Due to the multi-dimensional search in the near-field (NF), the excessive computational burden has become one of the major problems. To address this issue, this paper proposes a computationally efficient angle and distance estimation algorithm for extremely large uniform planar array (UPA) systems. To reduce computation, the proposed algorithm decouples 3D search into a series of 2D search and 1D search. The 2D search estimates the azimuth and elevation, followed by the 1D search that estimates the distance. While the proposed algorithm brings significant improvement in computational complexity, the estimation of the proposed algorithm is guaranteed to be accurate as long as the distance between the receiver and transmitter (or scatterer) exceeds a specific threshold. For UPAs, we establish that this threshold is around a quarter of the Rayleigh distance. The simulation results demonstrate that the proposed algorithm has a superior accuracy-complexity trade-off compared to existing works.

Filtering refers to the methods for inferring time-varying parameters and is a crucial task in cyber-physical systems. An important category of filtering is distributed filtering, where sensor nodes transmit observations via communication links to inference nodes that estimate the unknown states. Distributed filtering is challenging in the sense that the communication constraint of the sensor nodes limits the amount of information available to the inference node, calling for the co-design of communication and computing. This paper establishes a theoretical framework for the co-design of communication and computing in distributed filtering, building on an information-theoretic view of the Kalman–Bucy filtering. In particular, this paper considers a networked system consisting of two nodes, where each node aims to infer its own time-varying state in continuous-time scenarios. The two nodes are connected by a Gaussian feedback channel. Via the feedback link, one of the nodes can obtain the sensor observations and received signals of the other node. This paper develops an optimal linear strategy, namely the information difference encoding strategy, for generating signals transmitted via the Gaussian feedback channel. This paper also presents an inequality that relates Shannon information with Fisher information in distributed filtering. The inference accuracy and power efficiency of the information difference encoding strategy are quantified via simulations.

The massive amount of data related to spatiotemporal mobility offers new opportunities to understand human mobility with applications in various sectors, including transportation, logistics, and safety. However, the increase in the volume and in the dimension of mobility data makes it challenging to retrieve important information and critical features of spatiotemporal mobility. This paper develops a method to estimate probabilistic occurrences of travel demands considering interactions between origin, destination, and departure time. First, we reveal the important features in the complex structure of mobility data and identify mobility patterns. Then, we derive a data-driven model, accounting for mobility patterns, to estimate and predict travel demands. We quantify the accuracy of the proposed method for a case study using both New York city yellow taxi trip data and for-hire vehicles trip data over the entire city. Results show the accuracy of the proposed method compared to existing approaches.

Accurate location awareness is essential for various context-based applications. This calls for efficient methodologies to collect, communicate and process position-dependent measurements, especially in situations with limited computational resources. The soft information (SI) approach has recently shown significant improvements in accuracy over conventional localization methods. By developing efficient SI-based techniques, it is possible to achieve higher precision also in case of stringent computational constraints. This paper proposes new SI-based localization techniques that utilize belief condensation and maximum entropy methods to reduce both communication burden and computational complexity. In addition, the techniques presented enable the use of generic sensing measurements, including those taking discrete and categorical values. Through two case studies involving time and angle measurements, we demonstrate how the proposed approach can significantly improve localization accuracy and computational efficiency.

This paper presents a robust near-field (NF) beam tracking algorithm for terahertz communications based on deep Q-network (DQN). Traditional NF beam tracking methods relying on mobility models are fatal in ultra-massive MIMO systems, where even the slightest error could result in beam tracking failures. Thus, the proposed algorithm aims to maintain a stable beamforming gain by tracking the mobile station through the analysis of received signals without requiring mobile dynamics. By utilizing DQN, the proposed algorithm strengthens its tracking capability from online experiences and updates the combining beam towards positions expected to maximize beamforming gain. Throughout simulations, we compare the proposed algorithm with the Bayesian filter-based NF beam tracking algorithm. The simulation results confirm the robustness of the proposed algorithm for NF beam tracking, especially for abrupt changes in mobile dynamics.

Integrated localization and communication (ILC) will be a key enabler for providing accurate location information and high data rate in next generation networks. This paper proposes a transmission frame structure and a soft information (SI)-based localization algorithm for position-assisted communications. The proposed ILC achieves improved localization accuracy and enhanced communication rate simultaneously by accounting for the statistical characteristics of the wireless environment. Results in 3rd Generation Partnership Project (3GPP) industrial scenarios show that the SI-based localization algorithm can achieve decimeter-level accuracy. Moreover, the position-assisted communication enhances the achievable rate, especially in scenarios with high mobility.

Location awareness is crucial for emerging services and enhanced resource orchestration in next generation (xG) wireless networks. To provide efficient high-accuracy localization, xG networks require both algorithms for position inference and strategies for resource optimization. While the exploitation of soft information (SI) provides significant gains in localization performance, node activation strategies benefit resource utilization by selecting an adequate set of nodes to perform measurements. This paper develops a data-driven node activation strategy for efficient SI-based localization in xG networks. First, we formulate the node activation problem considering an SI-based position estimator. Then, we propose a data-driven node activation strategy for determining an adequate set of active nodes given only a position estimate. To validate the proposed strategy, we employ xG-Loc, a dataset for location-aware xG networks fully compliant with 3rd Generation Partnership Project (3GPP) specifications. Case studies in 3GPP scenarios show the benefits of the proposed node activation strategy.

Integrated terrestrial-satellite networks (ITSNs) play an essential role in providing global and ubiquitous connectivity for next generation networks. Spectral efficiency of ITSNs depends on their integrated architecture and the operational strategies, including interference management and resource allocation. This paper proposes an efficient integrated access and backhaul (IAB) architecture for terrestrial-satellite networks considering both uplink (UL) and downlink (DL) communications. We aim to integrate terrestrial access and satellite backhaul networks by developing a novel optimization framework for their joint operation. In particular, in-band access-backhaul transmission is considered for high spectral efficiency, where a reverse time division duplexing is used to prevent both self-interference and interference between access links and backhaul links. In addition, cooperation among gNodeB is taken into account to overcome harsh propagation conditions such as blockage effects and severe pathloss. A framework for joint optimization of cooperative beamforming and resource allocation is developed to maximize the UL-DL rate region of the in-band IAB. The proposed architecture is verified using the 3rd Generation Partnership Project (3GPP) channel models. Numerical results show that the proposed architecture significantly outperforms the classical out-of-band backhauling while approaching an outer bound of the UL-DL rate region.

Location awareness is crucial for a variety of emerging applications. The accuracy of localization depends heavily on the spatial topology of the network, especially in complex and infrastructure-limited wireless environments. In these environments, assisting nodes can be deployed to achieve desirable localization performance. This paper presents efficient strategies for deploying assisting nodes to improve the localization accuracy of a target agent. Specifically, it provides a methodology to determine a finite set of candidate positions for the assisting nodes. Based on this methodology, we present a convex relaxation method to select near-optimal positions for the assisting nodes and establish a theoretical limit on the localization accuracy provided by assisting nodes. We also propose an approximate dynamic programming algorithm to deploy assisting nodes with amenable complexity. A case study validates the proposed strategies and shows the benefits of deploying assisting nodes for accurate localization.

Quantum communication systems exchange information between a source and a destination by encoding such information into quantum states according to modulation techniques. The design of quantum modulation techniques, including quantum quadrature amplitude modulation (QAM), requires choosing and characterizing quantum states. This paper explores the use of non-Gaussian photon-added Gaussian states (PAGSs) for quantum QAM communications. First, we characterize pure PAGSs in the Fock space and derive a closed-form expression for their inner product. Then, we develop a quantum QAM technique employing PAGSs. In particular, we show how to construct quantum QAM constellations of PAGSs. Finally, we evaluate the performance of the developed quantum QAM technique with PAGSs, when a square root measurement (SRM) receiver is employed, and compare such performance with that of existing coherent states. Results show that PAGSs can provide a performance gain with respect to coherent states.

Location awareness is crucial for numerous applications in fifth generation (5G) and beyond ecosystems. Localization-of-things (LoT) via soft information (SI) enables accurate localization, tracking, and navigation of networked nodes. This paper demonstrates real-time SI-based LoT using ultrawideband (UWB) radios. We consider two data collection approaches and evaluate them via network experimentation in an indoor environment. Experimental results show the potential of SI-based LoT using UWB technology to satisfy service level requirements of 5G and beyond ecosystems.

