Beam management is an essential operation of next-generation (xG) wireless networks to compensate severe signal attenuation and ensure reliable communications over millimeter wave (mmWave) and terahertz (THz) bands. The primary objective of beam management is to determine beam directions that are properly aligned with the signal propagation paths. Conventional beam management techniques typically rely on geometric channel parameters such as angles, delays, and path gains. However, due to their lack of contextual awareness of the surrounding environment, these techniques fall short in handling the piecewise continuous changes in the geometric channel parameters caused by sudden obstructions or variations in scatterers. In this paper, we propose a novel beam management framework that utilizes environmental information extracted from sensor data (e.g., images) and pilot measurements to optimize beam directions. The main idea of the proposed scheme is to utilize visual channel parameters, including the positions of user equipment (UE), reflection points, and scatterers, which provide a direct visualization of the propagation environment. By tracking the visual channel parameters, dynamic changes in scatterers and propagation paths can be monitored over time. To analyze multimodal data and track these parameters, we exploit large multimodal model (LMM), a generative artificial intelligence (AI) model specialized in extracting correlated features across multimodal data and generating subsequent data. Simulation results show that the proposed scheme can accurately track the visual channel parameters and enhance data rate.

Integrated sensing and communication (ISAC) is a headline feature for the forthcoming IMT-2030 and 6G releases, yet a concrete solution that fits within the established orthogonal frequency division multiplexing (OFDM) family remains an open problem. Specifically, Doppler-induced inter-carrier interference (ICI) destroys subcarrier orthogonality of OFDM sensing signals, blurring range–velocity maps and severely degrading sensing accuracy. Building on multi-user multi-input-multi-output (MIMO) OFDM systems, this paper develops a model-driven ISAC framework that jointly optimizes transmit and receive beamforming to maximize multi-user communication sum-rate under sensing-performance constraints, while mitigating Doppler ICI. To this end, we propose a Doppler-correction filter network (DCFNet), an AI-native ISAC model that achieves fine-grained range-velocity estimation precision with minimal complexity and without altering the legacy frame structure. A bank of DCFs derived from the Doppler physics first shifts and suppresses dominant ICI components, and a subsequent deep neural network cancels the residual interference to yield a clean radar sensing image. To further enhance the range and velocity estimation precision, we propose DCFNet with local refinement (DCFNet-LR), which applies a generalized likelihood ratio test (GLRT) to refine target estimates of DCFNet to sub-cell precision. Simulation results show that DCFNet-LR runs 143 times faster than a maximumlike-lihood search and achieves significantly superior performance, reducing the range and velocity RMSE by factors of 2.7 × 10−4and 6.7 × 10−4compared to conventional detection methods.

Quantum sensing networks (QSNs) are expected to play a critical role in quantum networks by achieving measurement precision unattainable with classical methods, leveraging quantum properties such as superposition and entanglement. Distributed quantum sensing, a key application of QSNs, can reach Heisenberg-limited precision scaling with the number of sensors involved. However, practical implementation faces significant challenges due to noise effects, complicating the optimal selection of sensor configurations. In this paper, we propose applying a variational quantum algorithm (VQA) combined with a genetic algorithm to efficiently mitigate noise in quantum sensing protocols and to identify high-quality sensor configurations. Performance analysis demonstrates that our approach outperforms traditional sensor configuration methods in single- and multi-parameter sensing scenarios under dephasing and amplitude damping noises, significantly improving quantum sensing accuracy and scalability.

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.

QDE is an emerging paradigm in which an ancillary quantum system is used to remove entropy from a primary quantum system of interest. As a means for entropy reduction, quantum dissipation engineering (QDE) is useful across the domains of quantum sensing, computing, communication, and networking. Whereas most existing approaches concern QDE over an infinite time horizon, this paper studies the use of engineered environments to prepare the primary system in a pure state within finite time. This corresponds to completely transferring the primary system's initial entropy to the engineered environment in finite time. Necessary and sufficient conditions for performing this task are derived. These conditions elucidate the potential of finite-time QDE using real-world quantum systems.

Recently, wideband beamforming using extremely large-scale antenna array (ELAA) systems have gained much interest as a means to boost throughput in next generation (xG) networks. However, conventional phase shifter (PS)-based beamforming methods face challenges in wideband ELAA systems due to the beam squint effect, where beams at different frequencies become misaligned. Although the use of true time delay (TTD) can address this by creating frequency-dependent beamforming vectors, traditional TTD-based methods still experience considerable sidelobe leakage due to the mismatch between intended and generated beams. In this paper, we introduce a novel wideband beamforming architecture that dynamically configures connections between TTDs and PS subarrays using a switching network. By jointly optimizing subarray connections, TTD time delays, and PS phase shifts, wideband dynamic array-of-subarrays (WDAoSA) minimizes sidelobe gain and maximizes array gain in wideband ELAA systems. Numerical results show significant improvements in both array gain and data rate compared to conventional TTD-based methods.

Accurate wireless localization is crucial for multiple real-world applications such as modern autonomy, Internet-of-Things, and smart cities. Machine learning (ML) methods can significantly improve localization performance, especially through soft information (SI) techniques. However, existing ML methods are designed to determine positions within the same environment (network) where training data are collected and cannot exploit data from environments with assorted types. In addition, the usage of existing approaches across multiple environments would significantly increase privacy risks, as they require transferring large amounts of data to a central server. This paper presents a SI-based method for wireless localization that can leverage information from multiple environments and adapt to the specific characteristics of each environment. Differently from existing techniques, we propose methods that ensure privacy by only transfering summary statistics of the training data. The performance of the proposed method is evaluated using real-world datasets obtained in multiple localization environments. The results show that the proposed method significantly outperforms existing techniques and provides adaptation to assorted environments together with privacy protection of location data.

In the shift toward the quantum computing era, the foundational principles of classical cybersecurity, particularly in the realm of cryptographic algorithms, are facing unprecedented challenges. This demands comprehensive reevaluation and redesign of cryptographic infrastructures to withstand quantum adversarial attacks. With the emergence of the quantum Internet, a new approach to secure communication is possible, utilizing quantum properties that have no counterpart in classical systems. As the quantum Internet facilitates the exchange of quantum information, data publication protocols become essential in anonymizing and protecting privacy-sensitive data in quantum communication networks. This paper proposes two controlled quantum anonymous communication (QAC) protocols for publishing classical and quantum information on an Internet server (IS) with the assistance of a communication service provider. The first protocol allows for the controlled publication of classical information without revealing the publisher’s identity such that an adversary, even with access to all network resources, cannot trace the publication source—i.e., achieving perfect untraceability. The second protocol enables anonymous publication of quantum information on an IS in a controlled and untraceable manner. These protocols serve as essential building blocks for advancing the quantum Internet, which has the potential to transform communication and information exchange methods. We provide a detailed anonymity analysis of these QAC protocols for data publication, ensuring that the published symbol or qudit information remains untraceable to its publisher. Moreover, the performance analysis in terms of publication error probability, fidelity, and degree of anonymity in noisy environments demonstrates the robustness of the protocols against noise and adversarial attacks.

Recently, large multimodal models (LMMs) have been successfully adopted in various fields due to their outstanding adaptability and reasoning abilities. Despite their potential to automate diverse tasks in communications systems, application to the physical layer remains underexplored. The primary reason is that the traditional physical layer relies on analytic channel measurements (e.g., pilot measurements), which capture only quantitative changes in transmitted signals and fail to characterize qualitative physical interactions (e.g., reflections and blockages) with the environment. In this paper, we propose an LMM-based environment-aware channel estimation framework that captures the contextual channel information by leveraging both perceptual sensor data and numerical pilot measurements. The main idea of the proposed scheme is to utilize visual channel parameters (VCPs), i.e., positions of user equipment (UE), reflection points, and scatterers. Since VCPs provide a direct visualization of the propagation environment, we can identify how signals propagate and physically interacts with the surrounding objects. To extract the VCPs and learn their probability distributions, we develop a reflection learning technique based on LMM. By incorporating these parameters, we establish a fundamental channel knowledge map (CKM) between the UE position and the channel. Simulation results demonstrate that the proposed scheme can effectively predict the channel throughout the wireless environments.

As a means to provide ubiquitous connectivity across the ground-air-space 3D network, low Earth orbit (LEO) satellite mega-constellation systems comprising thousands of LEO satellites have attracted significant interest from both academia and industry recently. One major issue of LEO mega-constellation systems is the frequent handovers between satellites and beams, causing an increase in communication latency and deterioration of quality of service (QoS). In this paper, we propose a user-centric cooperative communication framework for next generation (xG) LEO satellite mega-constellation systems. In the proposed framework, a group of LEO satellites simultaneously serve all the user equipments (UEs) using the same timefrequency resources. By dynamically organizing the clusters of serving satellites and coordinating their joint transmission based on statistical channel state information (CSI), the handover frequency and inter-satellite interference can be reduced effectively, thereby achieving significant enhancements in the spectral efficiency and coverage probability. From the achievable rate analysis and extensive simulations on realistic xG LEO satellite communication environments, we show that the proposed scheme substantially improves the spectral efficiency and coverage over the conventional beam-centric systems.

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.

In recent years, cooperative positioning technologies have emerged as promising augmentation systems for providing high-accuracy positioning (HAP) in cooperative intelligent transportation systems (C-ITS). Among the approaches, implicit cooperative positioning (ICP) takes advantage of shared target detections between vehicles to create common reference points for localization refinement. Their performance, however, is limited by reliance on predefined parametric models, low scalability and communication overhead. To address these problems, this paper introduces a deep multi-agent reinforcement learning (MARL) framework modelled as a decentralized-partially observable Markov decision process (Dec-POMDP). We propose an ICP-multi-agent proximal policy optimization (MAPPO) algorithm, where distributed agents (i.e., the connected vehicles) learn their dynamics and those of the surrounding targets by performing belief estimation over dynamic cooperation graphs that are continuously adjusted by de/activating communication links with neighbors agents. A C-ITS scenario is simulated in a CARLA environment accounting for realistic vehicle dynamics and inter-vehicle communications. The findings reveal that our ICPMAPPO algorithm, leveraging dynamic decentralized execution and centralized training, outperforms ICP in terms of positioning accuracy and communication efficiency.

This paper explores filtering methods to protect range-based localization systems from spoofing attacks on vehicles with directional receivers. It focuses on scenarios where multiple spoofers, potentially from unmanned vehicles, disrupt vehicle localization by strategically positioning themselves between the target and the transmitter. The paper introduces an Adaptive Resilience Navigation Filter (ARNF) that detects ongoing attacks, identifies compromised signals, and mitigates their effects using statistical hypothesis testing. Simulations demonstrate the ARNF’s effectiveness under realistic Global Navigation Satellite System conditions, comparing it with the 2-Stage Extended Kalman Fitter and an ideal Clairvoyant Extended Kalman Filter.

Entangled quantum states serve as important resources in quantum communication, quantum computing, and quantum sensing. Creating entangled states between remote nodes is referred to as remote entanglement establishment (REE). REE typically consists of three types of quantum operations: entanglement generation, distillation, and swapping. By carefully designing the sequence describing the order of these operations, this paper investigates REE in a repeater chain under the requirement that the fidelity of the established entanglements be above a desired threshold. Specifically, the paper derives an asymptotically achievable upper bound on the maximum REE rate.

Seabed-to-space situational awareness (S3A) aims to organize, fuse, and synthesize the massive volume of information collected from heterogeneous sensors, i.e., underwater, terrestrial, and space-based sensors, and therefrom extract knowledge thence available to defence operators, enabling informed decision-making. Heterogeneous sensors provide observations of targets in different domains (e.g., air, water surface, undersea), and with different modalities, perspectives, latencies, and update rates. They complement each other, and an effective approach is needed to combine the data they generate. This paper introduces a comprehensive framework for multi-target tracking based on the sum-product algorithm (SPA) that models the different characteristics of the sensors and handles the appearance of targets in a complex multi-domain environment both through spontaneous births and through spawning from existing targets. The efficacy of this approach is demonstrated through a simulated maritime scenario informed by real-world observation streams.

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.

In the rapidly evolving domain of forthcoming 6th generation (6G) networks, achieving precise dynamic positioning down to the centimeter becomes critical, particularly in complex urban scenarios as those envisioned for cooperative intelligent transport systems (C-ITSs). To face the challenges introduced by severe path loss and blockages in new 6G frequency bands, machine learning (ML) provides innovative strategies to extract locational intelligence from wide-band space-time radio signals. This paper proposes the integration of Bayesian neural networks (BNNs) into cellular multi-base station (BS) tracking systems, where uncertainties of BNNs account for finite training sets and measurement errors. Our approach utilizes a deep learning (DL)-based autoencoder (AE) structure that exploits the full channel impulse response (CIR) to infer location-centric attributes in both line-of-sight (LoS) and non-LoS (NLoS) conditions. Validations in a 3rd Generation Partnership Project (3GPP) compliant urban micro (UMi) setting, simulated with ray-tracing and traffic simulations, demonstrate the superior performances of BNN-based tracking with respect to both traditional geometric-based tracking methods and state-of-the-art DL models.

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.

To meet the ever-increasing data rate demand expected in 6G networks, terahertz (THz) ultra-massive (UM) multiple-input multiple-output (MIMO) systems have gained much attention recently. One notable aspect of these systems is that the deployment of an extremely large-scale antenna array and high transmission frequency result in an expansion of the near-field region where the electromagnetic (EM) radiation is modeled as a spherical wave. In the near-field region, the channel becomes a function of a position of a user equipment (UE) rather than the direction, giving rise to a beam focusing operation that focuses the signal power onto the specific position. However, the traditional approaches relying on the sweeping of discretized beam codewords cannot support this ultra-sharp beam focusing operation in THz UM-MIMO systems. This paper proposes a novel beam focusing technique based on sensing and computer vision (CV) technologies. The essence of the proposed scheme is to estimate the UE’s position from the vision information using the CV technique and then generates the beam heading towards the estimated position. By replacing the discretized and time-consuming beam sweeping operation with a highly precise CV-based positioning, the positioning accuracy as well as the beam focusing gain can be improved significantly. Numerical results show that the proposed scheme achieves significant positioning accuracy and data rate gains over the conventional codebook-based beam focusing schemes.

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.

Entanglement is crucial for many applications such as quantum computing, quantum sensing, and quantum communication. Establishment of entanglement between remote nodes, referred to as remote entanglement establishment (REE), is a key element of the quantum internet. This paper develops a theoretical framework for establishing high-fidelity entanglement between two remote nodes of a quantum repeater chain via entanglement generation, distillation, and swapping operations. In particular, an upper bound on the optimal REE rate under minimum fidelity requirements is established, and an REE policy that achieves such a bound asymptotically is presented. Results in this paper provide guidelines for protocol design in the quantum internet.

In the evolving landscape of 5G new radio and related 6G evolution, achieving centimeter-level dynamic positioning is pivotal, especially in cooperative intelligent transportation system frameworks. With the challenges posed by higher path loss and blockages in the new frequency bands (i.e., millimeter waves), machine learning (ML) offers new approaches to draw location information from space-time wide-bandwidth radio signals and enable enhanced location-based services. This paper presents an approach to real-time 6G location tracking in urban settings with frequent signal blockages. We introduce a novel teacher-student Bayesian neural network (BNN) method, called Bayesian bright knowledge (BBK), that predicts both the location estimate and the associated uncertainty in real-time. Moreover, we propose a seamless integration of BNNs into a cellular multi-base station tracking system, where more complex channel measurements are taken into account. Our method employs a deep learning (DL)-based autoencoder structure that leverages the complete channel impulse response to deduce location-specific attributes in both line-of-sight and non-line-of-sight environments. Testing in 3GPP specification-compliant urban micro (UMi) scenario with ray-tracing and traffic simulations confirms the BBK’s superiority in estimating uncertainties and handling out-of-distribution testing positions. In dynamic conditions, our BNN-based tracking system surpasses geometric-based tracking techniques and state-of-the-art DL models, localizing a moving target with a median error of 46 cm.

QSNs (QSNs) incorporate quantum sensing and quantum communication to achieve Heisenberg precision and unconditional security by leveraging quantum properties such as superposition and entanglement. However, the QSNs deploying noisy intermediate-scale quantum (NISQ) devices face near-term practical challenges. In this paper, we employ variational quantum sensing (VQS) to optimize sensing configurations in noisy environments for the physical quantity of interest, e.g., magnetic-field sensing for navigation, localization, or detection. The VQS algorithm is variationally and evolutionarily optimized using a genetic algorithm for tailoring a variational or parameterized quantum circuit (PQC) structure that effectively mitigates quantum noise effects. This genetic VQS algorithm designs the PQC structure possessing the capability to create a variational probe state that metrologically outperforms the maximally entangled or product quantum state under bit-flip, dephasing, and amplitude-damping quantum noise for both single-parameter and multiparameter NISQ sensing, specifically as quantified by the quantum Fisher information. Furthermore, the quantum anonymous broadcast (QAB) shares the sensing information in the VQS network, ensuring anonymity and untraceability of sensing data. The broadcast bit error probability (BEP) is further analyzed for the QAB protocol under quantum noise, showing its robustness—i.e., error-free resilience—against bit-flip noise as well as the low-noise BEP behavior. This work provides a scalable framework for integrated quantum anonymous sensing and communication, particularly in a variational and untraceable manner.

This paper investigates the performance of key generation between two nodes assisted by a relay in the presence of correlated eavesdropping channels. A cooperative jamming scheme is utilized to impose superimposed channel measurements on the relay and eavesdropper. Both lower and upper bounds on key capacities for both secret key (SK) and private key (PK) generation are evaluated, where the lower bounds are derived by using minimum mean square error and zero forcing methods for channel estimation, and the upper bounds are derived by formulating several enhanced discrete memoryless source (DMS) models. The analytical expressions are further simplified in the high signal-to-noise ratio (SNR) regime. We discover that one of the two legitimate channels should specialize in playing a role of jamming the relay or eavesdropper. We also demonstrate that the derived lower and upper bounds are tight when the eavesdropping channels are lowly or highly correlated. When the eavesdropping channels are uncorrelated, the SK and PK capacities can be determined since the corresponding upper and lower bounds are equal. Moreover, at high SNRs, a constant gap exists between the SK/PK upper and lower bounds as the correlation coefficient becomes one.

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.

In mission-critical verticals such as automated driving, 5G-advanced networks must provide centimeter-level dynamic positioning along with ultra-reliable low-latency communication services. Massive Multiple-Input Multiple-Output (mMIMO) and millimeter waves (mmWave) are the key enablers, allowing high accuracy angle and delay estimation. Still, extracting such information from highly-dimensional Channel Impulse Responses (CIRs) results in a complex task, due to channel sparsity and intermittent blockage. In this paper we focus on non-line-of-sight (NLOS) identification from CIR data, proposing a Deep Autoencoding Kernel Density Model (DAKDM) to characterize the statistics of the channel latent features. We formulate the problem as a semi-supervised anomaly detection task in which only LOS samples, i.e., normal data, are adopted for training. DAKDM is a single-stage training model that takes as input the full CIR thanks to an AutoEncoder (AE) structure. The proposed method is able to learn the latent distribution by means of a Kernel Density Estimator (KDE) in combination with a deep learning likelihood network. We validate the proposed solution in a 5G Urban micro (UMi) vehicular scenario. Results show that the proposed model can significantly outperform conventional algorithms and obtain similar performances to variational Bayes algorithms at one tenth of the inference time.

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.

Integrating the full-duplex capability with quantum communication potentially equips emerging wireless networks with a quantum layer of security for the stringent communication efficiency and security requirements. This paper proposes two new full-duplex quantum communication protocols to exchange classical or quantum information between two remote parties simultaneously without transferring a physical particle over the quantum channel. The first protocol, called quantum duplex coding, enables the exchange of a classical bit using a preshared maximally entangled pair of qubits by means of counterfactual disentanglement. The second protocol, called quantum telexchanging, enables the exchange of an arbitrary unknown qubit without using preshared entanglement by means of counterfactual entanglement and disentanglement. We demonstrate that quantum duplex coding and quantum telexchanging can be achieved by exploiting counterfactual electron-photon interaction gates. It is shown that these tasks can be viewed as full-duplex transmission of bits and qubits via binary erasure channels and quantum erasure channels, respectively.

Distributed learning and multi-tier computing are the key ingredients to ensure ultra-reliable and low-latency communication (URLLC) in 6G networks. The distinct transition from connected things in 5G URLLC networks to connected intelligence in 6G URLLC networks requires ultra-secure communication due to the massive amount of private data. However, it is a challenging task to ensure stringent 6G URLLC requirements along with user privacy and data security in distributed networks. In this paper, we devise a distributed quantum computation protocol to perform a nonlocal controlled unitary operation on a bipartite input state in concealed and counterfactual manner and integrate it with anonymous quantum communication networks. This distributed protocol allows Bob to apply an arbitrary singlequbit unitary operator on Alice’s qubit in a controlled and probabilistic fashion, without revealing the operator to her and without transmitting any physical particle over the quantum channel-called the counterfactual concealed telecomputation (CCT). It is shown that the CCT protocol neither requires the preshared entanglement nor depends on the bipartite input state and that the single-qubit unitary teleportation is a special case of CCT. The quantum circuit for CCT can be implemented using the (chained) quantum Zeno gates. The protocol becomes deterministic with simplified circuit implementation if the initial composite state of Alice and Bob is a Bell-type state. Furthermore, we provide numerical examples of quantum anonymous broadcast networks using the CCT protocol and show their degrees of anonymity in the presence of malicious users.

