NextG (5G and beyond) networks, through the increasing integration of cloud/edge computing technologies, are becoming highly distributed compute platforms ideally suited to host emerging resource-intensive and latency-sensitive applications (e.g., industrial automation, extended reality, distributed AI). The end-to-end orchestration of such demanding applications, which involves function/data placement, flow routing, and joint communication/computation/storage resource allocation, requires new models and algorithms able to capture: (i) their disaggregated microservice-based architecture, (ii) their complex processing graph structures, including multiple-input multiple-output processing stages, and (iii) the opportunities to efficiently share and replicate real-time data streams that may be useful for multiple functions and/or end users. To this end, we first identify the technical gaps in existing literature that prevent efficiently addressing the optimal orchestration of emerging applications described by information-aware directed acyclic graphs (DAGs). We then leverage the recently proposed Cloud Network Flow optimization framework and a novel functionally-equivalent DAG-to-Forest graph transformation procedure to design IDAGO (Information-Aware DAG Orchestration), a polynomial-time multi-criteria approximation algorithm for the optimal orchestration of NextG media services over NextG compute-integrated networks. Results show that IDAGO's multiplicative cost reductions over leading baselines scale linearly with aggregate service load, reaching up to 3X gains in scenarios based on AWS and Unreal Engine data under moderate service loads.

Next-generation real-time compute-intensive applications, such as extended reality, multi-user gaming, and autonomous transportation, are increasingly composed of heterogeneous AI-intensive functions with diverse resource requirements and stringent latency constraints. While recent advances have enabled very efficient algorithms for joint service placement, routing, and resource allocation for increasingly complex applications, current models fail to capture the non-linear relationship between delay and resource usage that becomes especially relevant in AI-intensive workloads. In this paper, we extend thecloud network flowoptimization framework to support queueing-delay-aware orchestration of distributed AI applications over edge-cloud infrastructures. We introduce two execution models, Guaranteed-Resource (GR) and Shared-Resource (SR), that more accurately capture how computation and communication delays emerge from system-level resource constraints. These models incorporate M/M/1 and M/G/1 queue dynamics to represent dedicated and shared resource usage, respectively. The resulting optimization problem is non-convex due to the non-linear delay terms. To overcome this, we develop SPARQ, an iterative approximation algorithm that decomposes the problem into two convex sub-problems, enabling joint optimization of service placement, routing, and resource allocation under nonlinear delay constraints. The modeling approach is validated against real-world data. Simulation results demonstrate that the SPARQ not only offers a more faithful representation of system delays, but also substantially improves resource efficiency and the overall cost-delay tradeoff compared to existing state-of-the-art methods.

Timely delivery of delay-sensitive information over dynamic, heterogeneous networks is increasingly essential for a range of interactive applications, such as industrial automation, self-driving vehicles, and augmented reality. However, most existing network control solutions target onlyaveragedelay performance, falling short of providing strict End-to-End (E2E) peak latency guarantees. This paper addresses the challenge of reliably delivering packets within application-imposed deadlines by leveraging recent advancements in Multi-Agent Deep Reinforcement Learning (MA-DRL). After introducing the Delay-Constrained Maximum-Throughput (DCMT) dynamic network control problem, and highlighting the limitations of current solutions, we present a novel MA-DRL network control framework that leverages a centralized routing and distributed scheduling architecture. The proposed framework leverages critical networking domain knowledge for the design of effective MA-DRL strategies based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) technique, where centralized routing and distributed scheduling agents dynamically assign paths and schedule packet transmissions according to packet lifetimes, thereby maximizing on-time packet delivery. The generality of the proposed framework allows integrating both data-driven Deep Reinforcement Learning (DRL) agents and traditional rule-based policies in order to strike the right balance between performance and learning complexity. Our results confirm the superiority of the proposed framework with respect to traditional stochastic optimization-based approaches and provide key insights into the role and interplay between data-driven DRL agents and new rule-based policies for both efficient and high-performance control of latency-critical services.

Exploring underused spectrum bands such as the mmWave band is key to solve future wireless communications necessities. This work studies the problem of channel estimation in multi-antena systems in this high-frequency band considering the few propagation parameters that are unveiled due to the sparse propagation nature. We address the channel estimation by applying atomic norm as a gridless multidimensional spectral estimation. The applied methodology is compared in different scenarios with several on-grid spectral estimation approaches and also with traditional non-parametrical methods.

Data-intensive augmented information (AgI) services (e.g., metaverse applications such as virtual/augmented reality), designed to deliver highly interactive experiences resulting from the real-time combination of live data-streams and pre-stored digital content, are accelerating the need for distributed compute platforms with unprecedented storage, computation, and communication requirements. To this end, the integrated evolution of next-generation networks (5G/6G) and distributed cloud technologies (mobile/edge/cloud computing) have emerged as a promising paradigm to address the interaction- and resource-intensive nature of data-intensive AgI services. In this paper, we focus on the design of control policies for the joint orchestration of compute, caching, and communication (3C) resources in next-generation 3C networks for the delivery of data-intensive AgI services. We design the first throughput-optimal control policy that coordinates joint decisions around (i) routing paths and processing locations for live data streams, with (ii) cache selection and distribution paths for associated data objects. We then extend the proposed solution to include a max-throughput data placement policy and two efficient replacement policies. Numerical results demonstrate the superior performance obtained via the novel multi-pipeline flow control and 3C resource orchestration mechanisms of the proposed policy, compared with state-of-the-art algorithms that lack full 3C integrated control.

We consider a cellular network, where the uplink transmissions to a base station (BS) are interferenced by other devices, a condition that may occur, e.g., in cell-free networks or when using non-orthogonal multiple access (NOMA) techniques. Assuming that the BS treats this interference as additional noise, we focus on the problem of estimating the interference correlation matrix from received signal samples. We consider a BS equipped with multiple antennas and operating in the millimeter-wave (mmWave) bands and propose techniques exploiting the fact that channels comprise only a few reflections at these frequencies. This yields a specific structure of the interference correlation matrix that can be decomposed into three matrices, two rectangular depending on the angle of arrival (AoA) of the interference and the third square with smaller dimensions. We resort to gridless approaches to estimate the AoAs and then project the least square estimate of the interference correlation matrix into a subspace with a smaller dimension, thus reducing the estimation error. Moreover, we derive two simplified estimators, still based on the gridless angle estimation that turns out to be convenient when estimating the interference over a larger number of samples.

Emerging distributed cloud architectures, e.g., fog and mobile edge computing, are playing an increasingly important role in the efficient delivery of real-time stream-processing applications (also referred to as augmented information services), such as industrial automation and metaverse experiences (e.g., extended reality, immersive gaming). While such applications require processed streams to be shared and simultaneously consumed by multiple users/devices, existing technologies lack efficient mechanisms to deal with their inherent multicast nature, leading to unnecessary traffic redundancy and network congestion. In this paper, we establish a unified framework for distributed cloud network control with generalized (mixed-cast) traffic flows that allows optimizing the distributed execution of the required packet processing, forwarding, and replication operations. We first characterize the enlarged multicast network stability region under the new control framework (with respect to its unicast counterpart). We then design a novel queuing system that allows scheduling data packets according to their current destination sets, and leverage Lyapunov drift-plus-penalty control theory to develop the first fully decentralized, throughput- and cost-optimal algorithm for multicast flow control. Numerical experiments validate analytical results and demonstrate the performance gain of the proposed design over existing network control policies.

Next-generation distributed computing networks (e.g., edge and fog computing) enable the efficient delivery of delay-sensitive, compute-intensive applications by facilitating access to computation resources in close proximity to end users. Many of these applications (e.g., augmented/virtual reality) are also data-intensive: in addition to user-specific (live) data streams, they require access to shared (static) digital objects (e.g., image database) to complete the required processing tasks. When required objects are not available at the servers hosting the associated service functions, they must be fetched from other edge locations, incurring additional communication cost and latency. In such settings, overall service delivery performance shall benefit from jointly optimized decisions around (i) routing paths and processing locations for live data streams, together with (ii) cache selection and distribution paths for associated digital objects. In this paper, we address the problem of dynamic control of data-intensive services over edge cloud networks. We characterize the network stability region and design the first throughput-optimal control policy that coordinates processing and routing decisions for both live and static data-streams. Numerical results demonstrate the superior performance (e.g., throughput, delay, and resource consumption) obtained via the novel multi-pipeline flow control mechanism of the proposed policy, compared with state-of-the-art algorithms that lack integrated stream processing and data distribution control.

Parametrical channel estimation enables not only the expected large spectral efficiency in MIMO systems but also unveils relevant propagation parameters and allows low complexity representation of the channel matrix that facilitates massive deployments of antennas. In this work we propose to apply atomic norm as a gridless multidimensional spectral estimation approach to address parametrical channel estimation where both transmitter AoD and receiver AoA are identified. The proposed methodology is compared with several state of the art approaches based on grid recovery.

The DIWINE project aims to play a salient role in the Smart and Sustainable Agriculture industry by enabling creation of a Digital Twin platform for a vineyard. It is conceived as a disruptive solution based on the use of: Unmanned Aerial Vehicles (UAVs), 5G, edge/cloud computing, Machine Learning (ML) and Artificial Intelligence (AI). The platform leverages key 5G technologies such as 5G New Radio (NR) and Multi Access Edge computing (MEC) to remotely control the UAVs and to transfer captured high-resolution images to the cloud. Moreover, the computational power of MEC and central cloud computing enables the use of ML and AI algorithms to process captured data and transform it into a highly accurate Digital Twin. The winemaker has an immediate and flexible access to the Digital Twin platform and is also able to integrate existing technologies, such as IoT sensors and weather forecasts. DIWINE allows: an efficient management of the vineyard, accurately differentiating the final product, simulating different possible scenarios, and optimizing the farm’s consumption, supporting the winemaker to minimize missed harvests risk, and optimizing profitability.

We are entering a rapidly unfolding future driven by the delivery of real-time computation services, such as industrial automation and augmented reality, collectively referred to as augmented information (AgI) services, over highly distributed cloud/edge computing networks. The interaction intensive nature of AgI services is accelerating the need for networking solutions that provide strict latency guarantees. In contrast to most existing studies that can only characterize average delay performance, we focus on the critical goal of delivering AgI services ahead of corresponding deadlines on a per-packet basis, while minimizing overall cloud network operational cost. To this end, we design a novel queuing system able to track data packets’ lifetime and formalize thedelay-constrained least-cost dynamic network control problem. To address this challenging problem, we first study the setting with average capacity (or resource budget) constraints, for which we characterize the delay-constrained stability region and design a throughput-optimal control policy leveraging Lyapunov optimization theory on an equivalent virtual network. Guided by the same principle, we tackle the peak capacity constrained scenario by developing thereliable cloud network control(RCNC) algorithm, which employs a two-way optimization method to make actual and virtual network flow solutions converge in an iterative manner. Extensive numerical results show the superior performance of the proposed control policy compared with the state-of-the-art cloud network control algorithm, and the value of guaranteeing strict end-to-end deadlines for the delivery of next-generation AgI services.

The timely delivery of resource-intensive and latency-sensitive services (e.g., industrial automation, augmented reality) over distributed computing networks (e.g., mobile edge computing) is drawing increasing attention. Motivated by the insufficiency of average delay performance guarantees provided by existing studies, we focus on the critical goal of delivering next generation real-time services ahead of corresponding deadlines on a per-packet basis, while minimizing overall cloud network resource cost. We introduce a novel queuing system that is able to track data packets’ lifetime and formalize the optimal cloud network control problem with strict deadline constraints. After illustrating the main challenges in delivering packets to their destinations before getting dropped due to lifetime expiry, we construct an equivalent formulation, where relaxed flow conservation allows leveraging Lyapunov optimization to derive a provably near-optimal fully distributed algorithm for the original problem. Numerical results validate the theoretical analysis and show the superior performance of the proposed control policy compared with state-of-the-art cloud network control.