Quantum key distribution (QKD) is a key enabler toward unconditionally secure communications. The imperfections exhibited by non-ideal sources degrade the QKD performance. This raises the problem of engineering the employed quantum states to mitigate the impairments caused by such imperfections. This paper proposes to employ noisy photon-added squeezed states (PASSs) as QKD sources for the decoy-state protocol. First, noisy PASSs are characterized in the Fock space. Then, noisy PASSs are engineered for the decoy-state protocol. Finally, the performance of the decoy-state protocol with engineered noisy PASSs are quantified in a variety of settings.

The massive amount of data related to spatiotem-poral mobility offers new opportunities to understand human behaviors. However, with the increase of volume and complexity of mobility data, it has become challenging to retrieve important information and critical features of spatiotemporal mobility. In particular, predicting large-scale travel demands is challenging and requires a high computational load. This paper introduces a data-driven approach for estimating high-dimensional travel demands. We propose a method to identify mobility patterns using a probabilistic tensor decomposition approach for interpreting the complexity and uncertainty of mobility data. Expectation-maximization (EM) algorithm is applied for inferring mobility patterns. A case study is presented, where the proposed model is applied to New York city taxi data. The results show the model performance according to the number of origin and destination patterns and the number of trip data used. The probabilistic modeling results provide a deeper understanding of large-scale mobility data in the spatiotemporal dimension.

Integrated satellite-terrestrial networks (ISTNs) are essential for providing ubiquitous mobile ultra-broadband service in beyond 5G networks. The spectral efficiency and reliability of ISTN depend on the integrated architecture and its operational strategies including interference management and resource allocation. Our view is to integrate terrestrial access and satellite backhaul networks and to develop an optimization technique for their joint operation. This paper proposes an efficient integrated access and backhaul (IAB) architecture for satellite-terrestrial networks (STNs) based on reverse time division duplexing (TDD) considering both uplink (UL) and downlink (DL). In particular, in-band backhauling and gNodeB (gNB) cooperation are considered for high spectral efficiency and reliability. A framework for joint optimization of cooperative beamforming and resource allocation is developed to maximize the UL-DL rate region of the in-band IAB. The proposed scheme is verified using the 3rd Generation Partnership Project (3GPP) standard channel models. Results show that the proposed scheme significantly outperforms the conventional wireless backhauling, while approaching to an outer bound of the UL-DL rate region.

Optimizing the resource utilization is essential for efficiently providing reliable location awareness in complex wireless environments. This paper presents a data-driven approach to node prioritization for efficient localization based on neural networks. We develop a node prioritization strategy for power allocation consisting of offline training and online operation. In the offline phase, we train a neural network to approximate a mapping of node prioritization decisions obtained via model-based optimization. In the online phase, the trained neural network is employed to determine the resource allocation. A case study validates the proposed approach and compares it against conventional methods based on uniform power allocation.

Integrated localization and communication (ILC) at millimeter wave (mmWave MMWAVEinit) frequencies will be a key enabler for providing accurate location information and high data rate communication in beyond fifth generation (B5G) networks. This paper proposes a transmission frame structure and a soft information (SI)-based localization algorithm for position-assisted communications. In accordance with B5G specifications, we consider multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) networks. Theoretical limits are also derived to serve both as performance benchmark and as input for algorithm design. The proposed method enables cooperative ILC with improved localization accuracy and enhanced communication rate simultaneously. In particular, position-assisted communication at mmWave MMWAVEinit frequencies is explored accounting for the statistical characteristics of the wireless environment. Localization accuracy and communication rate are quantified in 3rd Generation Partnership Project (3GPP) network scenarios. Results show that the SI-based localization algorithm achieves decimeter-level accuracy, approaching the theoretical limit. Moreover, the position-assisted communication can provide higher communication rate with reduced overhead compared to existing techniques, especially in scenarios with high mobility.

Distributed learning is an important task in emerging applications such as localization and navigation, Internet-of-Things, and autonomous vehicles. This paper establishes a theoretical framework for learning states that evolve in real time over networks. Specifically, each agent node in the network aims to infer a time-varying state in a decentralized manner by using the node’s local observations and the messages received from other nodes within its communication range. As a result, the inference accuracy of a node is significantly affected by the quality of its received messages. This calls for carefully designed strategies for generating messages that are able to provide sufficient information for the receiver and are robust to channel impairments. This paper presents communication-efficient encoding strategies for generating transmitted messages and derives a sufficient condition for the boundedness of the distributed inference error of all the agent nodes over time. The findings of this paper provide guidelines for the design of communication-efficient distributed learning in complex networked systems.

Distributed learning is crucial for many applications such as localization and tracking, autonomy, and crowd sensing. This paper investigates communication-efficient distributed learning of time-varying states over networks. Specifically, the paper considers a network of nodes that infer their current states in a decentralized manner using observations obtained via local sensing and messages obtained via noisy inter-node communications. The paper derives a necessary condition in terms of the sensing and communication capabilities of the network for the boundedness of the learning error over time. The necessary condition is compared with the sufficient condition established in a companion paper and the gap between the two conditions is discussed. The paper provides guidelines for efficient management of the sensing and communication resources for distributed learning in complex networked systems.

Location awareness is vital for several applications in wireless ecosystems, including fifth generation (5G) and beyond networks defined by the 3rd Generation Partnership Project (3GPP). However, complex wireless environments such as indoor factories are characterized by harsh multipath propagation and non-line-of-sight (NLOS) conditions, which are detrimental to localization accuracy. This paper introduces the concept of blockage intelligence (BI) to provide a probabilistic description of wireless propagation conditions. Then, it discusses its integration in both conventional and soft information (SI)-based localization algorithms. Case studies are presented in the 3GPP indoor factory scenario with various gNodeBs (gNBs) deployments. Results show that BI together with SI-based localization significantly outperforms existing localization techniques. The rich information provided by BI is vital to perform accurate localization in 5G and beyond networks operating in complex wireless environments.

Accurate location information is crucial for a variety of new verticals and use cases enabled by 5th generation (5G) wireless networks. While existing localization techniques for cellular networks are continuously evolving in the standardization process, the stringent key performance indicator requirements defined by the 3rd Generation Partnership Project (3GPP) have not been met yet. This paper first provides an in-depth review of the standardized reference signals and time/angle-based measurements that can be used for localization of user equipments in current 5G networks. Then, the paper details the development of a soft information (SI)-based approach that significantly improves localization accuracy for 5G and beyond 5G networks. Results are obtained in full conformity with 3GPP standards in two standardized scenarios, namely urban microcell and indoor open office, using time/angle-based measurements. Results show that the proposed SI-based localization methods significantly outperform existing techniques, especially when harsh propagation conditions and higher (millimeter Waves) frequencies are considered, paving the way to new services and performance enhancements in 5G and beyond wireless networks.

Quantum state discrimination (QSD) is crucial for various applications of quantum sensing, communications, and computing. This paper characterizes the binary QSD with photon-subtracted squeezed states (PSSSs) affected by thermal noise during state preparation. First, noisy PSSSs are charac-terized and their Fock representation is given. Then, the binary QSD problem with noisy PSSSs affected by phase diffusion is presented. Finally, it is shown the suitability of PSSSs for QSD-based applications, especially when employing quantum states with unbalanced prior probabilities.

Network localization performance depends on the network geometry and, therefore, node deployment methods are critical for high-accuracy localization. Optimal node deployment is challenging in practical problems due to various uncertainties present in the position knowledge of the deployed nodes. In this paper, we propose a node-deployment method for network localization that accounts for such uncertainties. We develop a framework for the optimal deployment of location-aware networks under bounded disturbances in the positions of the sensing nodes. More specifically, by considering bounded discrepancies in the network geometry, we characterize the optimal deployment according to the D-optimality criterion and assert its implications for the A-optimality and E-optimality criteria. Results show that the proposed optimization-based design achieves a significative improvement according to the D-optimality criterion.

Nonorthogonal multiple access (NOMA) is recognized as an important technology to meet the connectivity requirements of fifth generation (5G), non-terrestrial, and beyond 5G wireless networks. In this paper, we consider network connectivity as a measure of NOMA robustness to variable overloading, and explore the relationship between NOMA overloading and the evolution of network connectivity through a temporal graph model. We derive a time-varying stochastic expression for the directed connectivity between any two network devices (ND) as a function of overloading, and extend this analysis to develop a probability mass function of initial connectivity between any two NDs. Our results provide an overloading-based characterization of time-varying network robustness that is generalizable to any underlying NOMA implementation. We present simulation results to support our analytical predictions.

