We extend the Rate-Distortion-Perception (RDP) framework to the Rényi information-theoretic regime, utilizing Sibson's $α$-mutual information to characterize the fundamental limits under distortion and perception constraints. For scalar Gaussian sources, we derive closed-form expressions for the Rényi RDP function, showing that the perception constraint induces a feasible interval for the reproduction variance. Furthermore, we establish a Rényi-generalized version of the Strong Functional Representation Lemma. Our analysis reveals a phase transition in the complexity of optimal functional representations: for $0.5<α< 1$, the coding cost is bounded by the $α$-divergence of order $α+1$, necessitating a codebook with heavy-tailed polynomial decay; conversely, for $α> 1$, the representation collapses to one with finite support, offering new insights into the compression of shared randomness under generalized notions of mutual information.

Due to their adaptability and mobility, Unmanned Aerial Vehicles (UAVs) are becoming increasingly essential for wireless network services, particularly for data harvesting tasks. In this context, Artificial Intelligence (AI)-based approaches have gained significant attention for addressing UAV path planning tasks in large and complex environments, bridging the gap with real-world deployments. However, many existing algorithms suffer from limited training diversity and limited generalization beyond the training distribution, which hampers their performance in highly dynamic environments. Moreover, they often overlook the inherently multi-objective nature of the task, treating it in an overly simplistic manner. To address these limitations, we propose an attention-based Multi-Objective Reinforcement Learning (MORL) architecture that explicitly handles the trade-off between data collection and energy consumption in urban environments, even without prior knowledge of wireless channel conditions. Our method learns a single model capable of adapting to varying trade-off preferences and dynamic scenario parameters without the need for fine-tuning or retraining. Extensive simulations show that our approach achieves substantial improvements in performance, model compactness, sample efficiency, and most importantly, generalization to previously unseen scenarios, outperforming existing RL solutions.

This paper addresses query scheduling for goaloriented semantic communication in pull-based status update systems. We consider a system where multiple sensing agents (SAs) observe a source characterized by various attributes and provide updates to multiple actuation agents (AAs), which act upon the received information to fulfill their heterogeneous goals at the endpoint. A hub serves as an intermediary, querying the SAs for updates on observed attributes and maintaining a knowledge base, which is then broadcast to the AAs. The AAs leverage the knowledge to perform their actions effectively. To quantify the semantic value of updates, we introduce agrade of effectiveness(GoE) metric. Furthermore, we integrate cumulative perspective theory(CPT) into the long-term effectiveness analysis to account for risk awareness and loss aversion in the system. Leveraging this framework, we compute effect-aware scheduling policies aimed at maximizing the expected discounted sum of CPT-based total GoE provided by the transmitted updates while complying with a given query cost constraint. To achieve this, we propose amodel-basedsolution based on dynamic programming andmodel-freesolutions employing state-of-the-art deep reinforcement learning (DRL) algorithms. Our findings demonstrate that effect-aware scheduling significantly enhances the effectiveness of communicated updates compared to benchmark scheduling methods, particularly in settings with stringent cost constraints where optimal query scheduling is vital for system performance and overall effectiveness.

In this work, we investigate low-complexity remote system state estimation over wireless multiple-input-multipleoutput (MIMO) channels without requiring prior knowledge of channel state information (CSI). We start by reviewing the conventional Kalman filtering-based state estimation algorithm, which typically relies on perfect CSI and incurs considerable computational complexity. To overcome the need for CSI, we introduce a novel semantic aggregation method, in which sensors transmit semantic measurement discrepancies to the remote state estimator through analog aggregation. To further reduce computational complexity, we introduce a constant-gain-based filtering algorithm that can be optimized offline using the constrained stochastic successive convex approximation (CSSCA) method. We derive a closed-form sufficient condition for the estimation stability of our proposed scheme via Lyapunov drift analysis. Numerical results showcase significant performance gains using the proposed scheme compared to several widely used methods.

We introduce a resource allocation framework for goal-oriented semantic networks, where participating agents assess system quality through subjective (e.g., context-dependent) perceptions. To accommodate this, our model accounts for agents whose preferences deviate from traditional expected utility theory (EUT), specifically incorporating cumulative prospect theory (CPT) preferences. We develop a comprehensive analytical framework that captures human-centric aspects of decision-making and risky choices under uncertainty, such as risk perception, loss aversion, and perceptual distortions in probability metrics. By identifying essential modifications in traditional resource allocation design principles required for agents with CPT preferences, we showcase the framework's relevance through its application to the problem of power allocation in multi-channel wireless communication systems.

Federated learning (FL) is a widely used and impactful distributed optimization framework that achieves consensus by averaging locally trained models. While effective, this approach may not align well with Bayesian inference, where the model space is more naturally represented as a distribution space. Taking an information-geometric perspective, we reinterpret FL aggregation as the problem of finding the barycenter of local posteriors using a predefined divergence metric, minimizing the average discrepancy across clients. This perspective provides a unifying framework that generalizes many existing methods and offers crisp insights into their theoretical underpinnings. We then propose BA-BFL, an algorithm that retains the convergence properties of Federated Averaging in non-convex settings. In non-independent and identically distributed scenarios, we conduct extensive comparisons with statistical aggregation techniques, showing that BA-BFL achieves performance comparable to state-of-the-art methods while also providing a geometric interpretation of the aggregation phase. Additionally, we extend our analysis to Hybrid Bayesian Deep Learning, exploring the impact of Bayesian layers on uncertainty quantification and model calibration.

Binary Neural Networks (BiNNs), which employ single-bit precision weights, have emerged as a promising solution to reduce memory usage and power consumption while maintaining competitive performance in large-scale systems. However, training BiNNs remains a significant challenge due to the limitations of conventional training algorithms. Quantum HyperNetworks offer a novel paradigm for enhancing the optimization of BiNN by leveraging quantum computing. Specifically, a Variational Quantum Algorithm is employed to generate binary weights through quantum circuit measurements, while key quantum phenomena such as superposition and entanglement facilitate the exploration of a broader solution space. In this work, we establish a connection between this approach and Bayesian inference by deriving the Evidence Lower Bound (ELBO), when direct access to the output distribution is available (i.e., in simulations), and introducing a surrogate ELBO based on the Maximum Mean Discrepancy (MMD) metric for scenarios involving implicit distributions, as commonly encountered in practice. Our experimental results demonstrate that the proposed methods outperform standard Maximum Likelihood Estimation (MLE), improving trainability and generalization.

In this paper, we investigate the rate-distortion-perception function (RDPF) of a source modeled as a Gaussian Process (GP) over a measure space$\Omega$, under mean squared error (MSE) distortion and squared Wasserstein-2 perception metrics. First, we show that the optimal reconstruction process is itself a GP, whose covariance operator shares the same set of eigenvectors as the source's covariance operator. This structural property, akin to the classical rate-distortion function (RDF), allows us to reformulate the RDPF problem in terms of the Karhunen-Loève (KL) transform coefficients of the involved GPs. Leveraging the similarities with the finite-dimensional Gaussian RDPF, we derive a tight analytical upper bound on the RDPF for GPs, which recovers the optimal solution in the “perfect realism” regime. Finally, for stationary GPs over the interval$[0, T]$with Lebesgue measure, we derive an upper bound on the rate and distortion for a fixed perceptual level and$T \rightarrow \infty$as a function of the spectral density of the source process. We complement our theoretical findings with relevant simulation studies.

In this paper, we investigate the problem of distributionally robust source coding, i.e., source coding under uncertainty in the source distribution, discussing both the coding and computational aspects of the problem. We propose two extensions of the so-called Strong Functional Representation Lemma (SFRL), considering the cases where, for a fixed conditional distribution, the marginal inducing the joint coupling belongs to either a finite set of distributions or a Kullback-Leibler divergence sphere (KL-Sphere) centered at a fixed nominal distribution. Using these extensions, we derive distributionally robust coding schemes for both the one-shot and asymptotic regimes, generalizing previous results in the literature. Focusing on the case where the source distribution belongs to a given KL-Sphere, we derive an implicit characterization of the points attaining the robust rate-distortion function (R-RDF), which we later exploit to implement a novel algorithm for computing the R-RDF. Finally, we characterize the analytical expression of the R-RDF for Bernoulli sources, providing a theoretical benchmark to evaluate the estimation performance of the proposed algorithm.

We study the real-time remote tracking of a two-state Markov process powered by an energy harvesting source. The source dynamically decides whether to transmit over an unreliable channel based on the state of the system. This problem is formulated as a Markov decision process (MDP) to determine the optimal transmission policy that minimizes the average Version Innovation Age (VIA) as a key performance metric. We demonstrate that the optimal transmission policy is threshold-based, determined by the battery level, source state, and VIA value. We numerically validate the analytical structure of the optimal policy and compare its performance against two baseline policies under various system parameters, establishing the superior performance of our approach.

This paper studies decision-making for goal-oriented effective communication. We consider an end-to-end status update system where a sensing agent (SA) observes a source, generates and transmits updates to an actuation agent (AA), while the AA takes actions to accomplish a goal at the endpoint. We integrate the push- and pull-based update communication models to obtain apush-and-pullmodel, which allows the transmission controller at the SA to decide whether to push an update to the AA and the query controller at the AA to pull updates by initiating queries at specific time instants. To gauge effectiveness, we utilize agrade of effectiveness(GoE) metric incorporating the updates’ freshness, usefulness, and the timeliness of actions as qualitative attributes. We then derive effect-aware policies to maximize the expected discounted sum of the updates’ effectiveness subject to induced costs. The effect-aware policy at the SA considers the potential effectiveness of communicated updates at the endpoint, while at the AA, it accounts for the probabilistic evolution of the source and importance of the generated updates. Our results show the proposed push-and-pull model outperforms models solely based on push- or pull-based updates both in terms of efficiency and effectiveness. Additionally, using effect-aware policies at both agents enhances effectiveness compared to periodic and/or probabilistic, effect-agnostic policies at either or both agents.

We consider the problem of real-time remote monitoring of a two-state Markov process, where a sensor observes the source state and decides whether to transmit updates over an unreliable channel. We introduce a change-aware randomized stationary policy, in which the source is sampled probabilistically whenever its state changes, and a semantics-aware randomized stationary policy, in which sampling is performed probabilistically based on the current source state and whether the system was in sync in the previous time slot. We then propose two new performance metrics:the Version Innovation Age (VIA), which measures significant changes in content between versions, andthe Age of Incorrect Version (AoIV), which quantifies the outdated versions at the receiver compared to the source when the system is in an incorrect state. We analyze their performance under the proposed and other state-of-the-art sampling policies. Specifically, we derive closed-form expressions for the distributions and averages of VIA, AoIV, and the Age of Incorrect Information (AoII), and formulate three constrained optimization problems to minimize them while accounting for constraints on the time-averaged sampling cost and the reconstruction error. Finally, we compare various sampling and transmission policies and identify the conditions under which each policy performs best.

We study the computation of the rate-distortion-perception function (RDPF) for discrete memoryless sources subject to a single-letter average distortion constraint and a perception constraint belonging to the family off-divergences. In this setting, the RDPF forms a convex programming problem for which we characterize optimal parametric solutions. We employ the developed solutions in an alternating minimization scheme, namely Optimal Alternating Minimization (OAM), for which we provide convergence guarantees. Nevertheless, the OAM scheme does not lead to a direct implementation of a generalized Blahut-Arimoto (BA) type of algorithm due to implicit equations in the iteration’s structure. To overcome this difficulty, we propose two alternative minimization approaches whose applicability depends on the smoothness of the used perception metric: a Newton-based Alternating Minimization (NAM) scheme, relying on Newton’s root-finding method for the approximation of the optimal solution of the iteration, and a Relaxed Alternating Minimization (RAM) scheme, based on relaxing the OAM iterates. We show, by deriving necessary and sufficient conditions, that both schemes guarantee convergence to a globally optimal solution. We also provide sufficient conditions on the distortion and perception constraints, which guarantee that the proposed algorithms converge exponentially fast in the number of iteration steps. We corroborate our theoretical results with numerical simulations and establish connections with existing results.

Federated learning (FL) has attracted vivid attention as a privacy-preserving distributed learning framework. In this work, we focus on cross-silo FL, where clients become the model owners after training and are only concerned about the model's generalization performance on their local data. Due to the data heterogeneity issue, asking all the clients to join a single FL training process may result in model performance degradation. To investigate the effectiveness of collaboration, we first derive a generalization bound for each client when collaborating with others or when training independently. We show that the generalization performance of a client can be improved by collaborating with other clients that have more training data and similar data distributions. Our analysis allows us to formulate a client utility maximization problem by partitioning clients into multiple collaborating groups. Ahierarchicalclustering-basedcollaborativetraining (HCCT) scheme is then proposed, which does not need to fix in advance the number of groups. We further analyze the convergence of HCCT for general non-convex loss functions which unveils the effect of data similarity among clients. Extensive simulations show that HCCT achieves better generalization performance than baseline schemes, whereas it degenerates to independent training and conventional FL in specific scenarios.