Distributed computing (cloud) networks, e.g., mobile edge computing (MEC), are playing an increasingly important role in the efficient hosting, running, and delivery of real-time stream-processing applications such as industrial automation, immersive video, and augmented reality. While such applications require timely processing of real-time streams that are simultaneously useful for multiple users/devices, existing technologies lack efficient mechanisms to handle their increasingly multicast nature, leading to unnecessary traffic redundancy and associated network congestion. In this paper, we address the design of distributed packet processing, routing, and duplication policies for optimal control of multicast stream-processing services. We present a characterization of the enlarged capacity region that results from efficient packet duplication, and design the first fully distributed multicast traffic management policy that stabilizes any input rate in the interior of the capacity region while minimizing overall operational cost. Numerical results demonstrate the effectiveness of the proposed policy to achieve throughput- and cost-optimal delivery of stream-processing services over distributed computing networks.

Distributed computing networks, tasked with both packet transmission and processing, require the joint optimization of communication and computation resources. We develop a dynamic control policy that determines both routes and processing locations for packets upon their arrival at a distributed computing network. The proposed policy, referred to as Universal Computing Network Control (UCNC), guarantees that packets i) are processed by a specified chain of service functions, ii) follow cycle-free routes between consecutive functions, and iii) are delivered to their corresponding set of destinations via proper packet duplications. UCNC is shown to be throughput-optimal for any mix of unicast and multicast traffic, and is the first throughput-optimal policy for non-unicast traffic in distributed computing networks with both communication and computation constraints. Moreover, simulation results suggest that UCNC yields substantially lower average packet delay compared with existing control policies for unicast traffic.

A full multi-dimensional characterization of the angle of arrival (AoA) has immediate applications to the efficient operation of modern wireless communication systems. In this work, we develop a compressed sensing based method to extract multi-dimensional AoA information exploiting the sparse nature of the signal received by a sensor array. The proposed solution, based on the atomicl0norm, enables accurate gridless resolution of the AoA in systems with arbitrary 3D antenna arrays. Our approach allows characterizing the maximum number of distinct sources (or scatters) that can be identified for a given number of antennas and array geometry. Both noiseless and noisy measurement scenarios are addressed, deriving and evaluating the resolvability of the AoA propagation parameters through a multi-level Toeplitz matrix rank\nolimits-minimization problem. To facilitate the implementation of the proposed solution, we also present a least squares approach regularized by a convex relaxation of the rank\nolimits-minimization problem and characterize its conditions for resolvability.

In this work, we study the joint detection and localization of multiple delay- and Doppler-spread targets through an opportunistic radar exploiting mmWave communication signals. The problem is formulated as the identification of an unknown number of active subspaces in a large family of subspaces, accounting for the possible positions of potential targets in the delay-Doppler domain: the resulting testing problem is composite and multi-hypothesis. At first, we derive a solution based on the generalized information criterion (GIC), whose complexity is however prohibitive; then, we propose an approximated form of the GIC-based receiver and derive two iterative data-adaptive strategies, both extracting and eliminating the superimposed back-scattered subspace signals one-by-one. Leveraging the IEEE 802.11ad standard, a short-range low-mobility application is discussed to validate the merits of the proposed procedures in terms of detection and localization capabilities, robustness to multi-target interference, and achievable resolution.

The geometric block model is a recently proposed generative model for random graphs that is able to capture the inherent geometric properties of many community detection problems, providing more accurate characterizations of practical community structures compared with the popular stochastic block model. Galhotra et al. recently proposed a motif-counting algorithm for unsupervised community detection in the geometric block model that is proved to be near-optimal. They also characterized the regimes of the model parameters for which the proposed algorithm can achieve exact recovery. In this work, we initiate the study of active learning in the geometric block model. That is, we are interested in the problem of exactly recovering the community structure of random graphs following the geometric block model under arbitrary model parameters, by possibly querying the labels of a limited number of chosen nodes. We propose two active learning algorithms that combine the use of motif-counting with two different label query policies. Our main contribution is to show that sampling the labels of a vanishingly small fraction of nodes (sub-linear in the total number of nodes) is sufficient to achieve exact recovery in the regimes under which the state-of-the-art unsupervised method fails. We validate the superior performance of our algorithms via numerical simulations on both real and synthetic datasets.

As mobile edge computing (MEC) finds widespread use for relieving the computational burden of compute- and interaction-intensive applications on end user devices, understanding the resulting delay and cost performance is drawing significant attention. While most existing works focus on single-task offloading in single-hop MEC networks, next generation applications (e.g., industrial automation, augmented/virtual reality) require advance models and algorithms for dynamic configuration of multi-task services over multi-hop MEC networks. In this work, we leverage recent advances in dynamic cloud network control to provide a comprehensive study of the performance of multi-hop MEC networks, addressing the key problems of multi-task offloading, timely packet scheduling, and joint computation and communication resource allocation. We present a fully distributed algorithm based on Lyapunov control theory that achieves throughput-optimal performance with delay and cost guarantees. Simulation results validate our theoretical analysis and provide insightful guidelines on the interplay between communication and computation resources in MEC networks.

In this work, we propose a new single-RF MIMO architecture which enjoys high scalability and energy-efficiency. The transmitter in this proposal consists of a single RF illuminator radiating towards a reflecting surface. Each element on the reflecting surface re-transmits its received signal after applying a phase-shift, such that a desired beamforming pattern is obtained. For this architecture, the problem of beamforming is interpreted as linear regression and a solution is derived via the method of least-squares. Using this formulation, a fast iterative algorithm for tuning of the reflecting surface is developed. Numerical results demonstrate that the proposed architecture is fully compatible with current designs of reflecting surfaces.

Semi-supervised classification, one of the most prominent fields in machine learning, studies how to combine the statistical knowledge of the often abundant unlabeled data with the often limited labeled data in order to maximize overall classification accuracy. In this context, the process of actively choosing the data to be labeled is referred to as active learning. In this paper, we initiate the non-asymptotic analysis of the optimal policy for semi-supervised classification with actively obtained labeled data. Considering a general Bayesian classification model, we provide the first characterization of the jointly optimal active learning and semi-supervised classification policy, in terms of the cost-performance tradeoff driven by the label query budget (number of data items to be labeled) and overall classification accuracy. Leveraging recent results on the Rényi Entropy, we derive tight information-theoretic bounds on such active learning cost-performance tradeoff.

Recent advances in network virtualization and programmability enable innovative service models such as Service Chaining (SC), where flows can be steered through a pre-defined sequence of service functions deployed at different cloud locations. A key aspect dictating the performance and efficiency of a SC is its instantiation onto the physical infrastructure. While existing SC Embedding (SCE) algorithms can effectively address the instantiation of SCs consuming computation and communication resources, they lack efficient mechanisms to handle the increasing data-intensive nature of next-generation services. Differently from computation and communication resources, which are allocated in a dedicated per request manner, storage resources can be shared to satisfy multiple requests for the same data. To fill this gap, in this paper, we formulate the data-intensive SCE problem with the goal of minimizing storage, computation, and communication resource costs subject to resource capacity, service chaining, and data sharing constraints. Using a randomized rounding technique that exploits a novel data-aware linear programming decomposition procedure, we develop a multi-criteria approximation algorithm with provable performance guarantees. Evaluation results show that the proposed algorithm achieves near-optimal resource costs with up to 27.8% of the cost savings owed to the sharing of the data.

The multi-version coding problem is a recently formulated information-theoretic framework to study the storage cost of consistent key-value data stores. Previous work on multi-version coding considered a completely decentralized asynchronous system where the nodes (servers) are not aware of which updates (versions) of the data are received by the other nodes. In this paper, we relax this assumption and study a system where a node acquires side information of the versions propagated to some other nodes based on the network topology. Specifically, we study a storage system with n nodes over a graph that stores ν totally ordered versions of an object (message). Each node receives a subset of these ν versions. A node is aware of which versions that were received by its neighbors in the network graph. Our code constructions show that the side information can result in a better storage cost as compared with the case where the nodes do not exchange side information for some regimes at the expense of the additional latency and the negligible communication overhead of exchanging the side information. Through an information-theoretic converse, we identify surprising scenarios where exchanging tremendous amount of side information does not reduce the storage cost. Finally, we present a case study over Amazon web services (AWS) that demonstrates the potential storage cost reductions of our code constructions.

This paper studies the fundamental limits of content delivery in a cache-aided broadcast network for correlated content generated by a discrete memoryless source with arbitrary joint distribution. Each receiver is equipped with a cache of equal capacity, and the requested files are delivered over a shared error-free broadcast link. A class of achievable correlation-aware schemes based on a two-step source coding approach is proposed. Library files are first compressed, and then cached and delivered using a combination of multiple-request caching schemes that are agnostic to the content correlations. The first step uses Gray-Wyner source coding to represent the library via private descriptions and descriptions that are common to more than one file. The second step then becomes a multiple-request caching problem, where the demand structure is dictated by the configuration of the compressed library, and it is interesting in its own right. The performance of the proposed two-step scheme is evaluated by comparing its achievable rate with a lower bound on the optimal peak and average rate-memory trade-offs in a two-file multiple-receiver network, and in a three-file two-receiver network. Specifically, in a network with two files and two receivers, the achievable rate matches the lower bound for a significant memory regime and it is within half of the conditional entropy of files for all other memory values. In the three-file two-receiver network, the two-step strategy achieves the lower bound for large cache capacities, and it is within half of the joint entropy of two of the sources conditioned on the third one for all other cache sizes.

The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network edge, in proximity to the end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be pre-stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in dense MEC networks with multidimensional constraints. We show that this problem generalizes several well-known placement and routing problems and propose an algorithm that achieves close-to-optimal performance using a randomized rounding technique. Evaluation results demonstrate that our approach can effectively utilize available storage, computation, and communication resources to maximize the number of requests served by low-latency edge cloud servers.

Caching at the wireless edge can be used to keep up with the increasing demand for high-definition wireless video streaming. By prefetching popular content into memory at wireless access points or end-user devices, requests can be served locally, relieving strain on expensive backhaul. In addition, using network coding allows the simultaneous serving of distinct cache misses via common coded multicast transmissions, resulting in significantly larger load reductions compared to those achieved with traditional delivery schemes. Most prior works simply treat video content as fixed-size files that users would like to fully download. This work is motivated by the fact that video can be coded in a scalable fashion and that the decoded video quality depends on the number of layers a user receives in sequence. Using a Gaussian source model, caching and coded delivery methods are designed to minimize the squared error distortion at end-user devices in a rate-limited caching network. The framework is very general and accounts for heterogeneous cache sizes, video popularities and user-file play-back qualities. As part of the solution, a new decentralized scheme for lossy cache-aided delivery subject to preset user distortion targets is proposed, which further generalizes prior literature to a setting with file heterogeneity.

The learning data requirements are analyzed for the construction of stealth attacks in state estimation. In particular, the training data set is used to compute a sample covariance matrix that results in a random matrix with a Wishart distribution. The ergodic attack performance is defined as the average attack performance obtained by taking the expectation with respect to the distribution of the training data set. The impact of the training data size on the ergodic attack performance is characterized by proposing an upper bound for the performance. Simulations on the IEEE 30-Bus test system show that the proposed bound is tight in practical settings.