Source localization is essential for a wide range of applications. The efficiency of source localization in complex wireless environments can be improved via the use of reconfigurable intelligent surfaces (RISs). In this paper, we investigate the information inequality of RIS-aided localization systems. We propose a general signal model for RIS-aided localization valid for both near-field and far-field scenarios. Based on the proposed model, we perform Fisher information analyses of localization performance in networks with RISs. Numerical results show that optimal-configured RISs can improve the localization accuracy significantly.

The wideband system plays a important role in high-accuracy location awareness. In beyond 5G networks, reconfigurable intelligent surfaces (RISs) are proposed to control the complex wireless environments. However, existing works related to RISs mainly focus on narrow-band systems due to the frequency-selectivity of conventional metasurfaces. This paper first presents the general signal model of wideband systems with RISs, and then performs Fisher information analysis to determine the theoretical limits of wideband localization with RISs. Furthermore, special scenarios including complete coupling scenarios and complete decoupling scenarios are discussed and evaluated. The simulation result shows that RISs in complete coupling scenarios provided more improvement, in terms of squared position error bound (SPEB), compared with that in complete decoupling scenarios and random configuration.

Achieving accurate location-awareness in wireless networks requires integrated sensing and communication (ISAC), where optimization, signal processing, and data fusion are performed under a common framework. The efficiency of ISAC in complex wireless environments can be improved via the use of reconfigurable intelligent surfaces (RISs). This paper introduces the concept of continuous intelligent surface (CIS) and establishes the fundamental limits of RIS-aided ISAC systems, specifically, an RIS-aided localization and communication system. In particular, this paper considers two types of RISs, namely CISs and discrete intelligent surfaces (DISs). First, this paper proposes a general signal model for RIS-aided localization and communication valid for both near-field and far-field scenarios, and then theoretical limits on the localization and communication performance are derived. Based on the proposed model, Fisher information analyses of the localization performance in networks with RISs are performed. Numerical results show that RISs with optimized phase responses can improve the received signal-to-noise ratio (SNR) and spectral efficiency of communication, and the localization accuracy significantly.

Location-aware networks enable new services and applications in fields such as autonomous driving, smart cities, and the Internet-of-Things. Network localization and navigation (NLN) is a recently proposed paradigm for accurate ubiquitous localization without the need for extensive infrastructure. In NLN, devices form an interconnected network for the purpose of cooperatively localizing one another. This paper introduces Peregrine, a system that combines real-time distributed NLN algorithms with ultra-wideband (UWB) sensing and communication. The Peregrine software integrates three NLN algorithms to jointly perform 3-D localization and network operation in a technology agnostic manner, leveraging both spatial and temporal cooperation. Peregrine hardware is composed of compact low-cost devices that comprise a microprocessor and a UWB radio. The contribution of each algorithmic component is characterized through indoor network experimentation. Results show that Peregrine is robust, scalable, and capable of sub-meter accuracy in challenging wireless environments.

Seven decades after the foundation of classical information theory and the invention of the transistor that launched the digital communication and computing revolutions, we are entering a new era of quantum information science and engineering (QISE). Despite holding its impressive sway for nearly 60 years, the celebrated Moore’s law is beginning to hit physical limits, as the ever-shrinking transistor size is making it necessary to account for quantum effects. Concurrently, the growing demand for high-rate processing is imposing unsustainable power and heat dissipation requirements. Thus, there is an urgent need to develop quantum information processing systems that can circumvent the limitations of existing technology.

Emerging healthcare radio technologies are designed to operate in the 2.4GHz industrial, scientific and medical (ISM) band. Since both standardized (Bluetooth and Wi-Fi) and non-standardized (proprietary) devices use the same frequency band, the aggregate interference may significantly affect the performance of medical wireless systems. This paper characterizes the spatiotemporal spectrum occupancy and proposes models for the aggregate interference in hospital environments. In particular, time-frequency and cluster-based statistical models for the aggregate interference are developed based on network experimentation. The proposed models enable the design of wireless networks for e-health applications and medical services.

The characterization of the latency is essential for operation of 5G and beyond 5G cellular networks. This paper develops a spatiotemporal model to characterize latency in the downlink of large-scale cellular networks with random scheduling. In particular, the framework integrates stochastic geometry and queueing theory, to capture the interwoven interactions between the microscopic behavior of each wireless link and the macroscopic mutual interference between all links in the network. The developed framework enables a traffic-aware characterization of the transmission success probability and of the latency across the network.

Location awareness is vital for emerging Internet-of-Things applications and opens a new era for Localization-of-Things. This paper first reviews the classical localization techniques based on single-value metrics, such as range and angle estimates, and on fixed measurement models, such as Gaussian distributions with mean equal to the true value of the metric. Then, it presents a new localization approach based on soft information (SI) extracted from intra- and inter-node measurements, as well as from contextual data. In particular, efficient techniques for learning and fusing different kinds of SI are described. Case studies are presented for two scenarios in which sensing measurements are based on: 1) noisy features and non-line-of-sight detector outputs and 2) IEEE 802.15.4a standard. The results show that SI-based localization is highly efficient, can significantly outperform classical techniques, and provides robustness to harsh propagation conditions.

The month’s regular papers issue covers machine learning at the wireless network edge, soft-informationbased localization techniques, and Antenna-in-Package technology.

Network secrecy is vital for a variety of wireless applications and can be accomplished by exploiting network interference. Recently, interference engineering strategies (IESs) have been developed to harness network interference, depending on the wireless environment (node distribution, transmission policy, and channel conditions). Typically, the node spatial distribution has been modeled according to a homogeneous Poisson point process for mathematical tractability. However, such a model can be inadequate for inhomogeneous (e.g., sensor and vehicular) networks. This paper develops a framework for the design and analysis of inhomogeneous wireless networks with intrinsic secrecy. Based on the characterization of the network interference and received signal-to-interference ratio for different receiver selection strategies. Local and global secrecy metrics are introduced for characterizing the level of intrinsic secrecy in inhomogeneous wireless networks from a link and a network perspective. The benefits of IESs are quantified by simulations in various scenarios, thus corroborating the analysis. Results show that IESs can elevate the network secrecy significantly.

The massive wireless networks (MWNs) enable surging applications for the Internet of Things and cyber physical systems. In these applications, nodes typically exhibit stringent power constraints, limited computing capabilities, and sporadic traffic patterns. This paper develops a spatiotemporal model to characterize and design uncoordinated multiple access (UMA) strategies for MWNs. By combining stochastic geometry and queueing theory, the paper quantifies the scalability of UMA via the maximum spatiotemporal traffic density that can be accommodated in the network, while satisfying the target operational constraints (e.g., stability) for a given percentile of the nodes. The developed framework is then used to design UMA strategies that stabilize the node data buffers and achieve desirable latency, buffer size, and data rate.

Network navigation is a promising paradigm for enabling location awareness in dynamic wireless networks. A wireless navigation network consists of agents (mobile with unknown locations) and anchors (possibly mobile with known locations). Agents can estimate their locations based on inter-and intra- node measurements as well as prior knowledge. With limited wireless resources, the key to achieve high navigation accuracy is to maximize the benefits of agents' channel usage. Therefore, it is critical to design scheduling algorithms that adaptively determine with whom and when an agent should perform inter-node measurements to achieve both high navigation accuracy and efficient channel usage. This paper develops a framework for the design of distributed scheduling algorithms in asynchronous wireless navigation networks, under which the algorithm parameters are optimized based on the evolution of agents' localization errors. Results show that the proposed algorithms lead to high-accuracy, efficient, and flexible network navigation.

Spatial field reconstruction is important for a variety of applications based on environmental monitoring via Internet-of-Things and vehicular communications. This work develops a framework for the analysis of multidimensional stochastic sampling in vehicular crowd sensing, where samples are gathered from sensors on vehicles. Vehicular crowd sensing performance in terms of reconstruction mean-square error is compared to that obtainable with fixed installation. In the first case, sensors are assumed to be randomly distributed over the monitored area, while, in the latter, they are considered as regularly placed in a lattice. In addition, the positions of the mobile nodes are assumed non perfectly known at the interpolator. By extending recent results on multidimensional stochastic sampling, it is shown that a high field reconstruction accuracy can be obtained by vehicular crowd sensing even in cases where fixed infrastructure would lead to insufficient sampling.

