted by an out-of-band reception/ in-band transmission relay (IC-OIR). An achievable rate region is derived for the IC-OIR that encompasses, besides the standard signal relaying, interference management via interference relaying, cancellation and precoding. The sum-capacity is found in a specific regime defined by the very strong relay-interference conditions. Numerical results validate the performance gains of interference mitigation via the relay.

A Gaussian interference channel (IC) with a relay is considered. The relay is assumed to operate over an orthogonal band with respect to the underlying IC, and the overall system is referred to as IC with an out-of-band relay (IC-OBR). The system can be seen as operating over two parallel interference-limited channels: The first is a standard Gaussian IC and the second is a Gaussian relay channel characterized by two sources and destinations communicating through the relay without direct links. We refer to the second parallel channel as OBR Channel (OBRC). The main aim of this work is to identify conditions under which optimal operation, in terms of the capacity region of the IC-OBR, entails either signal relaying and/or interference forwarding by the relay, with either a separable or nonseparable use of the two parallel channels, IC, and OBRC. Here, “separable” refers to transmission of independent information over the two constituent channels. For a basic model in which the OBRC consists of four orthogonal channels from sources to relay and from relay to destinations (IC-OBR Type-I), a condition is identified under which signal relaying and separable operation is optimal. This condition entails the presence of a relay-to-destinations capacity bottleneck on the OBRC and holds irrespective of the IC. When this condition is not satisfied, various scenarios, which depend on the IC channel gains, are identified in which interference forwarding and nonseparable operation are necessary to achieve optimal performance. In these scenarios, the system exploits the “excess capacity” on the OBRC via interference forwarding to drive the IC-OBR system in specific interference regimes (strong or mixed). The analysis is then turned to a more complex IC-OBR, in which the OBRC consists of only two orthogonal channels, one from sources to relay and one from relay to destinations (IC-OBR Type-II). For this channel, some capacity resuls are derived that parallel the conclusions for IC-OBR Type-I and point to the additional analytical challenges.

A standard assumption in network information theory is that all nodes are informed at all times of the operations carried out (e.g., of the codebooks used) by any other terminal in the network. In this paper, information theoretic limits are sought under the assumption that, instead, some nodes are not informed about the codebooks used by other terminals. Specifically, capacity results are derived for a relay channel in which the relay is oblivious to the codebook used by the source (oblivious relaying), and an interference relay channel with oblivious relaying and in which each destination is possibly unaware of the codebook used by the interfering source (interference-oblivious decoding). Extensions are also discussed for a related scenario with standard codebook-aware relaying but interference-oblivious decoding. The class of channels under study is limited to out-of-band (or “primitive”) relaying: Relay-to-destinations links use orthogonal resources with respect to the transmission from the source encoders. Conclusions are obtained under a rigorous definition of oblivious processing that is related to the idea of randomized encoding. The framework and results discussed in this paper suggest that imperfect codebook information can be included as a source of uncertainty in network design along with, e.g., imperfect channel and topology information.

A source communicates with a remote destination via a number of distributed relays. Communication from source to relays takes place over a (discrete or Gaussian) broadcast channel, while the relays are connected to the receiver via orthogonal finite-capacity links. Unknowns to the source and relays, link failures may occur between any subset of relays and the destination in a nonergodic fashion. Upper and lower bounds are derived on average achievable rates with respect to the prior distribution of the link failures, assuming the relays to be oblivious to the source codebook. The lower bounds are obtained by proposing strategies that combine the broadcast coding approach, previously investigated for quasi-static fading channels, and different robust distributed compression techniques. Numerical results show that lower and upper bounds are quite close over most operating regimes, and provide insight into optimal transmission design choices for the scenario at hand. Extension to the case of nonoblivious relays is also discussed.

A licensed multihop network that coexists with a set of unlicensed nodes is considered. Coexistence is regulated via a spectrum leasing mechanism that is based on cooperation and opportunistic routing. Specifically, the primary network consists of a source and a destination communicating via a number of primary relay nodes. In each transmission block, the next hop is selected in an on-line fashion based on the channel conditions (and thus the decoding outcome) in the previous transmissions, according to the idea of opportunistic routing. The secondary nodes may serve as extra relays, and hence potential next hops, for the primary network, but only in exchange for spectrum leasing. Namely, in return for their forwarding of primary packets, secondary nodes are awarded spectral resources for transmission of their own traffic. Secondary nodes enforce Quality-of-Service requirements in terms of rate and reliability when deciding whether or not to cooperate. Four policies that exploit spectrum leasing via opportunistic routing in different ways are proposed. These policies are designed to span different operating points in the trade-off between gains in throughput and overall energy expenditure for the primary network. Analysis is carried out for networks with a linear geometry and quasi-static Rayleigh fading statistics by using Markov chain tools. Different multiplexing techniques are considered for multiplexing of the primary and secondary traffic at the secondary nodes, namely orthogonal multiplexing (such as time, frequency or orthogonal code division multiplexing) and superposition coding. The optimality in terms of both throughput and primary energy consumption of superposition coding over all possible multiplexing strategies, for the given routing techniques, is proved. Finally, numerical results demonstrate the advantages of the proposed spectrum leasing solution based on opportunistic routing and illustrate the trade-offs between primary throughput and energy consumption.

Femtocells promise to increase the number of users served in a given macrocell by creating indoor hotspots through the deployment of home base stations (HBSs) connected to the mobile operator network via cheap backhaul links (i.e., the Internet). However, the interference created by femtocell transmissions may critically impair the performance of the macrocell users. In this paper, a novel approach to the operation of HBSs is proposed, whereby the HBSs act as relays with the aim of improving transmission reliability for femtocell users and, possibly, also macrocell users. The proposed approach enables cooperative strategies between HBS and macrocell base stations (BSs), and is unlike the conventional deployment of femtocells where HBSs operate as isolated encoders and decoders. The performance advantages of the proposed approach are evaluated by studying the transmission reliability of macro and femto users for a quasi-static fading channel in terms of outage probability and diversity-multiplexing trade-off for uplink and, more briefly, for downlink. Overall, the analytical and numerical results lend evidence to the fact that operating femtocells as relays may potentially offset the performance losses associated with the presence of additional active users in the cell due to femtocells and even provide overall performance gains.

Opportunistic routing is a well-known technique that exploits the broadcast nature of wireless transmissions and path diversity to form the route in an adaptive manner based on current channel conditions. This paper studies the throughput advantages of opportunistic routing over conventional multihop routing for linear multihop wireless networks with type-I Hybrid Automatic Repeat reQuest (HARQ) and quasi-static Rayleigh fading channels. The end-to-end throughput of opportunistic routing is derived using Markov chain tools and accounting for fading statistics. Both fixed-rate and optimal-rate transmissions are considered. Moreover, an investigation of the throughput using standard information-theoretic performance metrics for asymptotic signal-to-noise ratio regimes is provided. Specifically, the multiplexing gain and energy efficiency (i.e., minimum energy per bit) of both opportunistic and multihop routing are analyzed. Numerical results are given to corroborate the analysis.

