In vehicular communications, accurate modeling of real-world radio propagation channels is essential. To this end, we propose a novel stochastic model, namedVehicular-Tapped Delay-Line(V-TDL) that accurately captures the statistical behavior of multipath channels characterized by path-dependent gains, delays, and Doppler shifts. V-TDL supports diverse traffic conditions and road geometries by generating channel instances through well-established probability distributions. Also, it effectively models the parameters of the distributions through realistic geometry-based simulations, achieving the accuracy of a ray-tracing-based model while maintaining the low complexity of a purely stochastic approach. In contrast to existing models, V-TDL accounts for the correlation between propagation paths. Our findings show that this correlation is inherent in high-speed vehicular environments and neglecting it leads to a significant overestimation of channel diversity and system performance. We compare our model to existing alternatives to assess the performance of OTFS and OFDM modulations. The results demonstrate that, unlike traditional models such as the 3GPP EVA, V-TDL captures variations in channel diversity influenced by traffic intensity and road geometry, which impact the OTFS and OFDM performance. Although OTFS is penalized by path correlation, it consistently outperforms OFDM in all evaluated vehicular environments, confirming its suitability for high-speed vehicular communication scenarios.

The advent of Large Language Models (LLMs) and generative AI is fundamentally transforming information retrieval and processing on the Internet, bringing both great potential and significant concerns regarding content authenticity and reliability.This paper presents a novel quantitative approach to shed light on the complex information dynamics arising from the growing use of generative AI tools.Despite their significant impact on the digital ecosystem, these dynamics remain largely uncharted and poorly understood.We propose a stochastic model to characterize the generation, indexing, and dissemination of information in response to new topics.This scenario particularly challenges current LLMs, which often rely on real-time Retrieval-Augmented Generation (RAG) techniques to overcome their static knowledge limitations.Our findings suggest that the rapid pace of generative AI adoption, combined with increasing user reliance, can outpace human verification, escalating the risk of inaccurate information proliferation across digital resources.An in-depth analysis of Stack Exchange data confirms that high-quality answers inevitably require substantial time and human effort to emerge.This underscores the considerable risks associated with generating persuasive text in response to new questions and highlights the critical need for responsible development and deployment of future generative AI tools.

The growing interest in Orthogonal Time Frequency Space (OTFS) modulation for vehicular communication systems requires the validation of its advantages using suitable channel models capable of emulating the dynamic and geometric complexities of vehicle-to-infrastructure systems. This paper presents a novel, realistic geometric-based channel model, called V-CORE, specifically designed for evaluating the performance of OTFS in vehicular scenarios. Our model accurately characterizes the scattered paths by exploiting the radar cross-section of the vehicles that populate a road according to a given vehicular traffic intensity. Multiple road scenarios with different geometry and vehicle velocities are considered, resulting in a flexible tool to evaluate system performance in different traffic contexts. The V-CORE model provides the channel variables, particularly relevant to OTFS implementation, such as multipath Doppler shift and delay. We assess the performance of OTFS against that of OFDM under different road structures, traffic intensity, and vehicle velocities. Further, we compare the proposed V-CORE model to the Extended Vehicular A model and demonstrate that ours provides a deeper insight into performance in high-speed vehicular scenarios.

In5G and beyond (5GB) networks, the notion of cell tends to blur, as a set of points-of-access (PoAs) using different technologies often cover overlapping areas. In this context, highquality decisions are needed about (i) which PoA to use when serving an end user and (ii) how to manage PoAs, e.g., how to set their power levels. To address this challenge, we present Cluster-then-Match (CtM), an efficient algorithm making joint decisions about user assignment and PoA management. Following the human-centric networking paradigm, such decisions account not only for the performance of the network, but also for the level of electromagnetic field exposure to which human bodies incur and energy consumption. Our performance evaluation shows how CtM can match the performance of state-of-the-art network management schemes, while reducing electromagnetic emissions and energy consumption by over 80%.

In next-generation networks, cells will be replaced by a collection of points-of-access (PoAs), with overlapping coverage areas and/or different technologies. Along with a promise for greater performance and flexibility, this creates further pressure on network management algorithms, which must make joint decisions on (i) PoA-to-user association and (ii) PoA management. We solve this challenging problem through an efficient and effective solution concept called Cluster-then-Match (CtM). While state-of-the-art approaches tend to focus on performance-related metrics, e.g., network throughput, CtM makes human-centric decisions, where pure network performance is balanced against energy consumption and electromagnetic field exposure. Importantly, such human-centric metrics concern all humans in the network area — including those who are not network users. Through our performance evaluation, which leverages detailed models for EMF exposure estimation and standard-specified signal propagation models, we show that CtM outperforms state-of-the-art network management schemes that solely focus on network performance, including those utilizing machine learning, reducing energy consumption by over 80% in indoor scenarios, and over 36% in outdoor ones.