Wireless communications are highly susceptible to eavesdropping due to their inherent broadcast nature. Recent works have proposed the use of interference for impeding the capabilities of the eavesdropping receivers in wireless networks. To fully unleash the potential of interference for wireless information secrecy, it is necessary to engineer network interference such that it impedes the capability of eavesdropping receivers while having mild effect on legitimate receivers. The task of generating network interference desirable for wireless secrecy is particularly challenging in heterogeneous networks due to their ample set of configurations. This paper proposes a unified interference engineering strategy (IES) for wireless information secrecy in heterogeneous networks. The unified IES combines zero forcing beamforming, artificial noise generation, cooperative jamming, and interference alignment to fully exploit the network's capability in wireless secrecy. The proposed strategy enables multiple nodes with heterogeneous capabilities to achieve mutually beneficial coordination, thereby leading to a new level of wireless information secrecy.

The advent of new wireless technologies in recent years has created a tremendous increase in the demand for wireless connectivity. The estimated number of mobile device connections exceeds eight billion globally, more than the population of the world, and the growth trend is rapidly increasing as the developing world penetration rate is still in its infancy. With the Internet-of-Things (IoT), it is expected that the total number of connections will reach over fifty billion in the next few years. In conjunction with wireless connectivity technology, the need for location awareness is paramount.

This two-part special issue covers relevant aspects of network localization and navigation: theoretical foundations, localization technologies, positioning algorithms, and network operation.

This letter establishes a methodology to design threshold profiles for wideband ranging systems. Differently from conventional methods using a single threshold value that is designed based on the signal detection and false-alarm probability requirements, we propose a threshold profile that is designed based also on the ranging error requirement. The proposed method relies only on channel statistics without requiring channel estimation. A case study shows that, compared to conventional methods, the proposed method for threshold profiling significantly improves the performance in terms of false-alarm probability, detection probability, and ranging error.

The impressive growth of secrecy-sensitive wireless applications calls for methods to complement traditional cryptography. In particular, physical-layer security is attractive to enhance communication confidentiality by exploiting the characteristics of the wireless environment. Recent works on information-theoretic wireless network secrecy have shown how to exploit interference to enhance the level of information confidentiality for node spatial distribution according to a homogeneous Poisson point process. We propose a framework for design and analysis of inhomogeneous Poisson networks with maximum secrecy rate. We determine the effects of the inhomogeneous node spatial distribution on the secrecy metrics, which provide guidelines for network design.

The deployment of sensing nodes is crucial for applications relying on the reconstruction of spatial fields. Theoretical analysis usually assumes that nodes are distributed according to a homogeneous Poisson point process (PPP), in which nodes positions are stochastically independent. However, realistic scenarios for crowd sourcing and Internet of Things call for clustered layouts of sensing nodes, for which homogeneous PPP is not appropriate. This paper analyzes sampling and reconstruction of finite-energy signals in Rd, with samples gathered in space according to a Gauss-Poisson point process (G-PPP), which has been recently proposed to model node spatial distribution with attraction (as in clustering). In particular, it is shown that attraction between nodes modeled by G-PPP reduces the reconstruction accuracy with respect the case of homogeneous PPP with the same intensity. This represents the opposite case of the repulsion effect, which was investigated in a previous work relying on Ginibre point process sampling.

Counting people and things (targets) in a monitored area, also known as crowd-counting, enables several applications in diverse scenarios, including smart building, intelligent transportation, and public safety. In many scenarios, device-free systems relying on the signal backscattering from targets are preferred to device-based systems relying on the communication with the targets via dedicated or personal devices. However, the use of conventional radar techniques (e.g., for multi-target detection) requires us to associate a different set of measured data with each detected target. Data association is a redundant operation for counting and results in high complexity even with few targets. The need of lower dimensionality and complexity calls for signal features to associate the measured signals directly with the number of targets. This paper proposes a mathematical framework for the design of device-free counting systems. First, a maximum a posteriori algorithm is developed for counting via wideband signal backscattering by relying on model order selection. Then, a method that relies on low-level features is proposed to lower the computational complexity. The proposed method is verified via sample-level simulations in realistic operating conditions and compared with current solutions.

Network navigation is a promising paradigm for providing location awareness in wireless environments, where nodes estimate their locations based on sensor measurements and prior knowledge. In the presence of limited wireless resources, only a subset rather than all of the node pairs can perform inter-node measurements. The procedure of selecting node pairs at different times for inter-node measurements, referred to as network scheduling, affects the evolution of the localization errors. Thus, it is crucial to design efficient scheduling strategies for network navigation. This paper introduces situation-aware scheduling that exploits network states to select measurement pairs, and develops a framework to characterize the effects of scheduling strategies and of network settings on the error evolution. In particular, both sufficient and necessary conditions for the boundedness of the error evolution are provided. Furthermore, opportunistic and random situation-aware scheduling strategies are proposed, and bounds on the corresponding time-averaged network localization errors are derived. These strategies are shown to be optimal in terms of the error scaling with the number of agents. Finally, the reduction of the error scaling by increasing the number of simultaneous measurement pairs is quantified.

Network navigation is a promising paradigm for providing accurate location-awareness in wireless environments, where mobile nodes estimate their locations based on inter- and intra-node measurements. In the presence of limited wireless resources, only a subset rather than all of the node pairs can perform inter-node measurements. Therefore, it is crucial to design efficient scheduling algorithms for selecting node pairs at different times for inter-node measurements. This paper develops a framework for the design of scheduling algorithms based on random access for network navigation. The proposed algorithms are suitable for practical operation of wireless navigation networks due to their distributed nature, and the optimized access probabilities of the agents lead to significant performance improvement.

The spatial distribution of sensing nodes plays a crucial role in signal sampling and reconstruction via wireless sensor networks. Although homogeneous Poisson point process (PPP) model is widely adopted for its analytical tractability, it cannot be considered a proper model for all experiencing nodes. The Ginibre point process (GPP) is a class of determinantal point processes that has been recently proposed for wireless networks with repulsiveness between nodes. A modified GPP can be considered an intermediate class between the PPP (fully random) and the GPP (relatively regular) that can be derived as limiting cases. In this paper we analyze sampling and reconstruction of finite-energy signals in ℝdwhen samples are gathered in space according to a determinantal point process whose second order product density function generalizes to ℝdthat of a modified GPP in ℝ2. We derive closed form expressions for sampled signal energy spectral density (ESD) and for signal reconstruction mean square error (MSE). Results known in the literature are shown to be sub-cases of the proposed framework. The proposed analysis is also able to answer to the fundamental question: does the higher regularity of GPP also imply an higher signal reconstruction accuracy, according to the intuition? Theoretical results are illustrated through a simple case study.

Network navigation is an emerging paradigm for enabling location-awareness in wireless networks. The limited wireless resource calls for scheduling algorithms to select node pairs for inter-node measurements. The design of efficient scheduling algorithms relies on an understanding of the underlying localization error evolution. This paper unifies the error evolution analysis in both the non-Bayesian and Bayesian cases. We first develop a unified recursive equation for the error evolution in the two cases. We then provide sufficient conditions for the boundedness of the error evolution. These conditions are more general than our previous results and can be used to derive error bounds for different scheduling algorithms. Furthermore, we show the the reduction of the error scaling with respect to the number of agents by increasing the multiplexing factor. These results provide insights for the efficient operation of wireless navigation networks.

Wireless secrecy is essential for communication confidentiality, health privacy, public safety, information superiority, and economic advantage in the modern information society. Contemporary security systems are based on cryptographic primitives and can be complemented by techniques that exploit the intrinsic properties of a wireless environment. This paper develops a foundation for design and analysis of wireless networks with secrecy provided by intrinsic properties such as node spatial distribution, wireless propagation medium, and aggregate network interference. We further propose strategies that mitigate eavesdropping capabilities, and we quantify their benefits in terms of network secrecy metrics. This research provides insights into the essence of wireless network intrinsic secrecy and offers a new perspective on the role of network interference in communication confidentiality.