Hybrid analog-digital architectures are considered as promising candidates for implementing millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems since they enable a considerable reduction of the required number of costly radio frequency (RF) chains by moving some of the signal processing operations into the analog domain. However, the analog feed network, comprising RF dividers, combiners, phase shifters, and line connections, of hybrid MIMO architectures is not scalable due to its prohibitively high power consumption for large numbers of transmit antennas. Motivated by this limitation, in this paper, we study novel massive MIMO architectures, namely reflect-array (RA) and transmit-array (TA) antennas. We show that the precoders for RA and TA antennas have to meet different constraints compared to those for conventional MIMO architectures. Taking these constraints into account and exploiting the sparsity of mmWave channels, we design an efficient precoder for RA and TA antennas based on the orthogonal matching pursuit algorithm. Furthermore, in order to fairly compare the performance of RA and TA antennas with conventional fully-digital and hybrid MIMO architectures, we develop a unified power consumption model. Our simulation results show that unlike conventional MIMO architectures, RA and TA antennas are highly energy efficient and fully scalable in terms of the number of transmit antennas.

Real-time stream-processing (RTSP) services, such as telepresence, augmented reality, and real-time computer vision, allow end users to consume personalized media streams that result from the real-time processing of live sources via possibly multiple service functions (or stream processing operators) distributed throughout a cloud network. We consider the problem of optimizing the distribution of RTSP services over a cloud network, which requires the placement of stream processing operators, the routing of streams through the appropriate sequence of operators and the associated allocation of cloud and network resources. We show that existing formulations based on virtual network embedding cannot capture key features of RTSP services such as flow/function replication, and provide a new cloud network flow based formulation that captures arbitrary function and flow chaining, scaling, and replication. We then design two polynomial-time algorithms with bi-criteria approximation guarantees. To the best of our knowledge, these are the first approximation algorithms for the optimization of distributed computing services with arbitrary function/flow chaining, scaling, and replication. We finally illustrate the performance of our algorithms via simulations in practical cloud network settings.

The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network-periphery, in proximity to end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in MEC-enabled multi-cell networks with multidimensional (storage-computation-communication) constraints. We show that this problem generalizes several problems in literature and propose an algorithm that achieves close-to-optimal performance using randomized rounding. Evaluation results demonstrate that our approach can effectively utilize the available resources to maximize the number of requests served by low-latency edge cloud servers.

Network virtualization and programmability allow operators to deploy a wide range of services over a common physical infrastructure and elastically allocate cloud and network resources according to changing requirements. While the elastic reconfiguration of virtual resources enables dynamically scaling capacity in order to support service demands with minimal operational cost, reconfiguration operations make resources unavailable during a given time period and may incur additional cost. In this paper, we address the dynamic cloud network control problem under non-negligible reconfiguration delay and cost. We show that while the capacity region remains unchanged regardless of the reconfiguration delay/cost values, a reconfiguration-agnostic policy may fail to guarantee throughput-optimality and minimum cost under nonzero reconfiguration delay/cost. We then present an adaptive dynamic cloud network control policy that allows network nodes to make local flow scheduling and resource allocation decisions while controlling the frequency of reconfiguration in order to support any input rate in the capacity region and achieve arbitrarily close to minimum cost for any finite reconfiguration delay/cost values.

In Time Division Duplex reciprocity-based massive MIMO, it is essential to compute the downlink precoding matrix over all OFDM resource blocks within a small fraction of the uplink-downlink slot duration. Because of this harsh computation latency constraint, early implementations of massive MIMO considered the simple Conjugate Beamforming (ConjBF) precoding method. On the other hand, it is well-known that in the regime of a large but finite number of antennas, the Regularized Zero-Forcing (RZF) precoding is generally much more effective than ConjBF. In order to close the gap between ConjBF and RZF, while meeting the latency constraint, truncated polynomial expansion (TPE) methods have been proposed. In this paper, we present a novel TPE method that outperforms previously proposed methods in the non-symmetric case of users with different channel correlations, subject to the condition that the covariance matrices of the user channel vectors can be approximated, for a large number of antennas, by a family of matrices with common eigenvectors. This condition is met, for example, by uniform linear and uniform planar arrays in far-field conditions. The proposed method is computationally simple and lends itself to classical power allocation optimization such as min-sum power and max-min rate. We provide a detailed analysis of the computation latency vs computation resources, specifically targeted to a highly parallel FPGA hardware architecture. We conclude that the proposed TPE method can effectively close the performance gap between ConjBF and RZF with computation latency of less than one LTE OFDM symbol, as assumed in Marzetta's work on massive MIMO.

Distributed computing networks, tasked with both packet transmission and processing, require the joint optimization of communication and computation resources. We develop a dynamic control policy that determines both routes and processing locations for packets upon their arrival at a distributed computing network. The proposed policy, referred to as Universal Computing Network Control (UCNC), guarantees that packets i) are processed by a specified chain of service functions, ii) follow cycle-free routes between consecutive functions, and iii) are delivered to their corresponding set of destinations via proper packet duplications. UCNC is shown to be throughput-optimal for any mix of unicast and multicast traffic, and is the first throughput-optimal policy for non-unicast traffic in distributed computing networks with both communication and computation constraints. Moreover, simulation results suggest that UCNC yields substantially lower average packet delay compared with existing control policies for unicast traffic.

In applications of storage systems to modern key-value stores, the stored data is highly dynamic due to frequent updates from the system write clients. The multi-version coding problem has been formulated to study the cost of storing dynamic data in asynchronous distributed storage systems. In this problem, previous work considered a completely decentralized system where a server is not aware of which versions of the data are received by the other servers. In this paper, we relax this assumption and study a system where a server may acquire side information of the versions propagated to some other servers. In particular, we study a storage system with n servers that store v totally ordered independent versions of a message. Each server receives a subset of theseνversions that defines the state of that server. Assuming that the servers are distributed in a ring, a server is aware of which versions have been received by itsh-hop neighbors. If the server is aware of the states of (n- 2) other servers, we show that this side information can result in a better storage cost as compared with the case where there is no side information. Through an information-theoretic converse, we identify scenarios where, even if the server is aware of the states of (n-3) /2 other servers, the side information may not help in improving the worst-case storage cost beyond the case where servers have no side information.

In this paper we look after the secure delivery of multimedia content across an innovative Content Delivery Network comprising both satellite and terrestrial trunks. The CDN in question leverages state-of-the-art technologies both at the edges and in the core of the integrated distribution architecture and highly relies upon in-network caching strategies in order to improve its performance. We propose an architecture envisaging a combination of multicast, simulcast and unicast communication scenarios where satellite links are exploited to support local in-network caching. We analyse integrated network deployment scenarios where the satellite acts as the interconnection link between distributed in-network caches and a terrestrial CDN and/or feeds edge-network caches at micro-centre locations.

We study cloud resource control in the global-local distributed cloud infrastructure. We firstly model and formulate the problem while capturing the multiple challenges such as the inter-dependency between resources and the uncertainty in the inputs. We then propose a novel online algorithm which, via the regularization technique, decouples the original problem into a series of subproblems for individual time slots and solves both the subproblems and the original problem over every prediction time window to jointly make resource allocation decisions. Compared against the offline optimum with accurate inputs, our approach maintains a provable parameterized worst-case performance gap with only inaccurate inputs under certain conditions. Finally, we conduct evaluations with large-scale, real-world data traces and show that our solution outperforms existing methods and works efficiently with near-optimal cost in practice.

Cache-aided coded multicast leverages side information at wireless edge caches to efficiently serve multiple unicast demands via common multicast transmissions, leading to load reductions that are proportional to the aggregate cache size. However, the increasingly dynamic, unpredictable, and personalized nature of the content that users consume challenges the efficiency of existing caching-based solutions in which only exact content reuse is explored. This paper generalizes the cache-aided coded multicast problem to specifically account for the correlation among content files, such as, for example, the one between updated versions of dynamic data. It is shown that: 1) caching content pieces based on their correlation with the rest of the library and 2) jointly compressing requested files using cached information as references during delivery, can provide load reductions that go beyond those achieved with existing schemes. This is accomplished via the design of a class of correlation-aware achievable schemes, shown to significantly outperform the state-of-the-art correlation-unaware solutions. Our results show that as we move towards real-time and/or personalized media dominated services, where exact cache hits are almost non-existent but updates can exhibit high levels of correlation, network cached information can still be useful as references for network compression.

Augmented information (AgI) services allow users to consume information that results from the execution of a chain of service functions that process source information to create real-time augmented value. Applications include real-time analysis of remote sensing data, real-time computer vision, personalized video streaming, and augmented reality, among others. We consider the problem of optimal distribution of AgI services over a wireless computing network, in which nodes are equipped with both communication and computing resources. We characterize the wireless computing network capacity region and design a joint flow scheduling and resource allocation algorithm that stabilizes the underlying queuing system while achieving a network cost arbitrarily close to the minimum, with a tradeoff in network delay. Our solution captures the unique chaining and flow scaling aspects of AgI services while exploiting the use of the broadcast approach coding scheme over the wireless channel.

In an augmented information (Agi) service, users consume information that results from the execution of a chain of service functions that process source information to create real-time augmented value. Applications may include real-time analysis of remote sensing data, real-time computer vision, personalized video streaming, and augmented reality, among others. We consider the problem of optimal distribution of AgI services over a wireless computing network, in which nodes are equipped with both communication and computing resources. We characterize the wireless computing network capacity region and design a joint flow scheduling and resource allocation algorithm that stabilizes the underlying queuing system while achieving arbitrarily close to minimum network cost, with a tradeoff in network delay. Our solution captures the unique chaining and flow scaling aspects of AgI services, while exploiting the use of the broadcast approach over the wireless channel.

Distributed cloud networking builds on network functions virtualization (NFV) and software defined networking (SDN) to enable the deployment of network services in the form of elastic virtual network functions (VNFs) instantiated over general purpose servers at distributed cloud locations. We address the design of fast approximation algorithms for the NFV service distribution problem (NSDP), whose goal is to determine the placement of VNFs, the routing of service flows, and the associated allocation of cloud and network resources that satisfy client demands with minimum cost. We show that in the case of load-proportional costs, the resulting fractional NSDP can be formulated as a multi-commodity-chain flow problem on a cloud-augmented graph, and design a queue-length based algorithm, named QNSD, that provides an O(ε) approximation in time O (1/ε). We then address the case in which resource costs are a function of the integer number of allocated resources and design a variation of QNSD that effectively pushes for flow consolidation into a limited number of active resources to minimize overall cloud network cost.

This paper studies the fundamental limits of caching in a network with two receivers and two files generated by a two-component discrete memoryless source with arbitrary joint distribution. Each receiver is equipped with a cache of equal capacity, and the requested files are delivered over a shared error-free broadcast link. First, a lower bound on the optimal peak rate-memory trade-off is provided. Then, in order to leverage the correlation among the library files to alleviate the load over the shared link, a two-step correlation-aware cache-aided coded multicast (CACM) scheme is proposed. The first step uses Gray-Wyner source coding to represent the library via one common and two private descriptions, such that a second correlation-unaware multiple-request CACM step can exploit the additional coded multicast opportunities that arise. It is shown that the rate achieved by the proposed two-step scheme matches the lower bound for a significant memory regime and it is within half of the conditional entropy for all other memory values.

Coded caching is a problem where encoded broadcasts are used to satisfy users requesting popular files and having caching capabilities. Recent work by Maddah-Ali and Niesen showed that it is possible to satisfy a scaling number of users with only a constant number of broadcast transmissions by exploiting coding and caching. Unfortunately, all previous schemes required the splitting of files into an exponential number of packets before the significant coding gains of caching appeared. The question of what can be achieved with polynomial subpacketization (in the number of users) has been a central open problem in this area. We resolve this problem and present the first coded caching scheme with polynomial (in fact, linear) subpacketization. We obtain a number of transmissions that is not constant, but can be any polynomial in the number of users with an exponent arbitrarily close to zero. Our central technical tool is a direct connection between Ruzsa-Szeméredi graphs and coded caching schemes with linear file size.

