The detection of the COronaVIrus Disease 2019 (COVID-19) from Computed Tomography (CT) scans has become a very important task in modern medical diagnosis. Unfortunately, typical resolutions of state-of-the-art CT scans are still not adequate for reliable and accurate automatic detection of COVID-19 disease. Motivated by this consideration, in this paper, we propose a novel architecture that jointly affords the Single-Image Super-Resolution (SISR) and the reliable classification problems from Low Resolution (LR) and noisy CT scans. Specifically, the proposed architecture is based on a couple of Twinned Residual Auto-Encoders (TRAE), which exploits the feature vectors and the SR images recovered by a Master AE for performing transfer learning and then improves the training of a “twinned” Follower AE. In addition, we also develop a Task-Aware (TA) version of the basic TRAE architecture, namely the TA-TRAE, which further utilizes the set of feature vectors generated by the Follower AE for the joint training of an additional auxiliary classifier, so to perform automated medical diagnosis on the basis of the available LR input images without human support. Experimental results and comparisons with a number of state-of-the-art CNN/GAN/CycleGAN benchmark SISR architectures, performed by considering ×2, ×4, and ×8 super-resolution (i.e., upscaling) factors, support the effectiveness of the proposed TRAE/TA-TRAE architectures. In particular, the detection accuracy attained by the proposed architectures outperforms the corresponding ones of the implemented CNN, GAN and CycleGAN baselines up to 9.0%, 6.5%, and 6.0% at upscaling factors as high as ×8.

Abstract Bidirectional generative adversarial networks (BiGANs) and cycle generative adversarial networks (CycleGANs) are two emerging machine learning models that, up to now, have been used as generative models, i.e., to generate output data sampled from a target probability distribution. However, these models are also equipped with encoding modules, which, after weakly supervised training, could be, in principle, exploited for the extraction of hidden features from the input data. At the present time, how these extracted features could be effectively exploited for classification tasks is still an unexplored field. Hence, motivated by this consideration, in this paper, we develop and numerically test the performance of a novel inference engine that relies on the exploitation of BiGAN and CycleGAN-learned hidden features for the detection of COVID-19 disease from other lung diseases in computer tomography (CT) scans. In this respect, the main contributions of the paper are twofold. First, we develop a kernel density estimation (KDE)-based inference method, which, in the training phase, leverages the hidden features extracted by BiGANs and CycleGANs for estimating the (a priori unknown) probability density function (PDF) of the CT scans of COVID-19 patients and, then, in the inference phase, uses it as a target COVID-PDF for the detection of COVID diseases. As a second major contribution, we numerically evaluate and compare the classification accuracies of the implemented BiGAN and CycleGAN models against the ones of some state-of-the-art methods, which rely on the unsupervised training of convolutional autoencoders (CAEs) for attaining feature extraction. The performance comparisons are carried out by considering a spectrum of different training loss functions and distance metrics. The obtained classification accuracies of the proposed CycleGAN-based (resp., BiGAN-based) models outperform the corresponding ones of the considered benchmark CAE-based models of about 16% (resp., 14%).

Abstract We present a probabilistic method for classifying chest computed tomography (CT) scans into COVID-19 and non-COVID-19. To this end, we design and train, in an unsupervised manner, a deep convolutional autoencoder (DCAE) on a selected training data set, which is composed only of COVID-19 CT scans. Once the model is trained, the encoder can generate the compact hidden representation (the hidden feature vectors) of the training data set. Afterwards, we exploit the obtained hidden representation to build up the target probability density function (PDF) of the training data set by means of kernel density estimation (KDE). Subsequently, in the test phase, we feed a test CT into the trained encoder to produce the corresponding hidden feature vector, and then, we utilise the target PDF to compute the corresponding PDF value of the test image. Finally, this obtained value is compared to a threshold to assign the COVID-19 label or non-COVID-19 to the test image. We numerically check our approach’s performance (i.e. test accuracy and training times) by comparing it with those of some state-of-the-art methods.

In this paper, we consider the design of deep neural networks augmented with multiple auxiliary classifiers departing from the main (backbone) network. These classifiers can be used to perform early-exit from the network at various layers, making them convenient for energy-constrained applications such as IoT, embedded devices, or Fog computing. However, designing an optimized early-exit strategy is a difficult task, generally requiring a large amount of manual fine-tuning. In this paper, we propose a way to jointly optimize this strategy together with the branches, providing an end-to-end trainable algorithm for this emerging class of neural networks. We achieve this by replacing the original output of the branches with a 'soft', differentiable approximation. In addition, we also propose a regularization approach to trade-off the computational efficiency of the early-exit strategy with respect to the overall classification accuracy. We evaluate our proposed design approach on a set of image classification benchmarks, showing significant gains in accuracy and inference time.

Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP/IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP/IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP/IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces.