Passive and semipassive ultrawideband (UWB) radio-frequency identification (RFID) technology has been recently proposed to offer high-accuracy localization capabilities in next-generation RFID systems. This technology relies on the modulation of backscattered signals, i.e., backscatter modulation, from multiple tags present in the environment. The detection of multiple tags based on backscattered signals is challenging in harsh environments with nonideal conditions such as clutter, near-far interference effects, and clock drift. This paper analyzes the detection of multiple tags employing UWB backscatter modulation and proposes practical signaling, spreading codes, and detection schemes that are robust to nonideal conditions. A case study is presented to evaluate the performance of the proposed technique for the detection of multiple tags based on impulsive backscattered signals.

Network localization enables a variety of new applications that rely on the positional information of nodes. High-accuracy network localization is challenging in harsh propagation environments (such as indoor) and is limited by power emission constraints. We devise non-regenerative ultra-wideband relaying to improve the performance of network localization in terms of coverage and accuracy. Maximum likelihood inference of nodes' position for relayed network localization is developed, with perfect and imperfect channel knowledge. Results quantify the performance improvement that non-regenerative relaying offers despite its low-complexity.

Secrecy is a key enabler for various wireless applications in which distributed confidential information is communicated in a multilevel network from sources to destinations. Network secrecy can be accomplished by exploiting the intrinsic properties of multilevel wireless networks (MWNs). This paper introduces the concept of distributed network secrecy (DNS) and develops a framework for design and analysis of confidential MWNs. Our framework accounts for node distribution, network configuration, propagation medium, and communication protocol. This research offers the foundation of DNS and quantifies the impact of network configuration on DNS for self-organizing MWNs.

The demand of communication confidentiality in wireless network is rapidly increasing. The level of confidentiality can be enhanced by physical layer techniques exploiting intrinsic properties of a wireless network. We develop a framework for design and analysis of wireless network with secrecy that accounts for node distribution, propagation medium, and intentional interference. The framework enables the quantification of how intentional interference generated via legitimate network resources engineering mitigates the capability of the eavesdropping network. This research provides insight on the opportunistic use of legitimate network resources for enhancing network secrecy.

Secrecy is essential for a variety of emerging wireless applications where distributed confidential information is communicated in a multilevel network from sources to destinations. Network secrecy can be accomplished by exploiting the intrinsic properties of multilevel wireless networks (MWN). This paper introduces the concept of distributed network secrecy (DNS) and develops a framework for the design and analysis of secure, reliable, and efficient MWNs. Our framework accounts for node spatial distribution, multilevel cluster formation, propagation medium, communication protocol, and energy consumption. This research provides a foundation for DNS and offers a new perspective on the relationship between DNS and network lifetime.

Upper layer wireless security relies on the computational intractability assumption for solving certain number-theoretic problems. These methods can be complemented by techniques that exploit, at the physical layer, the intrinsic properties of the wireless channel and interference. This paper considers communications with intrinsic secrecy in the presence of spatially distributed nodes, namely legitimate users, eavesdroppers, and interferers. We characterize the role of aggregate interference on intrinsic network secrecy, providing insights into regimes in which interference is beneficial for network secrecy.

This paper introduces the notion of network experimentation and proposes an experimentation methodology particularly suited for cooperative wireless networks. Based on this methodology we performed extensive measurement campaigns and compare various cooperative localization techniques under a common setting. Network experiments enable (i) the quantification of cooperation benefits, (ii) the development of techniques for harnessing environmental information, and (iii) the characterization of network localization algorithms. As a case study, we consider ultrawide bandwidth cooperative location-aware networks in cluttered indoor environments and evaluate their performance based on measurements collected from network experiments.

Subset diversity (SSD) techniques, which select and combine the signals from a subset of the available diversity branches, are important for practical wireless systems. This paper characterizes the performance loss, or signal-to-noise ratio (SNR) penalty, of one SSD system with respect to another. Both ideal and non-ideal channel estimation are considered, and the analysis is valid for the important case of arbitrary two-dimensional signal constellations. Expressions are given for the asymptotic SNR penalty, for both small and large SNR, for all the comparisons considered. Additionally, we develop bounds and approximations to quantify the performance of one system in terms of another for all SNRs of interest. Furthermore, for some signal constellations, we derive the exact SNR penalty of a non-ideal system with respect to an ideal system, as well as the exact penalty associated with two non-ideal systems with varying degrees of estimation energy. The SNR penalty enables the assessment of system sensitivity to channel estimation energy, combining architecture, and signal constellation.

Wireless secrecy is becoming crucial in many commercial and governmental sectors. In this paper, the secrecy capacity of a wireless channel is analyzed in the presence of intentional network interference for denial of eavesdropping capability (DEC). We propose two different DEC strategies employing the closest interferer and the multiple interferers. We show the impact of the network interference on average and outage of secrecy capacity for the two DEC strategies with and without eavesdropper's countermeasures to create a region of interference neutralization. This work provides essentials for understanding how to increase wireless secrecy using intentional network interference.

Wireless sensor networks (WSNs) have a large number of applications, number of applications. Among them, the estimation of spatial processes from sparse sensing nodes is important for environmental monitoring. In this work we analyze the process estimation accuracy for a randomly deployed self-organizing WSN taking into account crucial aspects such as random sampling, nodes' connectivity, communications protocols, interpolation techniques, and uncertainty of nodes' positions. These aspects are typically not considered into a common framework. The joint analysis of all these aspects enables a WSN designer to understand the role of interpolation techniques and nodes' position uncertainty on process estimation to reach a given target estimation accuracy.

Relay assisted communications exploit cooperative diversity to efficiently extend area coverage or improve the performance in wireless channels. We propose a mathematical framework to analyze the frame error probability (FEP) at the destination depending on distributed coding, nodes spatial distribution, and power allocation among source and relay nodes. We evaluate the impact of cooperative links characterization on the overall FEP at the destination by considering simple approximations on the link FEP which depend on the signal-to-noise ratio and diversity. We consider a number of power allocation techniques such as uniform, ideal power control, outage-optimal, and FEP-optimal. Results are given for a case study in which pragmatic space-time codes are employed as distributed coding technique. Analytical results are confirmed by simulations that are given to serve as a benchmark for the considered cases. The framework enables the system designer to allocate power and individuate the best relay position to minimize the FEP.

Low-density parity-check (LDPC) codes are gaining interest for high data rate applications in both terrestrial and spatial communications. They can be designed and studied through a bipartite graph whose characteristics affect the performance. This paper proposes a low-complexity method to improve the performance of LDPC codes by selectively removing some cycles from the associated bipartite graph. The method is based on a modified version of the breadth first search (BFS) algorithm that we call modified BFS (MBFS), which is applied to find cycles, and a greedy procedure to eliminate them. Throughout the paper we will give a detailed description of the algorithm proposed and analytically study its complexity. Simulation results show that this girth conditioning method applied to some classes of codes, whose structure allows further optimization, can lead to a significative complexity reduction and a performance improvements with respect to other methods.

Link adaptation techniques are important modern and future wireless communication systems to cope with quality of service fluctuations in fading channels. These techniques require the knowledge of the channel state obtained with a portion of resources devoted to channel estimation instead of data and updated every coherence time of the process to be tracked. In this paper, we analyze fast and slow adaptive modulation systems with diversity and non-ideal channel estimation under energy constraints. The framework enables to address the following questions: (i) What is the impact of non-ideal channel estimation on fast and slow adaptive modulation systems? (ii) How to define a proper figure of merit which considers both resources dedicated to data and those to channel estimation? (iii) Does fast adaptive always outperform slow adaptive techniques? Our analysis shows that, despite the lower complexity and feedback rate, slow adaptive modulation (SAM) can achieve higher spectral efficiency than fast adaptive modulation (FAM) in the presence of energy constraint, diversity, and non-ideal channel estimation. In addition, SAM satisfies bit error outage requirements also in FAM-denied region.

Adaptive M-QAM systems with maximal-ratio diversity (MRD) over composite arbitrarily correlated Nakagami-m fading and log-normal shadowing channels are analyzed. The slow adaptive modulation (SAM) technique is considered which adapts the constellation signaling to slow channel variations and achieves good performance in terms of spectral efficiency (SE) and bit error outage (BEO) despite its low feedback rate. Non-ideal channel estimation (CE) based on pilot symbols is assumed. Analytical expressions and tight bounds for the bit error probability are obtained which are used for deriving analytical expressions and tight bounds for the BEO and achieved SE (ASE). Performance evaluation results have shown that fading correlation and CE errors degrade the performance of SAM systems. The partition of resources for CE and information data requires a careful definition of the ASE and results have shown the tradeoff between CE quality and ASE.