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.

Network localization is capable of providing accurate and ubiquitous position information for numerous wireless applications. This paper studies the accuracy of cooperative network localization in large-scale wireless networks. Based on a decomposition of the equivalent Fisher information matrix (EFIM), we develop a random-walk-inspired approach for the analysis of EFIM, and propose a position information routing interpretation of cooperative network localization. Using this approach, we show that in large lattice and stochastic geometric networks, when anchors are uniformly distributed, the average localization error of agents grows logarithmically with the reciprocal of anchor density in an asymptotic regime. The results are further illustrated using numerical examples.

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.

Millimeter wave communications show great potentials in many applications, one of which is the high-speed railway(HSR) communication system. However, a major challenge is the Doppler effect caused by the relative-movement between the train and the base station (BS), which leads to fast channel variation. To compensate for the Doppler shift, an accurate channel model is indispensable, and the far-field channel model is generally employed, which assumes that the dimensions of the antenna arrays are negligible compared to the distance between transmitter and receiver. This model is widely used in Cellular systems, but the underlining assumption is not always true for railway communication systems. In this paper, the modeling of the Doppler effect for millimeter wave in HSR communications is conducted, and data-aided Doppler estimation and compensation algorithms are designed based on the new model. We show that the conventional far-field channel model is based on the first-order Taylor expansion of the actually channel, and the second-order component cannot be ignored for HSR communications. Extensive simulations are conducted to verify the validity of the new model and the effectiveness of the proposed algorithms.

In this work, we propose a Labeled Multi-Bernoulli (LMB) filter for multi-object tracking with a merged measurement model. The finite resolution capabilities of practical sensing systems can lead to scenarios where multiple objects interact and generate merged measurements. In this work, we rely on the tractable LMB model for multi-object tracking and derive the Merged-Measurement LMB (MM-LMB) filter. Subsequently, we achieve an efficient implementation of the MM-LMB filter by relying on the K-shortest paths algorithm to find likely object-set partitions given a particular measurement set. Numerical results of our proposed filter show improved performance with respect to the standard LMB filter.

We propose a general sensor selection (SS) methodology for ocean-of-things (OoT) where a sensing network performs multiobject tracking (MOT) under resource constraints. SS methods address the combinatorial problem of determining the best subset of sensors that maximizes a suitable reward function for a fixed cardinality. The novelty of this article is twofold. First, we propose a tractable information-theoretic reward function for MOT-OoT with an unknown and time-varying number of objects such as ocean vessels. A tractable reward function is essential in order to rapidly evaluate a sensor subset, which is crucial in the high-dimensional problems encountered in OoT. Second, we propose a general cross-entropy SS (CE-SS) methodology that efficiently estimates the probabilities of sensor activations and determines the optimal sensor subset according to the proposed reward function and under the imposed cardinality constraint. The CE-SS algorithm avoids exhaustive searching over the space of all sensor subsets, which is intractable for most OoT applications. The CE-SS methodology, coupled with the proposed reward function, is capable of selecting sensors that lead to more accurate estimates than random selection for both the number of vessels and their trajectories. We demonstrate the effectiveness of our method via numerical simulation in serveral scenarios, including multivessel tracking for OoT with an emulated network of acoustic sensors deployed off the coast of Italy.

Distributing entanglement between distant nodes is an essential task in quantum networks. To achieve this task, quantum repeaters have been introduced to perform entanglement swapping. This paper offers a design of remote entanglement distribution (RED) protocols that maximize the entanglement distribution rate (EDR). We introduce the concept of enodes, representing the entangled quantum bit (qubit) pairs in the network. This concept enables us to design the optimal RED protocols based on the solutions of some linear programming problems. Moreover, we investigate RED in a homogeneous repeater chain, which is a building block for many quantum networks. In particular, we determine the maximum EDR for homogeneous repeater chains in a closed form. Our results enable the distribution of long-distance entanglement with noisy intermediate-scale quantum (NISQ) technologies and provide insights into the design and implementation of general quantum networks.

Queuing delay is an essential topic in the design of quantum networks. This paper introduces a tractable model for analyzing the queuing delay of quantum data, referred to as quantum queuing delay (QQD). The model employs a dynamic programming formalism and accounts for practical aspects such as the finite memory size. Using this model, we develop a cognitive-memory-based policy for memory management and show that this policy can decrease the average queuing delay exponentially with respect to memory size. Such a significant reduction can be traced back to the use of entanglement, a peculiar quantum phenomenon that has no classical counterpart. Numerical results validate the theoretical analysis and demonstrate the near-optimal performance of the developed policy.

The random propagation of molecules in a fluid medium is characterized by the spontaneous diffusion law as well as the interaction between the environment and molecules. In this paper, we embody the anomalous diffusion theory for modeling and analysis in molecular communication. We employ H-diffusion to model a non-Fickian behavior of molecules in diffusive channels. H-diffusion enables us to model anomalous diffusion as the subordinate relationship between self-similar parent and directing processes and their corresponding probability density functions with two H-variates in a unified fashion. In addition, we introduce standard H-diffusion to make a bridge of normal diffusion across well-known anomalous diffusions such as space-time fractional diffusion, Erdélyi-Kober fractional diffusion, grey Brownian motion, fractional Brownian motion, and Brownian motion. We then characterize the statistical properties of uncertainty of the random propagation time of a molecule governed by H-diffusion laws by introducing a general class of molecular noise-called H-noise. Since H-noise can be an algebraic-tailed distribution, we provide a concept of H-noise power using finite logarithm moments based on zero-order statistics. Finally, we develop a unifying framework for error probability analysis in a timing-based molecular communication system with a concept of signal-to-noise power ratio.

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.

In this article, we develop a comprehensive framework to characterize the performance of drone assisted Backscatter communication based Internet of things (IoT) sensor network. We consider a scenario such where drone transmits RF carrier which is modulated by IoT sensor node (SN) to transmit its data. The SN implements load modulation which results in amplitude shift keying (ASK) type modulation for the impinging RF carrier. In order to quantify the performance of the considered network, we characterize the coverage probability for the ground based SN node. The statistical framework developed to quantify the coverage probability explicitly accommodates dyadic backscatter channel which experiences deeper fades than that of the one-way Rayleigh channel. Our model also incorporates Line of Sight (LoS) and Non-LoS (NLoS) propagation states for accurately modeling large-scale path-loss between drone and SN. We consider spatially distributed SNs which can be modelled using spatial Binomial Point process (BPP). We practically implement the proposed system using Software Defined Radio (SDR) and a custom designed SN tag. The measurements of parameters such as noise figure, tag reflection coefficient etc., are used to parametrize the developed framework. Lastly, we demonstrate that there exists an optimal set of parameters which maximizes the coverage probability for the SN.

Remote state preparation (RSP) is a technique to transmit quantum states with classical communication and previously shared entanglement. In this paper, we consider RSP in a multiparty setting. A simple yet nontrivial case is studied, where there is one sender and two receivers. We put forth a broadcasting method for developing multiparty RSP protocols. We show that this method can remotely prepare arbitrary states by consuming classical and quantum resources. For preparing highly entangled states, the proposed method achieves the lower bound for the amount of consumed resources asymptotically.

The sum-product algorithm (SPA) was recently shown to provide a scalable methodology for multitarget tracking (MTT) using multiple sensors. Here, we focus on another advantage of the SPA frame-work, namely, its capacity for Bayesian fusion of heterogeneous data sources and auxiliary information. We develop extensions of the SPA-based multisensor MTT algorithm that integrate data from an auxiliary surveillance system and geographic information about standard target routes. The effectiveness of our approach is demonstrated for a simulated scenario and for a real maritime scenario.

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.

The position and time of mobile agents are essential information for many wireless network applications. In this paper, we propose a single-anchor localization and synchronization scheme of full-duplex (FD) agents in the line-of-sight scenario, where the anchor is equipped with an antenna array. The agents perform inter-node ranging using the FD radios, while the anchor only receives signals transmitted from the agents. We derive the Cramér-Rao lower bounds for the agent positions and clock offsets based on the frames received by the agents and anchor. In particular, the Fisher information matrix for the positions and clock offsets can be decomposed into the information from the measurements of the arrival angles and arrival times. Then, we design the channel estimation algorithm to obtain the arrival angles, arrival times, and signal-to-noise ratios, based on which we further develop the network localization and synchronization algorithms to obtain the agent positions and clock offsets. The simulation results illustrate the performance of our proposed scheme and algorithms.

Molecular communication in nature can incorporate a large number of nano-things in nanonetworks as well as demonstrate how nano-things communicate. This paper presents molecular communication where transmit nanomachines deliver information molecules to a receive nanomachine over an anomalous diffusion channel. By considering a random molecule concentration in a space-time fractional diffusion channel, an analytical expression is derived for the first passage time (FPT) of the molecules. Then, the bit error rate of the $\ell $ th nearest molecular communication with timing binary modulation is derived in terms of Fox's $H$ -function. In the presence of interfering molecules, the mean and variance of the number of the arrived interfering molecules in a given time interval are presented. Using these statistics, a simple mitigation scheme for timing modulation is provided. The results in this paper provide the network performance on the error probability by averaging over a set of random distances between the communicating links as well as a set of random FPTs caused by the anomalous diffusion of molecules. This result will help in designing and developing molecular communication systems for various design purposes.

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.

We present a simultaneous localization and mapping (SLAM) algorithm that is based on radio signals and the association of specular multipath components (MPCs) with geometric features. Especially in indoor scenarios, robust localization from radio signals is challenging due to diffuse multipath propagation, unknown MPC-feature association, and limited visibility of features. In our approach, specular reflections at flat surfaces are described in terms of virtual anchors (VAs) that are mirror images of the physical anchors (PAs). The positions of these VAs and possibly also of the PAs are unknown. We develop a Bayesian model of the SLAM problem and represent it by a factor graph, which enables the use of belief propagation (BP) for efficient marginalization of the joint posterior distribution. The resulting BP-based SLAM algorithm detects the VAs associated with the PAs and estimates jointly the time-varying position of the mobile agent and the positions of the VAs and possibly also of the PAs, thereby leveraging the MPCs in the radio signal for improved accuracy and robustness of agent localization. The algorithm has a low computational complexity and scales well in all relevant system parameters. Experimental results using both synthetic measurements and real ultra-wideband radio signals demonstrate the excellent performance of the algorithm in challenging indoor environments.

We revisit the opportunistic scheduling problem in which a server opportunistically serves multiple classes of users under time-varying multi-state Markovian channels. The aim of the server is to find an optimal policy minimizing the average waiting cost of those users. Mathematically, the problem can be recast to a restless multiarmed bandit one, and a pivot to solve restless bandit by the Whittle index approach is to establish indexability. Despite the theoretical and practical importance of the Whittle index policy, the indexability is still open for opportunistic scheduling in the heterogeneous multi-state channel case. To fill this gap, we mathematically identify a set of sufficient conditions on a channel state transition matrix under which the indexability is guaranteed and consequently, the Whittle index policy is feasible. Furthermore, we obtain the closed-form Whittle index by exploiting the structural property of the channel state transition matrix. For a generic channel state transition matrix, we propose an eigenvalue-arithmetic-mean scheme to obtain the corresponding approximate matrix which satisfies the sufficient conditions, and consequently can get an approximate Whittle index. This paper constitutes a small step toward solving the opportunistic scheduling problem in its generic form involving multi-state Markovian channels and multi-class users.

Location aware networks will introduce new applications and services for modern convenience, the military, and public safety. In this paper, we introduce a Bayesian method for network localization and navigation in the presence of measurement-origin uncertainty (MOU). In the envisioned cooperative scenario, the agents in a dynamic network aim to better localize themselves by performing pairwise observations with other agents in their environment and sharing their location information. Since pairwise observations suffer from MOU, a data association problem has to be solved before an agent can update its location information. In our approach, joint inference is performed through a factor graph formulation of the entire, network-wide estimation problem. Performing the loopy sum-product algorithm on the derived factor graph results in a distributed and scalable inference algorithm. Simulation results demonstrate that cooperation among agents can significantly improve the localization accuracy even in the presence of MOU.

In this paper, we present a detailed analysis of the coverage and spectral efficiency of a downlink cellular network. Rather than relying on the first order statistics of received signal-to- interference-ratio (SIR) such as coverage probability, we focus on characterizing its meta- distribution. Our analysis is based on the alpha- beta-gamma (ABG) path-loss model which provides us with the flexibility to analyze urban macro (UMa) and urban micro (UMi) deployments. With the help of an analytical framework, we demonstrate that selection of underlying degrees-of-freedom such as BS height for optimization of first order statistics such as coverage probability is not optimal in the network-wide sense. Consequently, the SIR meta-distribution must be employed to select appropriate operational points which will ensure consistent user experiences across the network. Our design framework reveals that the traditional results which advocate lowering of BS heights or even optimal selection of BS height do not yield consistent service experience across users. By employing the developed framework we also demonstrate how available spectral resources in terms of time slots/channel partitions can be optimized by considering the meta-distribution of the SIR.

We consider molecular communication in a nanonet-work in the presence of stochastic interfering molecules where transmit nanomachines deliver information messages to a receive nanomachine over an anomalous diffusion channel. Specifically, a set of interfering molecules is assumed to be scattered in a region according to a Cox process. We first characterize statistics (mean and variance) of the number of interfering molecules arrived in a given time interval using Campbell's theorem. Since the interfering molecules significantly degrade the bit error rate performance in timing modulation scheme, we provide a simple mitigation scheme using the statistics of the number of interfering molecules.

Wireless networks are growing in the value of application in many areas, in which accurate clock synchronization is required when tasks are performed in a collaborative fashion among nodes. Especially, cooperative synchronization techniques lead to significant performance improvement compared with traditional methods. However, the correlation among agents renders the performance analysis of cooperative network synchronization difficult. In this paper, we introduce the concept of information coupling intensity to the analysis of interaction between agents. Our approach enables us to derive closed-form asymptotic expressions under specific network topologies, and relate them to various network parameters.

We consider the multichannel opportunistic access problem, in which a user decides, at each time slot, which channel to access among multiple Gilbert-Elliot channels in order to maximize his aggregated utility (e.g., the expected transmission throughput) given that the observation of channel state is error-prone. The problem can be cast into a restless multiarmed bandit problem which is proved to be PSPACE-Hard. An alternative approach, given the problem hardness, is to look for simple channel access policies. Whittle index policy is a very popular heuristic for restless bandits, which is provably optimal asymptotically and has good empirical performance. In the case of imperfect observation, the traditional approach of computing the Whittle index policy cannot be applied because the channel state belief evolution is no more linear, thus rendering the indexability of our problem open. In this paper, we mathematically establish the indexability and establish the closed-form Whittle-index, based on which index policy can be constructed. The major technique in our analysis is a fixed point based approach which enable us to divide the belief information space into a series of regions and then establish a set of periodic structures of the underlying nonlinear dynamic evolving system, based on which we devise the linearization scheme for each region to establish indexability and compute the Whittle index for each region.

The Internet of Things (IoT) will seamlessly integrate a large number of densely deployed heterogeneous devices and will enable new location-aware services. However, fine-grained localization of IoT devices is challenging as their computation and communication resources are typically limited and different devices may have different qualities of internal clocks and different mobility patterns. To address these challenges, we propose a cooperative, scalable, and time-recursive algorithm for network localization and synchronization (NLS). Our algorithm is based on time measurements and supports heterogeneous devices with limited computation and communication resources, time-varying clock and location parameters, arbitrary state-evolution models, and time-varying network connectivity. These attributes make the proposed algorithm attractive for IoT-related applications. The algorithm is furthermore able to incorporate measurements from additional sensors for positioning, navigation, and timing such as receivers for global navigation satellite systems. Based on a factor graph representation of the underlying spatiotemporal Bayesian sequential estimation problem, the algorithm uses belief propagation (BP) for an efficient marginalization of the joint posterior distribution. To account for the nonlinear measurement model and nonlinear state-evolution models while keeping the communication and computation requirements low, we develop an efficient second-order implementation of the BP rules by means of the recently introduced sigma point belief propagation technique. Simulation results demonstrate the high synchronization and localization accuracy as well as the low computational complexity of the proposed algorithm. In particular, in sufficiently dense networks, the proposed algorithm outperforms the state-of-the-art BP-based algorithm for NLS in terms of both estimation accuracy and computational complexity.

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.

Situation-aware technologies enabled by multitarget tracking will lead to new services and applications in fields such as autonomous driving, indoor localization, robotic networks, and crowd counting. In this tutorial paper, we advocate a recently proposed paradigm for scalable multitarget tracking that is based on message passing or, more concretely, the loopy sum-product algorithm. This approach has advantages regarding estimation accuracy, computational complexity, and implementation flexibility. Most importantly, it provides a highly effective, efficient, and scalable solution to the probabilistic data association problem, a major challenge in multitarget tracking. This fact makes it attractive for emerging applications requiring real-time operation on resource-limited devices. In addition, the message passing approach is intuitively appealing and suited to nonlinear and non-Gaussian models. We present message-passing-based multitarget tracking methods for single-sensor and multiple-sensor scenarios, and for a known and unknown number of targets. The presented methods can cope with clutter, missed detections, and an unknown association between targets and measurements. We also discuss the integration of message-passing-based probabilistic data association into existing multitarget tracking methods. The superior performance, low complexity, and attractive scaling properties of the presented methods are verified numerically. In addition to simulated data, we use measured data captured by two radar stations with overlapping fields-of-view observing a large number of targets simultaneously.

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.

Reliable and accurate position information is of great importance for many mass-market and emerging applications. Network localization and navigation (NLN) is a promising paradigm to provide such information ubiquitously, where a network of nodes is used to aid in localizing its members. This paper explores various network operation strategies, which play an essential role in NLN as they determine the network lifetime and localization accuracy. Efficient network operation requires several functionalities, including node prioritization, node activation, and node deployment. The roles of these functionalities are described and different techniques for implementing respective functionalities via algorithmic modules are introduced. Some important concepts such as cooperative operation, robustness guarantee, and distributed design in the development of the network operation strategies are also introduced. Finally, numerical results are provided to demonstrate the localization performance improvement attributed to the optimized network operation strategies.

Network localization and navigation (NLN) is a promising paradigm, in which mobile nodes exploit spatiotemporal cooperation, to provide reliable location information for a diverse range of wireless applications. This paper presents a theoretical foundation of NLN, including a mathematical formulation for NLN, an introduction of equivalent Fisher information analysis, and determination of the fundamental limits of localization accuracy. Key ingredients such as spatiotemporal cooperation, array signal processing, and map exploitation are then studied. We also develop a geometric interpretation to provide insights into the essence of NLN for network design. Finally, the paper highlights the connection between the theoretical foundation and algorithm development for NLN, guiding the design and operation of practical localization systems.

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.

Network localization is an emerging paradigm for providing high-accuracy positional information in GPS-challenged environments. To enable efficient network localization, we propose node prioritization strategies for allocating transmission resources among network nodes. This paper develops a computational geometry framework for determining the optimal node prioritization strategy. The framework consists of transforming each node prioritization strategy into a point in a Euclidian space and exploiting geometric properties of these points. Under this framework, we prove the sparsity property of the optimal node prioritization vector (NPV) and reduce the search space of the optimal NPV. Our approach yields exact optimal solutions rather than ε-approximate solutions for efficient network localization. Numerical results show that the proposed approach can significantly reduce the computational complexity of prioritization strategies and improve the accuracy of network localization.

Network navigation, encompassing both spatial and temporal cooperation to locate mobile agents, is a key enabler for numerous emerging location-based applications. In such cooperative networks, the positional information obtained by each agent is a complex compound due to the interaction among its neighbors. This information coupling may result in poor performance: algorithms that discard information coupling are often inaccurate, and algorithms that keep track of all the neighbors' interactions are often inefficient. In this paper, we develop a principled framework to characterize the information coupling present in network navigation. Specifically, we derive the equivalent Fisher information matrix for individual agents as the sum of effective information from each neighbor and the coupled information induced by the neighbors' interaction. We further characterize how coupled information decays with the network distance in representative case studies. The results of this paper can offer guidelines for the development of distributed techniques that adequately account for information coupling, and hence enable accurate and efficient network navigation.