In this paper, we propose a dynamic resource provisioning scheduler to maximize the application throughput and minimize the computing-plus-communication energy consumption in virtualized networked data centers. The goal is to maximize the energy-efficiency, while meeting hard QoS requirements on processing delay. The resulting optimal resource scheduler is adaptive, and jointly performs: i) admission control of the input traffic offered by the cloud provider; ii) adaptive balanced control and dispatching of the admitted traffic; iii) dynamic reconfiguration and consolidation of the Dynamic Voltage and Frequency Scaling (DVFS)-enabled virtual machines instantiated onto the virtualized data center. The proposed scheduler can manage changes of the workload without requiring server estimation and prediction of its future trend. Furthermore, it takes into account the most advanced mechanisms for power reduction in servers, such as DVFS and reduced power states. Performance of the proposed scheduler is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces. The results confirm the delay-vs.-energy good performance of the proposed scheduler.

One of the current key challenges in wireless sensor networks is the development of routing protocols that provide stable cluster-head election, while prolonging network lifetime by saving energy. In this contribution, a new Stable Election Protocol (SEP), named New-SEP (N-SEP), is presented to prolong the stable period of Fog-supported sensor networks by maintaining balanced energy consumption. N-SEP takes into account some features of sensor nodes (e.g., distance from base station, network heterogeneity ratio, residual/consumed energy, distance between cluster heads (CHs)) in order to elect the best CHs. For this purpose, it exploits heterogeneous energy thresholds, in order to select CHs and prolong the time interval of the system. Simulation results support the capability of the proposed algorithm to maximize the network lifetime and preserve more energy as compared to the results obtained by using current heuristics, such as, Low Energy Adaptive Clustering Hierarchy (LEACH) and SEP protocols. Additionally, we found that N-SEP outperforms LEACH and SEP in prolonging the stability period of the network by 50% and 25%, respectively.

In this paper, a primary-secondary resource-management controller on Vehicular Networks is designed and tested. We cast the resource-management problem into a suitable constrained stochastic Network Utility Maximization problem and derive the optimal cognitive resource management controller, which dynamically allocates the access time-windows. We provide the optimal steady-state memoryless controllers under hard and soft primary-secondary collision constraints, showing as the hard controller does not present any optimality gap in the average utility with respect to the soft one, while, on the contrary, it is able to make the outage-probability vanishing. Then we generalize the framework integrating the controllers with different data fusion techniques, and test the controller behaviour in a non-stationary application scenario. Finally we provide the optimal steady-state hard controller with memory and compare it with the memoryless one.

In this paper, we design and test a full distributed and scalable resource-management scheduler for Vehicular Real-Time applications. We dynamically allocate the access time window (at the RoadSide Units) and the access rate and traffic flows (at the Vehicular Clients) under hard reliability collision constraints. We provide the optimal memoryless scheduler for network utility maximization, showing as it presents no loss in the network average utility with respect to not real-time soft reliability schedulers. Finally, the proposed scheduler exploits an ad-hoc designed soft-input/soft-output data fusion algorithm, able to supply in real-time reliable context-information, even in the presence of fading-affected and intermittent vehicular-to-infrastructure connectivity.

We design and test a distributed and adaptive resource management controller in Vehicular Access Networks, allowing energy and computing-limited car smart phones to opportunistically accede to a spectral-limited wireless backbone. We cast the resource management problem into a suitable constrained stochastic Network Utility Maximization problem and derive the optimal cognitive resource management controller, which dynamically allocates the access time-windows at the serving Roadside Units (i.e., the primary users) and the access rates and traffic flows at the served Vehicular Clients (i.e., the secondary users), allowing hard reliability guarantees to Roadside Units. We validated the controller performances in real-word application scenarios.

This paper focuses on the QoS-constrained jointly optimal adaptive distributed source coding, channel coding, network coding and power control for Co-Channel Interference (CCI)-limited wireless multiple class multicast networks, such as, for example, Wireless Sensor Networks (WSNs). The goal is to allocate the available system-wide resources by jointly performing Loss-Less Distributed Source Coding (LLDSC) and Intra-Session Network Coding (ISNC), while leveraging channel coding and power control for CCI-mitigation. Due to the presence of CCI, the resulting cross-layer optimization problem is inherently nonconvex. Hence, we develop a distributed, iterative and asynchronous algorithm for the optimal adaptive QoS management of the available bandwidth/power/flow resources. Actual performance and adaptive capability of the proposed resource management algorithm in the presence of: i) abrupt changes of the statistics of the source flows; ii) failures of the interior network nodes; and, iii) fast fading, are numerically tested.