With the widespread adoption of massive multiple-input multiple-output (MIMO) and millimeter wave (mmWave) communication techniques, the overhead of beam measurement and the complexity of beam management become even more severe issues due to the dramatic increase of the number of beams. Traditional methods often overlook the selection of optimal probing beams, thus limiting beam prediction performance. Additionally, existing solutions based on deep learning exhibit high complexity, which often hinders their practical deployment. In this work, we propose a lightweight, integrated neural network approach tailored for joint probing-beam pattern selection and beam prediction. Specifically, our solution includes two main components. First, formulating the selection of probing beams as a sampling operation, we envision a sampling network where, to enable gradient back-propagation of network parameters in spite the non-differentiable nature of sampling, the standard sampling function is approximated with a fitting function. Then, drawing inspiration from the physical structure of antenna arrays and 3D beam formation, we develop a beam-prediction network based on convolutional neural networks and self-attention mechanisms. Experimental results demonstrate that, thanks to the learned pattern, our proposed scheme achieves very good prediction performance (exceeding the state of the art by over 15% in top-1 accuracy), using a neural network with almost 90% less parameters than existing machine learning-based solutions.

Motivated by the challenges of future 6G communications where terahertz (THz) frequencies, intelligent reflective surfaces (IRSs) and ultra-wideband (UWB) signals coexist, we analyze and propose a set of efficient techniques for configuring the IRS when the signal bandwidth is a significant fraction of the central frequency (up to 50%). To the best of our knowledge this is the first time that IRS configuration techniques are analyzed for such huge bandwidths. In our work we take into account for the channel model, the power spectral density of the signal reflected by the IRS and the network geometry. We evaluate the proposed solutions in terms of achievable rate and compare it against an upper bound we derived. Our results hint rules for designing IRS-aided communication systems and allow to draw conclusions on the trade-off between performance and complexity required for configuring the IRS.

We consider a constellation of low-earth-orbit (LEO) satellites connected to a handheld device on the ground. Due to the very large orbital speed, an effective handover strategy becomes of paramount importance. In particular, we study the benefits of soft handover in the uplink from the physical-layer point of view. We give a realistic model for both the ground-tosatellite and the inter-satellite links, following the 3GPP channel model for the former. We suppose that, during handover from a serving satellite to a target satellite, one of the two satellites forwards the received signal from the ground user to the other, thus acting as a relay. We quantify through simulations the loss of hard handover, compared to soft handover. For the latter, we test both amplify-and-forward (AF) and decode-and-forward (DF) relaying techniques and verify that, at least in the simulated conditions, DF does not repay, in terms of block error rate (BLER), the increase of complexity with respect to AF. Also, we study the effect of the LEO constellation size on the network BLER. Finally, we show that, with soft handover, the impact of misalignment on the inter-satellite link is severe, especially at optical frequencies.

Intelligent reflecting surfaces (IRSs) have several prominent advantages, including improving the level of wireless communication security and privacy. In this work, we focus on the latter aspect and introduce a strategy to counteract the presence of passive eavesdroppers overhearing transmissions from a base station towards legitimate users that are facilitated by the presence of IRSs. Specifically, we envision a transmission scheme that cycles across a number of IRS-to-user assignments, and we select them in a near-optimal fashion, thus guaranteeing both a high data rate and a good secrecy rate. Unlike most of the existing works addressing passive eavesdropping, the strategy we envision has low complexity and is suitable for scenarios where nodes are equipped with a limited number of antennas. Through our performance evaluation, we highlight the trade-off between the legitimate users’ data rate and secrecy rate, and how the system parameters affect such a trade-off.

We consider the problem of accurately localizing$N$unmanned aerial vehicles (UAV) in 3D space where the UAVs are part of a swarm and communicate with each other through orthogonal time-frequency space (OTFS) modulated signals. Each receiving UAV estimates the multipath wireless channel on each link formed by the line-of-sight (LoS) transmission and by the single reflections from the remaining$N-2$UAVs. The estimated power delay profiles are communicated to an edge server, which is in charge of computing the exact location of the UAVs. To obtain the UAVs locations, we propose an iterative algorithm, named Turbo Iterative Positioning (TIP), which, using the belief-propagation approach, effectively exploits the time difference of arrival (TDoA) measurements between the LoS and the non-LoS paths. Enabling a full cold start (no prior knowledge), our solution first maps each TDoA's profile element to a specific ID of the reflecting UAV's. The localization of the$N$UAVs is then derived via gradient descent optimization, with the aid of turbo-like iterations that can progressively correct some of the residual errors in the initial ID mapping operation. Our numerical results, obtained also using real-world traces, show how the multipath links are beneficial to achieving very accurate localization of all UAVs, even with a limited delay resolution. Robustness of our scheme is proven by its performance approaching the Cramer-Rao bound.