Location-awareness enables a variety of emerging applications on mobile devices. For indoor applications, a desirable way of obtaining real-time locations is by combining different sources of positional information, such as the inertial measurements, ranging measurements, and map information with an infrastructure-free system that does not rely on any customized hardware. These sources of information can be incorporated into the paradigm of network localization and navigation (NLN). However, there still lacks an infrastructure-free localization system that applies the insights of NLN to effectively fuse different types of information. In this paper, we present the Mercury system, which realizes the key ideas of NLN, including the exploitation of spatiotemporal cooperation and the use of environmental knowledge. We design a real-time belief propagation algorithm to fuse inertial measurements as well as range measurements among different users with map information. We implement this algorithm in the Mercury system formed by a network of smartphones, and evaluate its localization accuracy through experimentation. Results show that Mercury provides reliable location information and that combining spatiotemporal cooperation with environmental knowledge remarkably reduces the location uncertainty of users. Moreover, the performance of Mercury is more robust to imperfect initial positional knowledge compared with that of existing systems.

One of the main challenges in high-speed mobile communications is the presence of large Doppler spreads. Thus, accurate estimation of maximum Doppler spread (MDS) plays an important role in improving the performance of the communication link. In this paper, we derive the data-aided (DA) and non-data-aided (NDA) Cramér-Rao lower bounds (CRLBs) and maximum likelihood estimators (MLEs) for the MDS in multiple-input multiple-output (MIMO) frequency-selective fading channel. Moreover, a low-complexity NDA-moment-based estimator (MBE) is proposed. The proposed NDA-MBE relies on the second- and fourth-order moments of the received signal, which are employed to estimate the normalized squared autocorrelation function of the fading channel. Then, the problem of MDS estimation is formulated as a non-linear regression problem, and the least-squares curve-fitting optimization technique is applied to determine the estimate of the MDS. This is the first time in the literature, when DA- and NDA-MDS estimation is investigated for MIMO frequency-selective fading channel. Simulation results show that there is no significant performance gap between the derived NDA-MLE and NDA-CRLB, even when the observation window is relatively small. Furthermore, the significant reduced-complexity in the NDA-MBE leads to low root-mean-square error over a wide range of MDSs, when the observation window is selected large enough.

Ultra-dense small cells operating in a cooperative manner, such as cloud radio access network (C-RAN), have been introduced to serve the numerous mobile devices in the next generation wireless networks. One of the major challenges in efficiently operating the C-RAN in a cooperative manner is the excessive overhead signaling and computation load, such as downlink channel state information (CSI) acquisition at the cloud for cooperative downlink transmission, which scales rapidly with the size of the network. In this paper, the exploitation of mobility information of devices is proposed to address the challenge of efficiently operating the C-RAN. We introduce a mobility-assisted CSI acquisition method to complement conventional pilot-based CSI acquisition methods. This method enables the C-RAN to avoid excessive overhead signaling. A low-complexity algorithm is designed to maximize the sum rate of all devices subject to the limits of backhaul capacity and base station transmit power. An adaptive algorithm has also been proposed to improve the robustness against channel uncertainty. Both theoretical and numerical analyses show that the exploitation of mobility information provides a new dimension to improve conventional transmission schemes for next-generation massive cooperative networks.

We investigate the opportunistic scheduling problem where a server opportunistically serves multiple classes of users under time varying multi-state Markovian channels. The aim of the server is to find an optimal policy minimizing the average waiting cost of users. Mathematically, the problem can be cast to a restless bandit one, and a pivot to solve restless bandit by index policy is to establish indexability. We mathematically propose a set of sufficient conditions on channel state transition matrix, and consequently, the index policy is feasible. Our work consists of a small step toward solving the opportunistic scheduling problem in its generic form involving multi- state Markovian channels and multi-class users.

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.

This paper addresses the problem of localizing an unknown number of static sources emitting unknown signals from time-difference of arrival (TDOA) measurements. Based on the framework of random finite sets and finite set statistics, we formulate the Bayesian estimation problem and develop a particle-based localization algorithm that overcomes the challenges related to the highly non-linear TDOA measurement model, the data associations uncertainty, and the uncertainty in the number of sources to be localized. Simulation results confirm that the number of sources can be determined correctly and accurate location estimates can be obtained when the number of false alarms is low and the probability of detection is high.

One of the major challenges in effectively operating a cloud radio access network (C-RAN) is the excessive overhead signaling and computation load that scale rapidly with the size of the network. In this paper, the exploitation of location information of the mobile devices is proposed to address this challenge. We consider an approach in which location-assisted channel state information (CSI) acquisition methods are introduced to complement conventional pilot-based CSI acquisition methods and avoid excessive overhead signaling. A low-complexity algorithm is designed to maximize the sum rate. An adaptive algorithm is also proposed to address the uncertainty issue in CSI acquisition. Both theoretical and numerical analyses show that location information provides a new dimension to improve throughput for next-generation massive cooperative networks.

Sharing users' positional information is often required for high accuracy localization, which is a cornerstone for numerous wireless applications in commercial, military, and social sectors. This raises a concern of leaking positional information to adversaries during the localization process. This paper establishes a mathematical foundation for location secrecy. In particular, we determine the location secrecy metric (LSM) for a localization network in which an eavesdropper has a generic measurement capability. We obtain a simple and closed-form expression of the LSM for the case where the eavesdropper's measurement of a user's position is corrupted by a Gaussian noise. We further extend this result to a more general measurement model, and design location secrecy protection strategies based on the insights gained from our analysis. The performance of the proposed algorithms is verified through numerical simulation.

Wireless network localization (WNL) is a paradigm proposed recently for providing reliable location services. The localization performance is highly dependent on the placement of nodes in the network. In this paper, we study the node placement problem for localization networks. We first propose a mathematical formulation of the node placement problem. Such a formulation takes into account the uncertainty in the position of the placed nodes as well as the constraints on the region where the nodes can be deployed. Based on this formulation, we propose a near-optimal node placement algorithm, and present an upper bound on the gap between its performance with that of the optimal placement strategy. The proposed algorithm is validated by numerical results.

Both localization and synchronization of mobile nodes are of fundamental importance for wireless networks. With the emergence of full-duplex (FD) communication technology, inter-node distances and clock offsets among a set of nodes can be simultaneously obtained through only two frames of communications, thus significantly improving the efficiency of node localization and synchronization. In this paper, we propose a localization and synchronization scheme using FD radios, and characterize its performance. Our study derives the Cramér-Rao lower bounds (CRLBs) for inter-node distances and clock offsets, the former of which can be translated into the estimation error bounds for localization. Comparison to conventional frequency division duplex (FDD) or time division duplex (TDD) demonstrates the high efficiency of localization and synchronization using FD radios. Our results reveal the potential of full-duplex technology beyond data communications in future wireless networks.

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.

Resource management in the power and time-frequency domains is an important issue in distributed network localization. Since highly accurate ranging requires a large amount of time-frequency resources, cooperation among nodes without proper link selection may not be feasible. To address this issue, two resource management games are formulated, and Stackelberg equilibrium and link bargaining equilibrium are proposed as the solution concepts for efficient link selection and power allocation. Distributed algorithms are derived and analyzed using game theoretical approaches. It is demonstrated that the proposed strategies can achieve a lower mean squared error of position estimation with fewer ranging measurements.

Millimeter wave (mmWave) communications use large transmission bandwidth and experience severe propagation conditions. This forces the communication system to operate in the so-called wideband regime, where signals must be increasingly “peaky” in order to attain a large fraction of the peak unconstrained wideband capacity. This paper investigates the capacity of the noncoherent channels as a function of bandwidth for signals with average and peak power constraints operating in the millimeter spectrum. Upper and lower bounds on capacity are provided, and the impact of features peculiar to mmWave channels is investigated. Numerical results for a scenario based on recent experimental campaigns are provided. It is shown that the rate achievable by a typical user in a mmWave cell with “non-peaky” signaling can be bounded away from the peak unconstrained wideband capacity. This suggests to reconsider the role of signaling “peakedness” in future mmWave communications.

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.

Two new non-data-aided (NDA) signal-to-noise ratio (SNR) estimators for multiple antenna systems are presented. The proposed estimators rely on higher-order moments of the received signal and employ the time-diversity of the fading channel. While one requires a priori knowledge about the number of transmit antennas, the other estimates the SNR without such knowledge. The performance of the proposed estimators is evaluated for different numbers of transmit antennas and over a wide SNR range. Experimental results show that the proposed estimators exhibit a good performance, over a wide range of SNRs, in terms of normalized-root-mean-square-error (NRMSE) with small bias.

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.

Massive MIMO is, in general, severely affected by pilot contamination. As opposed to traditional detectors, we propose a group-blind detector that takes into account the presence of pilot contamination. While sticking to the traditional structure of the training phase, where orthogonal pilot sequences are reused, we use the excess antennas at each base station to partially remove interference during the uplink data transmission phase. We analytically derive the asymptotic SINR achievable with group-blind detection, and confirm our findings by simulations. We show, in particular, that in an interference-limited scenario with one dominant interfering cell, the SINR can be doubled compared to non-group-blind detection.

Millimeter wave communications will use large bandwidth and experience severe attenuation due to the pathloss. The two above conditions may force the communication system to enter the so-called wideband regime, where transmitted signals must be increasingly “peaky” in order to achieve a large fraction of the wideband capacity. This paper investigates noncoherent capacity of millimeter wave communications with average and peak power constrained inputs as a function of bandwidth. The impact of several parameters, in particular coherence block size and strength of the normalized specular component in line-of-sight propagation, is also studied. Capacity upper and lower bounds are provided and suggest that in practical scenarios the communication system may have to operate in the wideband regime. Paired with non-line-of-sight propagation, dense signaling is shown to limit the maximum achievable rate for typical users in the cell.

The fundamental limits of localization accuracy can be characterized by the information-theoretic bounds on position estimation. Tight bounds can be used not only as performance benchmarks for practical localization systems, but also criterion for assessing the effects of various sources of information (e.g., map awareness and ranging measurements) on localization performance, thus providing guidelines for the design and operation of localization systems. In this paper, we derive the Ziv-Zakai bound (ZZB) and Weiss-Weinstein bound (WWB) for map-aware cooperative localization, in which agents cooperate for position estimation with the aid of map information. The benefits from exploiting map awareness and agent cooperation are corroborated from both theoretical analysis and numerical examples, yielding important insights into how and when the map awareness can significantly improve the performance of cooperative localization.

A multiple-access channel where point processes are randomly transformed by timing channels and then superposed is considered. An achievable rate region for the K-user channel is established. A single-user achievable rate in the presence of “many” interfering users is proposed. Results are applied to exponential server timing channels.

A queue timing channel with random arrival and service times is investigated. The message is encoded into a sequence of additional delays that packets are subject to before they depart from the queue. We derive upper and lower bounds of the channel capacity for general arrival and service processes, and general load. We establish the channel capacity for the queue with exponential server and no load constraint by keeping the queue stable. We discuss the consequences of this result and describe a possible application where the timing channel is used to send covert information.

Location-aware networks are of great importance and interest in both civil and military applications. This paper determines the localization accuracy of an agent, which is equipped with an antenna array and localizes itself using wireless measurements with anchor nodes, in a far-field environment. In view of the Cramér-Rao bound, we first derive the localization information for static scenarios and demonstrate that such information is a weighed sum of Fisher information matrices from each anchor-antenna measurement pair. Each matrix can be further decomposed into two parts: 1) a distance part with intensity proportional to the squared baseband effective bandwidth of the transmitted signal and 2) a direction part with intensity associated with the normalized anchor-antenna visual angle. Moreover, in dynamic scenarios, we show that the Doppler shift contributes additional direction information, with intensity determined by the agent velocity and the root mean squared time duration of the transmitted signal. In addition, two measures are proposed to evaluate the localization performance of wireless networks with different anchor-agent and array-antenna geometries, and both formulae and simulations are provided for typical anchor deployments and antenna arrays.

Cooperative localization can enhance the accuracy of wireless network localization by incorporating range information among agent nodes in addition to those between agents and anchors. In this paper, we investigate the optimal allocation of the restricted resources, namely, power and bandwidth, to different nodes. We formulate the optimization problems for both synchronous networks and asynchronous networks, where one way and round trip measurements are applied for range estimation, respectively. Since the optimization problems are nonconvex, we develop an iterative linearization-based technique, and show by comparison with brute-force search that it provides near-optimal performance in the investigated cases. We also show that especially in the case of inefficient anchor placement and/or severe shadowing, cooperation among agents is important and more resources should be allocated to the agents correspondingly.

High-precision positioning techniques have a promising future in various applications. Recent work shows that inter-node cooperation can increase the positioning accuracy. In general, such cooperation consumes additional power, and current power application techniques are limited in synchronous networks. To study the power allocation in asynchronous networks, this paper proposes a power management game for cooperative localization, where each user allocates power that minimizes its square error positioning bound (SPEB) penalized by the power cost. Such game-theoretic power allocation policy not only considers how to allocate power over different cooperative links, but also manages the total power consumed for a better performance and power trade-off. We show that, in two-user networks, the power management game admits a unique cooperating Nash Equilibrium (NE) in high SNR region. In addition, as the power budget goes to infinity, the proposed game-theoretic power allocation policy can achieve SPEB arbitrarily close to that under exhaustive power allocation, but requires much less power.

Wireless networks are subject to security vulnerability due to the broadcasting nature of radio transmission. Information-theoretic approaches for secure communication propose to generate secret keys from a common source, such as reciprocal channels, available to the transmitter and receiver. However, such approaches assume the probability distribution of the sources, which may not be available in many realistic scenarios. In this paper, we establish an information-theoretic framework for secret-key generation (SKG) using noisy observations of unknown deterministic parameters (UDPs). Based on an axiomatic definition of UDPs, we derive a new metric called intrinsic information between the UDP and its observation, characterizing the rate of the secret key that can be generated from the observation. This metric is then applied to quantify the use of wideband channels in mobile networks for SKG. Our results provide a non-Bayesian perspective for SKG as well as its practical implications.

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 network localization (WNL) is an emerging paradigm for providing high-accuracy positional information in GPS-challenged environments. The localization performance of a node in WNL is determined by the allocation of transmit resources among its neighboring nodes. To achieve the best localization performance, we develop a computational geometry framework for optimal resource allocation in WNL. We first determine an affine map that transforms each resource allocation strategy into a point in 3-D Euclidian space. By exploiting geometric properties of these image points, we prove the sparsity property of the optimal resource allocation vector, i.e., the optimal localization performance can be achieved by allocating resources to only a small subset of neighboring nodes. Moreover, these geometric properties enable the reduction of the search space for optimal solutions, based on which we design efficient resource allocation strategies. Numerical results show that the proposed strategies can achieve significant improvements in both localization performance and computation efficiency. Our approach provides a new methodology for resource allocation in network localization, yielding exact optimal solutions rather than ϵ-approximate solutions.

Network navigation is an emerging paradigm that enables high-accuracy location awareness in GPS-challenged environments via spatiotemporal cooperation. Two important operations of network navigation, location inference and power control, interrelate with each other, thus motivating the joint design of inference and control algorithms. In this paper, we develop efficient algorithms for network navigation with power control. In particular, we first determine the confidence region for location inference based on Fisher information analysis, and then design robust power control that minimizes the location inference errors of the agents within the confidence region. Both centralized and distributed algorithms are developed for energy-efficient network navigation. Simulation results show that the proposed algorithms significantly reduce the location inference errors compared to those without power control.

Device-to-device (D2D) communication in cellular networks is a promising concept that permits cooperation among mobile devices not only to increase data throughput but also to enhance localization services. In those networks, the allocation of transmitting power plays a critical role in determining network lifetime and localization accuracy. Meanwhile, it is a challenging task for implementation in cooperative D2D networks, since each device has only imperfect estimates of local network parameters in distributed settings. In this paper, we establish an optimization framework for robust power allocation in cooperative wireless network localization, and develop distributed power allocation strategies. In particular, we decompose the power allocation problem into infrastructure and cooperation phases, show the sparsity property of the optimal power allocation, and develop efficient power allocation strategies. Simulation results show that these strategies can achieve significant performance improvement in localization accuracy compared to the uniform strategies.

Network navigation is an emerging paradigm that enables high-accuracy location awareness in GPS-challenged environments. Two important operations of network navigation, location inference and power control, interrelate with each other, thus motivating the design of joint inference and control algorithms. In this paper, we develop efficient network navigation algorithms with optimized energy allocation. In particular, we first determine the confidence region for lzocation inference based on Fisher information analysis, and then design robust energy allocation strategies that minimize the position errors of the agents within the confidence region. Based on these strategies, both centralized and distributed energy-efficient network navigation algorithms are developed. Simulation results show that the proposed algorithms significantly reduce the position errors compared to the algorithms with uniform or non-robust power control.

Wireless localization has a great importance in a variety of areas including commercial, service, and military positioning and tracking systems. In harsh indoor environments, it is hard to localize an agent with high accuracy due to non-line-of-sight (NLOS) radio blockage or insufficient information from anchors. Therefore, NLOS identification and mitigation are highlighted as an effective way to improve the localization accuracy. In this paper, we develop a robust and efficient algorithm to enhance the accuracy for (ultrawide bandwidth) time-of-arrival localization through identifying and mitigating NLOS signals with relevance vector machine (RVM) techniques. We also propose a new localization algorithm, called the two-step iterative (TSI) algorithm, which converges fast with a finite number of iterations. To enhance the localization accuracy as well as expand the coverage of a localizable area, we continue to exploit the benefits of RVM in both classification and regression for cooperative localization by extending the TSI algorithm to a centralized cooperation case. For self-localization setting, we then develop a distributed cooperative algorithm based on variational Bayesian inference to simplify message representations on factor graphs and reduce communication overheads between agents. In particular, we build a refined version of Gaussian variational message passing to reduce the computational complexity while maintaining the localization accuracy. Finally, we introduce the notion of a stochastic localization network to verify proposed cooperative localization algorithms.

The H-transforms are integral transforms that involve Fox's H-functions as kernels. A large variety of integral transforms can be put into particular forms of the H-transform since H-functions subsume most of the known special functions including Meijer's G-functions. In this paper, we embody the H-transform theory into a unifying framework for modeling and analysis in wireless communication. First, we systematize the use of elementary identities and properties of the H-transform by introducing operations on parameter sequences of H-functions. We then put forth H-fading and degree-2 irregular H-fading to model radio propagation under composite, specular, and/or inhomogeneous conditions. The H-fading describes composite effects of multipath fading and shadowing as a single H-variate, including most of typical models such as Rayleigh, Nakagami-m, Weibull, α-μ, N*Nakagami-m, (generalized) K-fading, and Weibull/gamma fading as its special cases. As a new class of H-variates (called the degree-ζ irregular H-variate), the degree-2 irregular H-fading characterizes specular and/or inhomogeneous radio propagation in which the multipath component consists of a strong specularly reflected or line-of-sight (LOS) wave as well as unequal-power or correlated in-phase and quadrature scattered waves. This fading includes a variety of typical models such as Rician, Nakagami-q, κ-μ, η-μ, Rician/LOS gamma, and κ-μ/LOS gamma fading as its special cases. Finally, we develop a unifying H-transform analysis for the amount of fading, error probability, channel capacity, and error exponent in wireless communication using the new systematic language of transcendental H-functions. By virtue of two essential operations-called Mellin and convolution operations-involved in the Mellin transform and Mellin convolution of two H-functions, the H-transforms for these performance measures culminate in H-functions. Using the algebraic asymptotic expansions of the H-transform, we further analyze the error probability and capacity at high and low signal-to-noise ratios in a unified fashion.

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.

Accurate position information enables numerous location-based applications. Wireless network localization is a promising localization technique that permits cooperation among mobile objects to enhance localization services. The allocation of transmitting power for such networks plays a critical role since it determines network lifetime, throughput, as well as the localization accuracy. In this paper, we establish an optimization framework for power allocation in cooperative localization networks. We first show that the optimal solution for the power allocation problem can be obtained by semi-definite programs (SDPs). For implementation in cooperative localization networks, we develop efficient and distributed power allocation strategies via relaxation of the original problem. In particular, we decompose the power allocation problem into infrastructure and cooperation parts. We transform the former into SDPs and derive upper bounds for the localization accuracy of the latter. These bounds enable us to develop efficient distributed strategies. Simulation results show that these strategies can achieve significant performance improvement compared to the unoptimized strategies in terms of localization accuracy.

Location-awareness plays a key role in various applications in future wireless networks. In GPS-challenged environments, location-awareness can be achieved via wireless navigation networks, which call for an efficient scheduling algorithm to optimize the navigation performance under communication constraints. In this paper, we develop a general framework for the design and analysis of scheduling algorithms for navigation networks with multiple measurement pairs per time slot. In particular, we provide sufficient and necessary conditions for the stability of the error evolution, and derive bounds on the time-averaged network localization errors (NLEs) for opportunistic and random scheduling. Furthermore, we show that the two scheduling algorithms are optimal in terms of the error scaling with respect to the number of agents, and we quantify the performance gain from measurement pair selections exploiting the network states. These results provide guidelines for designing efficient scheduling algorithms for network navigation.