We study a pull-based communication system where a sensing agent updates an actuation agent using a query control policy, which is adjusted in the evolution of an observed information source and the usefulness of each update for achieving a specific goal. For that, a controller decides whether to pull an update at each slot, predicting what is probably occurring at the source and how much effective impact that update could have at the endpoint. Thus, temporal changes in the source evolution could modify the query arrivals to capture important updates. The amount of impact is determined by a grade of effectiveness (GoE) metric, which incorporates both freshness and usefulness attributes of the communicated updates. Applying an iterative algorithm, we derive query decisions that maximize the longterm average GoE for the communicated packets, subject to cost constraints. Our analytical and numerical results show that the proposed query policy exhibits higher effectiveness than existing periodic and probabilistic query policies for a wide range of query arrival rates.

A new approach for estimating confidence in machine learning model predictions, specifically in regression tasks utilizing Earth observation data with a particular focus on mosquito abundance (MA) estimation, is proposed here. We leverage the Variational AutoEncoder architecture to derive a confidence metric by the latent space representations of Earth observation datasets. This methodology is pivotal in establishing a correlation between the Euclidean distance in latent representations and the absolute error in individual MA predictions. Our study focuses on Earth observation datasets from the Veneto region in Italy and the Upper Rhine Valley in Germany, considering areas significantly affected by mosquito populations. A key finding is a notable correlation of 0.46 between the absolute error of MA predictions and the proposed confidence metric. This correlation signifies a robust, new metric for quantifying the reliability and enhancing the trustworthiness of the AI/ML model predictions in the context of both Earth observation data analysis and mosquito abundance studies.

In this paper, we propose a generic method for computing the rate-distortion-perception function (RDPF) of a multivariate Gaussian source under tensorizable distortion and perception metrics. Through the assumption of a jointly Gaussian reconstruction, we establish that the optimal solution of the RDPF belongs to the vector space spanned by the eigenvector of the source covariance matrix. Consequently, the multivariate optimization problem can be expressed as a function of the scalar Gaussian RDPFs of the source marginals, constrained by global distortion and perception levels. Utilizing this result, we devise an alternating minimization scheme based on the block nonlinear Gauss-Seidel method. This scheme solves optimally the optimization problem while identifying the optimal stage-wise distortion and perception levels. Furthermore, the associated algorithmic embodiment is provided, along with the convergence and the rate of convergence characterization. Lastly, in the regime of “perfect realism”, we provide the analytical solution for the multivariate Gaussian RDPF. We corroborate our findings with numerical simulations and draw connections to existing results.

We present a new method to estimate the rate-distortion-perception function in the perfect realism regime (PR-RDPF), for multivariate continuous sources subject to a single-letter average distortion constraint. The proposed approach is not only able to solve the specific problem but also two related problems: the entropic optimal transport (EOT) and the output-constrained rate-distortion function (OC-RDF), of which the PR-RDPF represents a special case. Using copula distributions, we show that the OC-RDF can be cast as an$I$-projection problem on a convex set, based on which we develop a parametric solution of the optimal projection proving that its parameters can be estimated, up to an arbitrary precision, via the solution of a convex program. Subsequently, we propose an iterative scheme via gradient methods to estimate the convex program. Lastly, we characterize a Shannon lower bound (SLB) for the PR-RDPF under a mean squared error (MSE) distortion constraint. We support our theoretical findings with numerical examples by assessing the estimation performance of our iterative scheme using the PR-RDPF with the obtained SLB for various sources.

The problem of goal-oriented semantic filtering and timely source coding in multiuser communication systems is considered here. We study a distributed monitoring system in which multiple information sources, each observing a physical process, provide status update packets to multiple monitors having heterogeneous goals. Two semantic filtering schemes are first proposed as a means to admit or drop arrival packets based on their goal-dependent importance, which is a function of the intrinsic and extrinsic attributes of information and the probability of occurrence of each realization. Admitted packets at each sensor are then encoded and transmitted over block-fading wireless channels so that served monitors can timely fulfill their goals. A truncated error control scheme is derived, which allows transmitters to drop or retransmit undelivered packets based on their significance. Then, we formulate the timely source encoding optimization problem and analytically derive the optimal codeword lengths assigned to the admitted packets which maximize a weighted sum of semantic utility functions for all pairs of communicating sensors and monitors. Our analytical and numerical results provide the optimal design parameters for different arrival rates and highlight the improvement in timely status update delivery using the proposed semantic filtering, source coding, and error control schemes.

In this work, we study the real-time tracking and reconstruction of an information source with the purpose of actuation. A device monitors the state of the information source and transmits status updates to a receiver over a wireless erasure channel. We consider two models for the source, namely anN-state Markov chain and anN-state Birth-Death Markov process. We investigate several joint sampling and transmission policies, including a semantics-aware one, and we study their performance for a set of metrics. Specifically, we investigate the real-time reconstruction error and its variance, the cost of actuation error, the consecutive error, and the cost of memory error. These metrics capture different characteristics of the system performance, such as the impact of erroneous actions and the timing of errors. In addition, we propose a randomized stationary sampling and transmission policy and we derive closed-form expressions for the aforementioned metrics. We then formulate two optimization problems. The first optimization problem aims to minimize the time-averaged reconstruction error subject to time-averaged sampling cost constraint. Then, we compare the optimal randomized stationary policy with uniform, change-aware, and semantics-aware sampling policies. Our results show that in the scenario of constrained sampling generation, the optimal randomized stationary policy outperforms all other sampling policies when the source is rapidly evolving. Otherwise, the semantics-aware policy performs the best. The objective of the second optimization problem is to obtain an optimal sampling policy that minimizes the average consecutive error with a constraint on the time-averaged sampling cost. Based on this, we propose await-then-generatesampling policy which is simple to implement.

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.

Personalization in federated learning (FL) functions as a coordinator for clients with high variance in data or behavior. Ensuring the convergence of these clients' models relies on how closely users collaborate with those with similar patterns or preferences. However, it is generally challenging to quantify similarity under limited knowledge about other users' models given to users in a decentralized network. To cope with this issue, we propose a personalized and fully decentralized FL algorithm, leveraging knowledge distillation techniques to empower each device so as to discern statistical distances between local models. Each client device can enhance its performance without sharing local data by estimating the similarity between two intermediate outputs from feeding local samples as in knowledge distillation. Our empirical studies demonstrate that the proposed algorithm improves the test accuracy of clients in fewer iterations under highly non-independent and identically distributed (non-i.i.d.) data distributions and is beneficial to agents with small datasets, even without the need for a central server.

In this paper, we study the computation of the rate-distortion-perception function (RDPF) for discrete memoryless sources subject to a single-letter average distortion constraint and a perception constraint that belongs to the family of f-divergences. For that, we leverage the fact that RDPF, assuming mild regularity conditions on the perception constraint, forms a convex programming problem. We first develop parametric characterizations of the optimal solution and utilize them in an alternating minimization approach for which we prove convergence guarantees. The resulting structure of the iterations of the alternating minimization approach renders the implementation of a generalized Blahut-Arimoto (BA) type of algorithm infeasible. To overcome this difficulty, we propose a relaxed formulation of the structure of the iterations in the alternating minimization approach, which allows for the implementation of an approximate iterative scheme. This approximation is shown, via the derivation of necessary and sufficient conditions, to guarantee convergence to a globally optimal solution. We also provide sufficient conditions on the distortion and the perception constraints which guarantee that our algorithm converges exponentially fast. We corroborate our theoretical results with numerical simulations, and we draw connections with existing results.

We consider a remote source coding problem subject to a distortion function. Contrary to the use of the classical separable distortion criterion, herein we consider the more general, f-separable distortion measure and study its implications on the characterization of the minimum achievable rates (also called f-separable indirect rate distortion function (iRDF)) under both excess and average distortion constraints. First, we provide a single-letter characterization of the optimal rates subject to an excess distortion using properties of the f-separable distortion. Our main result is a single-letter characterization of the f-separable iRDF subject to an average distortion constraint. As a consequence of the previous results, we also show a series of equalities that hold using either indirect or classical RDF under f-separable excess or average distortions. We corroborate our results with two application examples in which new closed-form solutions are derived, and based on these, we also recover known special cases.

The multi-user linearly-separable distributed computing problem is considered here, in which N servers help to compute the real-valued functions requested by K users, where each function can be written as a linear combination of up to L (generally non-linear) subfunctions. Each server computes a fraction γ of the subfunctions, then communicates a function of its computed outputs to some of the users, and then each user collects its received data to recover its desired function. Our goal is to bound the ratio between the computation workload done by all servers over the number of datasets.To this end, we here reformulate the real-valued distributed computing problem into a matrix factorization problem and then into a basic sparse recovery problem, where sparsity implies computational savings. Building on this, we first give a simple probabilistic scheme for subfunction assignment, which allows us to upper bound the optimal normalized computation cost as $\gamma \leq \frac{K}{N}$ that a generally intractable ℓ0-minimization would give. To bypass the intractability of such optimal scheme, we show that if these optimal schemes enjoy $\gamma \leq - r\frac{K}{N}W_{ - 1}^{ - 1}\left( { - \frac{{2K}}{{eNr}}} \right)$ (where W−1(•) is the Lambert function and r calibrates the communication between servers and users), then they can actually be derived using a tractable Basis Pursuit ℓ1-minimization. This newly-revealed connection opens up the possibility of designing practical distributed computing algorithms by employing tools and methods from compressed sensing.

A new variation of the classical point-to-point joint source-channel coding (JSCC) is studied here, which is relevant for identifying goal-oriented semantic aspects of a transmitted source message over a noisy channel in the presence of multiple distortion constraints. For this new formulation, coined goal-oriented JSCC, we first introduce optimality criteria followed by necessary and sufficient conditions for global optimality. The focus of our main theoretical results is on investigating the implications of a special subclass of block codes, namely, the single-letter codes, via a theorem in which we provide necessary and sufficient conditions for the probabilistic matching of a noisy source message with a noisy channel. We corroborate our theoretical results with two examples, in which goal-oriented single-letter codes and uncoded transmission perform optimally. Our results further highlight the important role of multiple fidelity constraints in goal-oriented communications.

Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users. In this paper, we present a novel random access scheme that addresses one of the most severe barriers of current strategies to achieve massive connectivity and ultra reliable and low latency communications for 6G. The proposed scheme utilizes wireless channels' angular continuous group-sparsity feature to provide low latency, high reliability, and massive access features in the face of limited time-bandwidth resources, asynchronous transmissions, and preamble errors. Specifically, a reconstruction-free goal oriented optimization problem is proposed which preserves the angular information of active devices and is then complemented by a clustering algorithm to assign active users to specific groups. This allows to identify active stationary devices according to their line of sight angles. Additionally, for mobile devices, an alternating minimization algorithm is proposed to recover their preamble, data, and channel gains simultaneously, enabling the identification of active mobile users. Simulation results show that the proposed algorithm provides excellent performance and supports a massive number of devices. Moreover, the performance of the proposed scheme is independent of the total number of devices, distinguishing it from other random access schemes. The proposed method provides a unified solution to meet the requirements of machine-type communications and ultra reliable and low latency communications, making it an important contribution to the emerging 6G networks.

We study a variant of a robust description source coding framework, which is a relevant model for goal-oriented semantic information transmission, via its corresponding characterization. Considering two individual single-letter separable distortion constraints and input and output data acting as the intrinsic and extrinsic message, respectively, we first derive a lower bound on the optimal rates of the problem, as well as necessary and sufficient conditions for this bound to be tight. Subsequently, we prove a general result that provides in parametric form the optimal solution of the characterization of this problem. Capitalizing on these results, we examine the structure of the solution for one case study of general binary alphabets under Hamming distortions and solve in closed form a special case. We also solve another general binary alphabet case where a Hamming and an erasure distortion coexist, as a means to highlight the importance of selecting the type of the distortion constraint in goal-oriented semantic communication. Furthermore, we develop a goal-oriented Blahut-Arimoto (BA) algorithm, which can be used for the computation of any finite alphabet intrinsic or extrinsic message under individual distortion criteria. Finally, we revisit the problem for multidimensional independent and identically distributed ($\mathop {\mathrm {i.i.d.}}$) jointly Gaussian processes with individual mean-square error (MSE) distortion constraints, providing new insights that have previously been overlooked. This work reveals the cardinal role of context-dependent fidelity criteria in goal-oriented semantic communication.