We consider the canonical shared link caching network formed by a source node, hosting a library of m information messages (files), connected via a noiseless multicast link to n user nodes, each equipped with a cache of size M files. Users request files independently at random according to an a-priori known demand distribution q. A coding scheme for this network consists of two phases: cache placement and delivery. The cache placement is a mapping of the library files onto the user caches that can be optimized as a function of the demand statistics, but is agnostic of the actual demand realization. After the user demands are revealed, during the delivery phase the source sends a codeword (function of the library files, cache placement, and demands) to the users, such that each user retrieves its requested file with arbitrarily high probability. The goal is to minimize the average transmission length of the delivery phase, referred to as rate (expressed in channel symbols per file). In the case of deterministic demands, the optimal min-max rate has been characterized within a constant multiplicative factor, independent of the network parameters. The case of random demands was previously addressed by applying the order-optimal min-max scheme separately within groups of files requested with similar probability. However, no complete characterization of order-optimality was previously provided for random demands under the average rate performance criterion. In this paper, we consider the random demand setting and, for the special yet relevant case of a Zipf demand distribution, we provide a comprehensive characterization of the order-optimal rate for all regimes of the system parameters, as well as an explicit placement and delivery scheme achieving order-optimal rates. We present also numerical results that confirm the superiority of our scheme with respect to previously proposed schemes for the same setting.

The problem of dynamic resource allocation for service provisioning in multi-tier distributed clouds is particularly challenging due to the coexistence of several factors: the need for joint allocation of cloud and network resources, the need for online decision-making under time-varying service demands and resource prices, and the reconfiguration cost associated with changing resource allocation decisions. We study this problem from an online optimization perspective to address all these challenges. We design an online algorithm that decouples the original offline problem over time by constructing a series of regularized subproblems, solvable at each corresponding time slot using the output of the previous time slot. We prove that, without prediction beyond the current time slot, our algorithm achieves a parameterized competitive ratio for arbitrarily dynamic workloads and resource prices. If prediction is available, we demonstrate that existing prediction-based control algorithms lack worst case performance guarantees for our problem, and we design two novel predictive control algorithms that inherit the theoretical guarantees of our online algorithm, while exhibiting improved practical performance. We conduct evaluations in a variety of settings based on real-world dynamic inputs and show that, without prediction, our online algorithm achieves up to nine times total cost reduction compared with the sequence of greedy one-shot optimizations and at most three times the offline optimum; with moderate predictions, our control algorithms can achieve two times total cost reduction compared with existing prediction-based algorithms.

One of the main challenges for continued wireless capacity growth is the difficulty in exploiting the multicast nature of the wireless medium: wireless end points rarely experience the same channel conditions or access the same content at the same time. In this paper, we present and analyze a novel wireless video delivery paradigm based on the combined use of channel-aware caching and coded multicasting that allows simultaneously serving multiple cache-enabled access points that may be requesting different content and experiencing different channel conditions. To this end, we reformulate the caching-aided coded multicast problem as a joint source-channel coding problem and design an achievable scheme that preserves the cache-enabled multiplicative throughput gains of the error-free scenario, by guaranteeing per-receiver (access point) rates unaffected by the presence of receivers with worse channel conditions.

Distributed cloud networking enables the deployment of network services in the form of interconnected virtual network functions instantiated over general purpose hardware at multiple cloud locations distributed across the network. The service distribution problem is to find the placement of virtual functions and the routing of network flows that meet a given set of demands with minimum cost. In this paper, we address the design of distributed online solutions that drive local routing, processing, and resource allocation decisions while providing global objective guarantees. We present a distributed joint transmission-processing flow scheduling and resource allocation algorithm that stabilizes the underlying cloud network queuing system, while achieving arbitrarily close to minimum average network cost (with a tradeoff in network delay) with probability 1. We further enhance our algorithm with a shortest transmission-plus-processing distance bias that improves the delay performance without compromising throughput or overall cloud network cost. We provide simulation results that confirm our theoretical analysis, illustrate the effect of the shortest transmission-plus-processing distance bias, and demonstrate remarkably good convergence to the optimal cloud network configuration.

In the emerging edge computing paradigm, small-scale highly distributed edge clouds are on the service path between end users and conventional large-scale clouds at the Internet core. A crucial problem that needs to be addressed in order to drive cost and performance in this multi-tier distributed infrastructure is the dynamic and joint allocation of cloud and network resources, which is particularly challenging due to the coexistence of several factors: the reconfiguration cost associated to changing resource allocation decisions over time, the constantly varying and often unpredictable nature of service demands, as well as the heterogeneity of distributed resources. We study the problem of resource allocation and reconfiguration in the multi-tier resource pool from an online optimization perspective that addresses all the challenges above. Our approach decouples the original problem over time by constructing a series of subproblems that are solvable at each corresponding time slot using the output of the previous time slot. Via solid formal analysis, we prove that, without any lookahead beyond the current time slot, our online algorithm provides a solution with a parameterized competitive ratio for any arbitrarily dynamic workload and operating price. We conduct extensive evaluations in a variety of settings based on a number of clouds and real-world workloads with regular and flash crowd fluctuations, and demonstrate that our online algorithm performs well in practice, achieving up to 9× total cost reduction than the sequence of one-shot optimizations and at most 3× the offline optimum.

Cache-aided coded multicast leverages side information at wireless edge caches to efficiently serve multiple groupcast demands via common multicast transmissions, leading to load reductions that are proportional to the aggregate cache size. However, the increasingly unpredictable and personalized nature of the content that users consume challenges the efficiency of existing caching-based solutions in which only exact content reuse is explored. This paper generalizes the cache-aided coded multicast problem to a source compression with distributed side information problem that specifically accounts for the correlation among the content files. It is shown how joint file compression during the caching and delivery phases can provide load reductions that go beyond those achieved with existing schemes. This is accomplished through a lower bound on the fundamental rate-memory trade-off as well as a correlation-aware achievable scheme, shown to significantly outperform state-of-the-art correlation-unaware solutions, while approaching the limiting rate-memory trade-off.

The impact of the Internet of Things (IoT) on the evolution toward next generation smart environments (e.g., smart homes, buildings, and cities) will largely depend on the efficient integration of IoT and cloud computing technologies. With the predicted explosion in the number of connected devices and IoT services, current centralized cloud architectures, which tend to consolidate computing and storage resources into a few large data centers, will inevitably lead to excessive network load, end-to-end service latencies, and overall power consumption. Thanks to recent advances in network virtualization and programmability, highly distributed cloud networking architectures are a promising solution to efficiently host, manage, and optimize next generation IoT services in smart environments. In this paper, we mathematically formulate the service distribution problem (SDP) in IoT-Cloud networks, referred to as the IoT-CSDP, as a minimum cost mixed-cast flow problem that can be efficiently solved via linear programming. We focus on energy consumption as the major driver of today's network and cloud operational costs and characterize the heterogeneous set of IoT-Cloud network resources according to their associated sensing, computing, and transport capacity and energy efficiency. Our results show that, when properly optimized, the flexibility of IoT-Cloud networks can be efficiently exploited to deliver a wide range of IoT services in the context of next generation smart environments, while significantly reducing overall power consumption.

Future Internet usage will be dominated by the consumption of a rich variety of online multimedia services accessed from an exponentially growing number of multimedia capable mobile devices. As such, future Internet designs will be challenged to provide solutions that can deliver bandwidth-intensive delay-sensitive on-demand video-based services over increasingly crowded and bandwidth-limited wireless access networks. One of the main reasons for the bandwidth stress facing wireless network operators is the difficulty to exploit the multicast nature of the wireless medium when wireless users or access points rarely experience the same channel conditions or access the same content at the same time. In this paper, we present and analyze a novel wireless video delivery paradigm based on the combined use of channel-aware caching and coded multicasting that allows simultaneously serving multiple cache-enabled receivers that may be requesting different content and experiencing different channel conditions. To this end, we reformulate the caching-aided coded multicast problem as a joint source-channel coding problem and design an achievable scheme that preserves the cache-enabled multiplicative throughput gains of the error-free scenario, by guaranteeing per-receiver rates unaffected by the presence of receivers with worse channel conditions.

We study a noiseless broadcast link serving K users whose requests arise from a library of N files. Every user is equipped with a cache of size M files each. It has been shown that by splitting all the files into packets and placing individual packets in a random independent manner across all the caches prior to any transmission, at most N/M file transmissions are required for any set of demands from the library. The achievable delivery scheme involves linearly combining packets of different files following a greedy clique cover solution to the underlying index coding problem. This remarkable multiplicative gain of random placement and coded delivery has been established in the asymptotic regime when the number of packets per file F scales to infinity. The asymptotic coding gain obtained is roughly t = K M/N. In this paper, we initiate the finite-length analysis of random caching schemes when the number of packets F is a function of the system parameters M, N, and K. Specifically, we show that the existing random placement and clique cover delivery schemes that achieve optimality in the asymptotic regime can have at most a multiplicative gain of 2 even if the number of packets is exponential in the asymptotic gain t = K(M/N). Furthermore, for any clique cover-based coded delivery and a large class of random placement schemes that include the existing ones, we show that the number of packets required to get a multiplicative gain of (4/3)g is at least O((g/K)(N/M)g-1). We design a new random placement and an efficient clique cover-based delivery scheme that achieves this lower bound approximately. We also provide tight concentration results that show that the average (over the random placement involved) number of transmissions concentrates very well requiring only a polynomial number of packets in the rest of the system parameters.

In this paper, we examine the information-theoretic security of multiple-input-multiple-output space-division multiplexed (MIMO-SDM) fiber-optic communication systems in the presence of multiple eavesdroppers. In particular, we analyze the achievable secrecy rate for the two special cases that reflect different capabilities of the eavesdroppers: independent eavesdroppers who do not share received information and colluding (cooperating) eavesdroppers who can combine and jointly process the received signals. Our results show that MIMO-SDM systems are robust against multiple independent tapping attacks, in the sense that the average achievable secrecy rate is strictly positive even with an infinite number of eavesdroppers. Though extremely difficult to implement in practice, if all the eavesdroppers could cooperate coherently, the average secrecy rate decreases quickly with the number of eavesdroppers. As such, to counter the colluding eavesdroppers, a combination of much higher SNR for the legitimate receiver and higher mode-dependent loss (MDL) for the eavesdroppers is needed.

The cloud service distribution problem (CSDP) is to find the placement of both content and virtual cloud service functions (vCSFs) over a distributed cloud network platform, that meets user requests, satisfies network resource capacities and minimizes overall network cost. We formulate the CSDP as a minimum cost mixed-cast flow problem in which cloud services are represented by a service graph that encodes the relationship between input and output information flows via the virtual functions that create them. As a result, the CSDP can be efficiently formulated using only linear constraints and solved via integer linear programming (ILP). Our solution jointly optimizes the use of compute, storage and transport resources in arbitrary cloud network topologies, and is able to capture flexible service chaining, resource consolidation savings, unicast and multicast delivery, and latency constraints. We further provide conditions for which a relaxed version of the presented ILP leads to optimal polynomial-time solutions. We finally present results for an illustrative sample of cloud services that show the advantage of optimizing the placement of content and vCSFs over a programmable distributed cloud network.