Millimeter-wave communication is widely seen as a promising option to increase the capacity of vehicular networks, where it is expected that connected cars will soon need to transmit and receive large amounts of data. Due to harsh propagation conditions, mmWave systems resort to narrow beams to serve their users, and such beams need to be configured according to traffic demand and its spatial distribution, as well as interference. In this work, we address the beam management problem, considering an urban vehicular network composed of gNBs. We first build an accurate, yet tractable, system model and formulate an optimization problem aiming at maximizing the total network data rate while accounting for the stochastic nature of the network scenario. Then we develop a graph-based model capturing the main system characteristics and use it to develop a belief propagation algorithmic framework, called CRAB, that has low complexity and, hence, can effectively cope with large-scale scenarios. We assess the performance of our approach under real-world settings and show that, in comparison to state-of-the-art alternatives, CRAB provides on average a 50% improvement in the amount of data transferred by the single gNBs and up to 30% better user coverage.

We consider the optimization of a smart radio environment where meta-surfaces are employed to improve the performance of multiuser wireless networks working at sub-THz frequencies. Motivated by the extreme sparsity of the THz channel we propose to model each meta-surface as an electronically steerable reflector, by using only two parameters, regardless of its size. This assumption, although suboptimal in a general multiuser setup, allows for a significant complexity reduction when optimizing the environment and, despite its simplicity, is able to provide high communication rates. We derive a set of asymptotic results providing insight on the system behavior when both the number of antennas at the transmitter and the meta-surfaces area grow large. For the optimization we propose an algorithm based on the Newton-Raphson method and a simpler, yet effective, heuristic approach based on a map associating meta-surfaces and users. Through numerical results we provide insights on the system behavior and we assess the performance limits of the network in terms of supported users and spatial density of the meta-surfaces.

Intelligent reflecting surfaces (IRSs) have several prominent advantages, including improving the level of wireless communications security and privacy. In this work, we focus on this aspect and envision a strategy to counteract the presence of passive eavesdroppers overhearing transmissions from a base station towards legitimate users. Unlike most of the existing works addressing passive eavesdropping, the strategy we consider has low complexity and is suitable for scenarios where nodes are equipped with a limited number of antennas. Through our performance evaluation, we highlight the trade-off between the legitimate users’ data rate and secrecy rate, and how the system parameters affect such a trade-off.

Full-duplex (FD)-enabled relay networks represent a relevant solution to two critical needs of next-generation networks, namely, radio coverage extension and high spectral efficiency of wireless communications. Under practical conditions, however, the FD mode may not be the best operational setting for the relay; rather, operating in half-duplex may be more convenient when harsh channel conditions add up to self-interference. One of the fundamental challenges in the design of FD relay networks is thus how to determine the relay operational mode and the value of transmit power at both the relay and the data source, so that the achievable data rate is maximized as time varies. We address this problem in a two-hop, MIMO network, accounting for practical operational conditions in which the source is unaware of the symbols that the relay is transmitting. In light of the problem complexity, we also derive a lower-bound to the maximum achievable rate, which proves to be tight, especially for low-medium SNR values. We then tackle massive MIMO networks, and exploit our asymptotic analysis in the number of antennas to derive a low-complexity, yet highly efficient, operational mode and transmit power allocation scheme for a finite-size scenario.

Mmwave bands are being widely touted as a very promising option for future 5G networks, especially in enabling such networks to meet highly demanding rate requirements. Accordingly, the usage of these bands is also receiving an increasing interest in the context of 5G vehicular networks, where it is expected that connected cars will soon need to transmit and receive large amounts of data. Mmwave communications, however, require the link to be established using narrow directed beams, to overcome harsh propagation conditions. The advanced antenna systems enabling this also allow for a complex beam design at the base station, where multiple beams of different widths can be set up. In this work, we focus on beam management in an urban vehicular network, using a graph-based approach to model the system characteristics and the existing constraints. In particular, unlike previous work, we formulate the beam design problem as a maximum-weight matching problem on a bipartite graph with conflicts, and then we solve it using an efficient heuristic algorithm. Our results show that our approach easily outperforms advanced methods based on clustering algorithms.

Due to their low complexity, Minimum Mean Squared Error (MMSE) and Zero-Forcing (ZF) emerge as two appealing MIMO receivers. Although they provide asymptotically the same achievable rate as the signal-to-noise ratio (SNR) grows large, a non-vanishing gap between the signal to interference and noise ratio (SINR) obtained through the two receivers exists, affecting the error and outage probability, and the multiuser efficiency. Interestingly, both the SINR and the multiuser efficiency gaps can be compactly expressed as quadratic forms of random matrices, with a kernel that depends solely on the statistics of the interfering streams. By leveraging, we derive the closed-form distribution of such indefinite quadratic forms with random kernel matrix, which turns out to be proportional to the determinant of a matrix containing the system parameters. Then, specializing our result to different fading conditions, we obtain the closed-form statistics of both the SINR gap and the multiuser efficiency gap. Although the focus of this work is on the finite-size statistics, for completeness we also provide some results on the doubly-massive MIMO case. We validate all our derivations through extensive Monte Carlo simulations.