Localization of wireless node is a key feature in many applications. Traditional localization has exploited the signal runtime between “agent” nodes that are to be localized and a set of “anchor” nodes with known position. Recently, cooperative localization that also uses runtime measurement between agent nodes has been shown to provide superior performance. This paper analyzes the optimum power and bandwidth allocation in such systems. We first formulate the general optimization problem and show that it is non-convex. We then develop an approximate algorithm based on Taylor expansion and iterative optimization of power and bandwidth separately to find an approximate solution; simulations show that results are close to the optimum solution (which is NP-hard). We also find that the importance of cooperative localization increases (and agents get assigned more resources) if the anchor deployment is bad in the sense that it provides high geometric dilution of precision and/or suffers from significant blockage between anchors and agents.

Reliable and accurate localization of mobile objects is essential for many applications in wireless networks. In range-based localization, the position of the object can be inferred using the distance measurements from wireless signals exchanged with active objects or reflected by passive ones. Power allocation for ranging signals is important since it affects not only network lifetime and throughput but also localization accuracy. In this paper, we establish a unifying optimization framework for power allocation in both active and passive localization networks. In particular, we first determine the functional properties of the localization accuracy metric, which enable us to transform the power allocation problems into second-order cone programs (SOCPs). We then propose the robust counterparts of the problems in the presence of parameter uncertainty and develop asymptotically optimal and efficient near-optimal SOCP-based algorithms. Our simulation results validate the efficiency and robustness of the proposed algorithms .

Novel non-coherent impulse communications receivers are proposed to alleviate excessive noise collection in clustered multipath channels. To this aim, a stop-and-go strategy based on energy detection in the autocorrelation receiver or in the energy detection receiver is employed. This strategy enables the selective collection of useful signal portions only, allowing the integration interval to be kept large without noise penalty. To implement this strategy, a blind method is employed, using model order selection based on information theoretic criteria, which optimizes the performance of the proposed receiver and does not require the estimation of channel parameters. The bit error probability of the proposed stop-and-go receivers is evaluated, and our results highlight the considerable performance gain at the expense of a small increase in complexity.

We consider the problem of localizing two devices using signals of opportunity from beacons with known positions. Beacons and devices have asynchronous local clocks or oscillators with unknown clock skews and offsets. We model clock skews as random, and analyze the biases introduced by clock asynchronism in the received signals. By deriving the equivalent Fisher information matrix for the modified Bayesian Cramér-Rao lower bound (CRLB) of device position and velocity estimation, we quantify the errors caused by clock asynchronism. We propose an algorithm based on differential time-difference-of-arrival (DTDOA) and frequency-difference-of-arrival (FDOA) that mitigates the effects of clock asynchronism to estimate the device positions and velocities. Simulation results suggest that our proposed algorithm is robust and approaches the CRLB when clock skews have small standard deviations.

Indoor navigation using the existing wireless infrastructure and mobile devices is a very active research area. The major challenge is to leverage the extensive smartphone sensor suite to achieve location tracking with high accuracy. In this paper, we develop a navigation algorithm which fuses the WiFi received signal strength indicator (RSSI) and smartphone inertial sensor measurements. A sequential Monte Carlo filter is developed for inertial sensor based tracking, and a radiolocation algorithm is developed to infer mobile location based on RSSI measurements. The simulation results show that the proposed algorithm significantly outperforms the extended Kalman filter (EKF), and achieves competitive location accuracy compared with the round trip time (RTT) based ultra-wideband (UWB) system.

Recent results have shown that interference alignment (IA) achieves the optimal throughput scaling law w.r.t. SNR. However, except for a few special cases, finding transceivers to achieve IA in MIMO interference networks is still an open problem. This problem is challenging due to the non-convex nature of the interference optimization problem. Inspired by recent success in using tools from algebraic geometry to analyze the feasibility conditions of IA, in this work, we adopt algebraic geometry analysis to guide IA algorithm design. Specifically, we first explore the relation between algebraic independence and feasibility of polynomial equation sets, and transform the IA problem into an equivalent polynomial form, whose policy space is convex. Then we reformulate the transformed IA problem into an interference optimization problem. By exploiting the connection between algebraic independence and full rankness of Jacobian matrix, we prove that in the interference optimization problem, there is no performance gap between local and global optimums when IA is feasible. This property enables us to easily design IA algorithms by adopting existing local search algorithms. Combining the propositions obtained in this work and those obtained in the authors' prior work on IA feasibility, we have established a unified algebraic framework for both IA feasibility analysis and algorithm design.

Wireless navigation networks enable location-awareness in GPS-challenged environments. For such networks, scheduling algorithms are needed to improve the navigation accuracy through measurement pair selections under limited communication resource. In this paper, we develop an analytical framework to determine the location error evolution for different scheduling algorithms and network settings. Under this framework, we provide sufficient conditions for the stability of the location error evolution, and we quantify the time-averaged network location errors (NLEs) for scheduling algorithms with and without exploiting the network states. Furthermore, we show the optimality of the proposed scheduling algorithms in terms of the error scaling with respect to the agent density. These results provide fundamental insights into the effects of scheduling algorithms and network settings on the location error evolution, leading to efficient scheduling algorithms for navigation networks.

In this paper, we develop a cooperative IMU/radio-location-based navigation system, where each node tracks the location not only based on its own measurements, but also via collaboration with neighbor nodes. The key problem is to design a nonlinear filter to fuse IMU and radiolocation information. We apply the Rao-Blackwellization method by using a particle filter and parallel Kalman filters for the estimation of orientation and other states (i.e., position, velocity, etc.), respectively. The proposed method significantly outperforms the extended Kalman filter (EKF) in the set of simulations here.

High-accuracy localization is essential for many location-based applications. The position of an object can be obtained from range measurements based on wireless transmissions. Transmitting power allocation not only affects network lifetime and throughput, but also determines localization accuracy. The number of active transmitting nodes is also crucial since it is related to communication load and computation complexity. In this paper, we formulate the power optimization problem that provides the best localization accuracy under power constraints. We first prove the sparsity of the optimal power allocation, i.e., the optimal localization accuracy can be achieved by activating no more than (J) transmitting nodes in d-dimensional networks. Inspired by such sparsity, we derive the expressions of the optimal power allocation in 2-D networks. We also put forth a near-optimal algorithm for the power allocation problem with individual power constraints. Our results provide a theoretical basis for designing transmitting node selection and power allocation algorithms for network localization.

In this paper, we integrate key wireless propagation effects such as the path loss, shadowing, and multipath fading into a single Fox's H-variate using the H-preserving property under products, powers, quotients, and their combinations. We then establish a unifying framework to analyze the error probability and channel capacity for V2V communication in a Cox field of vehicles, using again the language of Fox's H-functions. This framework enables us to characterize intervehicle communication in the doubly stochastic vehicular ad-hoc network (VANET) by averaging both small- and large-scale fading processes in time and (random) distance-dependent path losses in space.

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.

High-accuracy localization in harsh environments is a challenging research problem, mainly due to non-line-of-sight (NLOS) propagation, multipath effect, and multiuser interference. Many techniques have been proposed to address this problem; most of them focus on improving the accuracy of ranging estimation, e.g., NLOS identification and mitigation. In this paper, we take ranging one step further by introducing the concept of ranging likelihood (RL), showing that RL is the essential element for localization. Moreover, we present effective techniques for real-time RL estimation. We focus on ultra-wide bandwidth (UWB) localization systems and assess the performance of the proposed approach by using the data from an extensive indoor measurement campaign. The results show that the proposed approach can significantly improve the performance of wireless localization in harsh environments.

Accuracy and complexity represent fundamental aspects of localization and tracking systems. In this manuscript the impact of a priori knowledge about agent position on the accuracy and the complexity of localization algorithms is investigated. In particular, first Cramer-Rao bounds on localization accuracy are derived under the assumption that a priori information is described by a map restricting the agent position to a specific region. Then, the computational complexity of optimal map-aware and map-unaware localization techniques is assessed. Our results evidence that: a) map-aware localization accuracy can be related to some geometrical features of the map but usually exhibits a complicated dependence on them; b) in some scenarios map-aware localization algorithms provide better accuracy than their map-unaware counterparts at comparable computational complexity.

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.

Power resource allocation is an important task for active and passive network localization, since it affects the localization accuracy in addition to the lifetime and throughput of the network. In this paper, we propose a robust power allocation formulation that guarantees the localization accuracy in the presence of parameter uncertainty for network localization. We first consider wireless network localization (WNL) as an example of active localization, and derive sequential upper and lower bounds for the worst-case localization accuracy as well as their convergence rates. Built on these bounds, we transform the robust formulation into a sequence of second-order cone programs (SOCPs) that yield asymptotically optimal solutions. We also develop an efficient near-optimal SOCP-based algorithm using a relaxation method. Then, we extend all the results to passive localization through the example of radar network localization (RNL). Finally, the simulation results validate the efficiency and robustness of the proposed algorithms.

Safety message dissemination in a vehicular ad-hoc network (VANET) requires vehicle-to-vehicle (V2V) communication with low latency and high reliability. The dynamics of vehicle passing and queueing as well as high mobility create distinctive propagation characteristics of wireless medium and inevitable uncertainty in space-time patterns of the vehicle density on a road. It is therefore of great importance to account for random vehicle locations in V2V communication. In this paper, we characterize intervehicle communication in a random field of vehicles, where a beacon or head vehicle (transmitter) broadcasts safety or warning messages to neighboring client vehicles (receivers) randomly located in a cluster on the road. To account for a doubly stochastic property of the VANET, we first model vehicle's random locations as a stationary Cox process with Fox's H-distributed random intensity (vehicle concentration) and derive the distributional functions of the lth nearest client's distance from the beacon in such a Fox Cox field of vehicles. We then consolidate this spatial randomness of receiving vehicles into a path loss model and develop a triply-composite Fox channel model that combines key wireless propagation effects such as the distance-dependent path loss, large-scale fading (shadowing), and small-scale fading (multipath fading). In Fox channel modeling, each constituent propagation effect is described as Fox's H-variate, culminating again in Fox's H-variate for the received power or equivalently the instantaneous signal-to-noise ratio at the lth nearest client vehicle. Due to versatility of Fox's H-functions, this stochastic channel model can encompass a variety of well-established or generalized statistical propagation models used in wireless communication; be well-fitted to measurement data in diverse propagation environments by varying parameters; and facilitate a unifying analysis for fundamental physical-layer performances, such as error probability and channel capacity, using again the language of Fox's H-functions. This work serves to develop a unifying framework to characterize V2V communication in a doubly stochastic VANET by averaging both the small- and large-scale fading effects as well as the (random) distance-dependent path losses.

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.

The ability to exchange secret messages and protect against security attacks becomes increasingly important for providing information superiority and confidentiality in modern information systems. These systems require shared secret keys, which can be generated from common random sources with known distributions. However, the assumption on the distribution of the sources may not hold in many realistic scenarios. In this paper, we establish a mathematical framework for secret-key generation using common unknown deterministic sources (UDSs). In particular, we propose a new information measure called intrinsic information to characterize the achievable length of the secret key that can be generated from a UDS. As a case study, we consider a wideband propagation medium in mobile wireless networks as a UDS and derive its intrinsic information as a function of various network parameters. Our results provide a non-Bayesian perspective for secret-key generation as well as practical implications of this new perspective.

Establishing bounds on the accuracy achievable by localization techniques represents a fundamental technical issue. Bounds on localization accuracy have been derived for cases in which the position of an agent is estimated on the basis of a set of observations and, possibly, of some a priori information related to them (e.g., information about anchor positions and properties of the communication channel). In this paper, new bounds are derived under the assumption that the localization system is map-aware, i.e., it can benefit not only from the availability of observations, but also from the a priori knowledge provided by the map of the environment where it operates. Our results show that: a) map-aware estimation accuracy can be related to some features of the map (e.g., its shape and area) even though, in general, the relation is complicated; b) maps are really useful in the presence of some combination of low SNRs and specific geometrical features of the map (e.g., the size of obstructions); c) in most cases, there is no need of refined maps since additional details do not improve estimation accuracy.

In wireless location-aware networks, mobile nodes (agents) typically obtain their positions using the range measurements to the nodes with known positions. Transmit power allocation not only affects network lifetime and throughput, but also determines localization accuracy. In this paper, we present an optimization framework for robust power allocation in network localization with imperfect knowledge of network parameters. In particular, we formulate power allocation problems to minimize localization errors for a given power budget and show that such formulations can be solved via conic programming. Moreover, we design a distributed power allocation algorithm that allows parallel computation among agents. The simulation results show that the proposed schemes significantly outperform uniform power allocation, and the robust schemes outperform their non-robust counterparts when the network parameters are subject to uncertainty .

High-accuracy localization is crucial for numerous location-based applications. In wireless networks, the position information of a node (agent) can be obtained from range measurements with respect to nodes with known positions (anchors). The transmission power allocation among anchors not only affects network lifetime and throughput, but also determines the localization accuracy. In this paper, we formulate the power optimization problem that provides the best location accuracy under a total power constraint. For a given set of anchors, the minimum number of active anchors required for optimal localization in 2-D network is proven to be either two or three, depending on the network parameters. We then derive the closed-form expression for the optimal power allocation in the case of small networks and extend the results to general cases. We also develop a near-optimal strategy that requires less computational complexity, incurring negligible average performance loss. Our results provide a theoretical basis for designing anchor selection and power allocation algorithms for localization.

Safety message dissemination in a vehicular ad-hoc network (VANET) is in desperate need of vehicle-to-vehicle (V2V) communication with low latency and high reliability. The dynamics of vehicle passing and queueing as well as high mobility create distinctive propagation characteristics of wireless medium and inevitable uncertainty in space-time patterns of the vehicle density on a road. It is therefore of great importance to integrate stochastic geometry of vehicle locations into V2V channel modeling. In this paper, we characterize intervehicle communication in a random field of vehicles, where a beacon or head vehicle (transmitter) geobroadcasts safety or warning messages to neighboring client vehicles (receivers) randomly located in a cluster on the road. To account for a doubly stochastic spatial property of the VANET, we first model vehicle's random locations as a stationary Cox process with Fox's H-distributed random intensity (vehicle concentration) and derive the distributional functions of the `th nearest client's distance from the beacon in such a Fox Cox field of vehicles. We then consolidate this spatial randomness of receiving vehicles into a path loss model and develop a triply-composite Fox channel model that combines key wireless propagation effects such as the distance-dependent path loss, large-scale fading (shadowing), and small-scale fading (multipath fading).

The physical-layer multicast transmission that seeks to deliver the common information messages to the all users simultaneously is becoming more important in wireless systems with demand for various multimedia mobile applications such as Multimedia Broadcast Multicast Services. The spatial randomness of communicating nodes in a wireless network is one of inevitable uncertainty in design and analysis of network information flow and connectivity. In this paper, we characterize local information flow in a stochastic multiple-input multiple-output (MIMO) multicast network where a probe transmitter geobroadcasts common data with sectorized transmission to receivers sitting in a region R. We first put forth a measure of the total amount of common information flow, called the space-time capacity, into R in a spatial random field of receivers. We then derive the space-time capacity into a sectoral region R and the nth nearest ergodic capacity in a Poisson field to characterize the spatial average and ordering of MIMO ergodic capacity achieved by receivers in R. Using the Marčenko-Pastur law, we further assess the asymptotic space-time capacity and the nth nearest ergodic capacity per receive antenna as the antenna numbers tend to infinity. The framework developed in this work enables us to quantify the local information flow in random MIMO wireless networks by averaging both small-scale fading processes on time and large-scale path losses on space.

The availability of reliable positional information is a key enabler for numerous emerging location-based applications. Network navigation via joint spatial and temporal cooperation can provide mobile nodes with high-accuracy and robust positional information. Meanwhile, this joint cooperation incurs intricate information acquisition due to the correlation in inferred nodes' position, referred to as information coupling. In this paper, we quantify the information coupling in four representative scenarios by Fisher information analysis. We show that the information obtained by each node is a sum of the contribution from its own spatio-temporal cooperation and information coupling due to the cooperation of its neighbors. Our results shed lights on the complex information acquisition in network navigation, and can serve as a design guideline for efficient network navigation algorithms.

Power resource allocation is crucial for localization since it affects not only the conventionally recognized lifetime, throughput, and covertness, but also localization efficiency of the network. In this paper, we present an optimization framework for range-based localization to improve the power efficiency and localization accuracy. Our framework unifies the analysis for active and passive localization through the examples of wireless network localization (WNL) and multiple radar localization (MRL). In particular, we determine the functional properties of localization accuracy metric, and based on those properties we formulate the power allocation problem as conic programs. Moreover, we propose robust counterparts that retain the conic structures for power allocation in the presence of parameter uncertainty. Our simulation results validate the efficiency and robustness of the proposed methods.

This paper presents a new framework to evaluate the performance of wireless networks with intrinsic secrecy. Specifically, we put forth the notion of secure node packing (SNP), defined as the number of legitimate users capable of transmitting data with secrecy using stochastic geometry. Transmission with secrecy means a legitimate receiver can decode transmitted data (reliability) while its corresponding eavesdropper cannot decode (secrecy). The SNP is derived for two cases of secure transmission: weakly secure transmission against the nearest eavesdropper and strongly secure transmission against all eavesdroppers. We quantify the effect of network parameters, including spatial densities of legitimate transmitter and eavesdropper, on the throughput of wireless networks with secrecy. In addition, we determine the SNP in the presence of a coexisting network and show that the additional interference from a coexisting network with an appropriate spatial density is beneficial for the secure network throughput.

In wireless localization systems, mobile nodes (agents) typically obtain their positions through ranging with respect to fixed infrastructure (anchors). Transmission power allocation not only affects network lifetime, throughput and interference, but also determines the localization accuracy. In this paper, we develop a robust anchor power allocation strategy to combat imperfect network topology parameters. We formulate the problem to minimize the squared position error bound (SPEB), which characterizes the fundamental limit of localization accuracy, and show that such formulation can be efficiently solved via semidefinite programming (SDP). The simulation results show that the proposed robust scheme significantly outperforms both non-robust scheme and uniform power allocation.

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.

First passage time problems arise in many fields, but few of them are explicitly solved. Motivated by an application to synchronization of clocks, this paper obtains the probability distribution for the first passage time for a Brownian motion with quadratic drift to exit from two constant barriers that are not necessarily symmetric. The expression for the distribution is explicit, consisting of elementary functions and functions that are characterized by Laplace transforms. Applications of first passage times are demonstrated in the context of synchronization.

In cooperative localization networks, nodes estimate their locations through inter-node ranging, location information exchange, and information fusion. These operations cause packet collision, large communication overhead, and high computational complexity, which can be alleviated by proper scheduling techniques. In this paper, we design distributed scheduling algorithms for cooperative localization networks, which improve the efficiency of localization through neighbor selection and collision control. Then, we determine the evolution of each node's location error through cooperation and movement using Fisher information analysis. Furthermore, we analyze the convergence of the location error under the proposed scheduling algorithm. Simulation results show that the efficiency of localization is significantly improved by using the proposed scheduling algorithm.

In this paper, we consider a wideband cognitive radio network with limited available frame energy and treat a fundamental energy allocation problem: how available energy should be optimally allocated for sensing, probing, and data transmission to maximize the achievable average opportunistic spectrum access (OSA) throughput. By casting this problem into the multi-armed bandit framework under probably approximately correct learning, we put forth a proactive strategy for determining the optimal sensing cardinality and probing cardinality that maximize the average throughput of the secondary user. Numerical results show that our framework gives the the optimal diversity-energy tradeoff for the average OSA throughput.

Attributed by its breakthrough performance in interference networks, interference alignment (IA) has attracted great attention in the last few years. However, despite the tremendous works dedicated to IA, the feasibility conditions of IA processing remains unclear for most network typologies. The IA feasibility analysis is challenging as the IA constraints are sets of high-degree polynomials, for which no systematic tool to analyze the solvability conditions exists. In this work, by developing a new mathematical framework that maps the solvability of sets of polynomial equations to the linear independence of their first order terms, we propose a sufficient condition that applies to K-pairs MIMO interference networks with general typologies. We have further proved that the sufficient condition aligns with the necessary conditions under some special configurations.

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.

A femtocell technology-towards the deployment of small-cell networks-is a key enabler for improving indoor coverage and throughput per network area at a low cost in future wireless networks. However, these small-cell networking inevitably increases cochannel interference due to aggressive (even uncontrolled) reuse of spectral resources. One of the attractive approaches to alleviating the cochannel interference is a multiple-antenna technique for which accurately characterizing the effects of interference is crucial but challenging. To elucidate this important problem, we analyze the performance of interference rejection diversity combining, often called the optimum combining, in an uplink two-tier femtocell network. Specifically, we consider that a single-antenna femtocell user (transmitter) communicates with a closed femtocell access point (receiver) with multiple antennas in the presence of single-antenna cochannel interferers from co-tier (femtocells) and cross-tier (macrocell) networks. We introduce a new mathematical methodology to analyze the average symbol error probability of optimum combining diversity systems in Rayleigh fading, accounting for multiple unequal-power interferers, each is spatially correlated across receiving antennas. The analysis resorts to the so-called Berezin's supermathematics that treats both commuting and Grassmann anticommuting variables on an equal footing. This powerful supermathematical framework enables us to quantify the cross- and co-tier interference effects in terms of interference power heterogeneity and spatial correlation.