In this paper, we focus on the quality-of-service (QoS)-constrained jointly optimal congestion control, network coding, and adaptive distributed power control for connectionless wireless networks affected by multiple access interference (MAI). The goal is to manage the available network resources, so as to support multiple multicast sessions with QoS requirements when intrasession network coding (NC) is allowed. To cope with the nonconvex nature of the resulting cross-layer optimization problem, we propose a two-level decomposition that provides the means to attain the optimal solution through suitable relaxed convex versions of its comprising subproblems. Sufficient conditions for the equivalence of the primary nonconvex problem and its related convex version are derived, occurrence of such conditions investigated, and performance with respect to conventional routing-based layered solutions analyzed. Moreover, we develop a distributed algorithm to compute the actual solution of the resource allocation problem that quickly adapts to network time-evolutions. Performance of this algorithm and its adaptivity are evaluated in the presence of varying network/fading conditions and noisy measurements.

Emerging media overlay networks for wireless applications aim at delivering Variable Bit Rate (VBR) encoded media contents to nomadic end users by exploiting the (fading-impaired and time-varying) access capacity offered by the "last-hop” wireless channel. In this application scenario, a still open question concerns the closed-form design of control policies that maximize the average throughput sent over the wireless last hop, under constraints on the maximum connection bandwidth available at the Application (APP) layer, the queue capacity available at the Data Link (DL) layer, and the average and peak energies sustained by the Physical (PHY) layer. The approach we follow relies on the maximization on a per-slot basis of the throughput averaged over the fading statistic and conditioned on the queue state, without resorting to cumbersome iterative algorithms. The resulting optimal controller operates in a cross-layer fashion that involves the APP, DL, and PHY layers of the underlying protocol stack. Finally, we develop the operating conditions allowing the proposed controller also to maximize the unconditional average throughput (i.e., the throughput averaged over both queue and channel-state statistics). The carried out numerical tests give insight into the connection bandwidth-versus-queue delay trade-off achieved by the optimal controller.

Traffic Engineering application to the cross-layer design of Multiple Access Interference (MAI)-affected powerlimited wireless networks, when Quality of Service constraints are also present, leads to deal with nonconvex resource allocation problems. Although several manageable-complexity solutions have been proposed, they are based on specific capacity functions and, generally, fail to provide reliable results in low-SINR (Signal to Interference plus Noise Ratio) scenarios. We develop a two-level decomposition that is able to find the optimal solution of a wide nonconvex cross-layer problem, which combines user utility, flow control, QoS multipath routing,Medium Access Control (MAC) design and power control, by means of a suitable relaxed convex version of its comprising flow control and power-allocation sub-problems. Sufficient conditions for the equivalence of the primary (nonconvex) problem and its related (convex) version are provided. Moreover, we develop a distributed, iterative, asynchronous algorithm for computing the solution of the overall nonconvex resource allocation problem, that is able to (quickly) self-adapt to possible network time evolutions (as, for example, node failure events) and, most importantly, that may be implemented on top of connectionless networking platforms. Actual performance of the overall proposed solution and its robustness against node-failure events are numerically tested and compared with the corresponding ones of Destination Sequenced Distance Vector-based single-path routing algorithms.

The interference induced by UWB communication on systems operating in the frequency range between 0 and 10.6 GHz is the main reason why the essential role in these systems' design is played by the pulse shape selection. That's why an accurate shaping choice can lead to avoid severe performance reduction and to guard systems coexistence. To this end, FCC (and ETSI in the near future) gave strict indications about the spectral limits to be respected, so as to define, as the purpose of UWB systems realization, the compliance with this spectral mask. The, widely used, choice of Gaussian-like pulses is, however, largely suboptimal from a power emission point of view since they are unable to maximize the signal-to-noise ratio (SNR). Goal of this contribution is to achieve a good compromise, in a cognitive way, between spectral emission, rate and synchronization errors robustness, via a modified version of the Parks-McClellan method, considering channel impairments due to its frequency-selective nature, to the inter-pulse interference and in-band interference.

Emerging media overlay networks for wireless applications aim at delivering Variable-Bit-Rate (VBR) encoded media contents to nomadic end-users by exploiting the (fading-impaired and time-varying) access capacity offered by the "last-hop" wireless channel. In this application scenario, a still open question concerns the design of control policies maximizing the average throughput over the wireless last-hop, under constraints on the maximum connection bandwidth allowed at the Application (APP) layer, the queue-capacity available at the data-link (DL) layer, and the average and peak transmit energies sustained by the Physical (PHY) layer. The main feature of the approach we follow relies on the maximization (on a per-slot basis) of the throughput averaged over the fading statistics and conditioned on the queue-state. The resulting optimal controller is rate-based and operates in a cross-layer fashion that involves the APP, DL and PHY layers of the underlying protocol stack. This means that the proposed controller dynamically allocates connection bandwidth at the APP Layer, throughput at the DL layer and transmit energy at the PHY layer by basing on both current queue and channel states. The carried out numerical tests give insights about the connection bandwidth-vs.-queue delay tradeoff attained by optimal controller.