Femtocells promise to increase the number of users served in a given macrocell by creating indoor hotspots connected to the mobile operator network via cheap backhaul links (i.e., the Internet). However, the interference created by the femtocell transmissions may critically impair the performance of the macrocell users. This effect can be potentially alleviated via so called open-access home base stations. In this paper, the transmission reliability of macro (outdoor) and femto (indoor) users is studied for a quasi-static fading channel in the presence of both open and closed-access home base stations, in terms of outage probability and diversity-multiplexing trade-off. Analytical results are derived that shed light on the impact of femtocells and the advantages of open-access home base stations in different regimes of channel power gains and transmission rates.

The Dynamic Framed-ALOHA (DFA) protocol is studied for wireless sensor networks with energy limitations and energy-harvesting capability. The performance of DFA in this scenario is evaluated in terms of the time efficiency (or throughput), which is routinely used to evaluate medium access protocols, and by introducing a new metric, referred to as detection efficiency, which is tailored to scenarios with energy constraints. Specifically, detection efficiency measures the ability of a multiple access protocol to collect data from nodes without depleting their energy reserves. Analysis is first performed by assuming that DFA is operated with a perfect backlog (i.e., number of sensors left to be interrogated) knowledge. Then, a low-complexity backlog estimation algorithm is presented, which is shown by numerical results to perform close to the ideal case of perfect backlog knowledge.

The operation of communication protocols is conventionally independent of the information being transmitted. This paper puts forth the notion of protocol coding to refer to transmission strategies in which data can be encoded by modulating the protocol actions according to the information message. We focus on communication in the presence of half-duplex constraints, where the task of the protocol is to schedule the transmission/reception times of different nodes. Such a schedule is conventionally decided a priori in the form of time-sharing. While previous work has focused on protocol coding for standard relay channels, this paper tackles two-way communications aided by a relay. Since the techniques developed for standard relay channels cannot be applied to the scenario at hand, a novel simple strategy is proposed that is tailored to two-way communications. The proposed scheme is shown to significantly outperform conventional time-sharing for a deterministic two-way relay channel.

Spectrum leasing from a primary user to a set of secondary users may be implemented by requiring the secondary nodes to pay back the primary for the leased spectrum via cooperation (relaying). In this paper, this principle is studied from an information-theoretic standpoint by focusing on a scenario with one primary node and multiple secondary nodes, which may act as relays for the primary, communicating to a common receiver. The scenario is modelled as a multirelay channel where each relay (secondary user) has a private message for the destination. Achievable rate regions are derived for discrete memoryless and Gaussian models by considering Decode-and-Forward (DF), with both standard and parity-forwarding techniques, and Compress-and-Forward (CF), along with superposition coding at the secondary nodes. Numerical results for the Gaussian channel confirm that spectrum leasing via secondary cooperation is a promising framework to enable secondary spectrum access.

A critical performance criterion in backscatter modulation-based RFID sensor networks is the distance at which a RFID reader can reliably communicate with passive RFID sensors (or tags). This paper proposes to introduce a power amplifier (PA) and an energy storage device (such as a capacitor or a battery), in the hardware architecture of conventional passive RFID tags, with the aim of allowing amplification of the backscatter signal to increase the read range. This new tag architecture, referred to as Amplified Backscattering via Energy Harvesting (ABEH), can still be considered as passive, since the energy storage device is charged exclusively by harvesting energy from the RF-signal transmitted by the reader and received by the tag during idle periods. The harvested and stored energy is then used by the tags to opportunistically amplify the backscatter signal. It is noted that this architecture is significantly different from active RFID tags where the battery, charged at the time of installation, is used to supply a complete onboard transceiver so that no backscatter modulation is employed. Energy scheduling strategies, based on the trade-off between energy harvesting rate and successful transmission probability, are proposed. Performance analysis of tags with the proposed ABEH architecture is carried out over quasi-static fading channels by framing the design problem as a Markov Decision Process. Numerical results show remarkable improvement of the ABEH approach with respect to conventional passive RFID tags and provide insight into the effect of system parameters on the energy scheduling.

The capacity region for a multiple access channel (MAC) with arbitrary sets of common messages was derived by Han in 1979, extending a result by Slepian and Wolf from 1973. The general characterization by Han involves one auxiliary random variable per message and one inequality per subset of messages. In this paper, at first, a special hierarchy of common messages is identified for which the capacity region is characterized with generally fewer auxiliary random variables and inequalities. It is also shown that this characterization requires no auxiliary random variable for certain message structures. A procedure is then proposed to transform any common message structure to this special hierarchy, leading to a general capacity characterization which generally requires fewer auxiliary random variables than the one given by Han.

A Gaussian interference channel (IC) aided by a half-duplex relay is considered, in which the relay receives and transmits in an orthogonal band with respect to the IC. The system thus consists of two parallel channels, the IC and the channel over which the relay is active, which is referred to as Out-of-Band Relay Channel (OBRC). The OBRC is operated by separating a multiple access phase from the sources to the relay and a broadcast phase from the relay to the destinations. Conditions under which the optimal operation, in terms of the sum-capacity, entails either signal relaying and/or interference forwarding by the relay are identified. These conditions also assess the optimality of either separable or non-separable transmission over the IC and OBRC. Specifically, the optimality of signal relaying and separable coding is established for scenarios where the relay-to-destination channels set the performance bottleneck with respect to the source-to-relay channels on the OBRC. Optimality of interference forwarding and non-separable operation is also established in special cases.

This paper presents an overview of the theory and currently known techniques for multi-cell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacity-limiting factor, multi-cell cooperation can dramatically improve the system performance. Remarkably, such techniques literally exploit inter-cell interference by allowing the user data to be jointly processed by several interfering base stations, thus mimicking the benefits of a large virtual MIMO array. Multi-cell MIMO cooperation concepts are examined from different perspectives, including an examination of the fundamental information-theoretic limits, a review of the coding and signal processing algorithmic developments, and, going beyond that, consideration of very practical issues related to scalability and system-level integration. A few promising and quite fundamental research avenues are also suggested.

A cellular system where macrocells are overlaid with femtocells is studied. Each femtocell is served by a home base station (HBS) that is connected to the macrocell base station (BS) via an unreliable network access link, such as DSL followed by the Internet. A scenario with a single macrocell and a single femtocell is considered first, and is then extended to include multiple macrocells and femtocells, both with standard single-cell processing and with multicell processing (or network MIMO). Two main issues are addressed for the uplink channel: ({i}) Interference management between femto and macrocells; ({ii}) Robustness to uncertainties on the quality of the femtocell (HBS-to-BS) access link. The problem is formulated in information-theoretic terms, and inner and outer bounds are derived to achievable per-cell sum-rates for outdoor and home users. Expected sum-rates with respect to the distribution of the femtocells access link states are studied as well. Overall, the analysis lends evidence to the performance advantages of sophisticated interference management techniques, based on joint decoding and relaying, and of robust coding strategies via the broadcast coding approach (i.e., unequal error protection).