We investigate spatial patterns in mobile service consumption that emerge at national scale. Our investigation focuses on a representative case study, i.e., France, where we find that: (i) the demand for popular mobile services is fairly uniform across the whole country, and only a reduced set of peculiar services (mainly operating system updates and long-lived video streaming) yields geographic diversity; (ii) even for such distinguishing services, the spatial heterogeneity of demands is limited, and a small set of consumption behaviors is sufficient to characterize most of the mobile service usage across the country; (iii) the spatial distribution of these behaviors correlates well with the urbanization level, ultimately suggesting that the adoption of geographically-diverse mobile applications is linked to a dichotomy of cities and rural areas. We derive our results through the analysis of substantial measurement data collected by a major mobile network operator, leveraging an approach rooted in information theory that can be readily applied to other scenarios.

We consider a dual-hop, decode-and-forward network, where the relay can operate in full-duplex (FD) or half-duplex (HD) mode (X-duplex relay). We model the residual self-interference as an additive Gaussian noise with variance proportional to the relay transmit power, and we assume a Gaussian input distribution at the source. Unlike previous work, we assume that the source is only aware of the transmit power distribution adopted by the relay, but not of the symbols that the relay is currently transmitting. This assumption better reflects the practical situation, where the relay node forwards data traffic but modifies physical-layer or link-layer control information. We then identify the optimal power allocation strategy at the source and relay, which in some cases coincides with the HD transmission mode. We prove that such strategy implies either FD transmissions over an entire time frame or FD/HD transmissions over a variable fraction of the frame. We determine the optimal transmit power level at the source and relay for each frame, or fraction thereof. We compare the performance of our scheme against reference FD and HD techniques, which assume that the source is aware of the symbols instantaneously transmitted by the relay, and show that our solution closely approaches such strategies.

We present an information-theoretic analysis of a point-to-point multiple-input multiple-output (MIMO) link affected by Rayleigh fading and multiple scattering, under perfect channel state information at the receiver. Unlike previous work addressing this setting, we investigate the random coding error exponent, its associated cutoff rate and the expurgated error exponent, and derive closed-form expressions for them. Moreover, leveraging the average mutual information expression presented by Akemann et al., we derive another important metric, namely, the sum rate, under linear receive processing and independent stream decoding. In particular, we characterize the performance of the minimum mean squared error receiver in closed form, and that of the zero forcing receiver by resorting to bounding techniques. The bulk of the work relies on results about finite-dimensional random matrix products, a number of which are novel and detailed in the Appendices. The analysis, validated through numerical results, highlights the severe degradation in the performance of linear receivers due to multi-fold scattering. It also unveils the performance trend of multiple scattering MIMO channels as a function of the number of antennas and the number of scattering stages.

In this work, we consider a dual-hop, decode-and-forward network where the relay can operate in FD mode. We model the residual self interference as an additive Gaussian noise with variance proportional to the relay transmit power, and we assume a Gaussian input distribution at the source. Unlike previous work, however, we assume that the source is only aware of the transmit power distribution adopted by the relay over a given time horizon, not of the symbols that the relay is currently transmitting. This scenario better reflects practical situations in which the relay node may also have to forward signaling traffic, or data originated by other sources. Under these conditions, we show that the optimal communication strategy that source and relay can adopt is a time-division scheme, and, for each slot, we determine the optimal transmit power level that source and relay should adopt depending on the channel gains. Interestingly, the distribution of the optimal transmit power turns out to be discrete with two probability masses.

This paper presents the first systematic investigation of the potential performance gains for crowd work systems, deriving from available information at the requester about individual worker reputation. In particular, we first formalize the optimal task assignment problem when workers' reputation estimates are available, as the maximization of a monotone (submodular) function subject to Matroid constraints. Then, being the optimal problem NP-hard, we propose a simple but efficient greedy heuristic task allocation algorithm. We also propose a simple “maximum a-posteriori” decision rule and a decision algorithm based on message passing. Finally, we test and compare different solutions, showing that system performance can greatly benefit from information about workers' reputation. Our main findings are that: i) even largely inaccurate estimates of workers' reputation can be effectively exploited in the task assignment to greatly improve system performance; ii) the performance of the maximum a-posteriori decision rule quickly degrades as worker reputation estimates become inaccurate; iii) when workers' reputation estimates are significantly inaccurate, the best performance can be obtained by combining our proposed task assignment algorithm with the message-passing decision algorithm.

This paper presents a statistical characterization of the SNR gap between MIMO Zero-Forcing (ZF) and Minimum Mean Squared Error (MMSE) equalizers, beyond the Rayleigh assumption for the interfering streams amplitude fading. Results are valid for arbitrary transmit SNR values and number of transmit/receive antennas. Specifically, we provide the exact closed-form distribution of the random variable representing the difference between the output SNR on a generic receive filter branch, under MMSE and ZF equalization. Analytical results turn particularly useful for the study of heterogeneous cellular networks.