Wireless mesh networks (WMNs) are envisioned for extending the coverage of WLANs by interconnecting the underlying access points (APs) via high-capacity wireless backbones. Since the ultimate goal of a WMN is to provide Internet connectivity to residential clients, WMN traffic is mainly routed over the backbone either toward the Internet gateways (IGWs) or from the IGWs to the APs. In principle, the transport capacity of a WMN can further be upgraded by equipping the underlying backbone routers with multiantenna (MA) radio modules. Motivated by the above consideration, in this paper, we focus on the optimized node selection (e.g., path-routing) over MA mesh backbones when the target is to maximize the end-to-end routed information rate subject to a constraint on the total power available for the relays. Under the assumption of Rayleigh-distributed block fading, we assume that point-to-point capacity-achieving space-time codes (STCs) are used for the single-hop link. At first, we tackle the routing problem when neither interference mitigation (IM) nor transmit beamforming (TB) is performed at the relay nodes, and then, we extend the analysis to the cases when IM and/or TB are also carried out. The effects of channel-state-information (CSI) possibly available at the relay nodes are also investigated. So doing, we are able to gain insight about the combined effect of spatial multiplexing and IM capabilities of the overall MA architecture on both end-to-end capacity and access medium performance of the considered WMN.

Main goal of this work is to give insight on the possible performance improvement arising in the wireless local and/or ad-hoc access from the synergic cooperation of two emerging paradigms, e.g., multi-antenna and cognitive radios. As application scenario, we consider both the faded uplink of a WLAN working in infrastructure-mode, where noncooperative Multi-Antenna cognitive radios attempt to join to a (possibly multi-antenna) access point (AP) and an ad-hoc scenario where each node can communicate with each free node. The target can be twofold and two different approaches are considered. The first is the competitive maximization of own access throughput in the presence of multiple-access Interference (MAI) induced by the other accessing terminals, the second one is the BER minimization. Being the radios cognitive, they are capable to autonomously learn the ambient-context and, then, self-configure their access strategy via suitable power-allocation that is time-frequency-code-space signal-shaping. Furthermore, a generalized approach is developed that allows the node to access with a (possibly hybrid) scheme to the medium by combining different x-DMA strategies under QoS-guaranteed access policy.

In this contribution, the problem of the synchronization for ultra wideband impulse radio (UWB-IR) systems, through the adoption of a multi-antenna platform working on multipath faded channels, is afforded. In this regard, we propose a novel UWB-IR synchronizer devoted to jointly estimate (according to the maximum likelihood (ML) criterium) the arrival times and their (Poisson distributed) number, composing a typical UWB-IR multipath faded channel. Such estimate is effected without any knowledge on the value of the channel paths (noncoherent ML synchronizer). The architecture of such synchronizer is based on a single-input multiple-output (SIMO) platform, instead of a multiple-input multiple-output MIMO one, according to a previous result in the literature. Related performance of this joint ML estimation is expressed in closed via the Cramer-Rao bound (CRB) and is tested by simulations, under acquisition and tracking conditions.

In this contribution we develop a multiple-input multiple-output (MIMO) impulse radio ultra wide band (IR-UWB) transceiver for orthogonal signalling over (baseband) muhipath faded MIMO channels. The proposed maximum-likelihood receiver results to be "partially coherent", since it is optimized to work without any information about channel coefficients, and it is based on the knowledge of the arrivals' times of the transmitted signals' replicas. The behavior of the proposed transceiver has been evaluated via three suitable versions of the Union-Chernoff Bound related to several indoor and outdoor propagation scenarios, and these limits are considered to introduce a novel family of unitary orthogonal space-time block codes (e.g., the space-time OPPM (STOPPM) codes), that are able to attain maximum diversity and coding gains.

The emerging proxy-based wireless content delivery networks (CDNs) should to be designed to download huge-size files over fading-affected channels. However, from a radio resource management point of view, several basic problems still need to be solved for such wireless delivery systems to operate efficiently. Specifically, due to the fading nature of the downlink channel, a still open basic problem is how to design optimal energy-allocation (for example, scheduling) policies that minimize the requested download time when constraints on the total available energy and peak energy are simultaneously active. In this contribution, this problem is solved for application scenarios where the downlink channel is slotted and continuous-state, the carried out traffic is elastic, and the resulting conveyed throughput is measured by any desired increasing concave rate function. Specifically, the optimal energy-allocation policy minimizing the download time is computed in closed form, and its performance is compared against that of a basic on-off heuristic energy scheduler on some Rayleigh-faded multi-antenna delivery systems of practical interest. The carried out performance comparisons point out that the presented optimal policy typically may outperform the heuristic one up to two orders of magnitude, especially when the delivery system is strongly energy limited.