A novel distributed scheme that combines cooperative ARQ with the spectrum leasing paradigm is proposed and analyzed. The strategy harnesses the opportunistic gains of cooperative communications, while inherently providing a spectrum-rewarding incentive for the otherwise non-cooperative relays to assist the source's transmission. As in cooperative ARQ, the source might decide to hand over the possible retransmission slots to nearby stations that were able to decode the original transmission. In the proposed scheme, however, in exchange for the cooperation, the relaying station is also awarded an opportunity to exploit the retransmission slot for its own traffic. Arbitration of relays' retransmissions is performed via an auction mechanism, with the source, the competing relays and the transmission slot acting as the auctioneer, the bidders and the bidding article, respectively. Auction theory (more generally, the theory of Bayesian games) is applied to analyze the scheme performance. It is noted that the setting here can be alternatively seen as a practical framework for implementation of property-rights cognitive radio networks. Numerical results and analysis show that the proposed scheme enables an efficient dynamic resource allocation that provides relevant gains (e.g., transmission reliability) for both the original source (primary) and the cooperating nodes (secondary users).

The problem of simultaneous multicasting of multiple messages with the help of a relay terminal is considered. In particular, a model is studied in which a relay station simultaneously assists two transmitters in multicasting their independent messages to two receivers. The relay may also have an independent message of its own to multicast. As a first step to address this general model, referred to as the compound multiple access channel with a relay (cMACr), the capacity region of the multiple access channel with a “cognitive” relay is characterized, including the cases of partial and rate-limited cognition. Then, achievable rate regions for the cMACr model are presented based on decode-and-forward (DF) and compress-and-forward (CF) relaying strategies. Moreover, an outer bound is derived for the special case, called the cMACr without cross-reception, in which each transmitter has a direct link to one of the receivers while the connection to the other receiver is enabled only through the relay terminal. The capacity region is characterized for a binary modulo additive cMACr without cross-reception, showing the optimality of binary linear block codes, and thus highlighting the benefits of physical layer network coding and structured codes. Results are extended to the Gaussian channel model as well, providing achievable rate regions for DF and CF, as well as for a structured code design based on lattice codes. It is shown that the performance with lattice codes approaches the upper bound for increasing power, surpassing the rates achieved by the considered random coding-based techniques.

In this paper, the performance of cellular networks with joint multicell processing and dedicated relay terminals is investigated. It is assumed that each relay terminal is capable of full-duplex operation and receives the transmission of relay terminals in adjacent cells. Focusing on intra-cell time division multiple access and non-fading channels, a simplified relay-aided uplink cellular model is considered. Addressing the achievable per-cell sum-rate, two non-regenerative relaying schemes are considered. Interpreting the received signal at the base stations as the outcome of a two-dimensional linear time invariant system, the multicell processing rate of an amplify-and-forward scheme is derived and shown to decrease with the inter-relay interference level. A novel form of distributed compress-and-forward scheme with decoder side information is then proposed. The corresponding multicell processing rate, which is given as a solution of a simple fixed-point equation, reveals that the compress-and-forward scheme is able to completely eliminate the inter-relay interference, and it approaches a "cut-set-like" upper bound for strong relay terminal transmission power. The benefits of base-station cooperation via multicell processing over the conventional single site processing approach is also demonstrated for both protocols.

In this paper, a primary (licensed) user leases part of its resources to independent secondary (unlicensed) terminals in exchange for a tariff in dollars per bit, under the constraint that secondary transmissions do not cause excessive interference at the primary receiver (PRX). The PRX selects a power allocation (PA) for the secondary user that maximizes the secondary rate (and thus its revenue) and enforces it by the following mechanism: Upon violation of a predefined interference level, PRX keeps silencing randomly selected secondary users, until the aggregate secondary interference is below the required threshold. This mechanism ensures that secondary users may not be willing to deviate from the allocated PA. Specifically, the scenario gives rise to a Stackelberg game, in which the primary determines the PA and a Nash equilibrium (NE) constraint is imposed on the PA to ensure that secondary users do not have incentives to deviate, given their knowledge of the silencing mechanism run at the PRX. In principle, the primary should find the set of all PAs that are NE and among them choose the one that maximizes the aggregate secondary utility, and thereby the revenue of the primary. For the most general setting of channel gains, we investigate the conditions for NE for a subset of PAs. When the scenario is symmetric in the sense that all secondary users have the same channel gains in the direct/interfering links, we prove that only two optimal power allocations exist. Finally, for the case of general channel gains with strong interference, we show that there is a unique NE of the game.

We propose and analyze a novel decentralized mechanism that motivates otherwise non-cooperative stations to participate as relays in cooperative ARQ protocol. Cooperation incentive is provided by the possibility for the source to lease a portion of retransmission slot for the traffic of relaying terminals. To further leverage the opportunistic nature of cooperative ARQ and obtain a fully decentralized solution, the (motivated) relaying nodes compete for access to the retransmission slot by trying to make the best retransmission offer. Effective arbitration of cooperative retransmissions is performed using auction theory (bidding), with the source in the role of the auctioneer, the relaying nodes acting as the bidders and the (use of the) retransmission slot as the bidding article. It is noted that the proposed solution can be seen as a practical framework for the implementation of cognitive radio networks running according to the property-rights model (spectrum leasing). Numerical results and analysis confirm the efficient dynamic resource allocation property of the proposed scheme, revealing the relevant gains in terms of expected number of (re)transmissions required for successful data delivery for both the source (primary) and the cooperating (secondary) nodes.

Simultaneous multicasting of messages with the help of a relay is studied. A two-source two-destination network is considered, in which each destination can receive directly only the signal from one of the sources, so that the reception of the message from the other source (and multicasting) is enabled by the presence of the relay. An outer bound is derived, which is shown to be achievable in the case of finite-field modulo-additive channels by using linear codes, highlighting the benefits of structured codes in exploiting the underlying physical-layer structure of the network. Results are extended to the Gaussian channel model as well, providing achievable rate regions based on nested lattice codes. It is shown that for a wide range of power constraints, the performance with lattice codes approaches the upper bound and surpasses the rates achieved by the standard random coding schemes.

A Gaussian interference channel with an infrastructure relay (ICIR) is investigated. The relay has finite-capacity links to both sources and destinations that are orthogonal to each other and to the underlying interference channel. A general achievable rate region is presented by using the relay both to convey additional information from the sources (signal relaying) and to ease interference cancellation (interference forwarding). Outer bounds to the capacity region are also derived, and used to determine a number of regimes of interest where either signal relaying only or both signal relaying and interference forwarding are optimal.