Real-time and reliable location information of mobile nodes is a key enabler for many emerging wireless network applications. Such information can be obtained via network navigation, a new paradigm in which nodes exploit both spatial and temporal cooperation to infer their positions. In this paper, we establish a theoretical foundation for network navigation and determine the fundamental limits of navigation accuracy using equivalent Fisher information analysis. We then introduce the notion of carry-over information and provide a geometrical interpretation for the evolution of navigation information. Our framework unifies the navigation information obtained from spatial and temporal cooperation, leading to a deep understanding of information evolution and cooperation benefits in navigation networks.

Location-awareness is becoming increasingly important in wireless networks. Indoor localization can be enabled through wideband or ultra-wide bandwidth (UWB) transmission, due to its fine delay resolution and obstacle-penetration capabilities. A major hurdle is the presence of obstacles that block the line-of-sight (LOS) path between devices, affecting ranging performance and, in turn, localization accuracy. Many techniques have been proposed to address this issue, most of which make modifications to the localization algorithm. Since many localization algorithms work with distance or angle estimates, rather than received waveforms, information inherent in the wideband waveform is lost, leading to sub-optimal ranging error mitigation. To avoid this information loss, we present a novel approach to mitigate ranging errors directly in the physical layer. In contrast to existing techniques, which detect the non-line-of-sight (NLOS) condition, our approach directly mitigates the bias incurred in both LOS and non-LOS conditions. In particular, we apply two classes of non-parametric regressors to form an estimate of the ranging error. Our work is based on, and validated by, an extensive indoor measurement campaign with FCC-compliant UWB radios. The results show that the proposed regressors provide significant performance improvements in various practical localization scenarios, compared to conventional approaches.

The ability to exchange secret information is critical to many commercial, governmental, and military networks. Information-theoretic security-widely accepted as the strictest notion of security-relies on channel coding techniques that exploit the inherent randomness of the propagation channels to strengthen the security of digital communications systems. Motivated by recent developments in the field, we aim to characterize the fundamental secrecy limits of wireless networks. The paper is comprised of two separate parts. In Part I, we define the intrinsically secure communications graph (iS-graph), a random graph which describes the connections that can be securely established over a large-scale network. We provide conclusive results for the local connectivity of the Poisson iS-graph, in terms of node degrees and isolation probabilities. We show how the secure connectivity of the network varies with the wireless propagation effects, the secrecy rate threshold of each link, and the noise powers of legitimate nodes and eavesdroppers. We then propose sectorized transmission and eavesdropper neutralization as viable strategies for improving the secure connectivity. Our results help clarify how the spatial density of eavesdroppers can compromise the intrinsic security of wireless networks. In Part II of the paper, we study the achievable secrecy rates and the effect of eavesdropper collusion.

In Part I of this paper, we introduced the intrinsically secure communications graph (iS-graph)-a random graph which describes the connections that can be established with strong secrecy over a large-scale network, in the presence of eavesdroppers. We focused on the local connectivity of the iS-graph, and proposed techniques to improve it. In this second part, we characterize the maximum secrecy rate (MSR) that can be achieved between a node and its neighbors. We then consider the scenario where the eavesdroppers are allowed to collude, i.e., exchange and combine information. We quantify exactly how eavesdropper collusion degrades the secrecy properties of the network, in comparison to a noncolluding scenario. Our analysis helps clarify how the presence of eavesdroppers can jeopardize the success of wireless physical-layer security.

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.

The ability to exchange secret information is critical to many commercial, governmental, and military networks. The intrinsically secure communications graph (iS-graph) is a random graph which describes the connections that can be securely established over a large-scale network, by exploiting the physical properties of the wireless medium. This paper aims to characterize the global properties of the iS-graph in terms of (1) percolation on the infinite plane, and (2) full connectivity on a finite region. First, for the Poisson iS-graph defined on the infinite plane, the existence of a phase transition is proven, whereby an unbounded component of connected nodes suddenly arises as the density of legitimate nodes is increased. This shows that long-range secure communication is still possible in the presence of eavesdroppers. Second, full connectivity on a finite region of the Poisson iS-graph is considered. The exact asymptotic behavior of full connectivity in the limit of a large density of legitimate nodes is characterized. Then, simple, explicit expressions are derived in order to closely approximate the probability of full connectivity for a finite density of legitimate nodes. These results help clarify how the presence of eavesdroppers can compromise long-range secure communication.

Long term evolution (LTE) is considered to be a key technology for the next generation of cellular telecommunications. In LTE systems, each user equipment (UE) detects the surrounding cells by searching their identities (IDs) in the synchronization channels of the received waveform. Searching and tracking neighboring cells is important for cellular network management, such as handover and base station cooperation. In this paper, we establish a general framework for neighboring cell search (NCS) in LTE systems. In particular, we derive sufficient signal metrics (SSMs) for NCS under various channel conditions, and develop NCS algorithms based on the SSMs, which optimally combine multiple observations over space and/or time. Moreover, we develop a statistical model for NCS using probability analysis. The performance of NCS algorithms is characterized in terms of the number of detected cells and the cell detection probability, and simulation results validate the effectiveness of the proposed algorithms.

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.

Real-time navigation capability is a key enabler for many emerging applications in wireless networks. Localization of moving nodes via network navigation gives rise to a new paradigm, where nodes exploit both temporal and spatial cooperation to determine their positions based on intra- and inter-node measurements. In this paper, we establish a theoretical foundation for network navigation to determine the fundamental limits of navigation accuracy. In particular, we derive the accuracy limits in terms of navigation information by equivalent Fisher information analysis. Our framework unifies the navigation information obtained from temporal and spatial cooperation, leading to a deep understanding of information exchange in the network and benefit of cooperation.

This paper investigates the number of multipath components in realistic wireless indoor channels. A measurement campaign was performed to collect channel realizations in the Stata Center on the MIT campus. We develop an algorithm with low computational complexity to extract multipath components from the measured waveforms. Based on the results obtained from the measured data, we quantify the behavior of the number of paths with respect to both center frequency and bandwidth.

Many future wireless applications rely on the availability of position information for mobile wireless nodes (agents). Such information can be obtained through ranging and communication between agents and fixed infrastructure (anchors). Since the transmission power of the anchors affects network lifetime, throughput, and interference, in this paper we will investigate the problem of power allocation among anchors. We start with a formulation of minimizing the squared position error bound (SPEB), which characterizes the limits of location accuracy. However, the problem is difficult to solve due to its non-convexity. To address this issue, we leverage the insights obtained from the geometric interpretation of localization information and formulate the objective to maximum directional position error bound (DPEB). We first solve the problem for the single-agent case, and then extend the algorithm for the multiple-agent case. The simulation results show that the proposed scheme achieves close-to-optimal solution but with much lower computational complexity.

Traditional techniques for navigation such as the Kalman filter cannot capture the nonlinear and non-Gaussian models appearing in wireless localization systems deployed in harsh environments. Nonparametric filters as particle filters can cope with such models at the expense of a computational complexity beyond the reach of low-cost navigation devices. In this paper, we establish a general framework for parametric filters based on belief condensation (BC), which can express highly nonlinear and non-Gaussian system and measurement models. Our methodology exploits the specific structure of the problem and decomposes it in such a way that the linear and Gaussian part can be solved efficiently. The set of parameters for the posterior distribution is updated by an optimization process, referred to as BC. The simulation results show that the performance of the proposed parametric filter is close to that of the particle filter, but with a much lower complexity.

Opportunistic spectrum access creates the opening of under-utilized portions of the licensed spectrum for reuse, provided that the transmissions of secondary radios do not cause harmful interference to primary users. Therefore, it is important to characterize the effect of cognitive network interference due to such secondary spectrum reuse. In this paper, we show how a new statistical model for aggregate interference of a cognitive network, which accounts for the sensing procedure, secondary spatial reuse protocol, and environment-dependent conditions such as path loss, shadowing, and channel fading can be used to assess the aggregate interference in specific environments. Specifically, we consider scenarios like power controlled primary network, secondary network with interference avoidance mechanism, and non-circular coverage region.

A femtocell technology-towards the deployment of small-cell networks-is a key enabling feature for future wireless networks to improve indoor coverage and throughput per network area at a low cost. However, these small-cell networking inevitably increases cochannel interference due to aggressive (even uncontrolled) reuse of spectral resources. One of the attractive approaches to alleviating the cochannel interference is a multiple-antenna technique, where accurately characterizing the effects of cross- and co-tier interference on the performance of multiple-antenna communications is crucial but challenging. We apply superanalysis framework to provide further numerical results on the optimum combining diversity systems in an uplink two-tier femtocell network. Specifically, we consider that a single-antenna femtocell user (transmitter) communicates with a closed femtocell access point (receiver) with multiple antennas in the presence of single-antenna cochannel interferers from co-tier (femtocells) and cross-tier (macrocell) networks. We then quantify the cross- and co-tier interference effects in terms of degrees of interference power heterogeneity and spatial correlation.

We consider a wideband cognitive radio network, where the channel is modeled by doubly block fading in time and frequency. With a frame energy constraint, we propose a joint sensing and probing strategy, called active sensing, to access the best channel for secondary data transmission. By employing the multi-armed bandit problem, we establish the fundamental tradeoff between sensing, probing, and transmitting energies for an average throughput of the secondary user and proactively design the optimal cardinalities of sensing and probing sets of channels. Our design methodology provides a framework to determine the optimal energy tradeoff between exploration and exploitation in wideband cognitive radio networks.

Opportunistic spectrum access creates the opening of under-utilized portions of the licensed spectrum for reuse, provided that the transmissions of secondary radios do not cause harmful interference to primary users. Such a system would require secondary users to be cognitive-they must accurately detect and rapidly react to varying spectrum usage. Therefore, it is important to characterize the effect of cognitive network interference due to such secondary spectrum reuse. In this paper, we propose a new statistical model for aggregate interference of a cognitive network, which accounts for the sensing procedure, secondary spatial reuse protocol, and environment-dependent conditions such as path loss, shadowing, and channel fading. We first derive the characteristic function and cumulants of the cognitive network interference at a primary user. Using the theory of truncated-stable distributions, we then develop the statistical model for the cognitive network interference. We further extend this model to include the effect of power control and demonstrate the use of our model in evaluating the system performance of cognitive networks. Numerical results show the effectiveness of our model for capturing the statistical behavior of the cognitive network interference. This work provides essential understanding of interference for successful deployment of future cognitive networks.

We analyze the performance of optimum combining in a general cochannel interference environment with thermal noise. Specifically, we consider multiple unequal-power interferers, each is spatially correlated across receiving antennas. We develop a new mathematical methodology to analyze the average symbol error probability (SEP) of optimum combining diversity systems in Rayleigh fading. The analysis resorts to the so-called Berezin's supermathematics that treats both commuting and Grassmann anticommuting variables on an equal footing. This superanalysis framework enables us to derive the exact SEP expression for an arbitrary number of interferers with spatial correlation and possibly different power levels. Our results therefore encompass all the previous analytical results, based on the theory of multivariate statistics relating to complex Wishart matrices, for equal-power and/or spatially-uncorrelated interferers. Connecting the powerful supermathematical framework to the analysis of wireless diversity systems with optimum combining, we quantify the interference effects in terms of the degree of power unbalance and the amount of spatial correlation.

Information-theoretic security--widely accepted as the strictest notion of security--relies on channel coding techniques that exploit the inherent randomness of propagation channels to strengthen the security of communications systems. Within this paradigm, we explore strategies to improve secure connectivity in a wireless network. We first consider the intrinsically secure communications graph (iS-graph), a convenient representation of the links that can be established with information-theoretic security on a large-scale network. We then propose and characterize two techniques--sectorized transmission and eavesdropper neutralization--which are shown to dramatically enhance the connectivity of the iS-graph.

In this paper, we propose a new statistical interference model for cognitive network based on the amplitude aggregate interference, which accounts for the parameters related to the sensing procedure, spatial reuse protocol employed by secondary users, and environment dependent conditions like channel fading and shadowing. We derive the characteristic function and the nth cumulant of the cognitive network interference on the primary user. By using the theory of truncated-stable distribution, we show how we can approximate the cognitive network interference analytically. We further show how to apply our model to derive system performance measure such as bit error probability in the presence of cognitive network interference. Moreover, this work can serve to bring additional understanding of cognitive network interference for successful deployment of cognitive networks in the future.

Long term evolution (LTE) is envisioned to be a key technology for the 4G wireless communication. In LTE systems, each user (UE) detects the surrounding base stations by searching the primary and secondary synchronization channel symbols in the received signal. Searching and tracking neighboring base stations is of great interest for UEs' handover and other applications such as base station cooperation. In this paper, we formulate the problem of neighboring cell search (NCS) for LTE systems and investigate this problem from both theoretical and practical perspectives. In particular, we first derive the sufficient signal metrics for NCS under various channel conditions and develop NCS algorithms based on these metrics. We also implement the algorithms on the simulator to validate its effectiveness.

In this paper, we investigate performance of acquisition receivers employing randomized search strategies. In particular, we derive the expressions of the mean acquisition times (MATs) for the uniform randomized search strategy and the non-return-to-self uniform randomized search strategy (NRS). These two search strategies are robust against variation in the maximum dispersion of the channel, making them attractive from the standpoint of a receiver design. We also quantify the MATs of receivers that employ the uniform randomized search strategy and the multi-dwell detectors with optimal thresholds. The MATs are plotted for various search strategies over a range of signal-to-noise ratios, using a channel model that is suitable for wideband transmission. The numerical examples confirm the benefit of the uniform randomized search strategy and the NRS over the conventional serial search strategy.

The intrinsically secure communications graph (iS-graph) is a random graph which captures the connections that can be securely established over a large-scale network, in the presence of eavesdroppers. It is based on principles of information-theoretic security, widely accepted as the strictest notion of security. In this paper, we are interested in characterizing the global properties of the iS-graph in terms of percolation on the infinite plane. We prove the existence of a phase transition in the Poisson iS-graph, whereby an unbounded component of securely connected nodes suddenly arises as we increase the density of legitimate nodes. Our work shows that long-range communication in a wireless network is still possible when a secrecy constraint is present.

Sensor networks can benefit greatly from location-awareness, since it allows information gathered by the sensors to be tied to their physical locations. Ultra-wide bandwidth (UWB) transmission is a promising technology for location-aware sensor networks, due to its power efficiency, fine delay resolution, and robust operation in harsh environments. However, the presence of walls and other obstacles presents a significant challenge in terms of localization, as they can result in positively biased distance estimates. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight (NLOS) propagation. From these channel pulse responses, we extract features that are representative of the propagation conditions. We then develop classification and regression algorithms based on machine learning techniques, which are capable of: (i) assessing whether a signal was transmitted in LOS or NLOS conditions; and (ii) reducing ranging error caused by NLOS conditions. We evaluate the resulting performance through Monte Carlo simulations and compare with existing techniques. In contrast to common probabilistic approaches that require statistical models of the features, the proposed optimization-based approach is more robust against modeling errors.

Future advanced radio-frequency identification (RFID) systems are expected to provide both identification and high-definition localization of objects with improved reliability and security while maintaining low power consumption and cost. Ultrawide bandwidth (UWB) technology is a promising solution for next generation RFID systems to overcome most of the limitations of the current narrow bandwidth RFID technology such as: reduced area coverage, insufficient ranging resolution for accurate localization, sensitivity to interference, and scarce multiple-access capability. In this paper, a survey of current progress in the application of the UWB technology for RFID systems is presented with particular attention to low-complexity solutions for high-definition tag localization.

Resource allocation promises significant benefits in wireless networks. In order to fully reap these benefits, it is important to design efficient resource allocation algorithms. Here, we develop relay power allocation (RPA) algorithms for coherent and noncoherent amplify-and-forward (AF) relay networks. The goal is to maximize the output signal-to-noise ratio under individual as well as aggregate relay power constraints. We show that these RPA problems, in the presence of perfect global channel state information (CSI), can be formulated as quasiconvex optimization problems. In such settings, the optimal solutions can be efficiently obtained via a sequence of convex feasibility problems, in the form of second-order cone programs. The benefits of our RPA algorithms, however, depend on the quality of the global CSI, which is rarely perfect in practice. To address this issue, we introduce the robust optimization methodology that accounts for uncertainties in the global CSI. We show that the robust counterparts of our convex feasibility problems with ellipsoidal uncertainty sets are semi-definite programs. Our results reveal that ignoring uncertainties associated with global CSI often leads to poor performance, highlighting the importance of robust algorithm designs in practical wireless networks.

Accurate positional information is essential for many applications in wireless networks. Time-of-arrival (TOA) and angle-of-arrival (AOA) are the two most commonly used signal metrics for localizing nodes with unknown positions. In this paper, we consider a wireless network in which each node is equipped with a wideband antenna array capable of performing both TOA and AOA measurements. Since both the position and orientation of the agent are of interest, we propose a localization framework that jointly estimates these two parameters. The notion of equivalent fisher information is applied to derive the squared error bounds for the position and orientation. Since our analysis starts from the received waveforms rather than directly from the signal metrics, these bounds characterize the fundamental limits of the position and orientation accuracy. Surprisingly, our result reveals that AOA measurements obtained by wideband antenna arrays do not further improve position accuracy beyond that provided by TOA measurements.

Multiple-input multiple-output (MIMO) systems are being considered as one of the key enabling technologies for future wireless networks. However, the decrease in capacity due to the presence of interferers in MIMO networks is not well understood. In this paper, we develop an analytical framework to characterize the capacity of MIMO communication systems in the presence of multiple MIMO co-channel interferers and noise. We consider the situation in which transmitters have no channel state information, and all links undergo Rayleigh fading. We first generalize the determinant representation of hypergeometric functions with matrix arguments to the case when the argument matrices have eigenvalues of arbitrary multiplicity. This enables the derivation of the distribution of the eigenvalues of Gaussian quadratic forms and Wishart matrices with arbitrary correlation, with application to both single-user and multiuser MIMO systems. In particular, we derive the ergodic mutual information for MIMO systems in the presence of multiple MIMO interferers. Our analysis is valid for any number of interferers, each with arbitrary number of antennas having possibly unequal power levels. This framework, therefore, accommodates the study of distributed MIMO systems and accounts for different spatial positions of the MIMO interferers.

The availability of position information is of great importance in many commercial, public safety, and military applications. The coming years will see the emergence of location-aware networks with submeter accuracy, relying on accurate range measurements provided by wide bandwidth transmissions. In this two-part paper, we determine the fundamental limits of localization accuracy of wideband wireless networks in harsh multipath environments. We first develop a general framework to characterize the localization accuracy of a given node here and then extend our analysis to cooperative location-aware networks in Part II. In this paper, we characterize localization accuracy in terms of a performance measure called the squared position error bound (SPEB), and introduce the notion of equivalent Fisher information (EFI) to derive the SPEB in a succinct expression. This methodology provides insights into the essence of the localization problem by unifying localization information from individual anchors and that from a priori knowledge of the agent's position in a canonical form. Our analysis begins with the received waveforms themselves rather than utilizing only the signal metrics extracted from these waveforms, such as time-of-arrival and received signal strength. Hence, our framework exploits all the information inherent in the received waveforms, and the resulting SPEB serves as a fundamental limit of localization accuracy.

The availability of position information is of great importance in many commercial, governmental, and military applications. Localization is commonly accomplished through the use of radio communication between mobile devices (agents) and fixed infrastructure (anchors). However, precise determination of agent positions is a challenging task, especially in harsh environments due to radio blockage or limited anchor deployment. In these situations, cooperation among agents can significantly improve localization accuracy and reduce localization outage probabilities. A general framework of analyzing the fundamental limits of wideband localization has been developed in Part I of the paper. Here, we build on this framework and establish the fundamental limits of wideband cooperative location-aware networks. Our analysis is based on the waveforms received at the nodes, in conjunction with Fisher information inequality. We provide a geometrical interpretation of equivalent Fisher information (EFI) for cooperative networks. This approach allows us to succinctly derive fundamental performance limits and their scaling behaviors, and to treat anchors and agents in a unified way from the perspective of localization accuracy. Our results yield important insights into how and when cooperation is beneficial.

We present a mathematical model for communication subject to both network interference and noise. We introduce a framework where the interferers are scattered according to a spatial Poisson process, and are operating asynchronously in a wireless environment subject to path loss, shadowing, and multipath fading. We consider both cases of slow and fast-varying interferer positions. The paper is comprised of two separate parts. In Part I, we determine the distribution of the aggregate network interference at the output of a linear receiver. We characterize the error performance of the link, in terms of average and outage probabilities. The proposed model is valid for any linear modulation scheme (e.g., M-ary phase shift keying or M-ary quadrature amplitude modulation), and captures all the essential physical parameters that affect network interference. Our work generalizes the conventional analysis of communication in the presence of additive white Gaussian noise and fast fading, allowing such results to account for the effect of network interference. In Part II of the paper, we derive the capacity of the link when subject to network interference and noise, and characterize the spectrum of the aggregate interference.