In this contribution Time-Hopping (TH) codes are developed based upon the theory of congruences. These codes can be used for coherent multiuser asynchronous spread spectrum communication systems. They represent a compromise between Costas codes, which have nearly ideal auto-ambiguity properties, and congruential codes presenting nearly ideal cross-properties. The presented code applied not in ranging applications but communications system presents good performance in terms of Bit Error Rate (BER) for UltraWide Band Impulse Radio (UWB-IR) systems. Examples of typical auto- and cross-ambiguity functions are given to illustrate the performance of the presented codes jointly with BER evaluation.

This contribution analyzes the problem of multi-user interference suppression via estimation and subtraction. In particular it is shown that spatial diversity helps the suppression capability of a Base Station and this has an important impact on performance. By fact, we show that the best solution does not consist in transmit at maximum energy level but, in order to allow interference estimation and cancellation, a trade off has to be solved. So, interference suppression can give arise to performance improvements both for achieved throughput (that can be "enhanced") and for error probability (that can be reduced).

In this contribution, the problem of download time minimization, by considering channel state, is solved for application scenario where the downlink channel is slotted and continuous-state, while the resulting conveyed throughput is measured by any desired increasing concave rate-function. Specifically, the optimal energy-allocation policy minimizing the download-time is found and its performance is compared against that of a basic on-off type heuristic scheduler for some Rayleigh-faded multi-antenna downloading systems of practical interest

The paper focuses on competitively optimal power-control and signal-shaping for "ad-hoc" networks composed of multiple-antenna noncooperative transmit/receive terminals affected by spatially colored multi-access interference (MAI). The target is the competitive maximization of the information throughput of each link active over the network. For this purpose, the MAI-impaired network is modeled as a noncooperative strategic game, and sufficient conditions for the existence and uniqueness of the Nash equilibrium are provided. Specifically, the main contribution of the paper may be so summarized. First, we develop fully. distributed and scalable power-control and signal-shaping algorithms allowing the implementation of asynchronous space-division multiple access strategies (SDMACSs) able to guarantee the competitive maximization of the users' throughput under both best effort and contracted QoS access policies. Second, we give evidence that the developed SDMACSs outperform (in terms of aggregate throughput) the conventional centralized ones (such as TDMA/FDMA/CDMA), especially in operating scenarios affected by strong MAI.

The paper focuses on competitively optimal power-control and signal-shaping for "ad-hoc" networks composed of multiple-antenna noncooperative transmit/receive terminals affected by spatially colored multi-access interference (MAI). The target is the competitive maximization of the information throughput (measured in bits/slot) sustained by each link active over the network. For this purpose, the MAI-impaired network is modeled as a noncooperative strategic game, and sufficient conditions for the existence and uniqueness of the Nash equilibrium are provided. We develop fully distributed and scalable power-control and signal-shaping algorithms allowing the implementation of asynchronous space-division multiple access strategies (SDMACSs) able to guarantee the competitive maximization of the users' throughput under both best effort and contracted QoS access policies. We give evidence that the developed SDMACSs outperform (in terms of aggregate throughput) the conventional centralized ones (such as TDMA/FDMA/CDMA), especially in operating scenarios affected by strong MAI. We study the convergence property of the presented SDMACSs and show that, when the throughput set requested by the users is not achievable by the network, then the developed SDMACSs are able to move the working point of the system to the nearest one sustainable by the network. By exploiting the distributed feature of the presented SDMACSs, we propose two connection admission procedures (CAPs) able to optimize (in a competitive sense) the tradeoff between aggregate networking throughput and connection requirements advanced by the users.

In this letter, we introduce a simple co-decoding scheme for ultra wideband (UWB) impulse radio systems impaired by multiuser interference (MUI). The proposed scheme exhibits an adaptive capability with respect to MUI degrading effects and, depending on the MUI level, it may switch from a hard-detection operating mode to a soft-detection one. So doing, the presented scheme is able to gain resistance against near-far effects. Several numerical tests support the conclusion that the presented scheme outperforms conventional ones currently planned for noise-limited UWB systems up to orders of magnitude in MUI-limited application scenarios.

This contribution considers the power control and signal-shaping problem for ad-hoc networks composed by multi-antenna non-cooperative terminals affected by spatially colored multiple-access interference (MAI). The multi-antenna interference channel is modeled as a non cooperative and strategic game. Furthermore iterative power control and signal shaping algorithms are presented to efficiently achieve the Nash equilibrium under best effort and "contracted QoS" policies.

This paper focuses on the competitively optimal power-control and signal-shaping for "ad-hoc" networks composed by multiple-antenna noncooperative transmit/receive terminals affected by spatially colored multiple-access interference (MAI). For this purpose, the MAI-impaired network is modeled as a noncooperative strategic game, and sufficient conditions for the existence and uniqueness of the Nash equilibrium are provided. Connection admission procedures are presented with related performance.