The problem of multicasting multiple messages with the help of a relay, which may also have an independent message of its own to multicast, is considered. As a first step to address this general model, referred to as the compound multiple access channel with a relay (cMACr), the capacity region of the multiple access channel with a ldquocognitiverdquo relay is characterized, including the cases of partial and rate-limited cognition. Achievable rate regions for the cMACr model are then presented based on decode-and-forward (DF) and compress-and-forward (CF) relaying strategies. Moreover, an outer bound is derived for the special case in which each transmitter has a direct link to one of the receivers while the connection to the other receiver is enabled only through the relay terminal. Numerical results for the Gaussian channel are also provided.

A multi-relay network is considered in which communication from source to relays takes place over a (discrete or Gaussian) broadcast channel, while the relays are connected to the receiver via orthogonal finite-capacity links. Unbeknownst to the source and relays, link failures may take place between any subset of relays and destination in a non-ergodic fashion. Upper and lower bounds are derived on average achievable rates with respect to the prior distribution of the link failures, assuming the relays to be oblivious to the source codebook. The lower bounds are obtained via strategies that combine the broadcast coding approach, previously investigated for quasi-static fading channels, and various robust distributed compression techniques.

In cellular systems, confidentiality of uplink transmission with respect to eavesdropping terminals can be ensured by creating intentional interference via scheduling of concurrent downlink transmissions. In this paper, this basic idea is explored from an information-theoretic standpoint by focusing on a two-cell scenario where the involved base stations (BSs) are connected via a finite-capacity backbone link. A number of transmission strategies are considered that aim at improving uplink confidentiality under constraints on the downlink rate that acts as an interfering signal. The strategies differ mainly in the way the backbone link is exploited by the cooperating downlink to the uplink-operated BSs. Achievable rates are derived for both the Gaussian (unfaded) and the fading cases, under different assumptions on the channel state information available at different nodes. Numerical results are also provided to corroborate the analysis. Extensions to scenarios with more than two cells are briefly discussed as well. Overall, the analysis reveals that a combination of scheduling and base-station cooperation is a promising means to improve transmission confidentiality in cellular systems.

A two-user discrete memoryless compound multiple-access channel (MAC) with a common message and conferencing decoders is considered. The capacity region is characterized in the special cases of physically degraded channels and unidirectional cooperation, and achievable rate regions are provided for the general case. The results are then extended to the corresponding Gaussian model. In the Gaussian setup, the provided achievable rates are shown to lie within some constant number of bits from the boundary of the capacity region in several special cases. An alternative model, in which the encoders are connected by conferencing links rather than having a common message, is studied as well, and the capacity region for this model is also determined for the cases of physically degraded channels and unidirectional cooperation. Numerical results are also provided to obtain insights about the potential gains of conferencing at the decoders and encoders.

Cooperative decoding at the base stations (or access points) of an infrastructure wireless network is currently well recognized as a promising approach for intercell interference mitigation, thus enabling high frequency reuse. Deployment of cooperative multicell decoding depends critically on the tolopology and quality of the available backhaul links connecting the base stations. This work studies a scenario where base stations are connected only if in adjacent cells, and via finite-capacity links. Relying on a linear Wyner-type cellular model with no fading, achievable rates are derived for the two scenarios where base stations are endowed only with the codebooks of local (in-cell) mobile stations, or also with the codebooks used in adjacent cells. Moreover, both uni- and bidirectional backhaul links are considered. The analysis sheds light on the impact of codebook information, decoding delay, and network planning (frequency reuse) on the performance of multicell decoding as enabled by local and finite-capacity backhaul links. Analysis in the high-signal-to-noise ratio (SNR) regime and numerical results validate the main conclusions.

A single source communicates with a single destination via a remote wireless multiple-antenna (multiple-input multiple-output (MIMO)) transceiver. The source has access to each of the transmit antennas through a finite-capacity link, and likewise the destination is connected to the receiving antennas via capacity-constrained channels (e.g., as for wired or time-division multiple access (TDMA) channels). Targeting a nomadic communication scenario, in which the remote MIMO transceiver is designed to serve different standards or services, it is assumed that transmitters and receivers are oblivious to the encoding function shared by source and destination. Assuming a Gaussian symmetric interference network as the channel model (as for regularly placed transmitters and receivers), achievable rates are investigated and compared with an upper bound (that holds also for codebook-dependent operation). Closed-form expressions are derived for large numbers of antennas (and in some cases large signal-to-noise ratios (SNRs)), and asymptotics of the achievable rates are studied with respect to either link capacities or SNR. Overall, the analysis points to effective transmission/reception strategies for the distributed MIMO channel at hand, which are optimal under specified conditions. In particular, it is concluded that in certain asymptotic and nonasymptotic regimes there is no loss of optimality in designing the system for nomadic applications (i.e., assuming oblivious transmitters and receivers). Numerical results validate the analysis.

In this work, a multicell cooperative zero-forcing beamforming (ZFBF) scheme combined with a simple user selection procedure is considered for the Wyner cellular downlink channel. The approach is to transmit to the user with the ldquobestrdquo local channel in each cell. The performance of this suboptimal scheme is investigated in terms of the conventional sum-rate scaling law and the sum-rate offset for an increasing number of users per cell. We term this characterization of the sum-rate for large number of users ashigh-loadregimecharacterization, and point out the similarity of this approach to the standard affine approximation used in the high-signal-to-noise ratio (SNR) regime. It is shown that, under an overall power constraint, the suboptimal cooperative multicell ZFBF scheme achieves the same sum-rate growth rate and slightly degraded offset law, when compared to an optimal scheme deploying joint multicell dirty-paper coding (DPC), asymptotically with the number of users per cell. Moreover, the overall power constraint is shown to ensure in probability, equal per-cell power constraints when the number of users per-cell increases.

A sensor networks in which two nodes communicate a remote measurement to an access point is investigated (CEO problem). The focus is on assessing the performance advantages of cooperative encoding strategies as enabled by out-of-band and finite-capacity communication links between the sensors. The analysis assumes Gaussian source and observation noises, a quadratic (MSE) distortion metric and homogeneous sensors. With rate-constrained links between each sensor and the access point, an achievable rate-distortion trade-off is derived that reduces to known rate-distortion characterizations in the cases without and with perfect cooperation. This result is then extended to a Gaussian multiple access channel scenario by deriving achievable distortions with separate or joint source-channel coding. It is concluded that, for both scenarios, even modest values of the capacity of the inter-sensor links enable the optimal performance with full sensor cooperation to be approached.

In this paper, we consider the energy efficiency of truncated hybrid-ARQ (HARQ) protocols in a single-user link (i.e., non-collaborative HARQ), or with the inclusion of a relay station (i.e., collaborative HARQ). The total energy consumption accounts for both the transmission energy and the energy consumed by the transmitting and receiving electronic circuitry of all involved terminals (source, destination and, possibly, the relay). Using the transmission time and transmission energy of each packet as optimization variables, the overall energy is minimized under an outage probability constraint for HARQ Type I, HARQ chase combining and HARQ incremental redundancy protocols (in the latter case, a tight lower bound is considered). Numerical optimization provides insight into the optimal design choices that enhance energy efficiency with HARQ protocols. It is shown, for instance, that, if the circuitry energy consumption is not negligible, selection of the transmission energy is not only dictated by the outage constraint, but is also significantly affected by the need to reduce the number of retransmissions. Our results also demonstrate the performance limitations of collaborative HARQ protocols in terms of energy efficiency, when circuitry consumption is properly accounted for.