The dramatic increase in the number and sensing capabilities of mobile devices is fostering opportunistic sensing as a paramount data collection paradigm in smart cities. According to this paradigm, sensing of large-scale phenomena is autonomously performed by mobile devices that provide irregular samples in time and space. The collected data is then transferred to a central controller, and processed so as to obtain a representation of the phenomenon. In this paper, we investigate the factors that impact the accuracy of mobile opportunistic sensing. Specifically, we characterize the accuracy of a phenomenon representation obtained from samples collected by mobile devices and processed through the popular LMMSE filter. We do so by drawing on random matrix theory, which allows us to deal with irregularly spaced samples. Our analytical expressions capture the fundamental relationships existing between the accuracy and the parameters of mobile opportunistic sensing. We apply our analytical results to a realistic scenario where atmospheric pollution samples are collected by vehicular and pedestrian users. We validate the proposed analytical framework, and then exploit the model to investigate the impact on mobile sensing accuracy of a number of parameters. These include the pedestrian and vehicle density, the participation ratio to the sensing application, the type of phenomenon to be sensed, and the level of noise and position errors affecting the collected samples.

This paper presents the first systematic investigation of the potential performance gains for crowdsourcing systems, deriving from available information at the requester about individual worker earnestness (reputation). In particular, we first formalize the optimal task assignment problem when workers' reputation estimates are available, as the maximization of a monotone (submodular) function subject to Matroid constraints. Then, being the optimal problem NP-hard, we propose a simple but efficient greedy heuristic task allocation algorithm. We also propose a simple “maximum a-posteriori“ decision rule. Finally, we test and compare different solutions, showing that system performance can greatly benefit from information about workers' reputation. Our main findings are that: i) even largely inaccurate estimates of workers' reputation can be effectively exploited in the task assignment to greatly improve system performance; ii) the performance of the maximum a-posteriori decision rule quickly degrades as worker reputation estimates become inaccurate; iii) when workers' reputation estimates are significantly inaccurate, the best performance can be obtained by combining our proposed task assignment algorithm with the LRA decision rule introduced in the literature.

The information that can be transmitted through a wireless channel, with multiple-antenna-equipped transmitter and receiver, is crucially influenced by the channel behavior as well as by the structure of the input signal. We characterize in closed form, the probability density function (pdf) of the output of multiple-input and multiple-output block-fading channels, for an arbitrary signal-to-noise ratio value. Our results provide compact expressions for such output statistics, paving the way to a more detailed analytical information-theoretic exploration of communications in presence of block fading. The analysis is carried out assuming two different structures for the input signal: 1) the independent identically distributed (i.i.d.) Gaussian distribution and 2) a product form that has been proved to be optimal for noncoherent communication, i.e., in absence of any channel state information. When the channel is fed by an i.i.d. Gaussian input, we assume the Gramian of the channel matrix to be unitarily invariant and derive the output statistics in both the noise- and interference-limited scenario, considering different fading distributions. When the product-form input is adopted, we provide the expressions of the output pdf as the relationship between the overall number of antennas and fading coherence length varies. We also highlight the relation between our newly derived expressions and the results already available in the literature, and, for some cases, we numerically compute the mutual information based on the proposed expression of the output statistics.

We analyze the achievable data rate of cooperative relaying strategies in networks where nodes operate in half-duplex mode. Nodes have to deliver their data to a gateway, at a certain rate, and may have limited energy capabilities, as in the case of energy-harvesting communication networks. Both the requested data rate and the available energy capabilities may vary from node to node. Under such constraints, we take an information-theoretic approach and derive cut-set upper bounds to the achievable rate. Furthermore, we devise two kinds of communication strategies, each aiming at a different objective. The former ensures a fair rate allocation to the network nodes, but it neglects their energy constraints. The latter does consider energy constraints by meeting the requirements on the average power consumption at each node and by providing fairness in the data rate allocation. We show the performance of the aforementioned communication strategies, highlighting their effectiveness and providing useful insights on the system behavior.

We compute the optimal communication rate achieved by the nodes of a wireless multihop network with arbitrary topology. The network nodes operate in half-duplex mode and generate information that has to be delivered to a gateway node, possibly through a decode-and-forward relaying strategy. Nodes may make use of multiuser processing, thus transmissions from multiple nodes toward the same receiver are allowed. Additionally, nodes may be energy-constrained, as in the case of battery-powered or energy-harvesting communication networks. In such a scenario, we define the possible (and meaningful) network operational states. Then, by solving a linear optimization problem, we select the most efficient network states and for how long the network should work in each of them. More specifically, the resulting communication strategy maximizes the data rate achievable by the network while meeting the constraints that may exist on the node energy consumption. The results we present show how our approach can be effectively used for an optimal design and usage of wireless networks, as well as under which conditions multiuser processing and long-distance communication between nodes are most beneficial.

The information that can be conveyed through a wireless channel, with multiple-antenna equipped transmitter and receiver, crucially depends on the channel behavior as well as on the input structure. In this paper, we derive analytical results, concerning the probability density function (pdf) of the output of a single-user, multiple-antenna communication. The analysis is carried out under the assumption of an optimized input structure, and assuming Gaussian noise and a Rayleigh block-fading channel. Our analysis therefore provides a quite general and compact expression for the conditional output pdf. We also highlight the relation between such an expression and the results already available in the literature for some specific input structures.