In the last years a lot of attention has been paid to multiantenna systems with space-time coding (STC) and to ultra wide band (UWB). In this paper we aim to propose a transceiver scheme in order to evaluate if these two technologies can be considered adequate candidates for next generation WLANs. Merging ultra wide band signals with multiple antenna schemes allows us to achieve very high bit rate and error probability matched to QoS requested by 4GWLANs at low SNR. This contribution presents a simple way to combine ultra wide band and multiple antenna schemes so to improve performance and coverage with respect to the conventional UWB approach in order to promote this approach application scenarios by considering a base band channel model and selective are considered.

In the last few years, ultra-wideband (UWB) systems became an appealing technology for wireless communication applications. Unfortunately, when the transmission channel is affected by intersymbol interference (ISI), system performance of UWB systems equipped with receivers based on conventional matched filters presents error-floor phenomena. Aimed by these considerations, in this letter, we present a novel transmit-receive scheme allowing blind channel estimation and minimum mean-square error linear channel equalization. Essentially, the proposed scheme exploits a very short duration of the UWB pulse for achieving reliable blind deconvolution of the received signal. A nice feature of the resulting system is that blind deconvolution of the received signal is achieved without power and throughput losses. Simulation results support the effectiveness of the proposed scheme, and show that it is able to gain about 8 dB over current UWB receivers based on matched filtering on several test channels impaired by ISI.

In this contribution we propose a simple codec for ultra wideband (UW) radio networks limited by multi-user interference (MUI). The proposed codec exhibits adaptive capabilities with respect to MUI degrading effects and, depending on MUI level, it may switch from a hard-detection operating mode to a soft-detection one. So doing, the presented scheme gains resistance against near-far effect. Several numerical tests show that our scheme outperforms conventional ones currently planned for UWB systems up to several orders of magnitude in MUI-limited wireless local area networks (WLANs).

Multiple-antenna wireless systems with space-time block-codes exploit channel-diversity for improving performance without loss in spectral efficiency. Up to date, most work on this area assumed that perfect channel estimates are available at the receiver so to make feasible fully coherent decoding. However, accurate channel-estimates require long training sequences that lower spectral efficiency. Therefore, in this contribution we focus on architecture, optimized design and performance of multiple-antenna block-coded systems with imperfect channel-estimates. After considering emerging fourth-generation WLANs (4GWLANs) as a possible application scenario, new performance bounds and optimized design criteria are presented together with a new family of robust-space block-codes that "self-match" to channel estimation errors.

We present a novel iterative (turbo) receiver with tunable complexity for reliable detection of (uncoded) payload data transmitted over long intersymbol interference (ISI) channels affected by crosstalk, as those typically encountered in emerging HDSL2 services standardized by ANSI T1.418 recommendation. The proposed receiver combines in an original way "minimum mean square error (MMSE) estimation principle," "turbo-processing principle" and "crosstalk-prediction principle" for achieving both suboptimal maximum a posteriori probability channel equalization and reliable soft-mitigation of ISI tail plus crosstalk. More in detail, according to the turbo-processing principle, at each iteration suitable extrinsic information is extracted from the equalizing and interference-canceling modules and used as "a priori information" for the next iteration. Several simulation results on typical HDSL-like test-loops confirm the superiority of the proposed turbo-detector (TD) over current solutions based on conventional MMSE decision-feedback equalizers and precoders (such as the Tomlinson-Harashima precoder). The numerical tests also point out that performance of the presented TD on typical high bit-rate digital subscriber lines (HDSL) is not limited by error-floor phenomena, even at error-probabilities below 10/sup -7/.

We develop the solution for optimal loading of digital subscriber loop (DSL) multicarrier (MC) systems that present constraints both on overall available energy and maximum energy per carrier. In the emerging ADSL3 systems planned for high-throughput multimedia applications the constraint on the peak-energy arises from spectral compatibility issues. However, up till now none of the published loading algorithms apparently account for peak-energy limitations neither optimal solutions for loading peak-energy constrained MC systems seem available. Therefore, starting from some results about the maximization of /spl cap/-convex functions which generalize the usual Kuhn-Tucker conditions, we present the analytical relationships characterizing the optimal peak-energy limited Loading. Thus, a low-complexity iterative algorithm implementing the optimal solution is also developed and its performance is numerically tested on an ANSI-standard ADSL3-type DSL impaired by crosstalk. Furthermore, a suitably modified version of the presented loading algorithm which guarantees integer-bit rates is presented.

We present some novel results about the reliable information-rate supported by point-to-point multiple-antenna Rayleigh-faded wireless links for coded transmissions that employ two-dimensional (QAM or PSK) data constellations. After deriving the symmetric capacity of these links, we present fast-computable analytical upper and lower bounds that are asymptotically exact both for high and low SNRs, and give rise to a reliable evaluation of the link capacity when perfect channel state information (CSI) is available at the receiver. Furthermore, asymptotically exact simple upper bounds are also presented for a tight evaluation of the outage probability.