Coded multicasting has been shown to improve the caching performance of content delivery networks with multiple caches downstream of a common multicast link. However, the schemes that have been shown to achieve order-optimal performance require content items to be partitioned into a number of packets that grows exponentially with the number of users [1]. In this paper, we first extend the analysis of the order-optimal multiple-groupcast coded multicasting scheme in [2] to the case of heterogeneous cache sizes and demand distributions, providing an achievable scheme and an upper bound on the optimal performance when the number of packets goes to infinity. We then show that the scheme achieving this upper bound can very quickly loose its promising multiplicative caching gain for finite content packetization. To overcome this limitation, we design a novel polynomial-time algorithm based on greedy local graph-coloring that, while keeping the same content packetization, recovers a significant part of the multiplicative caching gain. Our results show that the achievable schemes proposed to date to quantify the fundamental limiting performance, must be properly designed for practical regimes of finite content packetization.

The capacity of caching networks has received considerable attention in the past few years. A particularly studied setting is the shared link caching network, in which a single source with access to a file library communicates with multiple users, each having the capability to store segments (packets) of the library files, over a shared multicast link. Each user requests one file from the library according to a common demand distribution and the server sends a coded multicast message to satisfy all users at once. The problem consists of finding the smallest possible average codeword length to satisfy such requests. In this paper, we consider the generalization to the case where each user places L ≥ 1 independent requests according to the same common demand distribution. We propose an achievable scheme based on random vector (packetized) caching placement and multiple groupcast index coding, shown to be order-optimal in the asymptotic regime in which the number of packets per file B goes to infinity. We then show that the scalar (B = 1) version of the proposed scheme can still preserve order-optimality when the number of per-user requests L is large enough. Our results provide the first order-optimal characterization of the shared link caching network with multiple random requests, revealing the key effects of L on the performance of caching-aided coded multicast schemes.

Consider a network of prosumers of media content in which users dynamically create and request content objects. The request process is governed by the objects' popularity, which may vary across network regions and over time. In order to meet user requests, content objects can be stored and transported over the network, characterized by the capacity and efficiency of its storage and transport resources. The energy-efficient dynamic content distribution problem aims at finding the evolution of the network configuration, in terms of the placement and routing of content objects over time, that meets user requests, satisfies network resource capacities and minimizes overall energy use. We present 1) an information-centric linear programming formulation for the energy efficient dynamic content distribution problem that captures multicasting and caching over the network, per-object system dynamics, and delivery deadlines; 2) an offline solution that characterizes the minimum energy use achievable with global knowledge of user requests and network resources; and 3) an efficient distributed online solution that allows network nodes to make caching decisions based on their local estimate of the global energy benefit. Using a custom-built content distribution network simulator as well as a real prototype implementation in an information-centric networking testbed, we show the significant energy savings that can be obtained via the efficient and lightweight cache cooperation induced by our service and energy aware distributed online solution with respect to state of the art approaches.

Space-division multiplexed (SDM) fiber optic transmission systems can not only increase system capacity, but also achieve physical-layer security against fiber tapping attacks. In this paper, we examine the information-theoretic security of optical multiple-input-multiple-output (MIMO) SDM by evaluating the tradeoff between the achievable information rate and the confidentiality for different channel dynamics. In particular, we provide problem formulations for secure communication over these channels and study three types of secrecy capacities: 1) guaranteed capacity; 2) outage capacity; and 3) average capacity, each serving as a performance metric for a coding strategy tailored to a specific type of MIMO-SDM channel. We also assess the impact of key system parameters, such as the number of modes, the mode-dependent loss (MDL), and the signal-to-noise ratio (SNR), on the various secrecy capacities. Our results indicate that, with a proper design of channel codes that balance information rate and security, an SDM system has the potential of offering confidential data transmission at a rate that could be orders of magnitude higher than what can be achieved through other means of encryption. Moreover, we show that MDL, unavoidably induced by fiber tapping, can allow information-theoretic security even if the SNR of the eavesdropper's receiver is better than that of the legitimate receiver.

We consider a MIMO (linear Gaussian) channel where the inputs are turned on and off at random, and the outputs are sampled at random with probability p. In particular, for a given probability of “on” input q (input sparsity), we consider a scenario where the transmitter wishes to send information to a family of possible receivers characterized by different random sampling rates p ∈ [0,1]. For this setting, we focus on the broadcast approach, i.e., a coding technique where the transmitter sends information encoded into superposition layers, such that the number of decoded layers depends on the receiver sampling rate p. We obtain a method for calculating the power allocation across the layers for given statistics of the MIMO channel matrix in order to maximize the system weighted sum rate for arbitrary non-negative weighting function w(p). In particular, we provide analytical solutions both for iid and Haar distributed MIMO channel matrices. The latter case accounts also for DFT matrices (see [1]), with application to sparse spectrum signals with random sub-Nyquist sampling.

A novel transmission scheme is developed for the downlink frame of cellular networks. Each base station (BS) aims at iteratively balancing the throughput at the mobile stations (MSs) of its cell with the interference it causes at the MSs of the neighboring cells, requiring negligible coordination between the BSs. A simplified version of the scheme that neither requires iterations nor cooperation is also proposed. Simulation results show that the proposed schemes achieve substantial gains over well-known schemes in the literature.

Consider a content delivery network in which storage and transport resources, characterized by their capacity and cost (e.g., energy) efficiency, are used to meet users' content object requests. The goal is to find the evolution of the objects being stored and transported by the network resources that meets user requests, satisfies network resource capacities and minimizes overall network cost. We first present a constructive offline solution that provides the maximum network efficiency (or minimum cost per object delivered) that can be achieved by dynamically exploiting network-coded caching and multicasting under arbitrary time-varying demands. We refer to the solution scheme as a dynamic network-coded caching-aided multicast (NCCAM) scheme, and illustrate it in a 6-node butterfly network. We then consider a single time period in which each user requests an arbitrary subset of content objects. We formulate the problem as a network coding problem on a caching-augmented graph and show that under uniform demand, random linear coded caching and multicasting is sufficient for achieving minimum cost caching-aided multicast. For the arbitrary demand scenario, we provide the transport-storage-popularity tradeoff of a polynomial-time solution that uses uncoded caching according to object popularity and random linear coded transmission. We show that while for skewed Zipf object popularity such a simple scheme achieves close to optimal performance, as the Zipf parameter approaches zero (uniform popularity), significant cost reductions can be obtained by optimizing the transport configuration at the expense of increased computational complexity.

Consider a network of prosumers of media content in which users dynamically create and request content objects. The request process is governed by the objects' popularity and varies across network regions and over time. In order to meet user requests, content objects can be stored and transported over the network, characterized by the capacity and energy efficiency of the storage and transport resources. The energy efficient dynamic in-network caching problem aims at finding the evolution of the network configuration, in terms of the content objects being cached and transported over each network element at any given time, that meets user requests, satisfies network resource capacities and minimizes overall energy use. We provide 1) an information-centric optimization framework for the energy efficient dynamic in-network caching problem, 2) an offline solution, EE-OFD, based on an integer linear program (ILP) that obtains the maximum efficiency gains that can be achieved with global knowledge of user requests and network resources, and 3) an efficient fully distributed online solution, EEOND, that allows network nodes to make local caching decisions based on their current estimate of the global energy benefit. Our solutions take into account the network heterogeneity, in terms of capacity, energy efficiency and content popularity, and adapt to changing network conditions minimizing overall energy use.

The secrecy rate of Wyner wiretap channels is analyzed for general classes of sources, sensing schemes, and channel distributions. Using the replica method, heuristic closed form expressions are obtained for the asymptotic secrecy rate as a function of the statistics of the system model. This result is then applied in practically oriented scenarios, leading to expressions that expose the existing trade-offs between system parameters and security requirements, including the region in which perfect secrecy is feasible. As a particular example of the broad class of sources that are considered in the main contribution, source distributions giving rise to sparse signals are studied. In that setting, the secrecy rate linked to the disclosure of information about the support of the signals is investigated.

The effect of prior knowledge when linear analog codes are used as joint source-channel codes for sources modeled as multivariate Gaussian processes is analyzed. We use information theoretic tools to evaluate the achievable performance gain obtained by exploiting prior knowledge. In order to assess the validity of linear codes in practical scenarios, where exact source statistics are not known, we study the effect of having partial knowledge of the statistics. We model the mismatch of the statistics as an additive perturbation matrix between the real covariance matrix and the postulated covariance matrix in the recovery process. In this setting, we obtain closed form expressions for a deterministic perturbation matrix and using random matrix theory tools we characterize the performance loss for i.i.d. random matrices.

In this paper, we provide an information-theoretic analysis of a Gaussian multiple-input multiple-output multiple access channel (MIMO MAC) with imperfect channel knowledge at the receiver. In particular, we derive inner and outer bounds for the MIMO MAC rate region when the inputs are Gaussian. We then apply these bounds to a Gaussian interference network with receiver cooperation, in which a central processor with incomplete channel state information must jointly decode all the received signals. Then, in the case where the channel knowledge at the receiver is obtained through training signals, we derive the structure of the optimum training signals for all users under a definite and semidefinite rank constraint. Numerical results show that the bounds we derive can be quite tight, confirming the asymptotic analysis conducted for the finite case. Finally, we also investigate the low-SNR and high-SNR regimes, specifically analyzing the minimum required energy per information bit and the wideband slope region in the first case, and the high-SNR slope in the second.

Consider a Bernoulli-Gaussian complexn-vector whose components areVi=XiBi, withXi~C N(0,Px) and binaryBimutually independent and iid acrossi. This randomq-sparse vector is multiplied by a square random matrixU, and a randomly chosen subset, of average sizen p,p∈ [0,1], of the resulting vector components is then observed in additive Gaussian noise. We extend the scope of conventional noisy compressive sampling models whereUis typically a matrix with iid components, to allowUsatisfying a certain freeness condition. This class of matrices encompasses Haar matrices and other unitarily invariant matrices. We use the replica method and the decoupling principle of Guo and Verdú, as well as a number of information-theoretic bounds, to study the input-output mutual information and the support recovery error rate in the limit ofn→ ∞. We also extend the scope of the large deviation approach of Rangan and characterize the performance of a class of estimators encompassing thresholded linear MMSE andl1relaxation.

We present inner and outer bounds of the rate region for the multiple-input-multiple-output mutiple access channel with imperfect channel estimates. We then employ them to compare different channel estimation techniques. We show that the benefit of using traditional compressed sensing recovery techniques, specifically orthogonal matching pursuit, for multipath wireless channels is dominant for high signal to noise ratio regimes, but does not provide a good performance for low signal to noise ratio regime.

The distortion increase in minimum mean-square error (MMSE) estimation of multivariate Gaussian sources is analyzed for the situation in which the statistics are mismatched, i.e., the covariance matrix is not perfectly known during the estimation process. First a deterministic mismatch model with an additive perturbation matrix is considered, for which we provide closed form expressions for the distortion excess caused by the mismatch. The mismatch study is then generalized by using random matrix theory tools which allow an asymptotic result for a broad class of perturbation matrices to be proved.

In cellular networks coordination among base stations (BSs) has been recognized as an important solution to handle inter-cell interference and increase spectral efficiency. In frequency division duplex systems one of the main issue that sensibly degrades the performance of coordinated multipoint (CoMP) transmission techniques is the imperfect channel state information (CSI) due to the limited bandwidth available for the feedback transmission. In this paper we focus on a CoMP scenario with data and CSI sharing among the BSs and we consider a feedback transmission scheme where each user equipment (UE) quantizes the different channels by using codebooks designed for a single-cell scenario. Due to the different propagation characteristics of the channels between a UE and each BS and by considering a constraint on the number of available feedback bits, we propose two practical algorithms depending on the large-scale fading to a) select the subset of BSs from whom the UE prefers to be served and b) optimize the number of feedback bits allocated to each channel. The developed techniques allow a UE to send more feedback bits to the BSs with a stronger signal and numerical results show the merits of the proposed approach.