This paper deals with the problem of stabilizing an unstable system through networked control over the wireless medium. In such a situation a remote sensor communicates the measurements to the system controller through a noisy channel. In particular, in the AWGN scenario, we show that protograph-based LDPC convolutional codes achieve anytime reliability and we also derive a lower bound to the signal-to-noise ratio required to stabilize the system. Moreover, on the Rayleigh-fading channel, we show by simulations that resorting to multiple sensors reduces the SNR required for system stabilization.

Wireless networks with linear topology, where nodes generate their own traffic and relay other nodes' traffic, have attracted increasing attention. Indeed, they well represent sensor networks monitoring paths or streets, as well as multihop networks for videosurveillance of roads or vehicular traffic. We study the performance limits of such network systems when (i) the nodes' transmissions can reach receivers farther than one-hop distance from the sender, (ii) the transmitters cooperate in the data delivery, and (iii) interference due to concurrent transmissions is taken into account. By adopting an information-theoretic approach, we derive analytical bounds to the achievable data rate in both the cases where the nodes have full-duplex and half-duplex radios. The expressions we provide are mathematically tractable and allow the analysis of multihop networks with a large number of nodes. Our analysis highlights that increasing the number of cooperating transmitters beyond two leads to a very limited gain in the achievable data rate. Also, for half-duplex radios, it indicates the existence of dominant network states, which have a major influence on the bound. It follows that efficient, yet simple, communication strategies can be designed by considering at most two cooperating transmitters and by letting half-duplex nodes operate according to the aforementioned dominant states.

We analyze the achievable data rate of relaying strategies in a wireless network where nodes are equipped with half-duplex radios. The nodes need to deliver their data to a destination node, possibly at different rates, through multi-hop communications. In such a scenario, we first derive a cut-set upper bound to the achievable rate(s), then we take into account the interference among simultaneous transmissions and devise a communication strategy. Such a strategy ensures a high rate allocation to the nodes, which is also fair, i.e., it provides the desired proportion among the average data rates of the nodes. Our results show that the proposed strategy closely approximates the bound for any value of SNR.

We analyze the capacity scaling laws of clustered ad hoc networks comprising significant inhomogeneities in the node spatial distribution over the area. In particular, we consider the class of networks in which nodes are distributed according to a doubly stochastic shot-noise Cox process, which allows to model a wide variety of inhomogeneous topologies. For this class of networks, we derive information theoretic upper-bounds to the capacity, identifying six operational regions. We also provide constructive lower bounds by devising, for each region, an optimal communication strategy to achieve the maximum network throughput. The performance of our communication schemes match, in terms of scaling exponent, the theoretical upper-bounds.

We study the delay performance of a sensor network, whose nodes access the medium by using the unslotted MAC protocol specified by the IEEE 802.15.4 standard. Unlike previous works, which focus on the average throughput and delay analysis, we develop a detailed model that allows us to obtain the delivery delay distribution of messages sent by concurrently contending sensors toward a central controller. We carry out a transient analysis that is of particular interest when sensor networks are deployed to provide k-coverage for real-time applications, and we study both single- and multi-hop network topologies. We validate our analytical results against simulation results obtained through ns2.

We analyze the capacity scaling laws of clustered ad hoc networks in which nodes are distributed according to a doubly stochastic shot-noise Cox process. We identify five different operational regimes, and for each regime we devise a communication strategy that allows to achieve a throughput featuring the same scaling exponent as the maximum theoretical capacity.

A wireless sensor network with randomly deployed nodes can be used to provide an irregular sampling of a physical field of interest. We assume that a sink node collects the data gathered by the sensors and uses a linear filter for the reconstruction of a bandlimited scalar field defined over a d -dimensional domain. Sensors' locations are assumed to be known at the sink node, up to a certain position error. We then take the mean square error (MSE) of the reconstructed field as performance metric, and evaluate the effect of both uniform and quasi-equally spaced sensor layouts on the quality of the reconstructed field. We define a parameter that provides a measure of the regularity of the sensors deployment, and, through asymptotic analysis, we derive the MSE in the case of different sensor spatial distributions. For two of them, an approximate closed form expression is obtained. We validate our analysis through numerical results, and we show that an excellent match exists between analysis and simulation even for a small number of sensors.

We study the delay performance of a sensor network, whose nodes access the medium by using the unslotted MAC protocol specified by the IEEE 802.15.4 standard. Unlike previous works which focus on the average throughput and delay analysis, we develop a detailed model that allows us to obtain the delivery delay distribution of messages sent by concurrently contending sensors toward a central controller. We carry out a transient analysis, which is of particular interest when sensor networks are deployed to provide k-coverage for real-time applications, and validate our analytical results against simulation results obtained through ns2.