The goal of this contribution is to give some insight into the cochannel interference (CCI) suppression capability of novel symbol-by-symbol-type maximum a posteriori probability single-user receivers for nonspread-spectrum multiuser narrow-band systems impaired by intersymbol interference and CCI. The presented multiuser receivers minimize the error probability in the detection of the desired user message on a per-symbol basis. Practical application environments for the developed detectors can be constituted by Ethernet-type LANs and DSL-based high-throughput links connecting central offices to subscribers.

In this article, quickly computable upper and lower bounds are presented on the symmetric capacity of flat-faded Rice and Nakagami channels with side information (SI) for data-transmissions via finite-size quadrature amplitude modulation (QAM) constellations. The proposed bounds exhibit the appealing feature to be tight and asymptotically exact both for high and low signal-to-noise ratios (SNRs). Furthermore, exponentially tight Chernoff-like formulas are also presented for an analytical evaluation of the resulting system outage probabilities when interleaved packet transmissions are carried out.

We present some analytical performance bounds that point out the key parameters dominating the performance of symbol-by-symbol combined-type maximum a posteriori equalizers/decoders for trellis-coded modulation (TCM) transmissions over fast-faded intersymbol-interference-impaired Rayleigh-channels with diversity reception. Guidelines are also provided for the design of good TCM codes matched to the statistical features of the multipath transmission links and application examples are developed that support the actual effectiveness of the presented results.

In this contribution, a novel version of the reduced-state maximum a posteriori probability/decision-feedback (MAP/DF) equalizer for intersymbol interference channels with long impulse responses is presented. The main feature of the proposed equalizer is that the soft statistics generated by the MAP receiver are employed to recursively compute a suitable index of the actual reliability of the (soft) decisions feeding the feedback filter. Therefore, in the presented equalizer, the usual (over-optimistic) assumption of error-free decisions at the input of the feedback filter is relaxed and improved performances are experienced, especially on channels with long impulse responses.

In this contribution, novel symbol-by-symbol (SbS)-type maximum a posteriori probability (MAP) receivers are presented for non spread-spectrum (SS) multiple-access (MAC) narrowband systems impaired by ISI and co-channel interference (CCI). The proposed decentralized type receivers minimize the error probability in the detection of the desired user message on a per-symbol basis. A practical application environment for the presented receivers can be constituted by Ethernet-type local area networks (LANs) where central offices are linked to subscribers via multitapped buses.

In this article, novel quickly computable analytical upper and lower bounds are presented on the symmetric capacity for flat-faded Rayleigh channels with finite-size quadrature amplitude modulation constellations when perfect channel-state information at the receiving site is available; the proposed bounds are asymptotically tight both for high and low signal-to-noise ratios. Furthermore, an easily computable expression is also provided for a reasonably tight evaluation of the resulting outage probability.

We present a new blind equalizer that achieves identification of a channel by exploiting only second-order statistics of the observations. The novelty of the proposed approach is that the receiver accomplishes channel identification by using soft statistics; roughly speaking, it consists of an Abend-Fritchman (1970) type maximum a posteriori (MAP) equalizer that feeds a nonlinear Kalman-like channel-estimator with the soft statistics constituted by the a posteriori probabilities (APPs) of the channel-state sequence. So, since the receiver employs second-order statistics only, it achieves channel identification with fewer symbols than most techniques based on higher-order statistics.

The symmetric cut-off rate is derived for quadrature-amplitude-modulated (QAM) block-coded data-transmissions over noisy links impaired by time-correlated multipath-phenomena which give rise to Rayleigh-distributed randomly time-variant intersymbol Interference (ISI) effects. The presented formulas for the cut-off rate are new; they are derived from a random coding bound developed "ad hoc" for bounding the ensemble-averaged block-error probability of a maximum likelihood (ML) decoder supported by perfect channel-state-information (CSI). The assumption of time-correlated ISI phenomena introduces memory in the link and makes the underlying ISI-channel a continuous-state channel with memory.

The paper presents a novel fast-adaptive nonlinear receiver which exploits soft statistics for tracking the random fluctuations experienced by time division multiple access (TDMA) mobile radio links impaired by frequency-selective time-variant multipath phenomena. The detection task is accomplished by an Abend-Fritchman-like symbol-by-symbol maximum likelihood (SbS-ML) detector which delivers both hard decisions and soft statistics in form of a posteriori probabilities (APPs) of the states of the intersymbol interference (ISI) channel. In the proposed adaptive receiver, these APPs are employed in place of the conventional hard-detected data to feed an ad hoc developed nonlinear recursive Kalman-type channel estimator. Extensive computer simulations show that the exploitation of soft statistics enhances the tracking capability of the channel estimator so that the proposed receiver generally outperforms the usual ones based on adaptive maximum likelihood sequence estimators (MLSEs) for signal-to-noise ratio (SNR) values over 12-13 dB. Furthermore, the experienced performance gap with respect to more complex per-survivor processing (PSP)-based multi-estimator detectors appears generally small on slowly and moderately fast time-varying channels characterized by values of the product Doppler bandwidth /spl times/ signaling period B/sub D/T/sub S/ below 5/spl times/10/sup -3/.