In Part I of this paper, we presented a mathematical model for communication subject to both network interference and noise, where the interferers are scattered according to a spatial Poisson process, and are operating asynchronously in a wireless environment subject to path loss, shadowing, and multipath fading. We determined the distribution of the aggregate interference and the error performance of the link. In this second part, we characterize the capacity of the link subject to both network interference and noise. Then, we put forth the concept of spectral outage probability (SOP), a new characterization of the aggregate radio-frequency emission generated by communicating nodes in a wireless network. We present some applications of the SOP, namely the establishment of spectral regulations and the design of covert military networks. The proposed framework captures all the essential physical parameters that affect the aggregate network emission, yet is simple enough to provide insights that may be of value in the design and deployment of wireless networks.

There has been an emerging interest in non-coherent ultra-wide bandwidth (UWB) communications, particularly for low-data rate applications because of its low-complexity and low-power consumption. However, the presence of narrowband (NB) interference severely degrades the communication performance since the energy of the interfering signals is also collected by the receiver. In this paper, we compare the performance of two non-coherent UWB receiver structures - the autocorrelation receiver (AcR) and the energy detection receiver (EDR) - in terms of the bit error probability (BEP). The AcR is based on the transmitted reference signaling with binary pulse amplitude modulation, while the EDR is based on the binary pulse position modulation. We analyze the BEPs for these two non-coherent systems in a multipath fading channel, both in the absence and presence of NB interference. We consider two cases: a) single NB interferer, where the interfering node is located at a fixed distance from the receiver, and b) multiple NB interferers, where the interfering nodes with the same carrier frequency are scattered according to a spatial Poisson process. Our framework is simple enough to enable a tractable analysis and provide insights that are of value in the design of practical UWB systems subject to interference.

Ultra-wide bandwidth (UWB) transmission is a promising technology for indoor localization due to its fine delay resolution and obstacle-penetration capabilities. However, the presence of walls and other obstacles introduces a positive bias in distance estimates, severely degrading localization accuracy. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight (NLOS) propagation. Based on this campaign, we extract key features that allow us to distinguish between NLOS and LOS conditions. We then propose a nonparametric approach based on support vector machines for NLOS identification, and compare it with existing parametric (i.e., model-based) approaches. Finally, we evaluate the impact on localization through Monte Carlo simulation. Our results show that it is possible to improve positioning accuracy relying solely on the received UWB signal.

With the increased competition for the electromagnetic spectrum, it is important to characterize the impact of interference in the performance of a wireless packet network, which is traditionally measured by its throughput. This paper presents a unifying framework for characterizing the throughput in wireless packet networks. We analyze the throughput from an outage perspective, in which a packet is successfully received if the signal-to-interference-plus-noise ratio (SINR) exceeds some threshold, considering the aggregate interference generated by all emitting nodes in the network. Our work generalizes and unifies various throughput results scattered throughout the literature. Furthermore, the proposed framework encompasses all types of wireless propagation effects (e.g, Nakagami-m fading, Rician fading, and log-normal shadowing), as well as traffic patterns (e.g., slotted-synchronous, slotted-asynchronous, and exponential-interarrivals traffic).

The combination of wideband transmission and cooperative techniques enables high-precision location-awareness. Wideband transmission provides fine delay resolution and multipath resolvability, while cooperation among nodes can yield significant performance benefit in harsh or infrastructure-limited environments. In this paper, we propose to exploit the geometric relationship inherent in multipath propagation, i.e., multipath geometry, via cooperation among nodes for localization. We characterize the contribution of this multipath geometry in terms of the nodes' squared position error bound, which is the fundamental limit of localization accuracy. Analytical and numerical results validate the benefit of using multipath geometry in wideband cooperative localization.

Fueled by the promises of high spectral efficiency, adaptive OFDMA has attracted enormous research interests over the last decade. The significant capacity gain of adaptive OFDMA comes from fast adaptation of resource allocation in response to instantaneous channel conditions. Despite years of efforts to improve the practicality of adaptive OFDMA, such promising technique is still far from real implementation due to the prohibitively high computational complexity and excessive control overhead. This paper is an endeavor to address the problem by proposing a slow adaptation scheme, where resource allocation is adapted on a much slower time scale than the fluctuation of wireless channel fading. Specifically, the slow adaptive OFDMA is formulated into a stochastic programming problem, which adapts resource allocation according to the channel statistics within an adaptation window rather than according to instantaneous channel conditions. By tuning the length of the adaptation window, we could engineer a desirable tradeoff between spectral efficiency and computational complexity. Furthermore, the proposed scheme can be modified to accommodate inelastic traffics. The modification, referred to as "safe" slow adaptation, ensures worst-case data rates to all users. In this work, safe slow adaptation is formulated into a conic linear program, which is efficiently solved via interior-point methods. Through extensive simulations, we show that the proposed schemes drastically reduce the computational complexity and control overheads, while achieving satisfactorily high spectral efficiency and QoS provisioning as their fast-adaptation counterpart does with a much higher cost.

We consider the fundamental security limits of stochastic wireless networks in the presence of colluding eavesdroppers. By establishing a direct connection with the single-input multiple-output (SIMO) Gaussian wiretap channel, we are able to provide a complete characterization of the secrecy capacity for the case in which the eavesdroppers are scattered according to a spatial Poisson process. Our analysis, which includes the probabilities of existence and outage of secrecy capacity, helps clarify how the spatial density of eavesdroppers can jeopardize the success of wireless physical-layer security based on information-theoretic principles.

With the increasing proliferation of different communication devices sharing the same spectrum, it is critical to understand the impact of interference in heterogeneous wireless networks. In this paper, we put forth a mathematical model for coexistence in networks composed of both narrowband (NB) and ultrawideband (UWB) wireless nodes, based on fundamental tools from stochastic geometry. Our model considers that the interferers are spatially scattered according to a Poisson field, and are operating asynchronously in a wireless environment. We first determine the statistical distribution of the aggregate interference for both cases of NB and UWB emitters. We then provide error probability expressions for two dual configurations: 1) a NB victim link subject to the aggregate UWB interference, and 2) a UWB victim link subject to the aggregate NB interference. The results show that while the impact of a single interferer on a link is often negligible due to restrictions on the transmitted power, the aggregate effect of multiple interferers may cause significant degradation. Therefore, aggregate interference must be considered to ensure coexistence in heterogeneous networks. The proposed analytical framework shows good agreement with physical-level simulations of the system.

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.

In this paper, we introduce a mathematical framework for the characterization of network interference in wireless systems. We consider a network in which the interferers are scattered according to a spatial Poisson process and are operating asynchronously in a wireless environment subject to path loss, shadowing, and multipath fading. We start by determining the statistical distribution of the aggregate network interference. We then investigate four applications of the proposed model: 1) interference in cognitive radio networks; 2) interference in wireless packet networks; 3) spectrum of the aggregate radio-frequency emission of wireless networks; and 4) coexistence between ultrawideband and narrowband systems. Our framework accounts for all the essential physical parameters that affect network interference, such as the wireless propagation effects, the transmission technology, the spatial density of interferers, and the transmitted power of the interferers.

Location-aware technologies will revolutionize many aspects of commercial, public service, and military sectors, and are expected to spawn numerous unforeseen applications. A new era of highly accurate ubiquitous location-awareness is on the horizon, enabled by a paradigm of cooperation between nodes. In this paper, we give an overview of cooperative localization approaches and apply them to ultrawide bandwidth (UWB) wireless networks. UWB transmission technology is particularly attractive for short- to medium-range localization, especially in GPS-denied environments: wide transmission bandwidths enable robust communication in dense multipath scenarios, and the ability to resolve subnanosecond delays results in centimeter-level distance resolution. We will describe several cooperative localization algorithms and quantify their performance, based on realistic UWB ranging models developed through an extensive measurement campaign using FCC-compliant UWB radios. We will also present a powerful localization algorithm by mapping a graphical model for statistical inference onto the network topology, which results in a net-factor graph, and by developing a suitable net-message passing schedule. The resulting algorithm (SPAWN) is fully distributed, can cope with a wide variety of scenarios, and requires little communication overhead to achieve accurate and robust localization.

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.

In this paper, we derive Gallager's random coding error exponent for multiple-input multiple-output (MIMO) Rayleigh block-fading channels, assuming no channel-state information (CSI) at the transmitter and perfect CSI at the receiver. This measure gives insight into a fundamental tradeoff between the communication reliability and information rate of MIMO channels, enabling to determine the required codeword length to achieve a prescribed error probability at a given rate below the channel capacity. We quantify the effects of the number of antennas, channel coherence time, and spatial fading correlation on the MIMO exponent. In addition, the general formulae for the ergodic capacity and the cutoff rate in the presence of spatial correlation are deduced from the exponent expressions. These formulae are applicable to arbitrary structures of transmit and receive correlation, encompassing all the previously known results as special cases of our expressions.

Random matrices play a crucial role in the design and analysis of multiple-input multiple-output (MIMO) systems. In particular, performance of MIMO systems depends on the statistical properties of a subclass of random matrices known as Wishart when the propagation environment is characterized by Rayleigh or Rician fading. This paper focuses on the stochastic analysis of this class of matrices and proposes a general methodology to evaluate some multiple nested integrals of interest. With this methodology we obtain a closed-form expression for the joint probability density function of k consecutive ordered eigenvalues and, as a special case, the PDF of the lscrthordered eigenvalue of Wishart matrices. The distribution of the largest eigenvalue can be used to analyze the performance of MIMO maximal ratio combining systems. The PDF of the smallest eigenvalue can be used for MIMO antenna selection techniques. Finally, the PDF the kthlargest eigenvalue finds applications in the performance analysis of MIMO singular value decomposition systems.

Cooperative diversity techniques can improve the transmission rate and reliability of wireless networks. For systems employing such diversity techniques in slow-fading channels, outage probability and outage capacity are important performance measures. Existing studies have derived approximate expressions for these performance measures in different scenarios. In this paper, we derive the exact expressions for outage probabilities and outage capacities of three proactive cooperative diversity schemes that select a best relay from a set of relays to forward the information. The derived expressions are valid for arbitrary network topology and operating signal-to-noise ratio, and serve as a useful tool for network design.

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.

Cooperative diversity is a useful technique to increase reliability and throughput of wireless networks. To analyze the performance gain from cooperative diversity, outage capacity is an important figure of merit that captures the inherent diversity-multiplexing trade-off in cooperative diversity schemes. In this paper we derive the outage capacity for several cooperative diversity schemes in decode-and-forward relay networks with a finite number of relay nodes. The obtained expressions are simple and applicable to arbitrary network topologies and signal-to-noise ratios in Rayleigh fading channels. The analysis shows that there exists a signal-to-noise ratio threshold, below which some cooperative diversity schemes are better than direct communication. We propose a new diversity scheme, which, compared to the conventional counterpart, offers improved performance and requires protocol overhead.

Location-awareness is of pivotal importance in future wireless systems. In these networks, energy efficiency is a critical issue since it affects network lifetime, throughput, and interference. In this paper, we investigate optimization issues associated with anchor power allocation, anchor selection, and anchor deployment. In particular, we minimize the squared position error bound (SPEB), which characterizes the limits of localization accuracy. To gain insights into the localization problem, we use the eigen-analysis and propose a geometric interpretation of the localization information. This approach facilitates the derivation of optimal solutions and serves guidelines for the design of algorithms in energy efficient location-aware networks. Our numerical results show that significant improvement in localization performance can be achieved with optimal resource allocation.

Accurate positioning is an essential issue in location- aware wireless networks. Wideband transmission is inherent suitable for indoor localization due to its potential of providing accurate range measurements. In this paper, we will investigate the effect of path-overlap phenomena on the localization accuracy, since wideband signals traveling between nodes in the network experience multipath propagation. The notion of equivalent Fisher information (EFI) is proposed and applied to derive the squared position error bound (SPEB), a measure of the localization accuracy. EFI unifies the contributions from individual anchors in a canonical form, i.e., a sum of ranging information (RI), which characterizes the ability of a received waveform for localization. We show that the effect of path-overlap on the RI can be quantified by the path-overlap coefficient. Our numerical results present the behaviors of the path-overlap coefficient for different propagation channels and transmitted waveforms.

Optical channels are limited in terms of data rate and fiber length due fiber non-linearity, chromatic dispersion and polarization mode dispersion. Electrical equalization is an attractive way to combat these forms of non-linear dispersion. With the advent of powerful error-correcting codes, equalizers must be able to work in an iterative fashion by exchanging soft information with the decoder. We will compare two such soft equalizers, the forward-backward equalizer and the Monte Carlo equalizer, and evaluate their performance.

This paper focuses on the stochastic analysis of Wishart matrices, which appear in many problems related to the performance analysis multiple-input-multiple-output (MIMO). We propose a general methodology to evaluate some multiple nested integrals of interest With this methodology we obtain a closed-form expression for the joint probability density function (pdf) of k consecutive ordered eigenvalues and, as a special case, the pdf of the lthordered eigenvalue of Wishart matrices.

In this paper, we assess the combined effects of rank deficiency and spatial fading correlation on the diversity performance of multiple-input multiple-output (MIMO) systems in terms of the symbol error probability, the effective fading figure (EFF), and the capacity at low signal-to-noise ratio (SNR). In particular, we consider a general family of MIMO channels known as double-scattering channels - i.e., Rayleigh product MIMO channels - which encompasses a variety of propagation environments from independent and identically distributed Rayleigh to degenerate keyhole cases by embracing both rank-deficient and spatial correlation effects. We quantify the combined effect of the spatial correlation and the lack of scattering richness on the EFF and the low-SNR capacity in terms of the correlation figures of transmit, receive, and scatterer correlation matrices. We further show the monotonicity properties of these performance measures with respect to the strength of spatial correlation, characterized by the eigenvalue majorization relations of the correlation matrices.

We derive Gallager's random coding error exponent for multiple-input multiple-output (MIMO) channels, assuming no channel-state information (CSI) at the transmitter and perfect CSI at the receiver. This measure gives insight into a fundamental tradeoff between the communication reliability and information rate of MIMO channels, enabling to determine the required codeword length to achieve a prescribed error probability at a given rate below the channel capacity. We quantify the effects of the number of antennas, channel coherence time, and spatial fading correlation on the MIMO exponent. In addition, the general formulae for the ergodic capacity and the cutoff rate in the presence of spatial correlation are deduced from the exponent expressions. These formulae are applicable to arbitrary structures of transmit and receive correlation, encompassing all the previously known results as special cases of our expressions.

Satellite channels are generally nonlinear and dispersive in nature, due to amplifiers being driven close to saturation. These effects can cause significant degradations when they are not taken into account at either the receiver (equalization) or at the transmitter (pre-distortion). State-of-the-art equalizers rely on the forward-backward algorithm and yield excellent performance. However, they have unreasonable complexity and storage requirements, especially for highly dispersive channels and/or large constellations. In this paper, we derive an equalization strategy for nonlinear channels based on Monte Carlo methods. We present a detailed performance, complexity and storage analysis. A significant performance gain compared to the linear equalizer is reported, and the proposed technique results in a significant reduction in both complexity and storage, compared to the forward-backward equalizer.

A cognitive radio can sense its environment and adapt some of its features, such as carrier frequency, transmission bandwidth, transmission power, and modulation, thus allowing dynamic reuse of the available spectrum. Due to their high degree of adaptability to environmental variations, cognitive radios are expected to utilize packet-based transmission with variable-length frames. Packet-based transmission requires the receiver to perform frame synchronization, an important enabling step that allows adaptation in cognitive radios. However, proper metrics to characterize the performance of frame synchronization for transmission of variable-length frames are currently unavailable. To address this issue, we put forth two performance metrics, namely the expected duration to complete frame synchronization and the probability of correct acquisition within a given duration. We then develop analytical expressions for these important metrics. This paper advances our understanding of frame synchronization for the continuous transmission of variable-length frames and for bursty transmission.

The need for accurate positioning has gained significant interest recently, especially in cluttered environments where signals from satellite navigation systems are not reliable. Positioning systems based on ultrawide bandwidth (UWB) technology have been considered for these environments because UWB signals are able to resolve multipath and penetrate obstacles. These systems usually obtain range measurements from time-of-arrival (TOA) estimation of the first path, which can be a challenge in dense multipath environments. In this paper, we analyze and compare the performance of matched filter (MF) and energy detector (ED) TOA estimators based on thresholding in UWB dense multipath channels. The main advantage of threshold-based estimators is that they have the potential for complete analog implementation and hence they are particularly attractive for applications that require low cost battery-powered devices. Closed-form expressions for the estimator bias and mean square error (MSE) are derived as a function of the signal- to-noise ratio. A comparison with results obtained from Monte Carlo simulation confirms the validity of our analytical approach. This analysis enables us to determine the threshold value that minimizes the MSE, a critical parameter for optimal estimator design. A simple criteria to determine the threshold value is also presented. It is shown that the estimation accuracy is mainly affected by the ambiguity in the selection of the correct peak at the output of the MF or ED, caused by the fading characteristics of the first path. We also evaluate the performance loss of ED estimators with respect to MF estimators. Finally, results based on experimental measurements in an indoor residential environment are presented in order to compare the performance of TOA estimators in realistic environments.

We address low-complexity, highly-accurate frequency offset estimation for multi-band orthogonal frequency division multiplexing (MB-OFDM) based ultra-wide band systems in time-invariant as well as time-variant channels. We investigate the unique characteristics of MB-OFDM systems, namely, different frequency offsets, channel responses, received energies, and preamble structures in different frequency bands. Utilizing them, we develop frequency offset estimators based on the best linear unbiased estimation principle. If compared to the reference estimators, our proposed methods achieve significantly better estimation performance (4 to 6.4 dB (5 to 20 dB) estimation mean-square error advantage in the time-invariant (time-variant) channels) for all preamble patterns of the MB-OFDM system in [8].

Error correction procedures are considered which are designed specifically for the amplitude damping channel. Amplitude damping errors are analyzed in the stabilizer formalism. This analysis allows a generalization of the[4,1]ldquoapproximaterdquo amplitude damping code. This generalization is presented as a class of[2(M+1),M] codes; quantum circuits for encoding and recovery operations are presented. A[7,3]amplitude damping code based on the classical Hamming code is presented. All of these are stabilizer codes whose encoding and recovery operations can be completely described with Clifford group operations. Finally, optimization options are described in which recovery operations may be further adapted according to the damping probabilitygamma.

The potential benefits of multiple-antenna systems may be limited by two types of channel degradations-rank deficiency and spatial fading correlation of the channel. In this paper, we assess the effects of these degradations on the diversity performance of multiple-input multiple-output (MIMO) systems, with an emphasis on orthogonal space-time block codes (OSTBC), in terms of the symbol error probability (SEP), the effective fading figure (EFF), and the capacity at low signal-to-noise ratio (SNR). In particular, we consider a general family of MIMO channels known as double-scattering channels-i.e., Rayleigh product MIMO channels-which encompasses a variety of propagation environments from independent and identically distributed (i.i.d.) Rayleigh to degenerate keyhole or pinhole cases by embracing both rank-deficient and spatial correlation effects. It is shown that a MIMO system with transmit and receive antennas achieves the diversity of order in a double-scattering channel with effective scatterers. We also quantify the combined effect of the spatial correlation and the lack of scattering richness on the EFF and the low-SNR capacity in terms of the correlation figures of transmit, receive, and scatterer correlation matrices. We further show the monotonicity properties of these performance measures with respect to the strength of spatial correlation, characterized by the eigenvalue majorization relations of the correlation matrices.

We consider the problem of decentralized detection in a network consisting of a large number of nodes arranged as a tree of bounded height, under the assumption of conditionally independent and identically distributed (i.i.d.) observations. We characterize the optimal error exponent under a Neyman-Pearson formulation. We show that the Type II error probability decays exponentially fast with the number of nodes, and the optimal error exponent is often the same as that corresponding to a parallel configuration. We provide sufficient, as well as necessary, conditions for this to happen. For those networks satisfying the sufficient conditions, we propose a simple strategy that nearly achieves the optimal error exponent, and in which all non-leaf nodes need only send 1-bit messages.

We consider the problem of Bayesian decentralized binary hypothesis testing in a network of sensors arranged in a tandem. We show that the rate of error probability decay is always subexponential, establishing the validity of a long-standing conjecture. Under the additional assumption of bounded Kullback-Leibler (KL) divergences, we show that for alld> 1/2, the error probability is Omega(e-cnd), wherecis a positive constant. Furthermore, the bound Omega(e-c(logn)d) , for alld> 1, holds under an additional mild condition on the distributions. This latter bound is shown to be tight.