Affine Frequency Division Multiplexing (AFDM), a new chirp-based multicarrier waveform for high mobility communications, is introduced here. AFDM is based on discrete affine Fourier transform (DAFT), a generalization of discrete Fourier transform, which is characterized by two parameters that can be adapted based on the Doppler spread of doubly dispersive channels. First, we derive the explicit input-output relation in the DAFT domain showing the effect of AFDM parameters in the input-output relation. Second, we show how the DAFT parameters underlying AFDM have to be set so that the resulting DAFT domain impulse response conveys a full delay-Doppler representation of the channel. Then, we show analytically that AFDM can achieve the optimal diversity order in doubly dispersive channels, where optimal diversity order refers to the number of multipath components separable in either the delay or the Doppler domain, due to its full delay-Doppler representation. Furthermore, we present a low complexity detection method taking advantage of zero-padding. We also propose an embedded pilot-aided channel estimation scheme for AFDM, in which both channel estimation and data detection are performed within the same AFDM frame. Finally, simulations corroborate the validity of our analytical results and show the significant performance gains of AFDM over state-of-the-art multicarrier schemes in high mobility scenarios.

We study a multiuser system in which an information source provides status updates to two monitors with heterogeneous goals. Semantic filtering is first performed to select the most useful realizations for each monitor. Packets are then encoded and sent so that each monitor can timely fulfill its goal. In this regard, some realizations are important for both monitors, while every other realization is informative for only one monitor. We determine the optimal real codeword lengths assigned to the selected packet arrivals in the sense of maximizing a weighted sum of semantics-aware utility functions for the two monitors. Our analytical and numerical results provide the optimal design parameters for different arrival rates and highlight the improvement in timely status update delivery using semantic filtering and source coding.

Affine Frequency Division Multiplexing (AFDM), which is based on discrete affine Fourier transform (DAFT), has recently been proposed for reliable communication in high-mobility scenarios. Two low complexity detectors for AFDM are introduced here. Approximating the channel matrix as a band matrix via placing null symbols in the AFDM frame in the DAFT domain, a low complexity MMSE detection is proposed by means of the LDL factorization. Furthermore, exploiting the sparsity of the channel matrix, we propose a low complexity iterative decision feedback equalizer (DFE) based on weighted maximal ratio combining (MRC), which extracts and combines the received multipath components of the transmitted symbols in the DAFT domain. Simulation results show that the proposed detectors have similar performance, while weighted MRC-based DFE has lower complexity than band-matrix-approximation LMMSE when the channel impulse response has gaps.

Decentralized learning enables edge users to collaboratively train models by exchanging information via device-to-device communication, yet prior works have been limited to wireless networks with fixed topologies and reliable workers. In this work, we propose an asynchronous decentralized stochastic gradient descent (DSGD) algorithm, which is robust to the inherent computation and communication failures occurring at the wireless network edge. We theoretically analyze its performance and establish a non-asymptotic convergence guarantee. Experimental results corroborate our analysis, demonstrating the benefits of asynchronicity and outdated gradient information reuse in decentralized learning over unreliable wireless networks.

A variant of a robust description source coding framework motivated by goal-oriented semantic information transmission is studied here. Considering two individual distortion constraints and input and output data that takes values in finite sets, we prove new bounds and structural properties for these bounds to be tight with respect to the information theoretic characterization of the problem. Then, we derive a general result that provides in parametric form the various cases of optimal solutions of this problem. Capitalizing on these results, we examine the structure of the solution for one case study of general binary alphabets under Hamming distortions and solve in closed form a special case. We also solve another general binary alphabet case where Hamming and erasure distortion are used, as a means to highlight the importance of selecting the type of the distortion constraint in the problem.

The conventional, widely used treatment of deep learning models as black boxes provides limited or no insights into the mechanisms that guide neural network decisions. Significant research effort has been dedicated to building interpretable models to address this issue. Most efforts either focus on the high-level features associated with the last layers, or attempt to interpret the output of a single layer. In this paper, we take a novel approach to enhance the transparency of the function of the whole network. We propose a neural network architecture for classification, in which the information that is relevant to each class flows through specific paths. These paths are designed in advance before training leveraging coding theory and without depending on the semantic similarities between classes. A key property is that each path can be used as an autonomous single-purpose model. This enables us to obtain, without any additional training and for any class, a lightweight binary classifier that has at least $60\%$ fewer parameters than the original network. Furthermore, our coding theory based approach allows the neural network to make early predictions at intermediate layers during inference, without requiring its full evaluation. Remarkably, the proposed architecture provides all the aforementioned properties while improving the overall accuracy. We demonstrate these properties on a slightly modified ResNeXt model tested on CIFAR-10/100 and ImageNet-1k.

With the advent of 5G technology, the notion oflatencygot a prominent role in wireless connectivity, serving as a proxy term for addressing the requirements for real-time communication. As wireless systems evolve toward 6G, the ambition to immerse the digital into physical reality will increase. Besides making the real-time requirements more stringent, this immersion will bring the notions of time, simultaneity, presence, and causality to a new level of complexity. A growing body of research points out that latency is insufficient to parameterize all real-time requirements. Notably, one such requirement that received significant attention is information freshness, defined through the Age of Information (AoI) and its derivatives. In general, the metrics derived from a conventional black-box approach to communication network design are not representative of new distributed paradigms, such as sensing, learning, or distributed consensus. The objective of this article is to investigate the general notion of timing in wireless communication systems and networks, and its relation to effective information generation, processing, transmission, and reconstruction at the senders and receivers. We establish a general statistical framework oftimingrequirements in wireless communication systems, which subsumes both latency and AoI. The framework is made by associating a timing component with the two basic statistical operations: decision and estimation. We first use the framework to present a representative sample of the existing works that deal with timing in wireless communication. Next, it is shown how the framework can be used with different communication models of increasing complexity, starting from the basic Shannon one-way communication model and arriving at communication models for consensus, distributed learning, and inference. Overall, this article fills an important gap in the literature by providing a systematic treatment of various timing measures in wireless communication and sets the basis for design and optimization for the next-generation real-time systems.

A new cooperative interference harnessing technique is proposed for non-orthogonal multiple access (NOMA) aided downlink multicell networks. The technique, coined as cooperative successive interference cancellation (Coop-SIC), leverages on the optimality conditions for SIC in the multiple access channel (MAC) seen at the receiver side without superposition coding at the transmitter. We derive SIC gain conditions that determine when is beneficial to reduce one user’s rate in order to enable SIC in another user potentially increasing its rate and maximize the sum-rate. The sum-rate maximization problem in multicell downlink systems is formulated and an algorithm to find the optimal solution is provided. Our simulation results show that our Coop-SIC technique is employed up to 80% of the iterations, providing up to 40% gains in the network’s spectral efficiency.

We consider distributed machine learning at the wireless edge, where a parameter server builds a global model with the help of multiple wireless edge devices that perform computations on local dataset partitions. Edge devices transmit the result of their computations (updates of current global model) to the server using a fixed rate and orthogonal multiple access over an error prone wireless channel. In case of a transmission error, the undelivered packet is retransmitted until successfully decoded at the receiver. Leveraging on the fundamental tradeoff between computation and communication in distributed systems, our aim is to derive how many edge devices are needed to minimize the average completion time while guaranteeing convergence. We provide upper and lower bounds for the average completion and we find a necessary condition for adding edge devices in two asymptotic regimes, namely the large dataset and the high accuracy regime. Conducted experiments on real datasets and numerical results confirm our analysis and substantiate our claim that the number of edge devices should be carefully selected for timely distributed edge learning.

Emerging 5G networks will need to efficiently support ultra-reliable, low-latency communication (URLLC), which requires extremely low latency (at msec order) with very high reliability (99.999%). In this work, we consider a URLLC system with incremental redundancy hybrid automatic repeat request (IR-HARQ) and investigate the effect of channel state information (CSI) at the transmitter on throughput and energy consumption optimization. For that, we analyze the feasibility region and the performance in block fading channels for the cases of full and statistical CSI. Our results show that in our setup the full CSI scheme is less robust and we also reveal a desirable balance between the trade-off quantities of energy and throughput.

Emerging 5G networks will need to efficiently support ultra-reliable, low-latency communications (URLLC) services, which require extremely low latency (msec order) with very high reliability (99.999%). We consider a URLLC system with short packets and incremental redundancy hybrid automatic repeat request (IR-HARQ). We aim at maximizing the throughput by optimally tuning the IR-HARQ mechanism subject to URLLC constraints and a fixed energy budget. We propose a dynamic programming algorithm for solving the throughput maximization problem in the finite blocklength regime and assess its performance numerically.

5G systems need to efficiently support both enhanced mobile broadband traffic (eMBB) and ultrareliable, low-latency communications (URLLC) traffic. In this paper, the problem of joint eMBB and URLLC scheduling is studied with the objective of supporting the URLLC requirements with minimal impact on eMBB performance. We propose a preemptive scheduler exploiting a precoder compatibility estimate, which measures the degree of similarity between different multiple-input multiple-output (MIMO) channels. This metric allows us to determine which eMBB transmission to pause for serving a URLLC user without recomputing the precoding matrix, thus reducing the processing time needed for multiuser MIMO precoder computation, while both relaxing cell pilot periodicity and reducing URLLC demodulation pilot overhead. This is achieved thanks to the fact that precoder compatibility metrics can be acquired at the user side in an opportunistic manner based on pilot symbols destined to ongoing downlink transmissions. Simulation results assess the performance gains of the proposed scheduler in terms of both URLLC block error rate and eMBB throughput.

Ultra-high reliability and low latency have not been in the mainstream in most wireless networks. Mobile networks have been driven so far by human-centric communications, delay-tolerant content, and non-critical services. The main target have been to boosting data rate and increasing coverage, adopting a rather best-effort networking approach. As wireless connectivity starts to get the status of a commodity, there is an increasing focus on support of services that rely critically on wireless links and therefore the reliability of the wireless connections. Next generation wireless systems, mainly 5G and beyond, are designed to provide wireless connectivity for massive machine-type communications (mMTC) and to support ultra-reliable, low latency communication (URLLC) for mission-critical services. URLLC scenarios impose stringent requirements in terms of latency (ranging from 1 ms and below to few milliseconds end-to-end latency depending on the use cases) and reliability (higher than 99.9999%). This does not mean that there are interesting applications where reliability is of paramount importance, while latency can be in the order of seconds, as in e.g. certain remote healthcare applications. Nevertheless, the coupling of low-latency networking and reliable communication is mainly driven by the need to push the technology boundaries and address a plethora of socially useful services and business domains that could benefit greatly from it. Some of the most challenging use cases are factory automation and industrial control, automated driving/flying, haptic communications, and real-time remote healthcare. URLLC is also expected to revolutionize processes in the areas of smart cities, smart farming, smart grid, remote manufacturing, and algorithmic trading. Although the URLLC constraints and the nature of real-time mission-critical applications imply the predominance of short packets and low-rate transmissions, future evolution of URLLC may also consider rate requirements. The emergence of immersive services, such as augmented and virtual reality (AR/VR), high-definition entertainment and gaming, and consumer robotics, calls for real-time, high-fidelity, broadband networks operating at latencies of few milliseconds.

We consider an ultra-reliable low-latency communication (URLLC) system with short packets employing hybrid automatic repeat request (HARQ). Depending on the delay of HARQ feedback and retransmissions, the latency constraint can be either violated or fulfilled at the expense of power consumption. We focus on the energy-latency tradeoff and explore whether it is better to do one-shot transmission or use HARQ. We analyze the energy consumption for incremental redundancy (IR) HARQ and compare it with the no HARQ case. The analysis relies on closed-form expressions for the outage probability of IR-HARQ with variables both the blocklength and the power. Our results show that for a wide range of blocklength, when the feedback delay is more than half the latency constraint, it is beneficial in terms of energy to use one-shot transmission (i.e., no HARQ).