A network with two sensors communicating a remote measurement to a common access point (AP) is investigated. The sensors are connected via out-of-band and finite-capacity communication links, e.g., thanks to an orthogonal radio interface, thus enabling partial cooperation. Focusing on a Gaussian model for source and observation noise and a quadratic (MSE) distortion metric, both the source coding problem (corresponding to error-free and orthogonal links to the AP), also known as the "CEO problem", and the joint source-channel coding problem over a Gaussian multiple access channel to the AP are considered. In the first case, an achievable rate-distortion tradeoff is derived that generalizes known results for the CEO problem in the absence of cooperation between the sensors. For the latter case, achievable distortions are derived with separate or joint source-channel coding. Optimality of the proposed schemes is established asymptotically with the capacity of the inter-sensor links. Moreover, it is concluded that, for both scenarios, even modest values of such capacity enable the optimal performance with full cooperation to be approached.

According to the commons model of cognitive radio, the activity of secondary (unlicensed) nodes is required to guarantee quality-of-service (QoS) constraints on the transmission of primary (licensed) terminals. Towards this goal, vertical handover between different radio interfaces is currently being investigated as a promising solution to enhance flexibility in unlicensed channel access. In this paper, we propose an analysis of cognitive radio with vertical handover capability in a simple scenario with one secondary node and two primary nodes that employ different radio interfaces with packet-based transmission. The maximum stable throughput of the secondary node is evaluated under maximum-delay QoS constraints on the primary activity as a function of system geometry, QoS constraints and sensing errors. Numerical results show the relevant advantages of optimal vertical handover in terms of the throughput of secondary nodes.

The concept of cognitive radio (or secondary spectrum access) is currently under investigation as a promising paradigm to achieve efficient use of the frequency resource. In this paper, we consider a decentralized cognitive radio model based on spectrum leasing, whereby a primary (licensed) user leases its bandwidth for a fraction of time to a network of independent secondary (unlicensed) terminals in exchange for cooperation. On one hand, the primary user decides whether to exploit (space-time coded) cooperation from the network of secondary terminals in order to maximize its own transmission rate. On the other hand, secondary terminals accept to cooperate with the primary only if compensated with a large enough fraction of time for their own transmission, towards the goal of maximizing their rate discounted by the overall cost of transmitted power. The considered model is studied in the framework of Stackelberg games, with the primary and the set of secondary users modelled as the (Stackelberg) game leader and the follower, respectively. Numerical results show that spectrum leasing based on trading secondary spectrum access for cooperation is a promising framework for cognitive radio.

A scenario in which a single source communicates with a single destination via a distributed MIMO transceiver is considered. The source operates each of the transmit antennas via finite-capacity links, and likewise the destination is connected to the receiving antennas through capacity-constrained channels. Targeting a nomadic communication scenario, in which the distributed MIMO transceiver is designed to serve different standards or services, transmitters and receivers are assumed to be oblivious to the encoding functions shared by source and destination. Adopting a Gaussian symmetric interference network as the channel model (as for regularly placed transmitters and receivers), achievable rates are investigated and compared with an upper bound. It is concluded that in certain asymptotic and non-asymptotic regimes obliviousness of transmitters and receivers does not cause any loss of optimality.

In this paper the advantages provided by multi-cell processing of signals transmitted by mobile terminals (MTs) which are received via dedicated relay terminals (RTs) are studied. It is assumed that each RT is capable of full-duplex operation and receives the transmission of adjacent relay terminals. Focusing on intra-cell TDMA and non-fading channels, a simplified relay-aided uplink cellular model based on a model introduced by Wyner is considered. Assuming a nomadic application in which the RTs are oblivious to the MTs’ codebooks, a form of distributed compress-and-forward (CF) scheme with decoder side information is employed. The per-cell sum-rate of the CF scheme is derived and is given as a solution of a simple fixed point equation. This achievable rate reveals that the CF scheme is able to completely eliminate the inter-relay interference, and it approaches a “cut-set-like” upper bound for strong RTs transmission power. The CF rate is also shown to surpass the rate of an amplify-and-forward scheme via numerical calculations for a wide range of the system parameters.

A novel approach for ensuring confidential communications over infrastructure-based wireless networks is proposed and analyzed from an information-theoretic standpoint. The considered techniques leverage the finite-capacity backbone connecting the base stations and the possibility to schedule uplink/downlink transmissions in order to create intentional interference. Two different methods are studied, one based on source coding and one on channel coding arguments, and corresponding rates achievable with perfect secrecy are derived.

Recent information theoretic results on cooperation in cellular systems are reviewed, addressing both multicell processing (cooperation among base stations) and relaying (cooperation at the user level). Two central issues are addressed, namely, first multicell processing is studied with either limited-capacity backhaul links to a central processor or only local (and finite-capacity) cooperation among neighboring cells. The role of codebook information, decoding delay and network planning (frequency reuse) are specifically highlighted along with the impact of different transmission/ reception strategies. Next, multicell processing is considered in the presence of cooperation at the user level, focusing on both out-of-band relaying via conferencing users and in-band relaying by means of dedicated relays. Non-fading and fading uplink and downlink channels adhering to simple Wyner-type, cellular system models are targeted.

In infrastructure-less wireless systems network-wise time and frequency synchronization can be achieved by exchanging mutual synchronization errors among neighboring nodes. Cooperative synchronization is based on the use of distributed digital locked loops (D-DLLs), as the extension to distributed systems of the classical concept of (analog or digital) locked loops. The convergence to a synchronized state depends ultimately on the degree of connectivity of the network. D-DLLs can be specialized for time or frequency synchronization by adopting an appropriate error detector, but preserving the same control loop. The focus of this paper is on distributed frequency synchronization for packet-based communication. A novel detector is proposed, which approximates the local mean frequency error from the uncoordinated transmission of packets by neighboring nodes. The performance of distributed frequency locked loops (D-FLLs) is evaluated for a wireless network employing packet-based cooperative relaying. Numerical validations are used to compare different frequency synchronization protocols in terms of speed of convergence and degradation of end-to-end performances.