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 consider a wireless sensor network monitoring a field of interest, and we study the benefits of grouping nodes into clusters. The data gathered within each cluster are compressed by the cluster head and sent to a sink node, where a reconstructed version of the field is obtained. We represent the compressed data through the Fourier coefficients of the field spectrum, and analyze both the case where the sensor positions are known to the sink, and the case where they are available at the cluster head only. We show that clustering significantly reduces the energy expenditure due to data transmission, and, most importantly, we derive the possible degradation of the quality of the reconstructed field due to compression.

In this paper we study wireless sensor networks for monitoring applications. We focus on the problem of sampling and reconstruction of multidimensional bandlimited signals, when the sensor locations are equally spaced points affected by some jitter, and the sensor measurements are affected by noise. We show how the mean square reconstruction error can be estimated from the eigenvalue distribution of a certain Toeplitz matrix. We analyze the d-dimensional case, and we show how the mean square error can be easily estimated by using asymptotic analysis.

We consider the problem of obtaining a high quality estimates of band-limited sensor fields when sensor measurements are noisy and the nodes are irregularly deployed and subject to random motion. We consider the mean square error (MSE) of the estimate and we analytically derive the performance of several reconstruction/estimation techniques based on linear filtering. For each technique, we obtain the mean value of the MSE, as well as its asymptotic expression in the case where the field bandwidth and the number of sensors grow to infinity, while their ratio is kept constant. Our results provide useful guidelines for the design of sensor networks when many system parameters have to be traded off.

alessandro.nordio © polito.it chiasserini © polito.it viterbo © deis.unical.it ABSTRACT The irregular sampling theory is concerned with the problem We consider wireless sensor networks whose nodes are randomly of recovering a bandlimited signal from a sequence of samples, deployed and, thus, provide an irregular sampling of the sensed which may be taken in an irregular way. Several reconstruction field. The field is assumed to be bandlimited; a sink node col- algorithms have been proposed in the literature (see e.g., [1]) and lects the data gathered by the sensors and reconstructs the field by have found application in a variety of fields, such as digital medical using a technique based on linear filtering. By taking the mean imaging [2,3], geophysics [4], weather forecast [5], astronomy, and square error (MSE) as performance metric, we evaluate the effect oceanography [6]. of quasi-equally spaced sensor layouts on the quality of the recon-Recently, a great deal of attention has been devoted to sensor netstructed signal. The MSE is derived through asymptotic analysis works, whose nodes sample a physical field, like air temperature, for different sensor spatial distributions, and for two of them we light intensity, pollution levels or rain falls, and report the data to are able to obtain an approximate closed form expression. The case a common processing unit (sink node). The sink node is in charge of uniformly distributed sensors is also considered for the sake of of reconstructing the sensed field. In general, sensors are not regcomparison. The validity of our asymptotic analysis is shown by ularly deployed in the area of interest and, if not synchronized to

We address the design of a receiver for the uplink of a direct sequence code division multiple access wireless communication system, endowed with multiple transmit and receive antennas and affected by multiple-access interference (MAI) for the presence of multiple transmitting users. The receiver is assumed to keep separate the tasks of mitigating the spatial and MAI in order to be implementable in two options: a baseline option accommodating the MAI mitigation interface only and an enhanced option accommodating the spatial interference mitigation interface as well. The MAI mitigating interface is based on standard linear multiuser detectors. The spatial interference mitigating interface is derived according to a linear minimum mean-square error criterion, which assumes the MAI mitigating interface to be given.

We consider a multiuser multiple-input multiple-output (MIMO) communication system using code-division multiple access (CDMA) and multiuser detection to discriminate the different users. Our focus is on the CDMA uplink of a frequency-nonselective Rayleigh fading channel. We study two types of receivers: joint receivers, which address simultaneously both spatial and multiple-access interference; and separate receivers, addressing the two types of interference individually. This approach allows assessing the benefits of adding MIMO processing capabilities to existing multiuser single-input single-output systems. For both receiver types, we analyze solutions based on linear (matched filter, decorrelator, minimum mean-square error) and maximum-likelihood receivers. For all the receivers considered, we provide closed-form expressions (as expectations of given functions) of the resulting pairwise error probabilities. Performance results are obtained in terms of frame-error rate versus Eb/N0, following two different approaches. An analytic approach using large-system asymptotic methods, whereby the system parameters (number of users and antennas, spreading gain) are assumed to grow to infinity with finite limiting ratios. A computer-simulation approach is used to illustrate the differences between asymptotic and simulation results

We consider the use of multiple transmit and receive antennas (MIMO) in a wideband code-division multiple-access system with space-time coding. Using large-system asymptotic analyses, we examine the performance of linear receivers whereby multiuser detection and MIMO decoding are kept separate.

We examine the performance of a reduced-complexity doubly iterative decoder for space-time turbo codes on a quasi-static fading channel. The decoder works by using preliminary soft values of the coded symbols, obtained after a limited number of turbo iterations, to reduce the spatial interference from the received signal. Then, new turbo iterations are performed to improve on the quality of the soft values, and so on. Using a number of approximations, we obtain a receiver interface that achieves a good tradeoff between performance and complexity, and allows the use of turbo space-time codes for a large number of transmit and receive antennas.