Ultra reliable, low latency communications (URLLC) are currently attracting significant attention due to the emergence of mission-critical applications and device-centric communication. URLLC will entail a fundamental paradigm shift from throughput-oriented system design towards holistic designs for guaranteed and reliable end-to-end latency. A deep understanding of the delay performance of wireless networks is essential for efficient URLLC systems. In this paper, we investigate the network layer performance of multiple-input, single-output (MISO) systems under statistical delay constraints. We provide a statistical characterization of MISO diversity-oriented service process through closed-form expressions of its Mellin transform and derive probabilistic delay bounds using tools from stochastic network calculus. In particular, we analyze transmit beamforming with perfect and imperfect channel knowledge and compare it with orthogonal space-time codes and antenna selection. The effect of transmit power and number of antennas on the delay distribution is also investigated. Our results provide useful guidelines for the design of communication systems that can guarantee the stringent URLLC latency requirements.

This paper studies Ultra-Reliable Low-Latency Communications (URLLC), an important service class of emerging 5G networks. In this class, multiple unreliable transmissions must be combined to achieve reliable latency: a user experiences a frame success when the entire L bits are received correctly within a deadline, and its latency performance is reliable when the frame success rate is above a threshold. When jointly serving multiple users, a natural URLLC scheduling question arises: given the uncertainty of the wireless channel, can we find a scheduling policy that allows all users to meet a target reliable latency objective? This is called the URLLC SLA Satisfaction (USS) problem. The USS problem is an infinite horizon constrained Markov Decision Process, for which, after establishing a convenient property, we are able to derive an optimal policy based on dynamic programming. Our policy suffers from the curse of dimensionality, hence for large instances we propose a class of knapsack-inspired computationally efficient - but not necessarily optimal - policies. We prove that every policy in that class becomes optimal in a fluid regime, where both the deadline and L scale to infinity, while our simulations show that the policies perform well even in small practical instances of the USS problem.

Quality of service (QoS) provisioning in next-generation mobile communications systems entails a deep under-standing of the delay performance. The delay in wireless networks is strongly affected by the traffic arrival process and the service process, which in turn depends on the medium access protocol and the signal-to-interference-plus-noise ratio (SINR) distribution. In this work, we characterize the conditional distribution of the service process given the point process in Poisson bipolar networks. We then provide an upper bound on the delay violation probability combining tools from stochastic network calculus and stochastic geometry. Furthermore, we analyze the delay performance under statistical queueing constraints using the effective capacity formulation. The impact of QoS requirements, network geometry and link distance on the delay performance is identified. Our results provide useful insights for guaranteeing stringent delay requirements in large wireless networks.

High-fidelity, real-time interactive applications are envisioned with the emergence of the Internet of Things and tactile Internet by means of ultra-reliable low-latency communications (URLLC). Exploiting time diversity for fulfilling the URLLC requirements in an energy efficient manner is a challenging task due to the nontrivial interplay among packet size, retransmission rounds and delay, and transmit power. In this paper, we study the fundamental energy-latency tradeoff in URLLC systems employing incremental redundancy (IR) hybrid automatic repeat request (HARQ). We cast the average energy minimization problem with a finite blocklength (latency) constraint and feedback delay, which is non-convex. We propose a dynamic programming algorithm for energy efficient IR-HARQ optimization in terms of number of retransmissions, blocklength, and power per round. Numerical results show that our IR-HARQ approach could provide around 25% energy saving compared with one-shot transmission (no HARQ).

In this paper, we propose a decentralized access control scheme for interference management in device-to-device (D2D) underlaid cellular networks. Our method combines signal-to-interference ratio (SIR)-aware link activation with cellular guard zones in a system, where D2D links opportunistically access the licensed cellular spectrum when the activation conditions are satisfied. Analytical expressions for the success/coverage probability of both cellular and D2D links are derived. We characterize the impact of the guard zone radius and the SIR threshold on the D2D potential throughput and cellular coverage. A tractable approach is proposed to find the SIR threshold and guard zone radius that maximize the potential throughput of the D2D communication while ensuring sufficient coverage probability for the cellular uplink users. Simulations validate the accuracy of our analytical results and show the performance gain of the proposed scheme compared to prior state-of-the-art solutions.

In this paper, we consider the two-user broadcast channel and we characterize its stable throughput region. We start the analysis by providing the stability region for the general case without any specific considerations on transmission and reception mechanisms. We also provide conditions for the stable throughput region to be convex. Subsequently, we study the case where the transmitter uses superposition coding and we consider two special cases for the receivers. The first one is when both receivers treat interference as noise. The second is when the user with a better channel uses successive decoding and the other receiver treats interference as noise.

In this paper, we analyze a shared access network with a fixed primary node and randomly distributed secondary nodes whose spatial distribution follows a poisson point process. The secondary nodes use a random access protocol allowing them to access the channel with probabilities that depend on the queue size of the primary node. Assuming a system with multipacket reception receivers, having bursty packet arrivals at the primary and saturated traffic at the secondary nodes, our protocol can be tuned to alleviate congestion at the primary. We analyze the throughput of the secondary network and the primary average delay, as well as the impact of the secondary node access probability and transmit power. We formulate an optimization problem to maximize the throughput of the secondary network under delay constraints for the primary node; in the case of no congestion control, the optimal access probability can be provided in closed form. Our numerical results illustrate the effect of network operating parameters on the performance of the proposed priority-based shared access protocol.

In this paper, we investigate the downlink performance of dense cellular networks with elevated base stations (BSs) using a channel model that incorporates lineof-sight (LOS)/non-line-of-sight (NLOS) propagation into both small-scale and large-scale fading. Modeling LOS fading with Nakagami-m fading, we provide a unified framework based on stochastic geometry that encompasses both closest and strongest BS association. This paper is particularized to two distancedependent LOS/NLOS models of practical interest. Considering the effect of LOS propagation alone, we derive closed-form expressions for the coverage probability with Nakagami-m fading, showing that the performance for strongest BS association is the same as in the case of Rayleigh fading, whereas for closest BS association it monotonically increases with the shape parameter m. Then, focusing on the effect of elevated BSs, we show that network densification eventually leads to near-universal outage even for moderately low BS densities: in particular, the maximum area spectral efficiency is proportional to the inverse of the square of the BS height.

In this paper, we propose a method for acquiring accurate and timely channel state information (CSI) by leveraging full-duplex transmission. Specifically, we propose a mobile communication system in which base stations continuously transmit a pilot sequence in the uplink frequency band, while terminals use self-interference cancellation capabilities to obtain CSI at any time. Our proposal outperforms its half-duplex counterpart by at least 50\% in terms of throughput while ensuring the same (or even lower) outage probability. Remarkably, it also outperforms using full duplex for downlink data transmission for low values of downlink bandwidth and received power.

This paper analyzes the downlink performance of ultra-dense networks with elevated base stations (BSs). We consider a general dual-slope pathloss model with distance-dependent probability of line-of-sight (LOS) transmission between BSs and receivers. Specifically, we consider the scenario where each link may be obstructed by randomly placed buildings. Using tools from stochastic geometry, we show that both coverage probability and area spectral efficiency decay to zero as the BS density grows large. Interestingly, we show that the BS height alone has a detrimental effect on the system performance even when the standard single-slope pathloss model is adopted.

Network densification is a promising cellular deployment technique that leverages spatial reuse to enhance coverage and throughput. Recent work has identified that at some point, ultra-densification will no longer be able to deliver significant throughput gains. In this paper, we provide a unified treatment of the performance limits of network densification. We develop a general framework, which incorporates multi-slope pathloss and the entire space of shadowing and small scale fading distributions, under strongest cell association in a Poisson field of interferers. First, our results show that there are three scaling regimes for the downlink signal-to-interference-plus-noise ratio, coverage probability, and average per-user rate. Specifically, depending on the near-field pathloss and the fading distribution, the user performance of 5G ultra dense networks (UDNs) would either monotonically increase, saturate, or decay with increasing network density. Second, we show that network performance in terms of coverage density and area spectral efficiency can scale with the network density better than the user performance does. Furthermore, we provide ordering results for both coverage and average rate as a means to qualitatively compare different transmission techniques that may exhibit the same performance scaling. Our results, which are verified by simulations, provide succinct insights and valuable design guidelines for the deployment of 5G UDNs.

Full-duplex (FD) technology is envisaged as a key component for future mobile broadband networks due to its ability to boost the spectral efficiency. FD systems can transmit and receive simultaneously on the same frequency at the expense of residual self-interference (SI) and additional interference to the network compared with half-duplex (HD) transmission. This paper analyzes the performance of wireless networks with FD multi-antenna base stations (BSs) and HD user equipments (UEs) using stochastic geometry. Our analytical results quantify the success probability and the achievable spectral efficiency and indicate the amount of SI cancellation needed for beneficial FD operation. The advantages of multi-antenna BSs/UEs are shown and the performance gains achieved by balancing desired signal power increase and interference cancellation are derived. The proposed framework aims at shedding light on the system-level gains of FD mode with respect to HD mode in terms of network throughput, and provides design guidelines for the practical implementation of FD technology in large small-cell networks.

Wireless content caching in small cell networks (SCNs) has recently been considered as an efficient way to reduce the data traffic and the energy consumption of the backhaul in emerging heterogeneous cellular networks. In this paper, we consider a cluster-centric SCN with combined design of cooperative caching and transmission policy. Small base stations (SBSs) are grouped into disjoint clusters, in which in-cluster cache space is utilized as an entity. We propose a combined caching scheme, where part of the cache space in each cluster is reserved for caching the most popular content in every SBS, while the remaining is used for cooperatively caching different partitions of the less popular content in different SBSs, as a means to increase local content diversity. Depending on the availability and placement of the requested content, coordinated multi-point technique with either joint transmission or parallel transmission is used to deliver content to the served user. Using Poisson point process for the SBS location distribution and a hexagonal grid model for the clusters, we provide analytical results on the successful content delivery probability of both transmission schemes for a user located at the cluster center. Our analysis shows an inherent tradeoff between transmission diversity and content diversity in our cooperation design. We also study the optimal cache space assignment for two objective functions: maximization of the cache service performance and the energy efficiency. Simulation results show that the proposed scheme achieves performance gain by leveraging cache-level and signal-level cooperation and adapting to the network environment and user quality-of-service requirements.

Most prior work on performance analysis of ultradense cellular networks (UDNs) has considered standard power-law path loss models and non-line-of-sight (NLOS) propagation modeled by Rayleigh fading. The effect of line-of-sight (LOS) on coverage and throughput and its implication on network densification are still not fully understood. In this paper, we investigate the performance of UDNs when the signal propagation includes both LOS and NLOS components. Using a stochastic geometry based cellular network model, we derive expressions for the coverage probability, as well as tight approximations and upper bounds for both closest and strongest base station (BS) association. Our results show that under standard singular path loss model, LOS propagation increases the coverage, especially with nearest BS association. On the contrary, using dual slope path loss, LOS propagation is beneficial with closest BS association and detrimental for strongest BS association.

Self-interference (SI) significantly limits the performance of full-duplex (FD) radio devices if not properly cancelled. State-of-the-art SI cancellation (SIC) techniques at the receive chain implicitly set an upper bound on the transmit power of the device. This paper starts from this observation and proposes a transmit beamforming design for FD multiple-antenna radios that: i) leverages the inherent SIC capabilities at the receiver and the channel state information; and ii) exploits the potential of multiple antennas in terms of spatial SIC. The proposed solution not only maximizes the throughput while complying with the SIC requirements of the FD device, but also enjoys a very low complexity that allows it to outperform state-of-the-art counterparts in terms of processing time and power requirements. Numerical results show that our transmit beamforming design achieves significant gains with respect to applying zero-forcing to the SI channel when the number of transmit antennas is small to moderate, which makes it particularly appealing for FD small-cell base stations.

In this paper, we propose an adaptive cluster-centric small cell network with cooperative caching and transmission design, in which small base stations (SBSs) are grouped into disjoint clusters and in-cluster cache space is utilized as an entity. We design a combined caching scheme where part of the available cache space is reserved for caching the most popular content in every SBS, while the remaining is used to increase the content diversity. Depending on the availability of the requested content, either joint transmission (JT) or parallel transmission (PT) is used to deliver the content to the served user. We provide analytical results on the successful content delivery probability of both schemes for a user located at the cluster center. The optimal cache utilization strategy to maximize the cache service probability is determined as a function of network parameters, revealing an interesting tradeoff between transmission diversity and content diversity. Simulation results show that our proposed cooperative design finely combines the benefits of cache-level and signal-level cooperation schemes.

Driven by new types of wireless devices and the proliferation of bandwidth-intensive applications, data traffic and the corresponding network load are increasing dramatically. Network densification has been recognized as a promising and efficient way to provide higher network capacity and enhanced coverage. Most prior work on performance analysis of ultra-dense networks (UDNs) has focused on random spatial deployment with idealized singular path loss models and Rayleigh fading. In this paper, we consider a more precise and general model, which incorporates multi-slope path loss and general fading distributions. We derive the tail behavior and scaling laws for the coverage probability and the capacity considering strongest base station association in a Poisson field network. Our analytical results identify the regimes in which the signal-to-interference-plus-noise ratio (SINR) either asymptotically grows, saturates, or decreases with increasing network density. We establish general results on when UDNs lead to worse or even zero SINR coverage and capacity, and we provide crisp insights on the fundamental limits of wireless network densification.