Radar sensor networks (RSNs) are gaining importance in the context of passive localization and tracking. The performance of RSNs is affected by disturbances, system's parameters, network topology, and the number of radar elements. In this paper, we derive a unified analytical framework that takes all this aspects into account and allows the derivation of probability of detection and localization uncertainty. The results enable the system designer to have a clear understanding on the effects of each system parameter and the trade-off between performance and complexity. Moreover, the potential for high-accuracy passive localization of ultrawide bandwidth (UWB) systems is shown.

Adaptive M-ary quadrature amplitude modulation (M-QAM) with subset diversity is a way to cope with the quality of service variations in small and large-scale fading channels. We consider a pilot-assisted slow adaptive modulation (SAM) technique that adapts the constellation size and the pilots energy to slow variations of the channel due to shadowing. The SAM technique is less complex and requires a lower feedback rate with respect to fast adaptive modulation techniques. We present an analytical framework to evaluate the bit-error probability for pilot-asisted slow adaptive QAM with subset diversity and imperfect channel state information (CSI) together with a utility-based approach to SAM. It accounts for the imperfect CSI, and is related to the game-theoretic approach, in which a particular strategy (the pilots energy and the constellation signaling) is chosen as a response to the set of possible channel conditions.

Over the coming decades, high-definition situationally-aware networks have the potential to create revolutionary applications in the social, scientific, commercial, and military sectors. Ultrawide bandwidth (UWB) technology is a viable candidate for enabling accurate localization capabilities through time-of-arrival (TOA)-based ranging techniques. These techniques exploit the fine delay resolution property of UWB signals by estimating the TOA of the first signal path. Exploiting the full capabilities of UWB TOA estimation can be challenging, especially when operating in harsh propagation environments, since the direct path may not exist or it may not be the strongest. In this paper, we first give an overview of ranging techniques together with the primary sources of TOA error (including propagation effects, clock drift, and interference). We then describe fundamental TOA bounds (such as the CramÉr–Rao bound and the tighter Ziv–Zakai bound) in both ideal and multipath environments. These bounds serve as useful benchmarks in assessing the performance of TOA estimation techniques. We also explore practical low-complexity TOA estimation techniques and analyze their performance in the presence of multipath and interference using IEEE 802.15.4a channel models as well as experimental data measured in indoor residential environments.

Diversity techniques play a key role in modern wireless systems, whose design benefits from a clear understanding of how these techniques affect system performance. To this aim we propose a simple class of bounds, whose parameters are optimized, on the symbol error probability (SEP) for detection of arbitrary two-dimensional signaling constellations with diversity in the presence of non-ideal channel estimation. Unlike known bounds, the optimized simple bounds are tight for all signal-to-noise ratios (SNRs) of interest. In addition, these bounds are easily invertible, which enables us to obtain bounds on the symbol error outage (SEO) and SNR penalty. As example applications for digital mobile radio, we consider the SEO in log-normal shadowing and the SNR penalty for both maximal ratio diversity, in the case of unequal branch power profile, and subset diversity, in the case of equal branch power profile, with non-ideal channel estimation. The reported lower and upper bounds are extremely tight, that is, within a fraction of a dB from each other.

Multi carrier code-division multiple access (MC-CDMA) is considered for beyond third generation wireless systems for its effectiveness in the presence of both multipath fading and interference. This paper analyzes MC-CDMA systems adopting an adaptive detection technique based on threshold orthogonality restoring combining (TORC). The mathematical framework here developed allows the evaluation of both the bit error probability and the bit error outage in downlink with perfect and imperfect channel state information and the derivation of the TORC threshold that optimizes the performance. The optimal threshold is analytically derived as a function of the number of subcarriers, the number of active users, and the mean signal-to-noise ratio. This enables an adaptive variation of the threshold following slow processes fluctuations. Numerical results show very good performance of TORC with optimal threshold comparing with others combining techniques with perfect and imperfect channel estimation. Results are shown both in uncorrelated Rayleigh fading as well as in time and frequency correlated fading channels.

A clear understanding of the behavior of error probability (EP) as a function of signal-to-noise ratio (SNR) and other system parameters is fundamental for assessing the design of digital wireless communication systems. We propose an analytical framework based on the log-concavity property of the EP which we prove for a wide family of multidimensional modulation formats in the presence of Gaussian disturbances and fading. Based on this property, we construct a class of local bounds for the EP that improve known generic bounds in a given region of the SNR and are invertible, as well as easily tractable for further analysis. This concept is motivated by the fact that communication systems often operate with performance in a certain region of interest (ROI) and, thus, it may be advantageous to have tighter bounds within this region instead of generic bounds valid for all SNRs. We present a possible application of these local bounds, but their relevance is beyond the example made in this paper.

Diversity techniques will play a key role in next generation wireless communication systems, thus system design will benefit from a clear understanding of how these techniques affect system performance. To this aim we propose simple bounds, optimized within a given class, on the symbol error probability (SEP) in the presence of non-ideal channel estimation for arbitrary two-dimensional signaling constellations. Unlike known bounds, the optimized simple bounds are tight for all signal-to-noise ratios (SNRs). In addition, these bounds are easily invertible, which enables us to obtain bounds on the symbol error outage (SEO) and SNR penalty. As an example application for digital mobile radio, we consider the SEO in log-normal shadowing for both maximal ratio combining with unequal branch power profile and subset microdiversity. The reported lower and upper bounds are extremely tight, that is, within a fraction of a dB from each other.

In this paper we analytically evaluate the downlink performance of a multi-carrier code division multiple access (MC-CDMA) system by employing partial combining when multiple antennas at the transmitter or at the receiver are considered. It is known that for single reception/transmission, the partial combining technique depends on a parameter which can be optimized as a function of the number of subcarrier, the number of active users and the SNR improving the performance with respect to other classical techniques (such as maximal ratio combining, equal gain combining, or orthogonality restoring combining). In this paper we extend the analysis considering also multiple antennas aiming at showing how the spatial diversity affects the performance as a function of the partial combining parameter.

Abstract This paper investigates the impact of diversity reception on the performance of Bluetooth (BT) packet transmission in wireless channels with fast fading and shadowing and in the presence of IEEE802.11g interference. In particular, selection diversity (SD) technique is considered and the adoption of this technique results in a coverage extension evaluated in the paper. We first derive a tight parametric exponential approximation for the instantaneous bit error probability (BEP) in additive white Gaussian noise (AWGN); then, from this expression, we derive the mean block error probability (BLEP) in Ricean fading channel by adopting a simple SD scheme. The analysis is then extended to the evaluation of the mean packet error probability (PEP) and the coverage probability (Pcov). In particular, the impact of the diversity order on the BLEP, PEP, andPcovis shown. Copyright © 2007 John Wiley & Sons, Ltd.

This paper addresses the performance evaluation of multi-carrier code division multiple access systems both at physical and TCP levels. A partial equalization technique improving the performance at physical layer in the presence of interference is investigated and its impact on the performance at upper layer is studied. The results are obtained through an integrated simulation platform carefully reproducing all main aspects affecting the quality of service perceived by the final user. Works of this nature enable a deep investigation of the real gain perceived by the user provided by proper signal processing techniques at the physical level.

This work reports some results of a joint activity research pursued within the Project C of NEWCom (European network of excellence in communications). The aim of the activity is to investigate adaptive cross-layer techniques for heterogeneous wireless networks and try to obtain some general rules. In particular the singular or joint adaptation of scheduling and link adaptation algorithms is studied by means of simulation platforms for which proper scenarios and metrics have been defined.