We introduce a doubly-iterative decoder for space–time turbo codes on a quasi-static fading channel. The decoder works by using preliminary soft values of the coded symbols, obtained from a limited number of turbo iterations, to reduce the spatial interference from the received signal. After this reduction, new turbo iterations are performed to improve on the quality of the soft values, and so on. Using a number of approximations, we obtain a receiver interface that achieves a good trade-off between performance and complexity, and allows the use of turbo space–time codes for a large number of transmit and receive antennas.

The authors propose a low complexity multiuser joint parallel interference cancellation (PIC) decoder and turbo decision feedback equalizer for code division multiple access (CDMA). In their scheme, an estimate of the interference signal (both multiple-access interference and intersymbol interference) is formed by weighting the hard decisions produced by conventional (i.e., hard-output) Viterbi decoders. The estimated interference is subtracted from the received signal in order to improve decoding in the next iteration. By using asymptotic performance analysis of random-spreading CDMA, they optimize the feedback weights at each iteration. Then, they consider two (mutually related) performance limitation factors: the bias of residual interference and the ping-pong effect. The authors show that the performance of the proposed algorithm can be improved by compensating for the bias in the weight calculation, and they propose a modification of the basic PIC algorithm, which prevents the ping-pong effect and allows higher channel load and/or faster convergence to the single-user performance. The proposed algorithm is validated through computer simulation in an environment fully compliant with the specifications of the time-division duplex mode of third-generation systems, contemplating a combination of time-division multiple access and CDMA and including frequency-selective fading channels, user asynchronism, and power control. The main conclusion of this work is that, for such application, soft-input soft-output decoders (e.g., implemented by the forward-backward BCJR algorithm) are not needed to attain very high spectral efficiency, and simple conventional Viterbi decoding suffices for most practical settings.

Abstract In this tutorial paper, we describe iterative receivers that combine a soft decoder for a general space‐time code with a spatial‐interference canceler. The performance of these receivers is analyzed by using EXIT charts, a convenient graphical description that yields quite accurate results. By properly combining the EXIT characteristics of the canceler and of the decoder, the convergence behavior of the iterative algorithms can be understood, and design guidelines derived. The idea here is to observe that both the interference canceler and the decoder can be studied by examining the evolution of the extrinsic information passed along to the connected blocks. Copyright © 2004 John Wiley & Sons, Ltd.

We compare complexities and performances of some suboptimum linear and nonlinear (BLAST) interfaces used for decoding space-time codes used over a Rayleigh fading channel. We observe how the introduction of an interleaver can be beneficial here. We introduce a new simple iterative linear interface, based on hard Viterbi decoding and offering a performance considerably improved with respect to noniterative receivers.

We propose a low-complexity multiuser joint parallel interference cancellation (PIC) decoder for direct-sequence CDMA. An estimate of the multiple-access interference (MAI) signal is formed by weighting the hard decisions produced by hard-output Viterbi decoders. Such MAI interference estimate is subtracted from the received signal in order to improve decoding in the next iteration. By using asymptotic performance analysis of random-spreading CDMA, we optimize the feedback weights at each iteration. Then, we consider two performance limitation factors: the bias of residual interference and the ping-pong effect. We propose then a modification of the basic PIC algorithm, which allows higher channel load and/or faster convergence to the single-user performance. The main conclusion of this work is that, in most practical cases, SISO decoders are not needed to attain very high spectral efficiency, and simple conventional Viterbi decoding suffices for most practical settings.

We consider initial timing acquisition in discrete-sequence code-division multiple-access (DS-CDMA) when propagation is affected by multipath and fading, and where the base-station broadcasts a synchronization pilot signal in the form of bursts of modulated chips transmitted periodically and separated by long silent intervals. Subject to certain simplifying assumptions, we derive the maximum-likelihood (ML) estimator by solving a constrained maximization problem. Our ML timing estimator has constant complexity per observation sample. The relation to other estimation methods is addressed, and performance comparisons are provided by simulation. The proposed estimator yields good performance independently of the multipath-intensity profile of the channel, provided that the delay spread is not larger than a given maximum spread. Moreover, our estimator is fairly robust to the mismatch in the fading Doppler spectrum and provides good performance for both fast and slow fading.

We consider timing initial acquisition in DS-CDMA when propagation is affected by multipath and fading and where the base-station broadcasts a synchronization pilot signal in the form of bursts of modulated chips transmitted periodically and separated by long silent intervals. Subject to certain simplifying assumptions we derive the maximum likelihood (ML) estimator by solving a constrained maximization problem by applying the Karush-Kuhn-Tucker conditions. Our ML timing estimator has constant complexity per observation sample. The relation to other estimation methods is addressed, and performance comparisons both in terms of estimation mean-square error and cumulative density function of the channel energy falling in a certain window are provided by simulation. The resulting estimator offers good results over all fading channels with delay spread not larger than a given maximum spread.