In this paper, we characterize the stability region of the two-user broadcast channel. First, we obtain the stability region in the general case. Second, we consider the particular case where each receiver treats the interfering signal as noise, as well as the case in which the packets are transmitted using superposition coding and successive decoding is employed at the strong receiver.

A two-tier heterogeneous cellular network (HCN) with intra-tier and inter-tier dependence is studied. The macro cell deployment follows a Poisson point process (PPP) and two different clustered point processes are used to model the cache-enabled small cells. Under this model, we derive approximate expressions in terms of finite integrals for the average delivery rate considering inter-tier and intra-tier dependence. On top of the fact that cache size drastically improves the performance of small cells in terms of average delivery rate, we show that rate splitting of limited-backhaul induces non-linear performance variations, and therefore has to be adjusted for rate fairness among users of different tiers.

In this paper, we analyze a shared access network with one primary device and randomly distributed smart objects with secondary priority. Assuming random traffic at the primary device and saturated queues at the smart objects with secondary priority, an access protocol is employed to adjust the random access probabilities of the smart objects depending on the congestion level of the primary. We characterize the maximum throughput of the secondary network with respect to delay constraints on the primary. Our results highlight the impact of system design parameters on the delay and throughput behavior of the shared access network with massive number of connected objects.

What would a cellular network designed for maximal energy efficiency look like? To answer this fundamental question, tools from stochastic geometry are used in this paper to model future cellular networks and obtain a new lower bound on the average uplink spectral efficiency. This enables us to formulate a tractable uplink energy efficiency (EE) maximization problem and solve it analytically with respect to the density of base stations (BSs), the transmit power levels, the number of BS antennas and users per cell, and the pilot reuse factor. The closed-form expressions obtained from this general EE maximization framework provide valuable insights on the interplay between the optimization variables, hardware characteristics, and propagation environment. Small cells are proved to give high EE, but the EE improvement saturates quickly with the BS density. Interestingly, the maximal EE is achieved by also equipping the BSs with multiple antennas and operate in a “massive MIMO” fashion, where the array gain from coherent detection mitigates interference and the multiplexing of many users reduces the energy cost per user.

With the foreseeable explosive growth of small cell deployment, backhaul has become the next big challenge in the next generation wireless networks. Heterogeneous backhaul deployment using different wired and wireless technologies may be a potential solution to meet this challenge. Therefore, it is of cardinal importance to evaluate and compare the performance characteristics of various backhaul technologies to understand their effect on the network aggregate performance. In this paper, we propose relevant backhaul models and study the delay performance of various backhaul technologies with different capabilities and characteristics, including fiber, xDSL, millimeter wave (mmWave), and sub-6 GHz. Using these models, we aim at optimizing the base station (BS) association so as to minimize the mean network packet delay in a macrocell network overlaid with small cells. Numerical results are presented to show the delay performance characteristics of different backhaul solutions. Comparisons between the proposed and traditional BS association policies show the significant effect of backhaul on the network performance, which demonstrates the importance of joint system design for radio access and backhaul networks.

Device-to-device (D2D) discovery is the inextricable prelude for the direct exchange of local traffic between cellular users in proximity. The D2D discovery process can be based on either autonomous actions taken by D2D-enabled devices, also known as network-assisted D2D discovery or core network functionalities to estimate proximity, also known as network-assisted D2D discovery. A key advantage of network-assisted D2D discovery is its potential to reduce the energy, signaling, and interference required for D2D discovery, by exploiting knowledge of the network layout. We analyze the performance of network-assisted D2D discovery in random spatial networks and derive useful guidelines for its design. We derive approximate expressions for the distance distribution between two D2D peers conditioned on the core network's knowledge of the cellular network layout, assuming that the base stations are distributed according to the Poisson point process. The expressions are used to assess the interplay between the D2D discovery probability and key system parameters, such as network intensity and transmit power, as well as to identify conditions to maximize the D2D discovery probability. Numerical results validate the accuracy of our findings and provide insights on the performance tradeoffs of network-assisted D2D discovery.

The downlink of a wireless network where multi-antenna base stations (BSs) communicate with single-antenna mobile stations (MSs) using maximal ratio transmission (MRT) is considered here. The locations of BSs are modeled by a homogeneous Poisson point process (PPP) and the channel gains between the multiple antennas of each BS and the single antenna of each MS are modeled as spatially arbitrarily correlated Rayleigh random variables. We first present novel closed-form expressions for the distribution of the power of the interference resulting from the coexistence of one intended and one unintended MRT over the considered correlated fading channels. The derived expressions are then used to obtain closed-form expressions for the success probability and area spectral efficiency of the wireless communication network under investigation. Simulation results corroborate the validity of the presented expressions. A key result of this work is that the effect of spatial correlation on the network throughput may be contrasting depending on the density of BSs, the signal-to-interference-plus-noise ratio (SINR) level, and the background noise power.

Full-duplex small-cell relays with multiple antennas constitute a core element of the envisioned 5G network architecture. In this paper, we use stochastic geometry to analyze the performance of wireless networks with full-duplex multi-antenna small cells, with particular emphasis on the probability of successful transmission. To achieve this goal, we additionally characterize the distribution of the self-interference power of the full-duplex nodes. The proposed framework reveals useful insights on the benefits of full-duplex with respect to half- duplex in terms of network throughput.

Device-to-Device (D2D) communications enable direct exchange of localized traffic between cellular users in proximity. Nonetheless, the employment of D2D communications heavily relies on the capability of the cellular devices to infer on their proximity. In this paper, we analyze the performance of network- assisted D2D discovery in random cellular networks and derive optimal deployment strategies for transforming the today's cellular network, which is optimized for coverage and capacity, into a D2D- centric network where the discovery and communications between cellular devices will be orchestrated by the core network. In this direction, we develop an analytical framework that exploits existing knowledge of the cellular network topology and effectively estimates the probability that two tagged devices are in proximity, a.k.a. D2D discovery probability. Also, we identify conditions under which the D2D discovery probability is maximized and assess the key performance trade-offs inherent to network-assisted D2D discovery.

We consider a system of multiple sources generating status update packets, which need to be sent by a single transmitter to a destination over a network. In the model we study, the packet generation time may vary at each source, and the packets go through the network with a random delay. Each update carries a time stamp of its generation, allowing the destination to calculate for each source the so called Age of Information, which measures the timeliness of each status update arriving. Considering that queuing delay can unnecessarily increase the age of a critical status update, we propose here a queue management technique, in which we maintain a queue with only the latest status packet of each source, overwriting any previously queued update from that source. This simple technique drastically limits the need for buffering and can be applied in systems where the history of source status is not relevant. We show that this scheme results in significantly less transmissions compared to the standard M/M/1 queue model. Furthermore, the proposed technique reduces the per source age of information, especially in settings not using queue management with high status update generation rates.

With the foreseeable explosive growth of small cell deployment, backhaul has become the next big challenge in the next generation wireless networks in terms of capacity and latency, especially for delay-sensitive services and network functionalities. Heterogeneous backhaul deployment using different wired and wireless technologies may be a potential solution to meet this challenge. Therefore, it is cardinal to evaluate and compare the performance characteristics of various backhaul technologies as a means to understand the effect of backhaul on the total network performance. In this paper, we propose relevant backhaul models and study the delay performance of promising technologies, including fiber, xDSL, millimeter wave (mmWave), and sub-6 GHz, which have different characteristics. Numerical results are presented to show the delay performance characteristics of different backhaul solutions.

Consumer demand for data has increased tremendously over the last years, and small cell networks are increasingly being considered as one of the key technologies to cope with this demand. Small cell network deployments within the conventional macro cellular networks are creating a significant amount of heterogeneity compared to traditional cellular networks. Nevertheless, the backhaul link is often the bottleneck in terms of system performance and cost. In this paper, we consider the backhaul issue in heterogeneous cellular networks and propose a hierarchical network model using superimposed independent homogeneous Poisson point processes. We derive the total expected delay by taking into account retransmissions over the wireless link, as well as the backhaul delay incurred from both wired and wireless backhaul. For the total expected deployment cost, we take into account infrastructure cost and the construction cost of wired backhaul deployment. Specifically, we are able to characterize the behavior of delay and deployment cost using our simple and tractable model. Furthermore, we propose a delay-based access control policy, which can provide better latency performance especially in dense deployments. Our theoretical framework provides a fundamental understanding of the tradeoff between wired and wireless backhaul and the effect on deployment cost and system performance in heterogeneous cellular networks.

Powering radio access networks using renewables, such as wind and solar power, promises dramatic reduction in the network operation cost and the network carbon footprints. However, the spatial variation of the energy field can lead to fluctuations in power supplied to the network and thereby affects its coverage. This warrants research on quantifying the aforementioned negative effect and designing countermeasure techniques, motivating the current work. First, a novel energy field model is presented, in which fixed maximum energy intensity γ occurs at Poisson distributed locations, called energy centers. The intensities fall off from the centers following an exponential decay function of squared distance and the energy intensity at an arbitrary location is given by the decayed intensity from the nearest energy center. The product between the energy center density and the exponential rate of the decay function, denoted as ψ, is shown to determine the energy field distribution. Next, the paper considers a cellular downlink network powered by harvesting energy from the energy field and analyzes its network coverage. For the case of harvesters deployed at the same sites as base stations (BSs), as γ increases, the mobile outage probability is shown to scale as (cγ-πψ+ p), where p is the outage probability corresponding to a flat energy field and c is a constant. Subsequently, a simple scheme is proposed for counteracting the energy randomness by spatial averaging. Specifically, distributed harvesters are deployed in clusters and the generated energy from the same cluster is aggregated and then redistributed to BSs. As the cluster size increases, the power supplied to each BS is shown to converge to a constant proportional to the number of harvesters per BS. Several additional issues are investigated in this paper, including regulation of the power transmission loss in energy aggregation and extensions of the energy field model.

Coordinated Multi-Point (CoMP) transmission for LTE-Advanced systems promises enhanced throughput and coverage performance, especially for cell edge users. However, the performance of CoMP systems heavily depends on the feedback quality and channel imperfections. In this paper, we investigate the impact of quantized and delayed channel state information (CSI) on the average achievable rate of joint transmission (JT) and coordinated beamforming (CBF) systems. We derive closed-form expressions and accurate approximations on the expected sum rate of CoMP systems with imperfect CSI assuming small-scale Rayleigh fading, pathloss attenuation, and other-cell interference (OCI). Furthermore, for analytical tractability, we employ a moment matching technique that approximates the distributions of the received desired and interference signals. Based on our analytical framework, we show that CBF and JT to multiple users are more sensitive to CSI imperfections than single-user JT and we identify switching points and optimal operating regimes for each scheme. Furthermore, we propose an adaptive multimode transmission technique that switches between CoMP schemes to maximize the sum rate. Finally, the proposed approximate framework enables us to identify key system parameters, such as feedback resolution, delay, pathloss, and transmit SNR for which CoMP becomes a judicious choice of transmission strategy as compared to non-cooperative transmission.

The effect of full-duplex cooperative relaying in a random access multiuser network is investigated here. First, we model the self-interference incurred due to full-duplex operation, assuming multi-packet reception capabilities for both the relay and the destination node. Traffic at the source nodes is considered saturated and the cooperative relay, which does not have packets of its own, stores a source packet that it receives successfully in its queue when the transmission to the destination has failed. We obtain analytical expressions for key performance metrics at the relay, such as arrival and service rates, stability conditions, and average queue length, as functions of the transmission probabilities, the self interference coefficient, and the links' outage probabilities. Furthermore, we study the impact of the relay node and the self-interference coefficient on the per-user and aggregate throughput, and the average delay per packet. We show that perfect self-interference cancelation plays a crucial role when the SINR threshold is small, since it may result to worse performance in throughput and delay comparing with the half-duplex case. This is because perfect self-interference cancelation can cause an unstable queue at the relay under some conditions.