Practical cooperative diversity protocols often rely on low-cost radios that treat multiple in-band signals as noise and thus require strictly orthogonal transmissions. We analyze the performance of a class of opportunistic relaying protocols that employ simple packet level feedback and strictly orthogonal transmissions. It is shown that the diversity-multiplexing tradeoff of the proposed protocols either matches or outperforms the multi-input-single-output (MISO), zero-feedback performance. These gains indicate that low complexity radios and feedback could be an appealing architecture for future user cooperation protocols.

In modern wireless systems employing diversity techniques, combining all the available diversity branches may not be feasible due to complexity and resource constraints. To alleviate these issues, subset diversity (SSD) systems have been proposed. Here, we develop a framework for evaluating the symbol error probability for antenna SSD, where the signals from a subset of antenna elements are selected and combined in the presence of channel estimation error. We consider independent identically distributed Rayleigh fading channels and use an estimator structure based on the maximum likelihood (ML) estimate which arises naturally as the sample mean of Nppilot symbols. The analysis is valid for arbitrary two-dimensional signaling constellations. The expressions give insight into the performance losses of non-ideal SSD when compared to ideal SSD. Due to estimation error, these losses occur in branch combining as well as in branch selection. However, our analytical results show that the practical ML channel estimator still preserves the diversity order of an ideal SSD system with Ndbranches. Finally, we investigate the asymptotic signal-to-noise ratio penalty due to estimation error.

In this paper, we derive the asymptotic statistics of mutual information for multiple-input multiple-output (MIMO) Rayleigh-fading channels in the presence of spatial fading correlation at both the transmitter and the receiver. We first introduce a class of asymptotic linear spectral statistics, calledcorrelants, for a structured correlation matrix. The mean and variance of MIMO mutual information are then expressed in terms of the correlants of spatial correlation matrices in the asymptotic regime where the number of transmit and receive antennas tends to infinity. In particular, using Szego's theorem on the asymptotic eigenvalue distribution of Toeplitz matrices, we give examples for special classes of correlation matrices with Toeplitz structure-exponential(orKac-Murdock-Szego),tridiagonal,andconstant(orintraclass) correlation matrices.

We study the asymptotic detection performance of large sensor networks, configured as trees with bounded height, in which information is progressively compressed as it moves towards the root of the tree. We show that the error probability decays exponentially fast, and we provide bounds for the error exponent. We analyze further the case where the tree has certain symmetry properties, and derive simple, easily implementable, suboptimal strategies

We consider the problem of decentralized binary hypothesis testing in a network of sensors arranged in a tandem. We show that the rate of error probability decay is always sub-exponential, establishing the validity of a long-standing conjecture. Under the additional assumption of bounded Kullback-Leibler divergences, we show that for all d > 1/2, the error probability is Omega(e-cnd), where c is a positive constant. Furthermore, the bound Omega(e-c(logn)d), for all d > 1, holds under an additional mild condition on the distributions. This latter bound is shown to be tight.

Relay power allocation has been shown to provide substantial performance gain in wireless relay networks when perfect global channel state information (CSI) is available. In this paper, we consider a more realistic scenario, where such global CSI is subject to uncertainty, and we aim to design robust power allocation protocols for both the coherent and noncoherent amplify-and-forward relay networks. The problem formulation is such that the output signal-to-noise ratio is maximized under both the aggregate and individual relay power constraints. Our previous results show that these optimization problems can be formulated as quasiconvex optimization problems, and are solved using the bisection method via a sequence of conic feasibility problems. We extend these results to the case of uncertain global CSI, and design robust relay power allocations using the robust optimization methodology. For simple ellipsoidal uncertainty sets, the robust counterparts of these optimization problems are semi-definite programs and can be solved efficiently via interior-point methods.

Relay power allocation has been shown to provide substantial performance gain in wireless relay channels when perfect global channel state information (CSI) is available. In this paper, we show that by using a class of conic optimization theory, we can solve the relay power allocation problem for amplify-and-forward (AF) relay networks in a straightforward manner. The problem formulation is such that the achievable rate with perfect global CSI is maximized under both the aggregate and individual relay power constraints for coherent and noncoherent AF relay networks. Numerical results quantify the performance gain using the optimal relay power allocation for both the coherent and noncoherent AF relay networks.

In this paper, the authors analyze the effects of system bandwidth, transmitter-receiver (TX-RX) separation distance and line-of-sight (LOS) blockage on the ranging error in realistic ultra-wideband (UWB) channels. Data collected from extensive UWB indoor propagation channel measurements conducted in various types of apartments with bandwidth of 7 GHz are used to study the above effects. Here, a threshold-based time-of-arrival (ToA) estimator is employed to estimate the ToA of the first arriving path. An optimum threshold value is set for each measurement data set according to the channel operating conditions by adapting the thresholding technique proposed in Dardari and Win, 2006, Analysis of the measurement results show that the ranging error is independent of the TX-RX separation distance. However, the presence of LOS blockage increases the ranging error due to the positive bias introduce by different materials that block the LOS path. We then develop a model to quantify the effect of bandwidth on the ranging error due to multipath dispersion, which is found to be well-modeled by an exponential function.

We address low-complexity, highly-accurate frequency offset estimation for MB-OFDM-based UWB systems in time-variant channels. We present a simple channel model capturing an abrupt channel change characteristic of UWB systems. We devise an abrupt channel change detector and develop an oscillator frequency offset estimator based on best linear unbiased estimation principle while taking into account the time-variant channel behavior as well as the MB-OFDM characteristics such as different carrier frequency offsets, different channel responses, different channel energies, and different preamble structures in different frequency bands. If compared to the conventional estimators using correlation, our proposed method achieves significantly better estimation performance for all preamble patterns of the MB-OFDM system.

Determination of position accuracy for geolocation is a fundamental issue in wireless sensor networks. This paper derives the position error bound (PEB), a fundamental limit for localization accuracy, by using information inequality. In particular, the authors consider all multipath propagation parameters, and hence our bound is tighter than those of previous work. To alleviate computation complexity, the authors put forth the notion of equivalent Fisher information (EFI) to characterize the localization accuracy. This approach also unifies the contributions from line-of-sight (LOS), non-line-of-sight (NLOS), and a priori knowledge to the PEB in a consistent form. These results are applicable to ultra-wide bandwidth (UWB) systems as a specific case.

Determination of position accuracy for geolocation is a fundamental issue in wireless sensor networks. In a dense obstacle environment, anchors (or base stations) may not be able to provide sufficient localization information to agents because of radio blockage or limited range. In such cases, cooperation among agents (or mobile stations) can be very helpful. In this paper, we develop a model for cooperative localization based on time-of-arrival (TOA) ranging information and derive the position error bound (PEB) for agents in the network using information inequality. Equivalent Fisher information (EFI), which has been applied in the single agent localization case (Sen and Win, 2007), is employed to characterize the localization accuracy. From analysis, we also show that anchors and agents are essentially equivalent in our unified cooperative localization model.

We study decentralized detection in tree networks with bounded height, and in which there are either sensor failures or unreliable communications between sensors. We characterize the asymptotically optimal performance of such tree networks, when certain parameters are allowed to become large, to model the case of dense sensor networks. We also develop simple strategies that nearly achieve the optimal performance.

Decentralized detection in a network of wireless sensor nodes involves the fusion of information about a phenomenon of interest (PoI) from geographically dispersed nodes. In this paper, we investigate the problem of binary decentralized detection in a dense and randomly deployed wireless sensor network (WSN), whereby the communication channels between the nodes and the fusion center are bandwidth-constrained. We consider a scenario in which sensor observations, conditioned on the alternate hypothesis, are independent but not identically distributed across the sensor nodes. We compare two different fusion architectures, namely, the parallel fusion architecture (PFA) and the cooperative fusion architecture (CFA), for such bandwidth-constrained WSNs, where each sensor node is restricted to send a I-bit information to the fusion center. For each architecture, we derive expression for the probability of decision error at the fusion center. We propose a consensus flooding protocol for CFA and analyze its average energy consumption. We analyze the effects of PoI intensity, realistic link models, consensus flooding protocol, and network connectivity on the system reliability and average energy consumption for both fusion architectures. We demonstrate that a trade-off exists among spatial diversity gain, average energy consumption, delivery ratio of the consensus flooding protocol, network connectivity, node density, and Poll intensity in CFA. We then provide insight into the design of cooperative WSNs.

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, we propose a search technique that takes advantage of multipath, which has long been considered deleterious for efficient communication, to aid the sequence acquisition in dense multipath channels. We consider a class of serial-search strategies and use optimization and convexity theories to determine fundamental limits of achievable mean acquisition times (MATs). In particular, we derive closed-form expressions for both the minimum and maximum MATs and the conditions for achieving these limits. We prove that a fixed-step serial search, a form of nonconsecutive serial search, achieves a near-optimal MAT. We also prove that the conventional serial search, in which consecutive cells are tested serially, should be avoided as it results in the maximum MAT. Our results are valid for all signal-to-noise ratio (SNR) values, regardless of the specifics of the detection layer and the fading distributions.

We consider the problem of decentralized binary detection in a sensor network where the sensors have access to side information that affects the statistics of their measurements, or reflects the quality of the available channel to a fusion center. Sensors can decide whether or not to make a measurement and transmit a message to the fusion center ("censoring"), and also have a choice of the mapping from measurements to messages. We consider the case of a large number of sensors, and an asymptotic criterion involving error exponents. We study both a Neyman-Pearson and a , Bayesian formulation, characterize the optimal error exponent, and derive asymptotically optimal strategies for the case where sensor decisions are only allowed to depend on locally available information. Furthermore, we show that for the Neyman-Pearson case, global sharing of side information ("sensor cooperation") does not improve asymptotic performance, when the Type I error is constrained to be small.

In this paper, we present simple opportunistic relaying with decode-and-forward (DaF) and amplify-and-forward (AaF) strategies under an aggregate power constraint. In particular, we consider distributed relay-selection algorithms requiring only local channel knowledge. We show that opportunistic DaF relaying is outage-optimal, that is, it is equivalent in outage behavior to the optimal DaF strategy that employs all potential relays. We further show that opportunistic AaF relaying is outage-optimal among single-relay selection methods and significantly outperforms an AaF strategy based on equal-power multiple-relay transmissions with local channel knowledge. These findings reveal that cooperation offers diversity benefits even when cooperative relays choose not to transmit but rather choose to cooperatively listen; they act as passive relays and give priority to the transmission of a single opportunistic relay. Numerical and simulation results are presented to verify our analysis.

One of the major issues for the design of ultra-wideband (UWB) receivers is the need to recover the signal energy dispersed over many multipath components, while keeping the receiver complexity low. To this aim we consider two schemes for reduced-complexity UWB Rake receivers, both of which combine a subset of the available resolved multipath components. The first method, called partial Rake (PRake), combines theirs/ arriving multipath components. The second is known as selective Rake (SRake) and combines the instantaneously strongest multipath components. We evaluate and compare the link performance of these Rake receivers in different UWB channels, whose models are based on extensive propagation measurements. We quantify the effect of the channel characteristics on the receiver performance, analyzing in particular the influence of small-scale fading statistics. We find that for dense channels the performance of the simpler PRake receiver is almost as good as that of the SRake receiver, even for a small number of fingers. In sparse channels, however, the SRake outperforms the PRake significantly. We also show that for a fixed transmitted energy there is an optimum transmission bandwidth

Transmitted-Reference (TR) signaling, in conjunction with an autocorrelation receiver (AcR), offers a low- complexity alternative to Rake reception in ultrawide bandwidth systems. This paper provides a unified performance analysis of various TR schemes by developing an analytical framework based on the sampling expansion approach. Specifically, we derive the uncoded bit error probability (BEP) of different TR signaling schemes, including TR and differential TR (DTR) signaling valid for a broad class of fading channels. We consider both AcRs and modified AcRs with noise averaging. We further develop a quasi-analytical method as well as an approximate analytical method to extend the BEP analysis to include the effect of narrowband interference (NBI). We show that the approximate analytical method is particularly useful in obtaining BEP expressions that provide insight into the effect of NBI. We quantify the effects of NBI and channel power dispersion profile on the optimum integration interval of an AcR. Finally, we compare TR and DTR signaling in terms of their sensitivity to NBI.

In a multiuser scenario, we study the capacity of multiple-input/multiple-output (MIMO) communication systems in the presence of multiple MIMO co-channel interferers. We assume that transmitters have no information about the channel status and that all links undergo Rayleigh distributed fading; no restrictions are made to the transmission power levels or on the number of interferers and antennas, so that many possible cases can be studied, including distributed MIMO. In order to be able to cover all possible scenarios, we generalize the known determinant representation of hypergeometric functions with matrix arguments to the case when the argument matrices have eigenvalues with arbitrary multiplicity. Possible extensions and numerical results are then sketched.

This paper puts forth the new concept of spectral outage probability (SOP), which can be used to quantify and limit the impact of the aggregate network interference on any given receiver operating in the same frequency band. We consider a scenario with asynchronous, slowly-moving interferers which are scattered according to a spatial Poisson process, and are operating in a wireless environment subject to path loss, shadowing, and multipath fading. We determine the power spectral density (PSD) of the aggregate interference at any location in the plane, and then define and provide expressions for the corresponding SOP. Lastly, we quantify the PSD and SOP as a function of various parameters, such as the path loss exponent, and the interferers' power and spatial density. Our analysis is valid for any linear modulation scheme, and provides insights that may be of value to the network designer.

We study sequential frame synchronization based on markers (or sync words), where the received samples are observed over a window of length equal to the marker length. A decision variable is derived over the observation window. A marker is declared if the decision variable exceeds a threshold; otherwise the observation window is time-shifted by one sample. This detection strategy can be characterized by the probabilities of false alarm and of correct detection, usually represented in terms of the receiver operating characteristic (ROC). However, once the detector (and hence its ROC) is given, it is crucial from a design perspective to select an optimal operating point on the ROC. In this paper we propose a general framework to connect the ROC to the performance of frame synchronization in terms of the probability of correct acquisition in an arbitrarily long time period, valid both for constant and variable length frames. This framework can also be used to compare different marker detectors for frame synchronization.

In this paper we put forth a simple threshold-based time-of-arrival (ToA) estimator and analyze its performance in ultra-wide bandwidth (UWB) dense multipath channels. Closed-from expressions for the estimator bias and mean square error are derived as a function of signal-to-noise ratio (SNR), overcoming the limitation of conventional asymptotic analysis valid only for high SNR. A comparison with results based on Monte Carlo simulation as well as those based on experimental measurements in an office environment confirms the validity of our analytical approach. Our analysis enables us to determine the threshold value that minimizes the mean-square error, critical parameter for optimal estimator design. It is shown that the estimation accuracy mainly depends on large estimation errors due to peak ambiguities at the output of the correlator and on the fading statistics of the first path.

This paper investigates time of arrival (ToA) estimation methods for ultra-wide bandwidth (UWB) propagation signals. Different algorithms are implemented in order to detect the direct path in a dense multipath environment. In particular, suboptimal low-complex techniques based on peak detection are used to deal with partial overlap of signal paths. A comparison in terms of ranging accuracy, complexity, and parameters sensitivity to propagation conditions is carried out also considering a conventional technique based on threshold detection. The algorithms are tested on experimental data collected from a measurement campaign performed in a typical office building.

Range-only localization techniques in dense cluttered environments typically lack accuracy due, notably, to partial and complete line-of-sight (LOS) blockage. Furthermore, the quality of the range measurements degrades with distance, and the geometric configuration of the beacons also affects the localization accuracy. In this paper we derive a fundamental limit of localization accuracy for an ultra-wide bandwidth (UWB) system operating in such environments, which we call the Position Error Bound (PEB). The impact of different ranging estimation errors due to beacons distance and biases on the best positioning accuracy is investigated. The statistical characterization of biases coming from measurement campaigns can easily be incorporated into this analysis. We show that the relative importance of information coming from different beacons varies depending on the propagation conditions, such as whether the beacon is LOS or non-line-of-sight (NLOS). We show in particular that NLOS beacons can significantly improve the localization accuracy compared to the case when they are not considered, especially in dense cluttered environments.

Decentralized detection in a network of wirelesensor sensor nodes involves the fusion of information about a phenomenon of interest (POI) from geographically dispersed nodes. In this paper, we investigate a binary decentralized detection problem in a dense wireless sensor network. We consider a scenario in which sensor observations, conditioned on the alternate hypothesis, are independent but not identically distributed across the sensor nodes. We consider two different fusion architectures, namely, the parallel fusion architecture and the cooperate fusion architecture. For each architecture, we derive expressions for the probability of decision error at the fusion center. We analyze the impact of a realistic sensing model, and flooding protocol on the system reliability and energy consumption for both architectures. We demonstrate that a trade-off exists among spatial diversity gain, average energy consumption, node density, and POI intensity in the cooperative architecture.

Variable-length block-coding schemes are investigated for discrete memoryless channels (DMC) with perfect feedback under cost constraints. Upper and lower bounds are found for the minimum achievable probability of decoding error Pepsi,minas a function of transmission rate R, cost constraint P, and expected block length taumacr. For given P and R, the lower and upper bounds to the exponent -(InPepsi,min)/taumacr are asymptotically equal as taumacr rarrinfin. The reliability function, limtaurarrinfin(-ln Pepsi,min)/taumacr, as a function of P and R, is concave in the pair (P, R) and generalizes the linear reliability function of Burnashev (M.V. Burnashev, 1976) to include cost constraints

Spread-spectrum systems with large transmission bandwidth present significant challenges from the standpoint of achieving acquisition before the communication commences. This paper investigates a rapid acquisition procedure that exploits multipath to aid the synchronization. In particular, we consider a randomized search strategy and derive the corresponding mean acquisition time (MAT). We show that the MAT of the randomized search is at most twice the MAT of the optimal serial search. Using our results, we compare various search techniques in a multipath fading channel

We consider the problem of Bayesian decentralized binary detection in a sensor network in which the sensors have access to some side information that affects the statistics of the measurements they make. Sensors can decide whether or not to make a measurement and transmit a message to the fusion center ("censoring"), and also have a choice of the transmission function from measurements to messages. We consider the case of a large number of sensors, characterize the optimal error exponent, and derive asymptotically optimal strategies. We show that the optimal strategy consists of dividing the sensors into two groups, with sensors in each group using the same policy

We study the diversity-multiplexing tradeoff in cooperative diversity systems involving multiple relays. We focus on low complexity architectures that do not require simultaneous transmissions on the same frequency band and therefore are amenable to practical implementation with low-cost radios. We show that smart relay selection protocols achieve the same performance as previously proposed protocols that rely on multi-terminal space-time coding. Our study includes both analog and digital relays under a variety of relay selection criteria, and considers the availibility of decision feedback in the network. Our results present an alternative to distributed space-time codes for realizing the potential gains in multiple relay cooperative systems and open new avenues for fruitful interaction between routing and cooperative diversity.

In this paper, we derive the asymptotic symbol error probability (SEP) of antenna subset diversity (SSD), where the signals from a subset of antenna elements are selected and combined in the presence of channel estimation error. The analysis is valid for arbitrary two-dimensional signaling constellations. We investigate the asymptotic SNR penalty, or performance loss between this system and an ideal system, caused by estimation error. We also compare this SNR penalty to that of a SSD system operating with perfect selection, but imperfect combining. We consider independent identically distributed (i.i.d.) Rayleigh fading channels and use an estimator structure based on the maximum likelihood (ML) estimate which arises naturally as the sample mean of Np pilot symbols. In both cases our analytical results show that the practical ML channel estimator still preserves the diversity order of an ideal SSD system with Nd branches.

Decentralized detection in a network of wireless sensor nodes involves the fusion of information about a phenomenon of interest (POI) from geographically dispersed sensor nodes. In this paper, we investigate a binary decentralized detection problem in a dense wireless sensor network (WSN). We assume that the strength of the POI varies spatially. We consider a fusion architecture which allows cooperation among the nodes. In order to allow the nodes to cooperate, we propose a consensus flooding protocol and analyze the average energy consumption when cooperation is present. Unlike similar works, we analyze the effect of a realistic sensing model, flooding protocol and network connectivity on the system reliability and energy consumption. We demonstrate that a trade-off exists among spatial diversity gain, average energy consumption, delivery ratio of the flooding protocol, network connectivity, node density, and POI intensity in the cooperative architecture. Consequently, we are able to determine when the additional performance gain provided by cooperation outweighs the increase in average energy consumption required to cooperate.

In this paper, we present single-selection-opportunistic-relaying with decode-and-forward (DaF) and amplify-and-forward (AaF) protocols under an aggregate power constraint. We show that opportunistic DaF relaying is equivalent to the outage bound of the optimal DaF strategy using all potential relays. We further show that opportunistic AaF relaying is outage-optimal with single-relay selection and significantly outperforms an AaF strategy with multiple-relay (MR) transmissions, in the presence of limited channel knowledge. These findings reveal that cooperative diversity benefits (under an aggregate power constraint) are useful even when cooperative relays choose not to transmit but rather choose to cooperatively listen; they act as passive relays and give priority to the transmission of a single opportunistic relay

Time-varying multipath fading associated with the wireless link limits the capacity of a wireless system. In order to adapt to this adverse radio environment efficiently, we investigate the use of a pilot-aided fade-resistant transmission scheme for the uplink of a chip-interleaved code-division multiple-access (CDMA) system. We analyze the tradeoff between the number of diversity branches and the channel estimation error. We derive the optimum ratio of pilot signal energy to information signal energy. Our numerical study indicates that the proposed system is capable of outperforming the conventional CDMA system depending on the transmitter energy and channel condition.