The concept of cognitive radio (or secondary spectrum access) is currently under investigation as a promising paradigm to achieve efficient use of the frequency resource by allowing the coexistence of licensed (primary) and unlicensed (secondary) users in the same bandwidth. According to the property-rights model of cognitive radio, the primary terminals own a given bandwidth and may decide to lease it for a fraction of time to secondary nodes in exchange for appropriate remuneration. In this paper, we propose and analyze an implementation of this framework, whereby a primary link has the possibility to lease the owned spectrum to an ad hoc network of secondary nodes in exchange for cooperation in the form of distributed space-time coding. On one hand, the primary link attempts to maximize its quality of service in terms of either rate or probability of outage, accounting for the possible contribution from cooperation. On the other hand, nodes in the secondary ad hoc network compete among themselves for transmission within the leased time-slot following a distributed power control mechanism. The investigated model is conveniently cast in the framework of Stackelberg games. We consider both a baseline scenario with full channel state information and information-theoretic transmission strategies, and a more practical model with long-term channel state information and randomized distributed space-time coding. Analysis and numerical results show that spectrum leasing based on trading secondary spectrum access for cooperation is a promising framework for cognitive radio.

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 latencyirrespective of the quality of the URLLC traffic predictor. The proposed method leverages recent advances inonline 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.

Achieving the sum-capacity of a multiple-input-multiple- output (MIMO) Gaussian broadcast channel is known to require full channel state information (CSI) at the base station, which implies the need of a large amount of feedback information from the users. Different asymptotics of the sum-capacity, such as its scaling law with respect to the number of users n or the multiplexing gain, are conventionally used to assess the performance of suboptimal schemes with reduced feedback, or equivalently with partial channel state information at the transmitter. In this correspondence, the optimal scaling law of the sum-rate with respect to n, for fixed signal-to-noise ratio (SNR), fixed number of transmit antennas M and any number of receiving antennasN(i.e.,Mlog log nN), is proved to be achievable with a deterministic feedback of only one bit per user. Moreover, the amount of feedback is shown to be further reduced with no asymptotic optimality loss by applying the selective feedback principle, leading to an average feedback rate that scales as log n. Finally, the asymptotic performance with respect to SNR is studied, by assessing how fast the number of users needs to increase with the SNR in order to guarantee a noninterference limited behavior.

In this correspondence, joint (cooperative) decoding at the base stations combined with collaborative transmission (relaying) is investigated as a means to improve the uplink throughput of current cellular systems over fading channels. Intracell orthogonal medium access control (e.g., TDMA, FDMA, or orthogonal CDMA) and Decode-and-Forward relaying by either a mobile terminal or a fixed relay are assumed. Moreover, the cellular system is modeled according to a simplified framework introduced by Wyner. The per-cell achievable ergodic throughput is calculated for different system configurations and then characterized in the low-power (wideband) regime in terms of minimum energy per bit required for reliable communication and slope of the spectral efficiency. The analysis allows to clearly assess the relative merits of both cooperation among base stations and at the terminal level within the considered model.

In this letter, packet-by-packet vertical handover is investigated as a means to improve the performance of unlicensed spectrum access. The analysis focuses on the optimization of a channel access strategy for a cognitive multi-standard radio node that has the capability to switch between two orthogonal uplink radio interfaces, based on long-term channel state information. The radio interfaces differ for coverage and quality of service (QoS) requirements of primary users. The proposed strategy prescribes a cross-layer selection of physical-layer (transmitting powers) and medium access control layer (handover probability) parameters and is designed to maximize secondary throughput while guaranteeing the primary QoS constraints. Analysis and numerical results bring insight into the impact of network topology, measurement errors and primary QoS constraints on the optimal system design and corresponding performance.

The performance of wireless multicasting is negatively affected by multipath fading. To improve reliability, cooperation among the nodes of the network can be used to create spatial diversity. This paper analyzes a two phase, space-time coded, cooperative multicast protocol and investigates two transmission and combination strategies: Maximal Ratio Combining and Incremental Redundancy. Moreover, it addresses two different channel state information (CSI) scenarios: i) no transmit CSI within the network and ii) broadcast transmit CSI at the source.

Cooperation is increasingly regarded as a key technology for tackling the challenges of a practical implementation of cognitive radio. In this paper, we first give a brief overview of the envisioned applications of cooperative technology to cognitive radio, distinguishing among cooperative sensing for detection of the primary activity, cooperative transmission between secondary nodes and cooperative transmission of primary traffic by secondary users (cognitive relaying). Then, we focus on the latter scenario and investigate a simple wireless network, where one secondary transmitter has the option to relay traffic of the primary. Assuming that the primary is oblivious to the presence of the secondary (thus excluding the possibility of spectrum leasing), the secondary transmitter optimizes transmission/ relaying parameters towards the goal of maximizing the rate towards the secondary receiver. Numerical results are provided in order to discuss the advantage and limits of cognitive relaying.

As the distance between terminals in modern wireless networks tends to decrease, the energy consumption issue, conventionally assumed to be exclusively dominated by the transmission power, needs to be revaluated. In particular, retransmission (ARQ) protocols that typically reduce the transmission energy required to obtain a given error probability on the channel (at the expense of a larger delay), also increase the energy consumed by the circuitry other than the power amplifier. In this paper, the energy efficiency of hybrid-ARQ type I, chase combining and incremental redundancy protocols in Rayleigh fading channels, is analyzed by accounting for the energy consumed by the transmitting and receiving electronic circuitry. It is shown that the advantages of hybrid-ARQ protocols in terms of energy consumption strictly depend on the transmission range.

In this paper the benefits provided by multi-cell processing of signals transmitted by mobile terminals which are received via dedicated relay terminals (RTs) are assessed. Unlike previous works, each RT is assumed here to be capable of full-duplex operation and receives the transmission of adjacent relay terminals. Focusing on intra-cell TDMA and non-fading channels, a simplified uplink cellular model introduced by Wyner is considered. This framework facilitates analytical derivation of the per-cell sum-rate of multi-cell and conventional single-cell receivers. In particular, the analysis is based on the observation that the signal received at the base stations can be interpreted as the outcome of a two-dimensional linear time invariant system. Numerical results are provided as well in order to provide further insight into the performance benefits of multi-cell processing with relaying.

In this paper, we consider downlink throughput performances of multiuser orthogonal frequency division multiplexing (multiuser OFDM) with reduced channel information feedback schemes. Specifically, two types of reduced feedback schemes, namely, 1-bit per sub-carrier and selective feedback scheme are considered and compared with each other in terms of average network throughput. For the latter, since the exact analysis is complicated, we resort to an approximate analysis. Simulations results will also be provided to verify the approximate analysis. Since the strict throughput comparison for given number of feedback bits per user is quite difficult, rather we compare their general behaviors in various system configurations with different system parameters, which can give us an insight into practical system design with those reduced feedback schemes.