Today's heterogeneous wireless network (HWN) is a collection of ubiquitous wireless networking elements (WNEs) that support diverse functional capabilities and networking purposes. In such a heterogeneous networking environment, proximity estimation will play a key role for the seamless support of emerging applications that span from the direct exchange of localized traffic between homogeneous WNEs (peer-to-peer communications) to positioning for autonomous systems using location information from the ubiquitous HWN infrastructure. Since most of the existing wireless networking technologies enable the direct (or indirect) estimation of the distances and angles between their WNEs, the integration of such spatial information is a natural solution for robustly handling the unprecedented demand for proximity estimation between the myriads of WNEs. In this paper, we develop an analytical framework that integrates existing knowledge of the HWN layout to enable proximity estimation between WNE supporting different radio access technologies (RATs). In this direction, we derive closed-form expressions for the distance distribution between two tagged WNEs given partial (or full) knowledge of the HWN topology. The derived expressions enable us to analyze how different levels of location-awareness affect the performance of proximity estimation between WNEs that are not necessarily capable of communicating directly. Optimal strategies for the deployment of WNEs, as means of maximizing the probability of successful proximity estimation between two WNEs of interest, are presented, and useful guidelines for the design of location-aware proximity estimation in the nowadays HWN are drawn.

In this paper, we propose a distributed interference and channel-aware opportunistic access control technique for D2D underlaid cellular networks, in which each potential D2D link is active whenever its estimated signal-to-interference ratio (SIR) is above a predetermined threshold so as to maximize the D2D area spectral efficiency. The objective of our SIR-aware opportunistic access scheme is to provide sufficient coverage probability and to increase the aggregate rate of D2D links by harnessing interference caused by dense underlaid D2D users using an adaptive decision activation threshold. We determine the optimum D2D activation probability and threshold, building on analytical expressions for the coverage probabilities and area spectral efficiency of D2D links derived using stochastic geometry. Specifically, we provide two expressions for the optimal SIR threshold, which can be applied in a decentralized way on each D2D link, so as to maximize the D2D area spectral efficiency derived using the unconditional and conditional D2D success probability respectively. Simulation results in different network settings show the performance gains of both SIR-aware threshold scheduling methods in terms of D2D link coverage probability, area spectral efficiency, and average sum rate compared to existing channel-aware access schemes.

Managing interference is a major technical challenge in large wireless networks. Distributed cooperation techniques, such as Interference Alignment (IA), exploit the available spatial degrees of freedom of the interference channel holding promise of enhanced spectral efficiency. Most prior results, however, consider isolated network settings, neglecting the interference from nodes that are not participating in the cooperation scheme. This paper analyzes the performance of IA in the presence of uncoordinated interference from a heterogeneous network. Specifically, we analyze perfect downlink IA in a fixed-size cell, where the interfering nodes are distributed according to a spatial point process, and compare it with a non-cooperative MIMO scheme. Furthermore, the performance gains by using a guard zone between the IA cluster and the interference field are evaluated and design guidelines for the necessary isolation distance from out-of-cluster interferers are provided.

The densification of the network infrastructure is a possible solution to meet the explosive growth of mobile data demand. In the resulting interference-limited networks, interference management techniques are of interest to increase the spectral efficiency. Successive interference cancellation (SIC) provides modest gains when users are connected to the access point (AP) which provides the maximum average received signal power. In this paper, we focus on alternative association policies where SIC gives rise to a substantial performance gain. Specifically, we present a probabilistic framework to evaluate the performance of heterogeneous networks with SIC capabilities considering the minimum load association policy and range expansion. Numerical results show the effectiveness of SIC for these association policies.

This work studies the improvement in the sum rate of downlink cellular networks using successive interference cancellation (SIC). First, we consider a two-cell cellular network and propose a cooperative SIC scheme, in which one user receives its data at the single-user capacity using SIC while the rate in the other cell is appropriately adapted to maximize the sum rate. Identifying the corner points of the rate region of a two-user interference channel, we derive conditions for which applying SIC increases the sum rate as compared to treating interference as noise (IaN). Conditions when both mobiles can perform SIC are also given. These conditions allow us to identify cell areas and user positions in which SIC gains can be achieved. Furthermore, a flexible user association policy is presented as a means to further increase the sum-rate gains by using SIC. Finally, we propose a centralized cell scheduling for performing cooperative SIC in multiuser multi-cell networks. Numerical results show that significant sum-rate improvement is achieved using SIC receivers, even with few users per cell and especially at the cell edge.

In this paper, we investigate the ergodic capacity of multiple-input multiple-output (MIMO) Rician fading channels in the presence of spatial correlation at both the transmitter and the receiver with perfect channel state information (CSI) at the receiver. Based on majorization theory and the distribution of quadratic forms in normal random variables, we propose a tight lower bound on the ergodic capacity in terms of Meijer G-function. Our analytical results are validated through extensive Monte Carlo simulations using the exponential correlation model.

Consumer demand for data traffic has increased tremendously, and small cell networks are increasingly being considered as one of the key technology drivers to cope with the so-called mobile date tsunami. The deployment of small cell networks within the conventional macro cellular networks creates a significant amount of heterogeneity compared to conventional cellular networks, and in this context, backhaul- related issues have important implications in terms of both cost and system performance. In this paper, we consider a hierarchical network structure to model the wireless backhaul in small cell networks. We derive the total expected delay by taking into account retransmission over the wireless links, as well as the backhaul delay in the wireless backhaul link. As for the total expected deployment cost, we take into account infrastructure cost as well as the construction cost of laying backhaul links. Specifically, we are able to characterize the behavior of delay and deployment cost using our simple and tractable model. In summary, our analysis provides a fundamental understanding of the wireless backhaul and its effect on the deployment cost and the system performance in heterogeneous cellular networks.

In this paper, we explore the physical limits of successful information transfer in a point-to-point communication system. In interest of studying the fundamental limits imposed by physics on communication systems in general, we model a simple generic system that enables us to make some basic inquiries about the energy efficiency in information transfer and processing. We use ideas from thermodynamics such as Szilard engine to represent information bits. We further use ideas from electromagnetic theory for transfer of information, and information theory to define the energy efficiency metric. We find the upper limit of this efficiency and conditions at which it can be achieved.

As energy consumption reduction has been an important concern for the wireless industry, energy-efficient communications is of prime interest for future broadband networks. In this paper, we study the energy efficiency of relay-assisted cellular networks using tools from stochastic geometry. We first derive the coverage probability for the macro base station (MBS) to user (UE), the MBS to relay station (RS), and the RS to UE links, and then we model the power consumption of MBSs and RSs. Based on the analytical model and expressions, the energy efficiency of relay-assisted cellular networks is then evaluated and is shown to be strictly quasi-concave on the transmit power for the MBS to UE link or the RS to UE link. Numerical results also show that the energy efficiency first improves while it hits a ceiling as the MBS density increases.

At present, operators address the explosive growth of mobile data demand by densification of the cellular network so as to reduce the transmitter-receiver distance and to achieve higher spectral efficiency. Due to such network densification and the intense proliferation of wireless devices, modern wireless networks are interference-limited, which motivates the use of interference mitigation and coordination techniques. In this work, we develop a statistical framework to evaluate the performance of multi-tier heterogeneous networks with successive interference cancellation (SIC) capabilities, accounting for the computational complexity of the cancellation scheme and relevant network related parameters such as random location of the access points (APs) and mobile users, and the characteristics of the wireless propagation channel. We explicitly model the consecutive events of canceling interferers and we derive the success probability to cancel the n-th strongest signal and to decode the signal of interest after n cancellations. When users are connected to the AP which provides the maximum average received signal power, the analysis indicates that the performance gains of SIC diminish quickly with n and the benefits are modest for realistic values of the signal-to-interference ration (SIR). We extend the statistical model to include several association policies where distinct gains of SIC are expected: (i) maximum instantaneous SIR association, (ii) minimum load association, and (iii) range expansion. Numerical results show the effectiveness of SIC for the considered association policies. This work deepens the understanding of SIC by defining the achievable gains for different association policies in multi-tier heterogeneous networks.

The use of large-scale antenna arrays can bring substantial improvements in energy and/or spectral efficiency to wireless systems due to the greatly improved spatial resolution and array gain. Recent works in the field of massive multiple-input multiple-output (MIMO) show that the user channels decorrelate when the number of antennas at the base stations (BSs) increases, thus strong signal gains are achievable with little interuser interference. Since these results rely on asymptotics, it is important to investigate whether the conventional system models are reasonable in this asymptotic regime. This paper considers a new system model that incorporates general transceiver hardware impairments at both the BSs (equipped with large antenna arrays) and the single-antenna user equipments (UEs). As opposed to the conventional case of ideal hardware, we show that hardware impairments create finite ceilings on the channel estimation accuracy and on the downlink/uplink capacity of each UE. Surprisingly, the capacity is mainly limited by the hardware at the UE, while the impact of impairments in the large-scale arrays vanishes asymptotically and interuser interference (in particular, pilot contamination) becomes negligible. Furthermore, we prove that the huge degrees of freedom offered by massive MIMO can be used to reduce the transmit power and/or to tolerate larger hardware impairments, which allows for the use of inexpensive and energy-efficient antenna elements.

The problem of throughput optimization in decentralized wireless networks with spatial randomness under queue stability and packet loss constraints is investigated in this paper. Two key performance measures are analyzed, namely the effective link throughput and the network spatial throughput. Specifically, the tuple of medium access probability, coding rate, and maximum number of retransmissions that maximize each throughput metric is analytically derived for a class of Poisson networks, in which packets arrive at the transmitters following a geometrical distribution. Necessary conditions so that the effective link throughput and the network spatial throughput are stable and achievable under bounded packet loss are determined, as well as upper bounds for both cases by considering the unconstrained optimization problem. Our results show in which system configuration stable achievable throughput can be obtained as a function of the network density and the arrival rate. They also evince conditions for which the per-link throughput-maximizing operating points coincide or not with the aggregate network throughput-maximizing operating regime.

Switching off base stations (BSs) when the activity in the cell is relatively low, often referred to as cell sleeping, is one possible method of reducing energy consumption in macrocellular networks. However, this method may as well reduce the coverage and hence it is not evident whether the energy savings can compensate for the network throughput reduction. In this paper, we propose and analyze two sleep mode strategies, namely random and strategic sleeping, for energy efficient cellular networks. Using stochastic geometry, we model the switching off BS operation and obtain analytical results for the coverage probabilities and the area spectral efficiency under different cell sleeping strategies. Specifically, the effect of proposed sleep mode policies on the power consumption and on the energy efficiency is investigated. We first show that the performance gains depend on the level of background noise. Furthermore, numerical results show the effectiveness of an activity-based sleeping strategy in maximizing the energy efficiency of macrocellular networks.

The stable throughput region of the two-user interference channel is investigated here. First, the stability region for the general case is characterized. Second, we study the cases where the receivers treat interference as noise or perform successive interference cancelation. Finally, we provide conditions for the convexity/concavity of the stability region and for which a certain interference management strategy leads to broader stability region.

In this paper we study the impact of a relay node in a two-user network. We assume a random access collision channel model with erasures. In particular we obtain an inner and an outer bound for the stability region.

The problem of adaptive feedback bit allocation for two different Coordinated Multi-Point (CoMP) transmission schemes, namely Joint Transmission (JT) and Coordinated Beamforming (CBF), is studied here. We formulate the average rate maximization problem in which channel information to the serving and interfering base stations is quantized with different resolution. Unlike previously proposed solutions, we show that it is possible to solve the optimization problem without decomposing it in two terms (desired signal and interference). The proposed adaptive bit allocation algorithm is evaluated through simulations for a realistic cellular layout, where users are placed exclusively in the cell edge, and results are averaged over different positions to avoid placement biasing. The gains of the proposed method vary from 5% to 50% showing that adaptive feedback allocation is an effective method to improve throughput performance in CoMP systems.

With the exponential increase in mobile internet traffic driven by a new generation of wireless devices, future cellular networks face a great challenge to meet this overwhelming demand of network capacity. At the same time, the demand for higher data rates and the ever-increasing number of wireless users led to rapid increases in power consumption and operating cost of cellular networks. One potential solution to address these issues is to overlay small cell networks with macrocell networks as a means to provide higher network capacity and better coverage. However, the dense and random deployment of small cells and their uncoordinated operation raise important questions about the energy efficiency implications of such multi-tier networks. Another technique to improve energy efficiency in cellular networks is to introduce active/sleep (on/off) modes in macrocell base stations. In this paper, we investigate the design and the associated tradeoffs of energy efficient cellular networks through the deployment of sleeping strategies and small cells. Using a stochastic geometry based model, we derive the success probability and energy efficiency in homogeneous macrocell (single-tier) and heterogeneous K-tier wireless networks under different sleeping policies. In addition, we formulate the power consumption minimization and energy efficiency maximization problems, and determine the optimal operating regimes for macrocell base stations. Numerical results confirm the effectiveness of switching off base stations in homogeneous macrocell networks. Nevertheless, the gains in terms of energy efficiency depend on the type of sleeping strategy used. In addition, the deployment of small cells generally leads to higher energy efficiency but this gain saturates as the density of small cells increases. In a nutshell, our proposed framework provides an essential understanding on the deployment of future green heterogeneous networks.