We consider the downlink of a multicell system with multiantenna base stations and single-antenna user terminals, arbitrary base station cooperation clusters, distance-dependent propagation pathloss, and general “fairness” requirements. Base stations in the same cooperation cluster employ joint transmission with linear zero-forcing beamforming, subject to sum or per-base station power constraints. Intercluster interference is treated as noise at the user terminals. Analytic expressions for the system spectral efficiency are found in the large-system limit where both the numbers of users and antennas per base station tend to infinity with a given ratio. In particular, for the per-base station power constraint, we find new results in random matrix theory, yielding the squared Frobenius norm of submatrices of the Moore-Penrose pseudo-inverse for the structured non-i.i.d. channel matrix resulting from the cooperation cluster, user distribution, and path-loss coefficients. The analysis is extended to the case of nonideal Channel State Information at the Transmitters obtained through explicit downlink channel training and uplink feedback. Specifically, our results illuminate the trade-off between the benefit of a larger number of cooperating antennas and the cost of estimating higher-dimensional channel vectors. Furthermore, our analysis leads to a new simplified downlink scheduling scheme that preselects the users according to probabilities obtained from the large-system results, depending on the desired fairness criterion. The proposed scheme performs close to the optimal (finite-dimensional) opportunistic user selection while requiring significantly less channel state feedback, since only a small fraction of preselected users must feed back their channel state information.

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

Consider a Bernoulli-Gaussian complex n-vector whose components are XiBi, with Bi~Bernoulli-q and Xi~ CN(0; σ2), iid across i and mutually independent. This random q-sparse vector is multiplied by a random matrix U, and a randomly chosen subset of the components of average size np, p ∈ [0; 1], of the resulting vector is then observed in additive Gaussian noise. We extend the scope of conventional noisy compressive sampling models where U is typically the identity or a matrix with iid components, to allow U that satisfies a certain freeness condition, which encompasses Haar matrices and other unitarily invariant matrices. We use the replica method and the decoupling principle of Guo and Verdú, as well as a number of information theoretic bounds, to study the input-output mutual information and the support recovery error rate as n → ∞.

In this work we provide an information-theoretic analysis of a Gaussian multiple-input multiple-output multiple access channel (MIMO MAC) with imperfect channel knowledge at the receiver. In particular we derive inner and outer bounds for the rate region of the MIMO MAC when the inputs are Gaussian. In the case where the channel knowledge at the receiver is obtained through training signals, we derive the structure of the optimum training signals for all users under a definite and semi-definite rank constraint. Numerical results show that the bounds we derive can be quite tight. Finally, we also investigate the low-SNR regime, specifically analyzing the minimum required energy per information bit and the wideband slope region.

The problem of robust waveform design for multiple-input, multiple-output radars equipped with widely-spaced antennas is addressed here. Robust design is needed as a number of parameters are unknown, e.g., the target scattering covariance matrix and, possibly, the clutter covariance matrix. A min-max approach is proposed, so that the code matrix is designed to minimize the worst-case cost (or equivalently maximize the corresponding figure of merit) under all possible target (or target and clutter) covariance matrices. Surprisingly, the same min-max solution applies to many commonly adopted performance measures, such as the average signal-to-clutter-plus-noise ratio, the mutual information between the received signal echoes and the unknown target response, and the approximation of the detection probability in the high- and low-signal regimes.

This paper studies the up-link of a multi-user Multiple-Input Multiple-Output (MIMO) system under a fairly general channel model, subsuming a number of situations of relevant practical interest. Concerning the available prior information, we consider the case of Prior Channel State Information at the Transmitter (PCSIT) wherein only a statistical channel characterization is available before transmission, while we assume perfect Channel State Information at the Receiver (CSIR). Under this assumptions, we characterize the sum-capacity-achieving input covariance matrix for such a system and we also examine the low-power regime in terms of both minimum energy contrast and multiple access slope region, validating our theoretical findings through a set of numerical results.

Information-theoretic performance analysis of a MIMO communication over Land Mobile Satellite (LMS) channels, under ergodic and non-ergodic regimes, is performed. The capacity-achieving input covariance matrix, and the corresponding ergodic capacity, assuming perfect receive-side information but making different assumptions on the amount of channel knowledge at the transmitter, are derived. We obtain exact results, but for the case when perfect channel knowledge is assumed at both ends of the link, for which we provide an upper bound to the ergodic capacity. In the non-ergodic scenario, we compute the outage capacity in absence of power-control, and discuss the asymptotic Gaussianity of the mutual information, which strongly depends on the overall number of degrees of freedom available on the channel. Design guidelines for multiantenna LMS channels are gained studying the low Signal-to-Noise Ratio (SNR) behavior of the capacity, still under the assumption of absence of knowledge of the channel matrix (or its statistics) at the transmitter. The results are illustrated through several examples, aimed at assessing the impact on the performance of the diversity order and/or the Line-of-Sight (LOS) fluctuations.

A statistical model for Land Mobile Satellite (LMS) channels, where transmitters and receivers are equipped with multiple antennas, is introduced. Several spectral statistics are given, which allow the theoretical performance analysis of the newly proposed channel model from both a communication and an information-theoretic point of view. Specifically, joint and marginal statistics of the squared singular-values of the channel matrix are evaluated, paving the way for the performance analysis under ergodic and nonergodic assumptions on the channel behavior. The capacity-achieving input covariance matrix, and the corresponding ergodic capacity, assuming perfect receive-side information but making different assumptions on the amount of channel knowledge at the transmitter, are derived. We obtain exact results, but for the case when perfect channel knowledge is assumed at both ends of the link, for which we provide an upper bound to the ergodic capacity. In the nonergodic scenario, we compute the outage probability in absence of power-control, and discuss the asymptotic Gaussianity of the mutual information, which strongly depends on the overall number of degrees of freedom available on the channel. Design guidelines for multiantenna LMS channels are gained studying the low signal-to-noise ratio (SNR) behavior of the capacity, still under the assumption of absence of knowledge of the channel matrix (or its statistics) at the transmitter. The results are illustrated through several examples, aimed at assessing the impact on the performance of the diversity order and/or the line-of-sight (LOS) fluctuations.

In this paper, we consider a multiple-input-multiple-output (MIMO) detection problem withMwidely spaced transmit antennas andLwidely spaced receive antennas, and we study the problem of designing the signal waveforms transmitted by each source node under non-Gaussian target scattering and temporally correlated Gaussian clutter. Two figures of merit are investigated for space-time code (STC) optimization under a semidefinite rank constraint: 1) the lower Chernoff bound (LCB) to the detection probability for fixed probability of false alarm, and 2) the mutual information (MI) between the observations available at the receive nodes and the “channel response” generated by a point-like target, assumed present tout court. Both receive and transmit power constraints are discussed. If the scattering distribution possesses some suitably defined properties of unitary invariance (see Section II-B), both MI-optimal and LCB-optimal STCs have a simple canonical structure: the same set of (clutter dependant) temporal codewords are employed at the transmit nodes, the only difference among the many solutions being the amount of power radiated by each antenna. Such a spatial power allocation critically depends upon the adopted figure of merit, the specified power constraint, and the underlying scattering model. Sufficient conditions to determine the optimal power allocation for all design criteria are provided. Asymptotic power distributions are also derived in the limit of vanishingly small and increasingly large signal-to-clutter ratios, proving that assuming Gaussian scattering at the design stage is a robust choice. A case study of relevant practical interest is examined in depth so as to compare the proposed design criteria and to assess the impact of signal non-Gaussianity on the system performances.

This paper investigates the achievable sum rate of multiple-input multiple-output (MIMO) wireless systems employing linear minimum mean-squared error (MMSE) receivers. We present a new analytic framework which exploits an interesting connection between the achievable sum rate with MMSE receivers and the ergodic mutual information achieved with optimal receivers. This simple but powerful result enables the vast prior literature on ergodic MIMO mutual information to be directly applied to the analysis of MMSE receivers. The framework is particularized to various Rayleigh and Rician channel scenarios to yield new exact closed-form expressions for the achievable sum rate, as well as simplified expressions in the asymptotic regimes of high and low signal-to-noise ratios (SNRs). These expressions lead to the discovery of key insights into the performance of MIMO MMSE receivers under practical channel conditions.

This paper finds the capacity of single-user discrete-time channels subject to both frequency-selective and time-selective fading, where the channel output is observed in additive Gaussian noise. A coherent model is assumed where the fading coefficients are known at the receiver. Capacity depends on the first-order distributions of the fading processes in frequency and in time, which are assumed to be independent of each other, and a simple formula is given when one of the processes is independent identically distributed (i.i.d.) and the other one is sufficiently mixing. When the frequency-selective fading coefficients are known also to the transmitter, we show that the optimum normalized power spectral density is the waterfilling power allocation for a reduced signal-to-noise ratio (SNR), where the gap to the actual SNR depends on the fading distributions. Asymptotic expressions for high/low SNR and easily computable bounds on capacity are also provided.

We study the performance of signal estimation and reconstruction systems, that exploit the linear minimum mean square error (LMMSE) technique. This model often occurs in signal processing and wireless communications; some examples are radar applications, MIMO communications, or sensor networks sampling a physical field. Our performance analysis implies the characterization of a random matrix product, involving a multifold Vandermonde matrix with complex exponential entries. We therefore derive the LMMSE by computing the eta-transform of this matrix product, which can be evaluated either by implicit as well as by explicit expression, using the matrix asymptotic moments. Finally, we show how our results can be applied in some cases of practical interest.

We present an interesting and powerful new framework connecting the achievable sum rate of multiple-input multiple-output (MIMO) wireless systems employing linear minimum mean-squared error (MMSE) receivers, and the ergodic MIMO mutual information. This allows the vast literature on ergodic MIMO mutual information to be directly applied to the analysis of MMSE receivers. As an example, the framework is particularized to spatially-correlated Rayleigh fading to yield new exact closed-form expressions for the achievable sum rate, as well as simplified expressions for high and low signal to noise ratios.

We consider a multiple-input multiple-output (MIMO) detection problem with widely-spaced antennas at both the transmitter and the receiver, and we assume that target scattering is modeled as an exchangeable and unitarily-invariant process. We illustrate optimal signal design (i.e., space-time coding) at the transmitter for two criteria, i.e. the lower Chernoff bound (LCB) to the detection probability for fixed probability of false alarm and the mutual information (MI) between the observations and the target scattering matrix, under a semi-definite rank constraint and a transmit power constraint, showing that the Gaussian scattering assumption is robust. A by-product, of not secondary importance, of our derivation is the proof of a number of new properties concerning concavity and Schur-concavity of MI and LCB.

This paper finds the capacity of a linear time-invariant system with a given transfer function, observed in additive Gaussian noise through a memoryless fading channel. A coherent model is assumed where the fading coefficients are known at the receiver (but not the transmitter). We show that the optimum normalized power spectral density is the waterfilling solution for reduced signal-to-noise ratio, where the gap to the actual signal-to-noise ratio depends on both the fading distribution and the channel transfer function.

Coordinating the reception and transmission of signals across spatially distributed base stations has been shown to improve sum-rate performance by mitigating the effects of intercell interference in Multiple-Input-Multiple-Output (MIMO) cellular networks. Relying on recent results on the freeness of certain non-Gaussian random matrices, we provide an information theoretic analysis of cooperative MIMO networks. This analysis applies to the case where full channel state information is known at a subset of the bases and where statistical information is known at all others. Tools for evaluating random matrix transforms traditionally exploited in Mean Square Error (MSE) and mutual information analysis are provided, and the general model formulation paves the way for future work, where specific scheduling and/or power assignment schemes could be embodied in the newly presented framework.