This paper investigates the performance of adaptive M-ary quadrature amplitude modulation (QAM) with antenna subset diversity. We consider a slow adaptive modulation (SAM) technique that adapts the constellation size to the slow variation of the channel due, for example, to shadowing. The proposed SAM technique is more practical than conventional fast adaptive modulation (FAM) techniques that require adaptation to fast-fading variations. Our results show that the SAM technique can provide a substantial increase in throughput with respect to fixed schemes while maintaining an acceptable low bit-error outage. We also compare SAM and FAM techniques, showing that the throughput of SAM can be, in many practical cases, close to that of FAM, despite the fact that SAM is less complex and requires a lower feedback rate. For example, using a set of possible modulations {4,16,64}-QAM with dual-branch maximal ratio combining reception, 5% outage at a bit-error probability of 10-2and a median signal-to-noise ratio of 22 dB, SAM is capable of improving the mean spectral efficiency of fixed schemes from about 1.9 to 4.7 b/s/Hz, which is close to the 5.5 b/s/Hz achieved by FAM

In this paper, the estimation of a scalar field over a bidimensional scenario (e.g., the atmospheric pressure in a wide area) through a self-organizing wireless sensor network (WSN) with energy constraints is investigated. The sensor devices (denoted as nodes) are randomly distributed; they transmit samples to a supervisor by using a clustered network. This paper provides a mathematical framework to analyze the interdependent aspects of WSN communication protocol and signal processing design. Channel modelling and connectivity issues, multiple access control and routing, and the role of distributed digital signal processing (DDSP) techniques are accounted for. The possibility that nodes perform DDSP is studied through a distributed compression technique based on signal resampling. The DDSP impact on network energy efficiency is compared through a novel mathematical approach to the case where the processing is performed entirely by the supervisor. The trade-off between energy conservation (i.e., network lifetime) and estimation error is discussed and a design criterion is proposed as well. Comparison to simulation outcomes validates the model. As an example result, the required node density is found as a trade-off between estimation quality and network lifetime for different system parameters and scalar field characteristics. It is shown that both the DDSP technique and the MAC protocol choice have a relevant impact on the performance of a WSN.

This paper addresses the performance evaluation of multi-carrier code division multiple access systems. A partial equalization technique depending on a parameter adapted to different conditions such as the number of sub-carriers, the number of active users and the mean signal-to-noise ratio is evaluated for the down-link. We analytically derive the performance of this system and the value of the equalization parameter that maximizes the performance. The partial equalization technique is shown to have the same complexity of the well known maximal ratio combining, equal gain combining and orthogonality restoring combining techniques, with significative performance improvement. Analytical results are compared with simulations showing a perfect agreement

In this work we propose an analytical framework aiming at studying the coexistence issue between Bluetooth (BT) and IEEE802.11g in fading channels. With the goal to improve the BT performance in the presence of interference, we consider a non coherent selection diversity (SD) scheme at the receiver side. In fact, due to its simplicity, the SD scheme is fully compliant with the low-cost requirement of BT equipped devices. Through the analysis, we derive the BT performance in terms of packet error probability averaged over Ricean fading when IEEE802.11g acts as interferer, taking into account all physical and medium access control (MAC) aspects. Results show the performance improvement and the reduction of the minimum coexistence distance due to the adoption of the simple SD receiving scheme

The paper address the coexistence of Bluetooth (BT) and IEEE802.11g systems. The interference on BT is analytically evaluated as well as the performance when diversity reception in Ricean fading is considered. We firstly derive the expression of the mean block error probability in the presence of interference, then we extend the analysis to the mean packet error probability and to the coexistence distance. Due to multiple receiving antennas, results show the not negligible performance improvement and the reduction of the minimum coexistence distance.

In this paper we present the results of an intensive experimental campaign performed at IEIIT-BO/CNR, Bologna, Italy, on an indoor localization system based on wireless sensors network. Received signal strength indications have been measured and collected using real devices. The data serve as an input data-base for an off-line investigation of different localization techniques. This enables us on one side to improve location accuracy through propagation model tuning, on the other side to compare different location algorithms in term of complexity and performance. Results of these optimizations and comparisons are presented in this paper

In this letter, we derive tight invertible bounds on the bit-error probability (BEP) for the coherent detection of M-ary quadrature amplitude modulation with Gray code bit mapping in Rayleigh fading channels. These bounds enable us to easily obtain tight lower and upper bounds on the bit-error outage (BEO), i.e., BEP-based outage probability, in a log-normal shadowing environment. As examples of applications, these bounds are used to investigate the BEO and mean spectral efficiency for slow adaptive modulation.

In this paper, we investigate the performance of adaptive M-ary quadrature amplitude modulation with antenna subset diversity. We consider a slow adaptive modulation (SAM) technique which adapts the constellation size to the slow variation of the channel due, for example, to shadowing. Our results show that the SAM technique can provide substantial increase in throughput with respect to fixed schemes while maintaining an acceptable low bit error outage. We also compare SAM with a fast adaptive modulation (FAM) technique, which tracks fast fading variations, showing that the throughput of SAM is close to that of FAM despite the fact that SAM is less complex and requires a lower feedback rate to the transmitter.

An analytical framework for the performance evaluation of a dense energy-efficient wireless sensor network (WSN), enabling distributed collaborative environment monitoring, is developed. We address the estimation of a target multidimensional process by means of samples captured by nodes randomly and uniformly distributed and transmitted to a collector through a self-organizing clustered network. The estimation in the presence and in the absence of collaborative signal processing with different types of data interpolators are compared in terms of both process estimation error and network life-time. Our analytical model, aimed at providing useful information for WSN design, takes many aspects into account, such as distance-dependent path loss and shadowing, energy consumption, information routing, process estimation quality, node density, transmission protocol and system parameters. As an example result, fixing requirements on estimation errors and network life-time, the node density is found as a function of the system/protocol parameters.

In this paper, we firstly derive tight invertible bounds on the bit error probability (BEP) for coherent detection of M-QAM in Rayleigh fading channels. These bounds enable us to easily obtain tight lower and upper bounds on the bit error outage (BEO). As examples of applications, these bounds are used to investigate the BEO in a log-normal shadowing environment. Moreover, using the bounds on the inverse BEP, the analysis of the mean spectral efficiency for slow adaptive modulation is assessed.

This paper investigates the impact of diversity reception techniques on the performance of Bluetooth (BT) packet transmission in fading channels. We firstly derive a tight parametric exponential approximation for the bit error probability in additive white Gaussian noise depending on GFSK modulation parameters according to the BT standard. Then, from this expression we derive the mean block error probability (BLEP) for DH packets transmission in Rayleigh fading channel by adopting different diversity reception techniques, such as selection diversity and maximal ratio combining (MRC). In particular, the impact of the diversity order and combining techniques on the BLEP is shown. For the MRC technique, we also obtain a tight bound on the BLEP. Furthermore, we extend the results for MRC to Nakagami-m fading and we obtain the parameters of exponential approximation for a general GFSK modulation, although out of BT standard, showing their impact on both the performance and spectrum.

This paper addresses indoor localization techniques through ad-hoc wireless networks, where anchors and unknown nodes are randomly positioned in a squared area. The position of unknown nodes is estimated starting from received signal strength (RSSI) measurements, since nodes are assumed to be not equipped with specialized localization hardware. The possibility to perform power measurements among any couple of nodes (either anchors or unknown nodes) enables the exploitation of collaborative localization techniques. In this case, the complexity of the optimum maximum likelihood (ML) technique becomes too huge to have any practical interest. Thus, we propose a sub-optimal but computational efficient, hierarchical algorithm to solve the ML problem. The performance in terms of mean localization error is evaluated by simulations for different scenarios and taking a realistic channel model into account. The sensitivity of the estimated position to the uncertainty on channel parameters is also evaluated.

This correspondence presents results of code search for convolutional codes over block-fading channels (BFCs). Search criteria are based on a union bound approach which exploits the concept of generalized transfer function (GTF) of the error trellis diagram. A new asymptotic bound is derived, and the performance of the codes found using our search criterion is compared analytically and by numerical simulation with already existing codes.

In this paper, the issue of Bluetooth and IEEE802.11b coexistence in a heterogeneous environment is addressed by means of an integrated analytical approach. The methodology proposed carefully takes both physical (i.e., thermal noise, propagation, interference, modulation formats, and coding techniques) and medium access control (frequency hopping, packet structures, traffic loads) aspects into account. This model can be easily implemented when developing network simulators, thus avoiding the need of extensive bit level Monte Carlo simulations at the physical level. The mean packet error probability is evaluated as a function of the relative distance between the two systems for different conditions (e.g., propagation, packet type, traffic loading, etc). In particular, how the presence or absence of line-of-sight propagation significantly affects the coexistence distance is emphasized. Furthermore, for a fixed quality-of-service level we derive the coexistence domain of the two considered systems in terms of relative distance.