A scenario with two single-user links, one licensed to use the spectral resource (primary) and one unlicensed (secondary or cognitive), is considered. According to the cognitive radio principle, the activity of the secondary link is required not to interfere with the performance of the primary. Therefore, in this paper, it is assumed that the cognitive link accesses the channel only when sensed idle. Moreover, the analysis includes: (1) random packet arrivals; (2) sensing errors due to fading at the secondary link; (3) power allocation at the secondary transmitter based on long-term measurements. In this framework, the maximum stable throughput of the cognitive link (in packets/slot) is derived for a fixed throughput selected by the primary link. The model is modified so as to allow the secondary transmitter to act as a ldquotransparentrdquo relay for the primary link. In particular, packets that are not received correctly by the intended destination might be decoded successfully by the secondary transmitter. The latter can, then, queue and forward these packets to the intended receiver. A stable throughput of the secondary link with relaying is derived under the same conditions as before. Results show that benefits of relaying strongly depend on the topology (i.e., average channel powers) of the network.

Cooperation between base stations and collaborative transmission between mobile terminals are two technologies currently under study as promising paradigms for next generation communications systems. In this paper, we provide a first look to the interplay between these two approaches by studying the per-cell achievable sum-rate (throughput) of different cooperative protocols under a simplified model for the uplink of a TDMA cellular system. The analysis is limited to non-regenerative (amplify-and-forward) cooperation schemes between terminals for their'simplicity and appeal to a practical implementation. A closed form expression for the (asymptotic) achievable rate of multicell processing combined with amplify-and-forward collaboration at the terminals is derived for an AWGN (i.e., no fading) scenario. Moreover, the impact of fading is investigated numerically, allowing to draw some conclusions on the impact of multicell diversity (or macrodiversity) on the performance of collaborative schemes among the terminals. In particular, we show that while AF cooperation is generally advantageous for single cell processing (i.e., with no collaboration between base stations), its benefits when combined with multicell processing are limited to the regime of low to moderate transmission rates.

For a multiple-input single-output (MISO) downlink channel with M transmit antennas, it has been recently proved that zero-forcing beamforming (ZFBF) to a subset of (at most) M "semi-orthogonal" users is optimal in terms of the sum-rate, asymptotically with the number of users. However, determining the subset of users for transmission is a complex optimization problem. Adopting the ZFBF scheme in a cooperative multi-cell scenario renders the selection process even more difficult since more users are involved. In this paper, we consider a multi-cell cooperative ZFBF scheme combined with a simple sub-optimal users selection procedure for the Wyner downlink channel setup. According to this sub-optimal procedure, the user with the "best" local channel is selected for transmission in each cell. It is shown that under an overall power constraint, a distributed multi-cell ZFBF to this sub-optimal subset of users achieves the same sum-rate growth rate as an optimal scheme deploying joint multi-cell dirty-paper coding (DPC) techniques, asymptotically with the number of users per cell. Moreover, the overall power constraint is shown to ensure in probability, equal per-cell power constraints when the number of users per-cell increases.

In this paper, we evaluate the capacity region of wireless ad hoc networks under a recently proposed space-time collaborative scheme. This protocol allows idle nodes to cooperate with the source opportunistically, i.e., whenever their wireless channel from the source is advantageous. The scheme is intrinsically distributed and well suited for an ad hoc scenario. It is shown analytically and through simulation that the capacity region of opportunistic collaboration is equal to or larger than (centralized) optimal multi-hop in case spatial reuse is not allowed by the transmission protocol. On the other hand, in case spatial reuse is possible, the relation between the two capacity regions has to be studied case by case. Simulation results prove that opportunistic collaborative communication is a promising paradigm for ad hoc networks that deserves further investigation.

The sum-capacity of a multi-antenna broadcast Gaussian channel is known to be achieved by dirty paper coding techniques, or, asymptotically in the number of users n, by beamforming methods, that require full channel state information at the base station. Based on the opportunistic beamforming principle, it has been recently shown that the optimal scaling law of the sum-rate with respect to n, for fixed signal to noise ratio and number of transmitting antennas M, (i.e., M log log n) can be achieved by employing a feedback of only one real and one integer number per user. Moreover, it was proved that a linear scaling with respect of M can be guaranteed only if M scales no faster than log n. In this paper, the optimal scaling law of the sum-rate with respect to n is proved to be achievable with only one bit of feedback per user. The proof builds on opportunistic beamforming and binary quantization of the signal to noise plus interference ratio. Moreover, the linear scaling of the sum-rate with M is demonstrated to hold for M growing no faster than log n even with such a reduced feedback. Finally, the results above are extended to the MIMO case, where each user is equipped with multiple antennas

The sum-capacity of the multi-antenna Gaussian broadcast channel is known to be achieved by Dirty Paper Coding techniques, that require full channel state information at the base station. It has been recently shown that a sum-rate having the same scaling law of the sum-capacity with respect to the number of users n for a fixed signal to noise ratio (i.e., M log log n where M is the number of transmitting antennas) can be achieved by using reduced feedback (or equivalently reduced channel state information at the transmitter). In particular, it has been proved that n real and n integer numbers are enough to guarantee the optimal scaling law. In this paper, the optimal scaling law of the sum-rate is shown to be achievable with an even smaller amount of feedback and, more precisely, with 1) n log2(M + 1) bits, if a deterministic feedback scheme is employed; 2) an average number of feedback bits that scales as M log2M log n with the number of users n, if a selective (random) feedback scheme is employed.

In this work, we propose an adaptive technique for interference mitigation based on minimum variance distortionless response (MVDR) beamforming for the uplink of a WiMAX-compliant system. This method is designed to cope with time-varying interference due to the asynchronous access of users in the neighboring cells. Channel parameters needed for beamforming are obtained by exploiting both the preambles in the transmitted frames and the pilot subcarriers embedded in each information-bearing OFDM symbol. The effectiveness of the proposed technique is shown through numerical simulations of a standard WiMAX uplink over standard multipath channels

Physical layer-based distributed timing synchronization among nodes of a wireless network is currently being investigated in the literature as an interesting alternative to packet synchronization. In this paper, we analyze the convergence properties of such a system through algebraic graph theory, by modelling the nodes as discrete-time oscillators and taking into account the specific features of wireless channels (e.g., reciprocity, fading). The analysis is corroborated by numerical results and by comparison with the performance of a practical implementation of the distributed synchronization algorithm over a bandlimited noisy channel

In this paper, we investigate the benefits of exploiting the a priori information about the structure of the multipath channel on the performance of channel estimation for multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) systems. We first approach this problem from the point of view of estimation theory by computing a lower bound on the estimation error and studying its properties. Then, based on the insight obtained from the analysis, efficient channel estimators are designed that perform close to the derived limit. The proposed channel estimators compute the long-term features of the multipath channel model through a subspace tracking algorithm by identifying the invariant (over multiple OFDM symbols) space/time modes of the channel (modal analysis). On the other hand, the fast-varying fading amplitudes are tracked by using least-squares techniques that exploit temporal correlation of the fading process (modal filtering). The analytic treatment is complemented by thorough numerical investigation in order to validate the performance of the proposed techniques. MIMO-OFDM with bit-interleaved coded modulation and MIMO-turbo equalization is selected as a benchmark for performance evaluation in terms of bit-error rate.