We develop a general downlink model for multi-antenna heterogeneous cellular networks (HetNets), where base stations (BSs) across tiers may differ in terms of transmit power, target signal-to-interference-ratio (SIR), deployment density, number of transmit antennas and the type of multi-antenna transmission. In particular, we consider and compare space division multiple access (SDMA), single user beamforming (SU-BF), and baseline single-input single-output (SISO) transmission. For this general model, the main contributions are: (i) ordering results for both coverage probability and per user rate in closed form for any BS distribution for the three considered techniques, using novel tools from stochastic orders, (ii) upper bounds on the coverage probability assuming a Poisson BS distribution, and (iii) a comparison of the area spectral efficiency (ASE). The analysis concretely demonstrates, for example, that for a given total number of transmit antennas in the network, it is preferable to spread them across many single-antenna BSs vs. fewer multi-antenna BSs. Another observation is that SU-BF provides higher coverage and per user data rate than SDMA, but SDMA is in some cases better in terms of ASE.

With the exponential increase in high rate traffic given by a new generation of wireless devices, data is expected to overwhelm cellular network capacity in the near future. Femtocell networks have been recently proposed as an efficient and cost-effective approach to provide unprecedented levels of network capacity and coverage. However, the dense and random deployment of femtocells and their uncoordinated operation raise important questions concerning interference pollution and spectrum allocation. Motivated by the flexible subchannel allocation capabilities of cognitive radio, we propose a cognitive hybrid division duplex (CHDD) that is suitable for heterogeneous networks in future mobile communication systems. Specifically, our CHDD scheme has a pair of frequency bands to perform frequency division duplex (FDD) on the macrocell, while time division duplex (TDD) is simultaneously operated in these bands by underlaid cognitive femtocells. By doing so, the proposed CHDD scheme exploits the advantages of both FDD and TDD schemes: operating in FDD at the macrocell tier controls inter-tier interference, whereas operating in TDD at the femtocell tier provides to femtocells the flexibility of adjusting uplink and downlink rates together with opportunistic access benefits. Using tools from stochastic geometry, we provide a methodology on how to design efficient switching mechanisms for cognitive TDD operation of femtocells. In particular, we derive closed-form expressions for the success probability and the area spectral efficiency of the proposed CHDD scheme when the macro tier is in downlink and uplink mode. Furthermore, we propose an open access policy as a means to improve the performance of macrocell transmissions. Our analysis and numerical results show the effectiveness of introducing cognition in femtocells so as to improve the system performance of two-tier femtocell networks.

Coordinated Multi-Point (CoMP) techniques for LTE-Advanced systems promise high spectral efficiency and enhanced coverage; however, the performance of such systems heavily depends on the feedback quality. In this paper, we study the impact of quantized and delayed feedback on the achievable rate of CoMP systems. We first derive closed-form expressions and tight approximations on the sum-rate of joint transmission processing (JT) and coordinated beamforming (CBF) with imperfect channel state information (CSI). Schemes with increasing interference power at high SNR, i.e. CBF and JT to multiple users, are more sensitive to CSI imperfections than single-user JT. Based on our analytical results, we identify switching points and optimal operating regimes for each scheme and we propose an adaptive mode switching transmit policy. Numerical results show that the number of users to serve under multiuser JT should be optimized based on average SNR, feedback resolution, and delay in order to optimally balance spatial multiplexing gain and diversity.

Small cell network architecture is considered as an effective solution to the ever growing demand for high data rate, with femtocells being a promising paradigm. The dense deployment and the uncoordinated operation of femtocells bring various challenges for interference management. Motivated by the flexible subchannel allocation capabilities of cognitive radio, we propose a cognitive hybrid division duplex (CHDD) for two-tier networks. In this scheme, macrocells operate in frequency division duplex (FDD), while the underlying cognitive femtocells employ time division duplex (TDD). The CHDD scheme has the flexibility of providing asymmetric data rates from the TDD mode, while managing inter-tier interference with FDD. Using a network model based on stochastic geometry in order to capture both interference and spatial randomness, we quantify the performance of the proposed CHDD scheme in terms of success probability, area spectral efficiency, and spatial average rate. Our analytical and numerical results show the effectiveness of introducing cognition in femtocells as a means to enhance the performance of femtocell-aided cellular networks.

Two-tier femtocell networks have been shown to provide superior performance in terms of indoor user coverage and data rate as compared to conventional macrocellular networks. Nevertheless, when macrocells and femtocells share the whole spectrum, macrocellular communication links suffer severely from inter-tier interference, further exacerbated in dense femtocell network deployments. Therefore, it is of interest to investigate optimal subchannel allocation strategies and characterize their dependence on network density in two-tier femtocell networks. In this paper, we consider a joint subchannel scheme, in which the whole spectrum is shared by both tiers, as well as a disjoint subchannel scheme, whereby disjoint sets of subchannels are assigned to each tier. We derive analytical expressions for the performance of different spectrum allocation schemes in terms of success probability and per-tier network throughput. Furthermore, we formulate the aggregate network throughput maximization problem subject to quality of service constraints in terms of success probabilities and per-tier minimum rate, and provide practically relevant solutions and insights on the optimal spectrum allocation scheme. Our results indicate that with closed access femtocells, the optimized joint and disjoint subchannel schemes provide the highest throughput among all schemes in sparse and dense femtocell network, respectively.

Coordinated Multi-Point (CoMP) transmission is a promising technique for LTE-Advanced systems, especially due to the enhanced throughput performance of cell edge users. In this paper, we investigate joint transmission (JT) and coordinated beamforming (CBF) with quantized and delayed feedback, and we derive ergodic rate and outage probability expressions assuming large-scale fading (pathloss), small-scale Rayleigh fading, and other-cell interference (OCI). Furthermore, we employ a moment matching technique that approximates the distributions of the received desired and interference signals with Gamma distributions as a means to facilitate analysis and simulation. The performance of CoMP transmission is quantified and compared with non-cooperative transmission, and operating regions in which CoMP gains are more pronounced are provided. The versatility of the proposed approximate framework enables us to identify key system parameters, such as feedback resolution, pathloss, and transmit SNR for which CoMP becomes a judicious choice of transmission strategy.

Two-tier femtocell networks, which comprise macro-cells underlaid with short range femtocells, are under intensive study due to their improved indoor coverage and data rate advantages as compared to conventional single-tier macrocellular networks. Nevertheless, the implementation of two-tier networks with universal frequency reuse causes severe cross-tier interference from the femtocells to the macrocellular users, exacerbated by dense femtocell deployment. In this paper, we investigate the effect of access control and cell association in two-tier networks, where the macrocells employ closed access, whereas the femtocells can operate in either open or closed access. By introducing a tractable model, we derive the success probability for each tier under different femtocell access schemes using stochastic geometric tools. For the case of open access, we derive the enhanced success probability for a macrocell user, and we show that the effect of inter-tier interference could be eliminated through optimal cell association policy for the open access.

In this paper, a new measure of outage-constrained network area spectral efficiency, coined as stable transmission capacity, is introduced, which is achievable under finite delay and queue-length stability. Specifically, this framework extends the transmission capacity formulation to scenarios with packet retransmissions and transmitters' queues with independent packet arrivals. This approach is applied to single-hop wireless networks with nodes being spatially distributed as Poisson point process, slotted ALOHA medium access protocol, packet arrivals following a geometrical distribution, and bounded number of retransmissions. The stable transmission capacity is then obtained as the solution of a constrained optimization problem with respect to the probability that a transmitter access the network, the link spectral efficiency, and the maximum number of retransmissions. Using the unconstrained stable transmission capacity as an upper bound, it is shown under which operating points and network parameters such limit can be achieved. Our numerical results also evince how the spatial density and the arrival process affect the network performance.

The deployment of femtocells in a macrocell network is an economical and effective way to increase network capacity and coverage. Nevertheless, such deployment is challenging due to the presence of inter-tier and intra-tier interference, and the ad hoc operation of femtocells. Motivated by the flexible subchannel allocation capability of OFDMA, we investigate the effect of spectrum allocation in two-tier networks, where the macrocells employ closed access policy and the femtocells can operate in either open or closed access. By introducing a tractable model, we derive the success probability for each tier under different spectrum allocation and femtocell access policies. In particular, we consider joint subchannel allocation, in which the whole spectrum is shared by both tiers, as well as disjoint subchannel allocation, whereby disjoint sets of subchannels are assigned to both tiers. We formulate the throughput maximization problem subject to quality of service constraints in terms of success probabilities and per-tier minimum rates, and provide insights into the optimal spectrum allocation. Our results indicate that with closed access femtocells, the optimized joint and disjoint subchannel allocations provide the highest throughput among all schemes in sparse and dense femtocell networks, respectively. With open access femtocells, the optimized joint subchannel allocation provides the highest possible throughput for all femtocell densities.

The tremendous capacity gains promised by space division multiple access (SDMA) depend critically on the accuracy of the transmit channel state information. In the broadcast channel, even without any network interference, it is known that such gains collapse due to interstream interference if the feedback is delayed or low rate. In this paper, we investigate SDMA in the presence of interference from many other simultaneously active transmitters distributed randomly over the network. In particular we consider zero-forcing beamforming in a decentralized (ad hoc) network where each receiver provides feedback to its respective transmitter. We derive closed-form expressions for the outage probability, network throughput, transmission capacity, and average achievable rate and go on to quantify the degradation in network performance due to residual self-interference as a function of key system parameters. One particular finding is that as in the classical broadcast channel, the per-user feedback rate must increase linearly with the number of transmit antennas and SINR (in dB) for the full multiplexing gains to be preserved with limited feedback. We derive the throughput-maximizing number of streams, establishing that single-stream transmission is optimal in most practically relevant settings. In short, SDMA does not appear to be a prudent design choice for interference-limited wireless networks.

Recently, there is an increasing interest in the deployment of femto access points (FAPs), which are short-range low-power home base stations, over a macro cellular network to improve the total network capacity. Due to the random deployment of FAPs, an analytical understanding of such two-tier networks via stochastic geometry is essential for network operators to fully exploit the potential of femto cells. In this paper, we propose a two-tier network model that captures the geometries of macrocells and femtocells realistically. The success probabilities in spectrum sharing and unsharing for both tiers in the uplink and downlink are derived. Finally, the downlink performance of such two-tier network is discussed numerically.

The effect of limited feedback on point-to-multipoint communication is investigated in multi-antenna wireless ad hoc networks. We consider zero-forcing beamforming with quantized channel direction information and derive new closed-form expressions for the outage probability, throughput, transmission capacity, and average user rate. Expressions for the performance degradation due to finite rate feedback, the optimal number of streams, and the required feedback rate scaling are provided. Our results indicate that the optimized system operating points depend on different network parameters such as pathloss exponent, node density, and outage constraints.

In this paper, we consider a hierarchical two-tier cellular network where a macrocell is overlaid with a tier of randomly distributed femtocells. We evaluate the combined effect of uncoordinated cross-tier interference, feedback delay, and quantization errors on the achievable rate of transmit beamforming with imperfect channel state information (CSI). We model the femtocell spatial distribution as a Poisson point process (PPP) and the temporal correlation of the channel according to a Gauss-Markov model. Using stochastic geometry tools, we derive the probability of outage at the macrocell users as a function of the temporal correlation, the femtocell density, and the feedback rate. We compute the maximum average achievable rate on the downlink of the macrocell network using a properly designed rate backoff scheme. We show that transmit beamforming with imperfect CSI is a viable option for the downlink of a two-tier cellular network, and that rate backoff recovers the loss in rate due to packet outage.

This paper proposes an adaptive multi-mode transmission strategy to improve the spectral efficiency achieved in the multiple-input multiple-output (MIMO) broadcast channel with delayed and quantized channel state information. The adaptive strategy adjusts the number of active users, denoted as the transmission mode, to balance transmit array gain, spatial division multiplexing gain, and residual inter-user interference. Accurate closed-form approximations are derived for the achievable rates for different modes, which help identify the active mode that maximizes the average sum throughput for given feedback delay and channel quantization error. The proposed transmission strategy can be easily combined with round-robin scheduling to serve a large number of users. As instantaneous channel information is not exploited, the proposed algorithm cannot provide multiuser diversity gain, but it is still able to provide throughput gain over single-user MIMO at moderate signal-to-noise ratio. In addition, it has a light feedback overhead and only requires feedback of instantaneous channel state information from a small number of users. In the system with a feedback load constraint, it is shown that the proposed algorithm provides performance close to that achieved by opportunistic scheduling with instantaneous feedback from a large number of users.