This paper formulates power allocation policies that maximize the region of mutual informations achievable in multiuser downlink OFDM channels. Arbitrary partitioning of the available tones among users and arbitrary modulation formats, possibly different for every user, are considered. Two distinct policies are derived, respectively for slow fading channels tracked instantaneously by the transmitter and for fast fading channels known only statistically thereby. With instantaneous channel tracking, the solution adopts the form of a multiuser mercury/waterfilling procedure that generalizes the single-user mercury/waterfilling introduced in [1], [2]. With only statistical channel information, in contrast, the mercury/waterfllling interpretation is lost. For both policies, a number of limiting regimes are explored and illustrative examples are provided.

This paper finds the capacity of linear time-invariant systems observed in additive Gaussian noise through a memoryless erasure channel. This problem requires obtaining the asymptotic spectral distribution of a submatrix of a nonnegative definite Toeplitz matrix obtained by retaining each column/row independently and with identical probability. We show that the optimum normalized power spectral density is the water filling solution for reduced signal-to-noise ratio, where the gap to the actual signal-to-noise ratio depends on both the erasure probability and the channel transfer function. We find asymptotic expressions for the capacity in the sporadic erasure and sporadic non-erasure regimes as well as the low and high signal-to-noise regimes.

This paper formulates the power allocation policy that maximizes the region of ergodic mutual informations achievable in multiuser downlink OFDM channels known only statistically by the base station. Arbitrary partitioning of the available tones among users and arbitrary modulation formats, possibly different for every user, are considered. The derivation relies on the nexus between the mutual information of Gaussian channels and the minimum mean-square error incurred in the nonlinear estimation of the transmit constellation points given their noisy receive observations.

This paper characterizes the marginal probability density function of an unordered eigenvalue of finite-dimensional random matrices of particular interest in MIMO (multiple-input multiple-output) wireless communications. Specifically, a technique is presented for deriving the eigenvalue statistics in one-side correlated Rayleigh-faded channels and in Ricean-faded channels, with or without cochannel interferers. The exact expressions found turn out to be extremely useful in calculating information-theoretic quantities. As an application, we calculate the ergodic mutual information for all the abovementioned channel fading conditions, obtaining a closed form formula for the Rayleigh case and, in turn, a series expression for the Ricean faded one.

The mutual information of independent parallel Gaussian-noise channels is maximized, under an average power constraint, by independent Gaussian inputs whose power is allocated according to the waterfilling policy. In practice, discrete signaling constellations with limited peak-to-average ratios (m-PSK, m-QAM, etc.) are used in lieu of the ideal Gaussian signals. This paper gives the power allocation policy that maximizes the mutual information over parallel channels with arbitrary input distributions. Such policy admits a graphical interpretation, referred to as mercury/waterfilling, which generalizes the waterfilling solution and allows retaining some of its intuition. The relationship between mutual information of Gaussian channels and nonlinear minimum mean-square error (MMSE) proves key to solving the power allocation problem.

Artstein, Ball, Barthe, and Naor have recently shown that the non-Gaussianness (divergence with respect to a Gaussian random variable with identical first and second moments) of the sum of independent and identically distributed (i.i.d.) random variables is monotonically nonincreasing. We give a simplified proof using the relationship between non-Gaussianness and minimum mean-square error (MMSE) in Gaussian channels. As Artstein , we also deal with the more general setting of nonidentically distributed random variables

We characterize the capacity-achieving input covariance for multi-antenna channels known instantaneously at the receiver and in distribution at the transmitter. Our characterization, valid for arbitrary numbers of antennas, encompasses both the eigenvectors and the eigenvalues. The eigenvectors are found for zero-mean channels with arbitrary fading profiles and a wide range of correlation and keyhole structures. For the eigenvalues, in turn, we present necessary and sufficient conditions as well as an iterative algorithm that exhibits remarkable properties: universal applicability, robustness and rapid convergence. In addition, we identify channel structures for which an isotropic input achieves capacity.

This paper characterizes the asymptotic distribution of the input-output mutual information of multiantenna channels. Using recent results on random matrix theory, we prove asymptotic normality of the unnormalized mutual information for arbitrary signal-to-noise ratios and fading distributions, allowing for correlation between the antennas at either transmitter or receiver.

In the high-SNR regime, the multi-antenna mutual information behaves as an affine function of SNR|/sub dB/, described by the multiplexing gain, which quantifies the multiplicative increase as function of the number of antennas, and the power offset (zero-order term in dB). The conventional high-SNR analysis that considers only the multiplexing gain is unable to assess the impact of channel features such as the Rician factor since, irrespective thereof, the multiplexing gain equals the minimum of the number of transmit and receive antennas. The impact of the Rician factor at high SNR can be conveniently quantified through the corresponding power offset, which this paper evaluates in closed-form.

For parallel independent Gaussian-noise channels with an aggregate power constraint, independent Gaussian inputs whose powers are allocated according to the waterfilling policy maximize the sum mutual information. In practice, however, discrete signalling constellations such as m-PSK or m-QAM are used in lieu of the ideal Gaussian signals. This paper gives the power allocation policy, referred to as mercury/waterfilling, that maximizes the sum mutual information over parallel channels with arbitrary input constellations

The analysis of the multiple-antenna capacity in the high-SNR regime has hitherto focused on the high-SNR slope (or maximum multiplexing gain), which quantifies the multiplicative increase as a function of the number of antennas. This traditional characterization is unable to assess the impact of prominent channel features since, for a majority of channels, the slope equals the minimum of the number of transmit and receive antennas. Furthermore, a characterization based solely on the slope captures only the scaling but it has no notion of the power required for a certain capacity. This paper advocates a more refined characterization whereby, as a function of SNR|/sub dB/, the high-SNR capacity is expanded as an affine function where the impact of channel features such as antenna correlation, unfaded components, etc., resides in the zero-order term or power offset. The power offset, for which we find insightful closed-form expressions, is shown to play a chief role for SNR levels of practical interest.

We analyze the spectral efficiency (sum-rate per subcarrier) of randomly spread synchronous multicarrier code-division multiple access (MC-CDMA) subject to frequency-selective fading in the asymptotic regime of number of users and bandwidth going to infinity with a constant ratio. Both uplink and downlink are considered, either conditioned on the subcarrier fading coefficients (for nonergodic channels) or unconditioned thereon (for ergodic channels). The following receivers are analyzed: a) jointly optimum receiver, b) linear minimum mean-square error (MMSE) receiver, c) decorrelator, and d) single-user matched filter.

This paper applies random matrix theory to obtain analytical characterizations of the capacity of correlated multiantenna channels. The analysis is not restricted to the popular separable correlation model, but rather it embraces a more general representation that subsumes most of the channel models that have been treated in the literature. For arbitrary signal-to-noise ratios (SNR), the characterization is conducted in the regime of large numbers of antennas. For the low- and high-SNR regions, in turn, we uncover compact capacity expansions that are valid for arbitrary numbers of antennas and that shed insight on how antenna correlation impacts the tradeoffs among power, bandwidth, and rate.

In this paper, the high-SNR multiantenna capacity with coherent receivers on the multiplexing gain, i.e., the multiplicative increase as function of the number of antennas is analyzed. For most channels of interest, such multiplexing gain equals the minimum of the number of transmit and receive antennas. This traditional characterization, however, is unable to quantify the impact of many relevant channel features. As a function of SNR, the capacity is very well approximated, from moderate SNR on, as an affine function. The impact of the various channel features is captured in the power offset (in dB) or zero-order term in the affine expansion.

We characterize the power allocation that maximizes the rate per unit bandwidth supported with arbitrary reliability over single-user multiantenna channels known instantaneously by the receiver and in distribution by the transmitter. The characterization is valid for arbitrary channels and numbers of antennas. Although, in general, it leads to a fixed-point solution, at low and high signal-to-noise it provides explicit allocations. For arbitrary signal-to-noise ratios, we present an iterative algorithm that exhibits remarkable properties: robustness, rapid convergence and universal applicability. Further, when applied to the proper set of signalling eigenvectors, the algorithm converges to the power allocation that attains capacity.

Reduced-rank minimum mean-squared error (MMSE) multiuser detectors using asymptotic weights have been shown to reduce receiver complexity while maintaining good performance in long-sequence code-division multiple-access (CDMA) systems. In this paper, we consider the design of reduced-rank MMSE receivers in a general framework which includes fading, single and multiantenna receivers, as well as direct-sequence CDMA (DS-CDMA) and multicarrier CDMA (both uplink and downlink). In all these cases, random matrix results are used to obtain explicit expressions for the asymptotic eigenvalue moments of the interference autocorrelation matrix and for the asymptotic weights used in the reduced-rank receiver.

Reduced-rank MMSE receivers using asymptotic weights reduce receiver complexity while maintaining good performance in long-sequence DS-CDMA systems. In this paper, we analyze such receivers in multipath fading channels and extend their design to multicarrier CDMA (both uplink and downlink). An explicit expression is obtained for the asymptotic eigenvalue moments of the interference autocorrelation matrix and for the asymptotic weights derived there from and used in the reduced-rank receiver. The full-rank MMSE receiver is also considered for multicarrier CDMA and a fixed point of equation of the asymptotic maximum output SINR is derived, which particularizes to the Tse-Hanly fixed point equation for the special case of DS-CDMA. An explicit expression of the MMSE spectral efficiency is proposed for multicarrier CDMA.

We present an analytical characterization of multi-antenna capacity in the limit of a large number of antennas. In contrast to previous studies, the entries of the channel matrix are not restricted to be identically distributed, thus incorporating diversity mechanisms that are otherwise excluded, such as those based on the use of antennas with distinct polarizations and radiation patterns. In addition to the capacity, first-order expressions in the low- and high-power regimes are also evaluated both asymptotically and non-asymptotically.

This paper provides analytical characterizations of the impact on the multiple-antenna capacity of several important features that fall outside the standard multiple-antenna model, namely: (i) antenna correlation, (ii) Ricean factors, (iii) polarization diversity, and (iv) out-of-cell interference; all in the regime of low signal-to-noise ratio. The interplay of rate, bandwidth, and power is analyzed in the region of energy per bit close to its minimum value. The analysis yields practical design lessons for arbitrary number of antennas in the transmit and receive arrays.

We study the asymptotic behavior of space-time codes when a linear receiver interface is used in lieu of the maximum-likelihood interface. Specifically, we determine the behavior of pairwise error probabilities with maximum-likelihood decoding and with four types of receiver interfaces: the maximum-likelihood interface, the linear zero-forcing interface, the linear minimum-mean-square-error interface, and the matched-filter interface. An asymptotic analysis is performed by assuming that the number of receiving antennas grows to infinity while the number of transmitting antennas is finite, and that both numbers grow to infinity but their ratio remains constant. We show that with all the interfaces studied here the asymptotic performance of space-time codes is determined by the Euclidean distances between code words. Moreover, the performance of linear interfaces comes close to maximum-likelihood if the number r of receive antennas is sufficiently larger than the number t of transmit antennas. The dependence of error probabilities on Euclidean distance is valid for intermediate signal-to-noise ratios even when the number of antennas is small.

In emerging mobile systems users must operate very often in the low-power regime. Specifically, almost 40 % of geographical locations experience signal-to-noise ratios (SNR) below 0 dB. Despite its relevance, the multi-antenna low-power regime had not been analyzed in depth until the paper by S. Verdu (see IEEE Trans. on Inform. Theory, p.1319-43, June 2002), where the figure of merit is not the SNR, but rather the normalized energy per information bit, E/sub b//N/sub 0/. This paper expands these findings using a channel model that realistically describes the conditions found in typical wireless systems. The focus is on channels that are known to the receiver, but unknown to the transmitter.