This letter addresses the optimum energy partition between the midamble and data fields, given the total energy per burst, in channel estimation for joint detected time-division code-division multiple-access systems. We show that, given the lengths of the data and midamble, in general, the optimal solution requires different amplitude levels. We also derive the burst structure leading to the optimum energy partition with equal amplitude symbols, and the performance degradation by using traditional choices. The analysis is validated by simulations.

This paper addresses the problem of finding the inverse symbol-error probability (SEP) expression for coherent detection of M-ary phase-shift keying with multichannel reception and maximal ratio combining in Rayleigh fading. To this aim, we derive upper and lower bounds on SEP that are simply invertible and uniformly tight for all values of signal-to-noise ratio. This enables us to obtain tight bounds on the inverse SEP and on the symbol-error outage (SEO), i.e., SEP-based outage probability. As an example of application to digital mobile radio, the SEO in a log-normal shadowing environment is analyzed.

This paper addresses the design of bit-interleaved pragmatic space-time codes (BI-PSTC) over block fading channels (BFC). BI-PSTC are obtained with the serial concatenation of a convolutional encoder, a bit-interleaver and a space-time mapper, whereas the decoder is based on a Viterbi algorithm which uses suitably defined branch metrics. Here, a method for good BI-PSTC construction, which exploits the concept of generalized transfer function for space-time codes over BFC, is proposed. Codes for QPSK constellations achieving a considerable coding gain over known space-time codes are then derived. Different spectral efficiencies and fading rates (taken into account by the BFC model) are investigated.

In this article the issue of Bluetooth and IEEE802.11b coexistence in a heterogeneous environment is addressed by means of an analytical approach. The methodology proposed carefully takes physical (i.e., thermal noise, propagation, interference, modulation formats and coding techniques) as well as medium access control (frequency hopping, packet structures, traffic loads) aspects into account. This model can be easily implemented when developing system simulators avoiding, thus, the need of extensive bit level Monte-Carlo simulations at the physical level. Furthermore, having preliminarily fixed a quality of service level, namely the target mean PEP, we derive the coexistence domain of the two considered systems in terms of relative distance.

In a digital mobile radio system with fast fading superimposed on slow fading, the symbol error probability (SEP) alone is not sufficient to describe the link quality. In this case, a reasonable performance measure related to the slow channel variations is the outage probability (OP). This paper addresses the problem of evaluating SEP-based OP for multichannel reception with maximal ratio combining (MRC). To this end, we derive upper and lower bounds on the OP from upper and lower bounds on the inverse SEP respectively. As an example of application to digital mobile radio, the SEP-based outage probability in a log-normal shadowing environment is analyzed.

The performance in terms of the mean packet error probability (PEP) in a Rayleigh fading channel with thermal noise is analytically derived for a Bluetooth transmission interfered by IEEE 802.11b. The methodology proposed carefully takes PHY as well MAC aspects into account. Simulations are used to verify methodology assumptions. Moreover, we derive the coexistence domain, in terms of relative distances between the two systems, for a fixed quality of service, i.e., a target mean PEP.

In this paper we introduce bit-interleaved pragmatic space time codes (BI-PSTC) and evaluate their performance over correlated fading channels taken into account by a block fading channel (BFC) model. More precisely, the BI-PSTC proposed here are obtained with the serial concatenation of a convolutional encoder, a bit-interleaver and a space-time mapper. A simple decoder based on a Viterbi algorithm with suitably defined branch metrics can be used, but iterative decoding can be also applied. In this paper we also discuss the diversity achievable and the performance of BI-PSTC obtained by using the classical optimal convolutional codes for AWGN channel and QPSK constellations. Performance evaluation shows that a considerable gain over known space-time codes can be achieved.

This paper presents a reliable and low complexity acquisition and tracking technique for a DSP-based satellite CDMA modem. An analysis of the performance, in terms of false alarm probability, detection probability and mean acquisition time, is carried out as a function of the complexity and the level of interference. The proposed scheme, based on a dedicated frame format, appears able to cope with the DSP complexity constraint and high levels of interference. The signal structure can be also used for frequency tracking. A DSP implementation and numerical results, obtained by means of analytical evaluations and measurements on a real test-bed, are given.

The design of code-division multiple-access (CDMA) transmission systems for satellite communications requires an appropriate consideration of the distortion effects due to on-board nonlinear amplification. The aim of this paper is to provide an analytical framework for the evaluation of the in-band nonlinear distortion effects on the performance of CDMA systems. Both synchronous systems with orthogonal codes and asynchronous systems are considered. It is first shown that, when the users accessing the channel have the same power and their number is sufficiently large, the nonlinear distortion in the decision variables at the receiver can be simply described by a complex scale factor, which depends on the high-power-amplifier (HPA) characteristics only, and an additive noise, which is uncorrelated to the useful signal. Moreover, an analytical formulation of the bit error probability and the total degradation as a function of the output back-off and number of users is given. In the results, which are obtained for three classes of HPA models (i.e., the traveling wave tube amplifier, the solid-state power amplifier, and the amplifier with ideal predistortion), the performance and the capacity of power-limited systems is also discussed.

A pragmatic approach to space-time codes (STC) over block fading channels (BFC) is proposed. The new approach consists in using common convolutional codes to obtain STC, simplifying the encoder and the decoder. It is shown that pragmatic space-time codes (P-STC) achieve good performance, similar to that of the best known STC, and that they are suitable for systems with different spectral efficiencies and fading velocity (taken into account by the BFC model). To design P-STC we propose a search algorithm based on a new formulation of the pairwise error probability and the error enumerating function for geometrically uniform STC over BFC.

Channel estimation in the uplink of a TD-CDTMA system can be accomplished by inserting 11 known sequence of symbols at the chip rate, usually in the middle of the burst, and therefore called midamble. In this paper we found the analytical solution to the problem of optimum energy partition, given the total energy per burst, between the midamble and data fields, in joint detected TD-CDMA systems. We show that, given the lengths of the data and midamble, in general the optimal solution requires different amplitude levels. We also show the burst structure in order to reach the optimum energy partition with equal amplitude symbols, and the performance degradation with traditional choices. The analysis is validated, for different channels, by comparison with simulation results of the TD-CDMA radio interlace specified for third generation cellular systems, where gains in terms of signal-to-noise ratio of 0.5-0.9 dB are achieveable.

Richardson, Shokrollahi, Urbanke have proposed irregular low-density parity-check codes (LDPCCs) that outperform, on memoryless channels, the best known turbo-codes. These results have been obtained by allowing the degree of each node (variable or check) of a LDPCC to vary according to some distribution. In this paper we investigate the performance of such new codes over block fading channels (i.e. channels with memory), in terms of bit and codeword error rates adopting the standard decoding algorithm and a modified version which slightly improves performance. Also, a numerical comparison with conventional convolutional codes is carried out. For a code rate 1/2, it results that irregular LDPCCs are convenient only for large codeword size (greater than 500 bits), and that the gain with respect to a constraint length 7 convolutional code decreases considerably with the channel memory.

In a shadowing-free environment, the improvement introduced by slow frequency hopping (SFH) on a TDMA-based mobile radio system can be taken into account by properly re-defining the minimum carrier-to-interference protection ratio. This protection ratio, with SFH, is dependent on the transmission system, channel model, traffic and frequency reuse parameters. The approach is used in order to analytically investigate the up-link capacity of a SFH mobile radio system, by taking into account a complete scenario, i.e. shadowing, fast fading, power control, antenna diversity, discontinuous transmission and forward error correction (FEC) with non-ideal interleaving and sectorization.

In a shadowing-free environment, the improvement introduced by slow frequency-hopping (SFH) on a time-division multiple-access based mobile radio system can be taken into account by redefining the minimum carrier-to-interference ratio. This protection ratio, with SFH, is dependent on the transmission system, channel model, traffic, and frequency reuse parameters. In this paper, the above-mentioned analysis is used in order to investigate the capacity of a SFH mobile radio system, with reference to both the uplink and downlink, by taking into account a complete scenario, i.e., shadowing, fast fading, power control, antenna diversity, discontinuous transmission, and forward error correction with nonideal interleaving and sectorization. Outage probability is evaluated by a completely analytical methodology for the uplink, whereas the downlink requires a semianalytical approach to take users' positions into account. Comparison with a pure simulative approach is used to validate the results.