We analyze the sum rate performance in multicell single-hop networks where access points are allowed to cooperate in terms of a joint resource allocation. The resource allocation policies considered here combine power control and user scheduling. Although promising from a conceptual point of view, the optimization of the sum of per-link rates hinges on tough issues such as computational complexity and the requirement for heavy receiver-to-transmitter and cell-to-cell channel information feedback. In this paper, however, we show that simple distributed algorithms can scale optimally in terms of rates, when the number of users per cell U is allowed to grow large. We use extreme value theory to provide scaling laws for upper and lower bounds for the network sum-rate (sum of single user rates over all cells), corresponding to zero-interference and worst-case interference scenarios. We show that the scaling is either dominated by path loss statistics or by small-scale fading, depending on the regime and user location scenario. A surprising result is that the well known log log U rate behavior exhibited in i.i.d. fading channels with maximum rate schedulers is transformed into a log U behavior when path loss is accounted for. Additionally, by showing that upper and lower rate bounds behave in fact identically, asymptotically, our results suggest, remarkably, that the impact of multicell interference on the rate (in terms of scaling) actually vanishes asymptotically, when appropriate resource allocation policies are used.

We develop a new metric for quantifying end-to-end throughput in multihop wireless networks, which we term random access transport capacity, since the interference model presumes uncoordinated transmissions. The metric quantifies the average maximum rate of successful end-to-end transmissions, multiplied by the communication distance, and normalized by the network area. We show that a simple upper bound on this quantity is computable in closed-form in terms of key network parameters when the number of retransmissions is not restricted and the hops are assumed to be equally spaced on a line between the source and destination. We also derive the optimum number of hops and optimal per hop success probability and show that our result follows the well-known square root scaling law while providing exact expressions for the preconstants, which contain most of the design-relevant network parameters. Numerical results demonstrate that the upper bound is accurate for the purpose of determining the optimal hop count and success (or outage) probability.

In two-tier networks - comprising a conventional cellular network overlaid with shorter range femtocell hotspots - with universal frequency reuse, the near-far effect from crosstier interference diminishes coverage for users in either tier. Equipping the macrocell and femtocell basestations (BSs) with multiple antennas can enhance robustness against the near-far problem. Given a per-tier outage probability constraint, this work derives coverage radii wherein cross-tier interference bottlenecks cellular and hotspot coverage. Single-user (SU) multiple antenna transmission at each tier is shown to provide significantly superior coverage and spatial reuse relative to multiuser (MU) transmission. We propose a decentralized carrier sensing approach to regulate femtocell transmission powers for ensuring reliable cellular coverage. Simulations using typical path loss scenarios show that our interference management strategy provides reliable cellular coverage with about 60 femtocells per cellsite.

In the multiple-input multiple-output (MIMO) broadcast channel with imperfect channel state information (CSI), neither the capacity nor the optimal transmission technique have been fully discovered. In this paper, we derive achievable ergodic rates for a multi-antenna fading broadcast channel when CSI at the transmitter (CSIT) is delayed and quantized. It is shown that not all possible users should be supported with spatial division multiplexing due to the residual inter-user interference caused by imperfect CSIT. Based on the derived achievable rates, we propose a multi-mode transmission strategy to maximize the throughput, which adaptively adjusts the number of active users based on the channel statistics information.

We consider transmission of packets in two-hop wireless ad hoc networks in which relay nodes are deployed between the source-destination pairs. Based on results from extreme value theory and product tails, we derive throughput scaling laws when opportunistic relay selection is performed. Assuming partial channel state information at each transmitter (CSIT) and decode- and-forward, half-duplex relays, we investigate how the per-hop throughput depends on the channel gain asymptotic distribution and the relay deployment. In dense networks with lambdatnodes per m2and fixed relay distances, we provide specific scaling laws for Rayleigh, lognormal, and Weibull fading, showing that the throughput is upper bounded by thetas(radic(lambdat)). Interestingly, with variable relay distances and location-aware relay selection, we analytically show that regularly varying channel distributions result in enhanced multi-relay diversity gain, achieving linear throughput scaling thetas(radic(lambdat)).

In two-tier networks comprising a conventional cellular network overlaid with shorter range hotspots (e.g. femtocells, distributed antennas, or wired relays) with universal frequency reuse, the near-far effect from cross-tier interference creates dead spots where reliable coverage cannot be guaranteed to users in either tier. Equipping the macrocell and femtocells with multiple antennas enhances robustness against the near-far problem. This work derives the maximum number of simultaneously transmitting multiple antenna femtocells meeting a per-tier outage probability constraint. Coverage dead zones are presented wherein cross-tier interference bottlenecks cellular and femtocell coverage. Two operating regimes are shown namely 1) a cellular-limited regime in which femtocell users experience unacceptable cross-tier interference and 2) a hotspot-limited regime wherein both femtocell users and cellular users are limited by hotspot interference. Our analysis accounts for the per-tier transmit powers, the number of transmit antennas (single antenna transmission being a special case) and terrestrial propagation such as the Rayleigh fading and the path loss exponents. Single-user (SU) multiple antenna transmission at each tier is shown to provide significantly superior coverage and spatial reuse relative to multiuser (MU) transmission. We propose a decentralized carrier-sensing approach to regulate femtocell transmission powers based on their location. Considering a worst-case cell-edge location, simulations using typical path loss scenarios show that our interference management strategy provides reliable cellular coverage with about 60 femtocells per cell-site.

In this work we study the capacity of multi-user multiple-input multiple-output (MU-MIMO) downlink channels with codebook-based limited feedback using real measurement data. Several aspects of MU-MIMO channels are evaluated. Firstly, we compare the sum rate of different MU-MIMO precoding schemes in various channel conditions. Secondly, we study the effect of different codebooks on the performance of limited feedback MU-MIMO. Thirdly, we relate the required feedback rate with the achievable rate on the downlink channel. Real multi-user channel measurement data acquired with the Eurecom MIMO OpenAir Sounder (EMOS) is used. To the best of our knowledge, these are the first measurement results giving evidence of how MU-MIMO precoding schemes depend on the precoding scheme, channel characteristics, user separation, and codebook. For example, we show that having a large user separation as well as codebooks adapted to the second order statistics of the channel gives a sum rate close to the theoretical limit. A small user separation due to bad scheduling or a poorly adapted codebook on the other hand can impair the gain brought by MU-MIMO. The tools and the analysis presented in this paper allow the system designer to trade-off downlink rate with feedback rate by carefully choosing the codebook.

In multi-user multiple-input multiple-output (MU-MIMO) systems, channel state information at the transmitter (CSIT) allows for multi-user spatial multiplexing and thus increases the system throughput. We assume that CSIT is obtained by means of a finite-rate feedback channel through channel vector quantization (CVQ) at the receiver. In this paper we use real channel measurements to study the effect of CVQ on the sum rate of a MU-MIMO system employing linear precoding. The measurement data has been acquired using Eurecom's MIMO Openair Sounder (EMOS). The EMOS can perform realtime MIMO channel measurements synchronously over multiple users. We consider CVQ using a Fourier codebook, a random codebook and a random codebook exploiting the second order statistics of the channel. For comparison, we also show the capacity of a single-user system using time division multiple access (TDMA) with no CSIT at all. The results show that the Fourier codebook shows very poor performance in the measured channels. Random codebooks - although suboptimal - provide a much better performance in the measured channels.

In multi-user multiple-input multiple-output (MU-MIMO) systems, spatial multiplexing can be employed to increase the throughput without the need for multiple antennas and expensive signal processing at the user equipments. In theory, MU-MIMO is also more immune to most of propagation limitations plaguing single-user MIMO (SU-MIMO) systems, such as channel rank loss or antenna correlation. However, in this paper we show that this is not always true. We compare the capacity and the correlation of measured MU-MIMO channels for both outdoor and indoor scenarios. The measurement data has been acquired using Eurecompsilas MIMO openair sounder (EMOS). The EMOS can perform real-time MIMO channel measurements synchronously over multiple users. The results show that in most scenarios MU-MIMO provides a higher throughput than SU-MIMO also in the measured channels. However, in outdoor scenarios with a line of sight, the capacity drops significantly when the users are close together, due to high correlation at the transmitter side of the channel. In such a case, the performance of SU-MIMO and MU-MIMO is comparable.

We consider the downlink of a wireless system with an M-antenna base station and K single-antenna users. A limited feedback-based scheduling and precoding scenario is considered that builds on the multiuser random beamforming (RBF). Such a scheme was shown to yield the same capacity scaling, in terms of multiplexing and multiuser diversity gain, as the optimal full CSIT-based (channel state information at transmitter) precoding scheme, in the large number of users K regime. Unfortunately, for more practically relevant (low to moderate) K values, RBF yields degraded performance. In this work, we investigate solutions to this problem. We introduce a two-stage framework that decouples the scheduling and beamforming problems. In our scenario, RBF is exploited to identify good, spatially separable, users in a first stage. In the second stage, the initial random beams are refined based on the available feedback to offer improved performance toward the selected users. Specifically, we propose beam power control techniques that do not change the direction of the second-stage beams, offering feedback reduction and performance tradeoffs. The common feature of these schemes is to restore robustness of RBF with respect to sparse network settings (low K), at the cost of moderate complexity increase.

We consider a downlink channel where a base station equipped with M transmit antennas communicates with K ≥ M single-antenna receivers and has partial channel knowledge obtained via a limited rate feedback channel. We propose scalar feedback metrics that provide an estimate of the received signal-to-noise plus interference ratio (SINR), which are combined with efficient user selection algorithms and zero-forcing beamforming. The asymptotic system sum rate for large K is analyzed and numerical results are provided, showing the performance of each metric in different scenarios.

The problem of joint linear beamforming and scheduling in a MIMO broadcast channel is considered. We show how orthogonal linear beamforming (OLBF) can be efficiently combined with a low-complexity user selection algorithm to achieve a large portion of the multiuser capacity. The use of orthogonal transmission enables the transmitter to calculate exact signal-to-interference plus noise ratio (SINR) values during the user selection process. The knowledge of multiuser interference proves to be of particular importance for user scheduling as both the number of users in the cell and the average signal-to-noise ratio (SNR) decrease. The sum capacity of our scheme is characterized in the low-SNR regime, providing analytical results on the performance gain over zero-forcing beamforming (ZFBF). Numerical results show gains over both suboptimal and optimal ZFBF techniques in different scenarios.

The problem of joint scheduling and beamforming for a multiuser multiple-input multiple-output (MIMO) network with partial channel state information at the transmitter (CSIT) is addressed here. Unlike most previous work that rely on full instantaneous CSIT and require unacceptable overhead feedback to the transmitter, we point out here that useful information relevant to the scheduler lies untapped in the long term statistical information of the user's channels. We show how statistical CSIT can be efficiently combined with partial instantaneous CSIT to derive a scheduling rule for the downlink of multiuser MIMO systems

MIMO-OFDMA systems using opportunistic beamforming are a promising solution to satisfy the increasing demand in terms of data rate and quality-of-service (QoS). An important practical issue in MIMO-OFDMA systems is the feedback load. As a large number of carriers (e.g. 2048 for WiMax) is usually used in such systems, feeding back full channel state information at the transmitter (CSIT) for each carrier is prohibitive. In this paper, the problem of feedback reduction in MIMO-OFDMA opportunistic beamforming is addressed. We present different partial CSIT schemes that reduce significantly the feedback overload at little expense of system throughput. We additionally investigate different power control strategies that show significant capacity gain for low to moderate number of users over standard opportunistic beamforming approaches

A scheme exploiting memory in opportunistic multiuser beamforming is proposed. The scheme builds on recent advances realized in M. Sharif and B. Hassibi, 2005, in the area of multi-user downlink preceding and scheduling based on partial transmitter channel state information (CSIT). Although the preceding and scheduling done in M. Sharif and B. Hassibi, 2005 is optimal within the set of unitary precoders, it is only so asymptotically for large number of users. Secondly, this scheme is unable to exploit potential time correlation of the channel. In this paper, we show (1) that exploiting memory in the transmitter allows to fill the gap to optimality for fixed (even low) number of users for time correlated channels, (2) how such schemes can be extended to take fairness into account in the proportional fair sense