We study the asymptotic behavior of space-time codes when the number of transmit and receive antennas grows to infinity. Specifically, we determine the behavior of pairwise error probabilities with maximum-likelihood (ML) decoding and with three types of receiver interfaces: the ML interface, the linear zero-forcing (ZF) interface, and the linear minimum-mean-square-error (MMSE) interface. Two situations are studied: when the number of receiving antennas grows to infinity while the number of transmitting antennas is finite, and when both numbers grow to infinity but their ratio remains constant. We show that with ML or linear interfaces the asymptotic performance of space-time codes is determined by the Euclidean distances between codewords. Moreover, with the two linear interfaces examined here the number r of receive antennas must be much larger than the number t of transmit antennas to avoid a sizeable loss of performance; on the other hand, when r /spl Gt/ t, the performance of these linear interfaces comes close to that of ML. The dependence of error probabilities on Euclidean distance is valid for intermediate signal-to-noise ratios (SNRs) even when the number of antennas is small. Simulations validate our theoretical findings, and show how asymptotic results may be substantially valid even in a nonasymptotic regime: thus, even for few antennas, off-the-shelf codes may outperform space-time codes designed ad hoc.

It has been shown that in a direct-sequence/code-division multiple-access (DS/CDMA) system employing binary phase-shift keying (BPSK) modulation the baseband equivalent of the CDMA multiplex is, under very mild assumptions, an improper complex random process, i.e., it has a nonzero pseudoautocorrelation function. The problem of linear multiuser detection for asynchronous DS/CDMA systems with improper multiaccess interference (MAI) is considered. A new mean-output-energy (MOE) cost function is introduced, whose constrained minimization leads to two new linear multiuser detectors, exploiting the information contained in the pseudoautocorrelation of the observables, and which generalize the classical decorrelating and minimum mean-square error (MMSE) receivers. The problem of blind adaptive receiver implementation based on subspace tracking is also tackled. Finally, the superiority of the new detectors with respect to the classical linear detection structures present in the literature is demonstrated through both theoretical considerations and computer simulations.

The capacity of wireless communication architectures equipped with multiple transmit and receive antennas and impaired by both noise and cochannel interference is studied. We find a closed-form solution for the capacity in the limit of a large number of antennas. This asymptotic solution, which is a sole function of the relative number of transmit and receive antennas and the signal-to-noise and signal-to-interference ratios (SNR and SIR), is then particularized to a number of cases of interest. By verifying that antenna diversity one can substitute for time and/or frequency diversity at providing ergodicity, we show that these asymptotic solutions approximate the ergodic capacity very closely even when the number of antennas is very small.

We examine a multiple-access AWGN channel with synchronous DS-CDMA in which the channel amplitude and noise variance parameters are unknown a priori. We derive an iterative joint multiuser decoder and parameter estimator based on soft interference cancellation and on soft decision-driven least-squares estimation. Our derivation is obtained by applying the sum-product algorithm to the factor graph of the joint a posteriori probability measure of the information bits and of the unknown channel parameters.

In this paper, the authors consider an asynchronous direct-sequence code division multiple access (DS/CDMA) system wherein users are allowed to transmit their symbols at one out of two available data rates. Three possible access schemes are considered, namely, the variable spreading length (VSL), the variable chip rate (VCR), and the variable chip rate with frequency shift (VCRFS) formats. Their performance is compared for the case that a linear one-shot multiuser receiver is employed. It is also shown that detection of the users transmitting at the higher rate requires a periodically time-varying processing of the observables. Moreover, the problem of blind adaptive receiver implementation is studied, and a cyclic blind recursive-least-squares (RLS) algorithm is provided which is capable of converging to the periodically time-varying high-rate users detection structure. Numerical results show that the proposed receivers are near-far resistant, and that the VCRFS access technique achieves the best performance. Finally as to the adaptive blind receiver implementation, computer simulations have revealed that the cyclic RLS algorithm for blind adaptive high-rate users demodulation outperforms the conventional RLS algorithm in most cases of primary importance.

In this paper, we design and analyze a new class of linear multiuser detectors, which can be applied when the users employ BPSK modulation and the fading coefficients of the active users are known at the receiver (such as base-station demodulation). The tools of asymptotic distribution of the spectrum of large random matrices are used to show that relative to the classical minimum mean-square-error (MMSE) receiver, the output signal-to-noise ratio (SNR) improves by halving the number of effective interferers and adding 3 dB to the input SNR. We also propose sensible approximations to the proposed linear receivers so as to facilitate their use in CDMA systems that employ long codes.

In this work, the problem of joint suppression of multiple-access and narrow-band interference (NBI) for an asynchronous direct-sequence code-division multiple-access (CDMA) system operating on a frequency-selective fading channel is addressed. The receiver structure we consider can be deemed as a two-stage one: the first stage consists of a bank of minimum mean-square-error (MMSE) filters, each keyed to a given replica of the useful signal, and aimed at suppressing the overall interference; the second stage, assuming knowledge of the fading channel coefficients realizations, combines the MMSE filters outputs according to a maximal-ratio combining rule. Due to the presence of the NBI, the resulting structure is in general time-varying, and becomes periodically time-varying if the NBI bit-rate has a rational ratio to that of the CDMA system. Moreover, enlarging the observation window beyond the signaling interval and oversampling the signal space may yield a noticeable performance improvement. For the relevant case that the said ratio is rational, a new cyclic blind recursive least squares (RLS)-based algorithm is introduced, capable of tracking the periodically time-varying receiver structure, and allowing adaptive interference cancellation with a moderate complexity increase. We also come up with a closed-form expression for the conditional bit-error rate (BER), which is useful both to evaluate semi-analytical methods to assess the unconditional BER and to derive bounds on the system near-far resistance. The results indicate that the receiver achieves very satisfactory performance in comparison to previously known structures. Computer simulations also demonstrate that the cyclic blind RLS algorithm exhibits quite fast convergence dynamics.

We deal with interference suppression in asynchronous direct-sequence code-division multiple-access (CDMA) systems employing binary phase-shift keying modulation. Such an interference may arise from other users of the network, from external low-rate systems, as well as from a CDMA network coexisting with the primary network to form a dual-rate network. We derive, for all of these cases, a new family of minimum mean-square-error detectors, which differ from their conventional counterparts in that they minimize a modified cost function. Since the resulting structure is not implementable with acceptable complexity, we also propose some suboptimum systems. The statistical analysis reveals that both the optimum and the suboptimum receivers are near-far resistant, not only with respect to the other users, but also with respect to the external interference. We also present a blind and a recursive least squares-based, decision-directed implementation of the receivers wherein only the signature and the timing of the user to be decoded and the signaling time and the frequency offset of the external interferer are assumed known. Finally, computer simulations show that the proposed adaptive algorithm outperforms the classical decision-directed RLS algorithm.

In this work, we propose an MMSE multiuser detector for asynchronous DS/CDMA systems operating over frequency-selective fading channels. It is shown that computation of a conditional MMSE estimate of the bit to be decoded may be carried out with a computational burden linear in the processing gain N. We also give a closed-form formula for the error probability and the near-far resistance of the proposed detector, and curves of such performance measures, showing that the new receiver is near-far resistant and outperforms the previously derived decorrelating detector for frequency-selective fading channels.

In this work we introduce a new multiuser detector for interference suppression in DS/CDMA systems, based on the minimization of a modified MMSE-like cost function, and which may possibly entail an independent processing of the data and of their complex conjugate. The proposed detection structure is then specialized to the relevant case that the interference consists of a secondary CDMA network whose bit-rate is slower than that of the primary network: interestingly, interference suppression entails a periodically time-varying (PTV) detection rule. We also address the issue of adaptive detection, and present a new RLS algorithm suited for the tracking of the PTV solution. As to the performance assessment, we give a closed-form formula for the system error probability, while numerical results show that the new detection structure, and its adaptive version, outperform previously known receivers.

In this paper, we handle the problem of joint suppression of multiple-access interference (MAI) and narrowband interference (NBI) in fading, dispersive channels. The detectors we consider are linear, one-shot structures, which allow for possible window enlargement and signal-space oversampling to improve performance. We focus on both zero-forcing and minimum-mean square-error design strategies, showing that the presence of NBI generally requires a time-varying processing of the observables, no matter what the optimization criterion. A thorough performance assessment of the proposed detectors is also presented, either through analytical formulas or through computer simulations. We finally deal with the problem of blind suppression of both MAI and NBI, introducing batch-estimation procedures to be implemented offline, which require very little and sometimes no prior knowledge as to the interference structure.

This paper addresses the problem of narrow-band interference (NBI) cancellation in direct-sequence spread-spectrum systems. The proposed procedure amounts to a preliminary nonlinear processing, wherein, upon projection of the received signal onto a Fourier basis, a number of samples having the largest modula are excluded from further processing. The structure of the optimum detector operating on censored observations is obtained, showing that the optimum detector performs matched filtering on the censored data. The performance assessment demonstrates that this receiver is able to suppress narrow-band interferers, no matter what their structure, provided that the censoring depth is properly chosen. A blind version of such a receiver is presented also, and a comparative performance assessment demonstrates that, unlike other suppression procedures, the proposed system allows suppression of NBI with no prior knowledge on its structure.

The authors consider an asynchronous DS/CDMA system wherein users are allowed to transmit their symbols at one out of two available data-rates. Besides the variable spreading length (VSL) access technique, described in the literature, two other types of access methods are considered, namely the variable chip-rate (VCR) and the variable chip-rate frequency shifted (VCRFS), wherein users with different data-rates are accommodated by means of signature waveforms with different chip-rates. The authors derive, through an unified approach valid for each of the above access techniques, an MMSE-based multiuser detector, and show that detection of the users transmitting at the higher data-rate requires a periodically time varying processing. As to the performance analysis, results show that the VCRFS access technique presents the best performance, as well as that enlarging the processing window length and sampling at rates larger than the chip-rate yield beneficial effects on the system performance.

The authors present a new MMSE-based multiuser detector for asynchronous DS/CDMA communications over frequency-selective fading channels. The proposed detector is able to simultaneously suppress both the multiaccess interference and a narrowband interference with known second-order statistics. It is shown that narrowband interference suppression requires in general a time-varying processing of the observables. As to the performance assessment, we give formulas for the system bit error rate for Rayleigh fading and we show that the detector is near-far resistant. Results demonstrate that the proposed system is effective in combating the overall interference. Moreover, enlarging the processing window and oversampling the signal space yield a remarkable improvement in the system performance, especially for large number of users.

This paper addresses the problem of narrowband interference suppression in direct-sequence spread-spectrum (DS/SS) techniques, which have been adopted to implement code division multiple access (CDMA) systems for wireless mobile communications. The theory of cyclic Wiener filtering, based on the cyclostationarity assumption for the signals involved in the reception problem, is applied to design single-channel adaptive frequency-shift filters which exploit both temporal and spectral correlation properties, i.e., the correlation between time- and frequency-shifted versions of the received signal. The numerical results show that receiving structures based on the proposed cyclostationarity-based interference suppression schemes largely outperform receivers that utilize conventional linear time-invariant suppressors, when they operate in highly contaminated interference environments.

This paper handles the simultaneous suppression of narrow-band and multiaccess interference in code division multiple-access (CDMA) direct-sequence spread-spectrum (DSSS) systems. The basic structure we refer to is reminiscent of the decorrelating detector, but here the design strategy relies on the concept of combating jointly the two interference sources-precisely, a decision as to the bit transmitted by each user is made based on the projection of the observables onto the orthogonal complement to the subspace spanned by the other users' signatures and the narrow-band interference. We focus on several different implementations of such a strategy, assuming a different degree of prior knowledge as to the narrow-band interference. An important side result of the proposed approach is that, in general, complete suppression of data-like interference may be achieved through periodically time-varying processing. An adaptive version of such a receiver is also presented, wherein the projection direction is estimated based on suitable estimates of the covariance properties of the observables. The value of this method is also assessed by studying the rate of convergence of the estimated direction to the true projection direction.