In this paper, we investigate the benefits of exploiting the a priori information about the structure of the multipath channel on the performance of channel estimation for multiple-input multiple-output-orthogonal frequency-division multiplexing (MIMO-OFDM) systems. We first approach this problem from the point of view of estimation theory by computing a lower bound on the estimation error and studying its properties. Then, based on the insight obtained from the analysis, efficient channel estimators are designed that perform close to the derived limit. The proposed channel estimators compute the long-term features of the multipath channel model through a subspace tracking algorithm by identifying the invariant (over multiple OFDM symbols) space/time modes of the channel (modal analysis). On the other hand, the fast-varying fading amplitudes are tracked by using least-squares techniques that exploit temporal correlation of the fading process (modal filtering). The analytic treatment is complemented by thorough numerical investigation in order to validate the performance of the proposed techniques. MIMO-OFDM with bit-interleaved coded modulation and MIMO-turbo equalization is selected as a benchmark for performance evaluation in terms of bit-error rate

In the downlink of a broadcast fading channel, the base station can capitalize on multiuser diversity through channel aware scheduling. In MIMO systems, the design of the scheduler has to take into account the processing performed at the transmitter and the receivers. In this work, we consider channel aware scheduling for orthogonal linear precoding at the base station that guarantees interference free reception for each scheduled users. The problem is set in a novel mathematical framework and a scheduling algorithm is proposed that is shown by simulation to guarantee superior performance as compared to know techniques. Moreover, fairness constraints inspired by the proportional fair criterion are introduced in the scheduling process in order to guarantee the desired long term fairness properties.

In the downlink of a multi-user MIMO system over a fading channel, the base station can assign the available spatial streams to different users by capitalizing on multiuser diversity or enforcing fairness constraints. Assuming linear precoding (beamforming) at the base station, the problem amounts to the joint design of precoding matrices and channel aware scheduling, according to the cross-layer paradigm. In this paper we formulate the joint optimization of scheduling and linear precoding within a physical layer-oriented framework, where the performance metric is the transmission rate. Moreover, we constraint the precoding scheme to ensure interference-free reception of spatial streams (orthogonal space division multiple access, OSDMA). Fairness constraints are either inspired by the max-min or the proportional fair criterion. Performance of different schemes is evaluated by numerical simulations allowing to assess the tradeoffs between sum-rate (multi-user diversity) and fairness

In OFDM communication systems over fading channels, link adaptation based on the available channel state information at the transmitter (i.e., power allocation, adaptive modulation and coding rate) increases the spectral efficiency and reliability of the link. In this work, we explore the possibility to extend the set of transmission parameters to be adaptively selected to the pilot arrangement (i.e., to the collocation of pilot subcarriers on the time-frequency grid). Based on the prediction of the channel estimation error at the receiver, the transmitter can minimize the number of pilot subcarriers that guarantees a sufficiently reliable channel estimate according to quality of service requirements. Prediction of the channel estimation error is performed by computing the error covariance matrix of Kalman filters. The adaptive pilot arrangement problem is formulated and a "greedy" solution is proposed. Simulations show the effectiveness of the algorithm with respect to periodic re-training.

In orthogonal frequency division multiplexing (OFDM) systems over fast-varying fading channels, channel estimation and tracking is generally carried out by transmitting known pilot symbols in given positions of the frequency-time grid. The traditional approach consists of two steps. First, the least-squares (LS) estimate is obtained over the pilot subcarriers. Then, this preliminary estimate is interpolated/smoothed over the entire frequency-time grid. In this paper, we propose to add an intermediate step, whose purpose is to increase the accuracy of the estimate over the pilot subcarriers. The presented techniques are based on the observation that the wireless radio channel can be parametrized as a combination of paths, each characterized by a delay and a complex amplitude. The amplitudes show fast temporal variations due to the mobility of terminals while the delays (and their associated delay-subspace) are almost constant over a large number of OFDM symbols. We propose to track the delay-subspace by a subspace tracking algorithm and the amplitudes by the least mean square algorithm (or modifications of the latter). The approach can be extended to multiple input multiple output OFDM or multicarrier code-division multiple-access systems. Analytical results and simulations prove the relevant benefits of the novel structure.

A transceiver structure for frequency-flat multiple-input multiple-output (MIMO) systems that comprises linear/nonlinear preequalization/equalization is optimized according to the minimum mean square error (MMSE) criterion under the assumption that only long-term channel state information (i.e., correlation matrices of fading channel and noise) is available at the transmitter. The structure generalizes different techniques known from the literature, such as BLAST, linear preequalization and equalization, and Tomlinson-Harashima precoding (THP). Simulations show that relevant benefits can be obtained by exploiting the long term channel state information at the transmitter in both dense multipath channels with relatively large correlation at the transmitter side and in sparse multipath channels.

In antenna array systems, downlink beamforming and uplink maximum likelihood structured channel estimation can be formulated under a common framework related to the algebraic structure of the two problems. The slow variations of the uplink and downlink spatial subspaces, due to moving terminals, can be tracked by using an adaptive structure based on a common processing block, namely a subspace tracker. Simulations for realistic propagation conditions show that the structure is able to efficiently cope with fast-varying fading channels, allowing relevant gains compared to conventional techniques.

In this paper, we derive a lower bound on the MSE matrix of training-based channel estimators for MIMO systems over fast-varying fading channels. To this end, we consider an ideal estimator that is able to estimate the long-term features of the channel (e.g., second order statistics, delays...) with high accuracy while tracking the fast-varying fading fluctuations in an optimum (MMSE) way. The bound on the MSE matrix is a valuable tool as a reference to assess the performance of any proposed estimator and it is proved to reduce to known results for simplified settings.

In mobile communications, the movement of terminals renders the multipath channel time varying. Even though the faded amplitudes are fast varying, the delays can be considered as stationary on a large temporal scale. We propose a new subspace-based method that estimates the channel response from multiple slots by capitalizing on these different varying rates without explicitly computing the delays of the multipath. The temporal subspace is obtained from multiple single-slot training-based estimates of the (single-user or multiuser) channel response. Provided that the number of slots is large enough, the time basis can be calculated with some accuracy. As a consequence, the mean-square error of the channel response depends only on the number of fast-varying parameters that have to be estimated in a slot-by-slot fashion. Performance analysis and simulations confirm the expected benefits of the multislot approach in improving the efficiency of systems with short training sequences.

In mobile communications, the multipath space-time channel has some fast-varying (faded amplitudes of the paths) and slowly-varying (delays and directions of arrival) features. We propose to estimate the channel from limited-length training sequences by exploiting these different varying rates without explicitly computing delays and directions of arrival. Therefore, the multi-slot channel estimate is composed of two terms: the slowly-varying space-time bases estimated from L consecutive slots and the fast-varying amplitudes estimated on a slot-by-slot basis. Performance analysis and simulations confirm the expected benefits of the multi-slot approach and demonstrate that for large L the mean square error (MSE) on the channel estimate depends only on the number of fast-varying parameters.