The symmetric cutoff rate is derived for quadrature-amplitude-modulated (QAM) block-coded data transmissions over noisy links affected by time-correlated multipath-phenomena which introduce intersymbol interference (ISI) in the received signal. Maximum likelihood (ML) decoding supported by perfect channel-state information (CSI) is assumed. Due to the presence of time-correlated ISI phenomena, the resulting transmission link constitutes an example of a continuous-state channel with memory.

A novel adaptive nonlinear equalizer for fast time-varying multipath channels that combines the channel estimation and data detection tasks is presented. The a posteriori probabilities (APPs) of the states of the intersymbol interference (ISI) channel are recursively computed from the received data by a symbol-by-symbol (SbS) detector and are then employed by a Kalman-type nonlinear channel estimator. Robust channel tracking and good data-detection performance are obtained, with a reasonable receiver complexity.

New upper bounds for the performance of the optimum combined symbol-by-symbol (SbS) Abend-Fritchman-like equalizer and decoder are presented, and a related criterion for the actual design of good trellis-coded-modulated (TCM) schemes effectively matched to the distortion introduced by the intersymbol interference (ISI)-corrupted transmission channel is developed. The actual application of the proposed design criterion is also addressed.

The design of good TCM schemes matched to ISI channels requires that analytical performance bounds are also available. In this paper, starting from an application of the union-Bhattacharyya bound, upper-bounds for the performance of the theoretically optimum combined symbol-by-symbol (SbS) Abend-Fritchman like equalizer and decoder are presented for the first time in literature. The combined detector acts on the distorted version of the TCM signal received at the output of a channel impaired by (known) ISI and AWGN. On the basis of these bounds a novel simple criterion for the design of TCMs optimally matched to any assigned ISI-corrupted transmission channel is then derived and its application to some actual test-channels is illustrated.

GMSK is an efficient modulation technique for digital mobile communications. In this paper a simple solution to the modeling and simulation of GMSK systems impaired by fading channels and additive white Gaussian noise is given, and its effects on the channel characteristics are also considered. The modeling is based on the well-known linearized representation of phase-modulated signals. The analysis is not only useful for modeling and simulation purposes, but also gives a tool for the design of simple receivers.

A new tight analytical upper bound is presented for the performance of finite-delay symbol-by-symbol (SBS) Abend-Fritchman-like decoding of trellis-encoded data crossing arbitrary (eventually time-varying) discrete memoryless channels (DMCs). On the basis of this bound, a related criterion for the optimal design of trellis codes with SBS decoding is then proposed. It gives rise to an effective procedure for the construction of good trellis codes (generally time varying and nonlinear) with assigned decoding complexity.

New analytical upper bounds are derived for the performance of symbol-by-symbol maximum-likelihood (ML) Abend-Fritchman-type detectors for multi-amplitude/phase-modulated digital sequences transmitted over time-dispersive (generally) time-variant linear channels introducing finite intersymbol interference (ISI) and additive white Gaussian noise (AWGN). The proposed bounds are obtained starting from a novel form of the standard Bhattacharyya bound and take explicitly into account the allowed decoding delay. Both deterministic and random multipath slow-fading Rayleigh transmission ISI channels are considered. Simulation results obtained for some test channels of practical interest show the tightness of the derived analytical bounds.

The optimal analog discrete-time feedback communication system is designed for the transmission of a Markov process, such as that generated by a combined source/channel encoder. The forward channel is affected by an additive white Gaussian noise, while the feedback channel is causal and noiseless; no repetition of the transmitted sample is assumed. On the basis of the minimum mean square error (MMSE) criterion, the optimal transmitter and receiver are derived under the constraint of a maximum power level for the transmission over the forward channel. The resulting optimized system is adaptive and fully exploits the available power, thus maximizing the information rate transferred by the system itself. For comparison purposes, the optimal MMSE communication system without feedback link is also designed. The MSE performance of both systems, with and without feedback link, is evaluated by means of closed-form expressions and their convergence rate to the steady state is also computed. Using the feedback link gives significant performance improvements when the input-encoded process is moderately or highly correlated; in this case, the advantages offered by the combined source-channel coding techniques are fully exploited by the proposed optimized feedback communication systems.

The least square estimation problem for pinned-to-zero discrete-index reciprocal Gaussian processes in additive white noise is solved, thus completing and extending some previous results available in the literature. In particular, following the innovations approach a (finite) set of recursive equations is obtained for the filter and for the three standard classes of smoothers (fixed-point, fixed-interval, fixed-lag). Recursive expressions for the mean square performance of the proposed estimators are also given.>