We present a shot noise based model for a large family of ultrawide bandwidth (UWB) signals. These include time-hopping and direct-sequence signaling with pulse position, interval, and amplitude modulations. Each specific signal is constructed by adding features to a basic model in a modular, simple, and tractable way. Our work unifies the contributions scattered in the literature and provides a general approach that allows various extensions of previous works as well as new results. The exact power spectrum is then evaluated using shot noise spectral theory, which provides a simpler, systematic, and rigorous approach to the spectra evaluation of complicated UWB signals. The strength of our methodology is that different features of the signal model contribute clearly and separately to the resulting spectral expressions.

In hybrid selection/maximal ratio combining (H-S/MRC) the receiver selects the L branches with the largest signal-to-noise ratios (SNRs) from N available diversity branches and performs maximal ratio combining (MRC). A penalty is incurred with respect to MRC which can be defined as the increase in SNR required for H-S/MRC to achieve the same symbol error probability as MRC. Recently, this penalty was investigated specifically for two-dimensional modulations. In this paper, we derive the asymptotic SNR penalties in slow Rayleigh fading for small and large values of SNR for general uncoded signaling constellations.

Physical limitations due to fading and interference in wireless channels pose technical challenges in achieving reliable communication. To adapt to this adverse radio environment efficiently, we propose a fade-resistant transmission system based on code division multiple access (CDMA) technology. In this system, all the symbols of a user are transmitted simultaneously using parallel sequences over a time-varying wireless channel. We derive the relationship between the self-interference and the multiple-access interference in such a system. We prove that instantaneous signal-to-interference-plus-noise ratio (SINR) at the matched filter output of a system with parallel sequences is statistically higher than that of the conventional CDMA system. Numerical results demonstrate that the parallel sequence transmission scheme can provide capacity improvement over the conventional CDMA system depending on variations in the channel.

We put forth a saddlepoint approximation for the outage capacity of multiple-input multiple-output (MIMO) systems using the exact moment generating function of the capacity. We consider both uncorrelated and spatially correlated Rayleigh-fading channels. Our results show that the saddlepoint method gives a remarkably accurate approximation to the outage capacity even at extremely low outage probabilities

In this paper, we analyze the capacity of multiple-input multiple-output (MIMO) Rayleigh-fading channels in the presence of spatial fading correlation at both the transmitter and the receiver, assuming the channel is unknown at the transmitter and perfectly known at the receiver. We first derive the determinant representation for the exact characteristic function of the capacity, which is then used to determine the trace representations for the mean, variance, skewness, kurtosis, and other higher-order statistics (HOS). These results allow us to exactly evaluate two relevant information-theoretic capacity measures - ergodic capacity and outage capacity - and the HOS of the capacity for such a MIMO channel. The analytical framework presented in the paper is valid for arbitrary numbers of antennas, and generalizes the previously known results for independent and identically distributed or one-sided correlated MIMO channels to the case when fading correlation exists on both sides. We verify our analytical results by comparing them with Monte Carlo simulations for a correlation model based on realistic channel measurements as well as a classical exponential correlation model

The focus of this paper is to derive the symbol error probability (SEP) of a Rake receiver with a limited number of fingers that track the strongest multipath components in a frequency-selective Rayleigh fading channel. We develop an analytical framework that allows the computation of the SEP for nonuniform power dispersion profiles (PDPs) and spreading bandwidth. By transforming the physical Rake receiver with correlated ordered paths into the domain of a 'virtual Rake" receiver with conditionally independent virtual paths, analytical expressions for the SEP are derived in terms of the spreading bandwidth, the channel profile, and the number of combined paths. We show how our analytical results can be used to predict the performance of various Rake architectures in environments with nonuniform PDPs using for example, the channel models defined for the next generation wireless standards. Furthermore, we validate our methodology by comparison to data obtained from channel measurements, showing good agreement with our analytically derived results.

We address the problem of modeling received pulse trains in a multipath fading channel and of computing their exact spectrum. We propose a model based on point processes. Such model is very general, simple and tractable and it allows to account for various phenomena that affect the transmission. We then give the exact spectrum of the output of such a model. Spectral formula of specific configurations are then derived from a singular general formula, where the various features of the channel and the pulse transmission appear explicitly

Transmitted-reference (TR) signaling, in conjunction with an autocorrelation receiver (AcR), offers a low-complexity alternative to Rake reception. Due to its simplicity, there is renewed interest in TR signaling for ultrawide bandwidth (UWB) systems. To assess the performance of these systems, we develop an analytical framework based on the sampling expansion approach. In particular, we derive closed-form expression for the bit-error probability (BEP) of TR signaling with AcR that can be used to exploit multipath diversity inherent in wideband channels. We further extend our analysis to the BEP derivation of modified AcR with noise averaging. Our methodology does not require the Gaussian approximation and is applicable for any fading scenario, provided that the correlator output signal-to-noise ratio (SNR) can be characterized in terms of a characteristic function. We show that the validity of the conventional Gaussian approximation depends on the time-bandwidth product and the number of transmitted pulses per symbol. Our results enable the derivation of a computationally simple lower bound on the BEP of TR signaling with AcR. This lower bound allows us to obtain the SNR penalty associated with an AcR, as compared with All-Rake and Partial-Rake receivers.

In this letter, we derive a closed-form symbol-error probability expression for adaptive antenna array with optimum (or, equivalently, linear minimum mean-square error) combining. We consider coherent detection of M-ary phase-shift keying signals in the presence of unequal-power interferers and thermal noise. The analysis is based on our new results on the eigenvalues distribution of central Wishart matrices with correlation.

This paper evaluates the performance of wideband communication systems in the presence of narrowband interference (NBI). In particular, we derive closed-form bit-error probability expressions for spread-spectrum systems by approximating narrowband interferers as independent asynchronous tone interferers. The scenarios considered include additive white Gaussian noise channels, flat-fading channels, and frequency-selective multipath fading channels. For multipath fading channels, we develop a new analytical framework based on perturbation theory to analyze the performance of a Rake receiver in Nakagami-m channels. Simulation results for NBI such as GSM and Bluetooth are in good agreement with our analytical results, showing the approach developed is useful for investigating the coexistence of ultrawide bandwidth systems with existing wireless systems.

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 in terms of symbol error probability (SEP) of multiple-input-multiple-output (MIMO) systems with high spectral efficiency. In particular, we consider the coherent detection of M-PSK signals in a flat Rayleigh-fading environment. We focus on spectrally efficient MIMO systems where, after serial-to-parallel conversion, several substreams of symbols are simultaneously transmitted by using an antenna array, thereby increasing the spectral efficiency. The reception is based on linear minimum mean-square-error (MMSE) combining, eventually followed by successive interference cancellation. Exact and approximate expressions are derived for an arbitrary number of transmitting and receiving antenna elements. Simulation results confirm the validity of our analytical methodology.

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.

We present a framework for evaluating the bit error probability of N/sub d/-branch diversity combining in the presence of non-ideal channel estimates. The estimator structure is based on the maximum likelihood (ML) estimate and arises naturally as the sample mean of N/sub p/ pilot symbols. The framework presented requires only the evaluation of a single integral involving the moment generating function of the norm square of the channel-gain vector, and is applicable to channels with arbitrary distribution, including correlated fading. Our results show that the diversity order of a system with practical channel estimation matches that of an ideal system operating in the same correlated fading environment, regardless of the number of pilot symbols used in the estimation process.

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 investigate the performance of time hopping (TH) pulse position modulation (PPM) systems in the presence of narrowband interference. In particular, we derive closed-form expressions for the bit error probability (BEP) of a TH-PPM system with independent asynchronous tone interferers with arbitrary amplitudes and frequencies. Different scenarios are taken into consideration with fading on the interferer and/or on the useful signal. Simulation results show that the assumption of tone interferers is a good approximation for narrowband interferers. It is shown that our analytical results are useful in assessing the possible coexistence of TH-PPM systems with existing wireless systems.

Interference and fading inherent to the radio channel limit the capacity of wireless systems. To overcome these impairments, we adopt parallel symbol transmission scheme and propose a protocol that adjusts the duration (or length) of symbols based on the past instantaneous channel and interference conditions. We demonstrate that the proposed system can provide performance improvements over the existing systems.

Transmitted-reference (TR) signaling, in conjunction with an autocorrelation receiver (AcR) offers a low-complexity alternative to Rake reception. Due to its simplicity, there is renewed interest in TR systems for UWB systems. To assess the performance of such systems, we develop an analytical framework based on a sampling expansion approach. In particular, we derive closed-form expressions for the exact SEP of TR systems with AcR that can be used to exploit multipath diversity inherent in wideband channels. We further extend the SEP derivation to include noise averaging. Our methodology does not require the Gaussian approximation and is applicable for any fading scenario given that the correlator output signal-to-noise ratio can be characterized by a moment generating function.

The capacity of a wireless system is subject to bandwidth constraints, fading, as well as temporal and spatial variations in data traffic. To adapt to this adverse radio environment efficiently, we propose a fade resistant transmission system using parallel sequences. Our analysis shows that instantaneous signal-to-interference-plus-noise ratio at the matched filter output of the proposed system is statistically higher than that of the conventional system. Numerical results demonstrate that the proposed parallel sequence transmission scheme can provide capacity improvement over the conventional system depending on variations in the channel.

We investigate convergence properties of both the steepest descent (SD) and least mean-square (LMS) algorithms applied to a multiple-input-multiple-output system in a Rayleigh fading environment with correlated fading. For a given value of the adaptation step, we evaluate the probability that the algorithms are stable. Then, we compare two stable strategies to choose the adaptation step of SD and analyze their speed of convergence. Our results are valid for an arbitrary number of transmit and receive antennas for the uncorrelated fading, and for an arbitrary number of receive antennas less or equal to the number of transmit antennas for the case of correlated fading.

Performance of a parallel symbol transmission scheme is analyzed in time-varying Nakagami fading channels. We show that instantaneous signal-to-interference-plus-noise ratio of the parallel symbol transmission scheme at the matched-filter output is higher than that of a conventional system. Furthermore, the performance gain of the parallel symbol transmission scheme over the conventional system increases as the number of independent fades within the frame increases and/or the Nakagami parameter m decreases.

Lower and upper bound expressions for the penalty of hybrid diversity used with any two-dimensional (2-D) signaling constellation with polygonal decision regions are derived. Using these bounds with well-known results for maximal ratio combining, the error probability performance of 2-D signaling with hybrid selection/maximal ratio combining can be approximated to a high accuracy.

We derive a simple closed-form upper bound on the symbol error probability for coherent detection of M-ary phase-shift keying using antenna arrays with optimum combining, in the presence of multiple uncorrelated equal-power cochannel interferers and thermal noise in a Rayleigh fading environment. The new bound, based on Laguerre polynomials, is valid for an arbitrary number of antenna elements as well as arbitrary number of interferers, and it is proven to be asymptotically tight. Comparisons with Monte Carlo simulation are also provided, showing that our bound is useful in many cases of interest.

We derive an exact bit-error probability (BEP) expression for coherent detection of binary signals with optimum combining in wireless systems in the presence of multiple cochannel interferers and thermal noise. A flat Rayleigh fading environment with space diversity, uncorrelated equal-power interferers, and additive white Gaussian noise is considered. The approach is to use the chain rule of conditional expectation together with the joint probability density function (pdf) of the eigenvalues of the interference correlation matrix. This joint pdf is related to the Vandermonde determinant. Let N/sub A/ denote the number of antennas and N/sub I/ the number of interferers. We consider both the cases of an overloaded system, in which N/sub I//spl ges/N/sub A/, and an underloaded system, in which N/sub I/

We derive closed-form expressions for the single-user capacity of maximal ratio combining diversity systems taking into account the effect of correlation between the different branches. We consider a Rayleigh fading channel with two kinds of correlation: 1) equal branch signal-to-noise ratios (SNRs) and the same correlation between any pair of branches and 2) unequal branch SNRs and arbitrary correlation between branches such that the eigenvalues of the branch covariance matrix are all distinct. Three adaptive transmission schemes are analyzed: 1) optimal simultaneous power and rate adaptation; 2) optimal rate adaptation with constant transmit power; and 3) channel inversion with fixed rate.

We investigate the capacity distribution of spatially correlated MIMO channels. In particular, we consider MIMO systems with arbitrary correlation among the transmitting antennas or among the receiving antennas in frequency-flat Rayleigh fading environments. We derive a simple expression for the distribution of the eigenvalues of a Wishart matrix including correlation, and then a closed-form expression for the characteristic function (CF) of MIMO system capacity. Using the exact expression of the CF, the probability density function and the cumulative distribution function can be easily obtained, thus enabling the exact evaluation of the outage and mean capacity of spatially correlated MIMO channels.

An analytical framework to assess the performance of a given diversity system in wireless environments characterized by their power dispersion profiles is established. In particular, we consider maximal-ratio diversity systems and proved the monotonicity of their matched-filter bound performance. Our results are general in that fading distributions are not required to compare and/or bound the performance of a given diversity system in different wireless environments. Hence, these results are valid for arbitrary fading distributions.

We investigate the performance, in terms of symbol error probability (SEP), of multiple-input-multiple-output (MIMO) systems in a flat Rayleigh fading environment with coherent detection of M-PSK signals. The reception is based on linear minimum mean square error (MMSE) combining, followed by successive interference cancellation. Compared to conventional vertical-Bell Laboratories layered space-time (V-BLAST) MIMO systems, we assume a low complexity strategy that does not include the ordering phase. To reduce the difference in terms of SEP on the sub-streams, we consider a MIMO scheme where the symbols transmitted by the different antennas have different energy. Our methodology is completely analytical, is valid for an arbitrary number of receive and transmit antennas (provided that N/sub R//spl ges/N/sub T/) and provides a very fast evaluation of MIMO performance. Results show that the unequal power transmission scheme gains about 3-4 dB over the conventional equal-power transmission case.

We consider an ultra-wide bandwidth system using reduced-complexity RAKE receivers, which are based on either selective (called SRake) or partial (called PRake) combining of a subset of the available resolved multipath components. We investigate the influence of the spreading bandwidth on the system performance using the two considered types of RAKE receivers. We show that, for a fix number of RAKE fingers and a fix transmit power, there is an optimum bandwidth. This optimal bandwidth increases with the number of RAKE fingers, and is higher for an SRake than for a Prake. We also investigate the effects of the fading statistics (Rayleigh or Nakagami) on the optimal spreading bandwidth. We find that the optimal spreading bandwidth is approximately the same for both types of fading, but that the actual performance of an SRake can be better or worse in Rayleigh fading (compared to Nakagami), depending on the spreading bandwidth and the number of fingers.

In hybrid selection/maximal ratio combining the receiver selects the L branches with the largest signal-to-noise ratios from N available diversity branches and performs maximal ratio from N available diversity branches and performs maximal ratio combining. A penalty is incurred with respect to maximal ratio combining which can be defined as the increase in signal-to-noise ratio required for hybrid combining to achieve the same symbol error probability as maximal ratio combining. In this paper, we derive the asymptotic signal-to-noise ratio penalties for small and large values of signal-to-noise ratio for arbitrary signaling constellations in Rayleigh fading. We also present new analytical expressions for the symbol error probabilities of 4-ary orthogonal signaling and 8-ary biorthogonal signaling with hybrid diversity combining in Rayleigh fading.

We derive a closed form expression of the symbol error probability (SEP) for coherent detection of M-ary PSK signals using an array of antennas with optimum (or, equivalently, linear MMSE) combining in the presence of unequal power interferers and thermal noise. This result is based on a new expression for the eigenvalues distribution of central Wishart matrices with correlation.

An exact expression for the symbol-error probability (SEP) for coherent detection of M-ary phase-shift keying using an array of antennas with optimum combining in a Rayleigh fading environment is derived, based on the theory of orthogonal polynomials. In particular, performance analysis in the presence of multiple uncorrelated equal-power cochannel interferers and thermal noise is considered, starting from problems related to the eigenvalues distribution of complex Wishart matrices. We give an effective technique to derive the SEP involving only one integral with finite integration limits. The result is general and valid for an arbitrary number of receiving antennas and/or cochannel interferers. Based on our efficient method, new results that are useful for the design of wireless systems are obtained.

We derive an upper bound and investigate some approximations on the symbol error probability (SEP) for coherent detection of M-ary phase-shift keying, using an array of antennas with optimum combining in wireless systems in the presence of multiple uncorrelated equal-power cochannel interferers and thermal noise in a Rayleigh fading environment. Our results are general and valid for an arbitrary number of antenna elements as well as an arbitrary number of interferers. In particular, the exact SEP is derived for an arbitrary number of antennas and interferers; the computational complexity of the exact solution depends on the minimum number of antennas and interferers. Moreover, closed-form approximations are provided for the cases of dual optimum combining with an arbitrary number of interferers, and of two interferers with an arbitrary number of antenna elements. We show that our bounds and approximations are close to Monte Carlo simulation results for all cases considered in this paper.

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.

Closed-form expressions that lower and upper bound the penalty of hybrid selection/maximal ratio combining relative to maximal ratio combining (MRC) for M-ary phase-shift keying (MPSK) modulation are proved. The bounds offer simple-to-evaluate explicit expressions, and are typically within 0.6 dB for hybrid systems with diversity order up to eight that use at least two branches, yet are independent of signal-to-noise ratio (SNR). Contrary to conclusions conjectured in a previously published paper, it is proved that the SNR penalty is not a constant, independent of SNR. It is also shown that previous estimates of the performance losses of selection diversity relative to MRC underestimate or lower bound the losses for MPSK modulation systems, and that the true loss can be significantly larger than previously believed. An upper bound to this loss is also obtained.

In this paper, we investigate the capacity distribution of spatially correlated, multiple-input-multiple-output (MIMO) channels. In particular, we derive a concise closed-form expression for the characteristic function (c.f.) of MIMO system capacity with arbitrary correlation among the transmitting antennas or among the receiving antennas in frequency-flat Rayleigh-fading environments. Using the exact expression of the c.f., the probability density function (pdf) and the cumulative distribution function (CDF) can be easily obtained, thus enabling the exact evaluation of the outage and mean capacity of spatially correlated MIMO channels. Our results are valid for scenarios with the number of transmitting antennas greater than or equal to that of receiving antennas with arbitrary correlation among them. Moreover, the results are valid for an arbitrary number of transmitting and receiving antennas in uncorrelated MIMO channels. It is shown that the capacity loss is negligible even with a correlation coefficient between two adjacent antennas as large as 0.5 for exponential correlation model. Finally, we derive an exact expression for the mean value of the capacity for arbitrary correlation matrices.

We use a virtual-branch technique to derive higher order statistics of the output signal-to-noise ratio (SNR) of antenna subset diversity in a multipath fading environment. In particular, closed-form expressions of the cumulants, central moments, skewness, and kurtosis for various antenna subset diversity schemes are derived. These measures characterize the statistical behavior of the output SNR distribution.

In this paper, we derive an exact expression for the symbol error probability (SEP) for coherent detection of M-ary PSK signals using array of antennas with optimum combining in a Rayleigh fading environment. The proposed analytical framework is based on the theory of orthogonal polynomials and we give an effective technique to derive the SEP involving only one integral with finite integration limits. The result is general and valid for an arbitrary number of receiving antennas or co-channel interferers.

In hybrid selection/maximal ratio combining the receiver selects the L branches with the largest signal-to-noise ratios from N available diversity branches and performs maximal ratio combining. This incurs a loss or penalty with respect to maximal ratio combining which can be defined as the increase in signal-to-noise ratio required for hybrid combining to achieve the same symbol error probability as maximal ratio combining. In this paper, we derive lower and upper bound expressions for the penalty of any two-dimensional signaling constellation with polygonal decision regions. Using these bounds with well known results for maximal ratio combining, the error probability performance of two-dimensional signaling with hybrid diversity can be approximated at small and large values of signal-to-noise ratio, to a high accuracy.

We present an analysis of a hybrid selection/maximal-ratio combining (H-S/MRC) diversity system over an evenly correlated slow frequency-nonselective Nakagami fading channel. In this system, the L branches with the largest instantaneous signal-to-noise ratio (SNR) out of N available branches are selected and combined using maximal-ratio combining. From the joint characteristic function (c.f.) of the instantaneous branch SNR, we obtain an expression for the c.f. of the combiner output SNR as a series of elementary c.f.'s. The expression can be conveniently used to obtain the symbol error probability of coherent detection of different M-ary modulation schemes.

We analyze the acquisition of direct-sequence-spread-spectrum signals by an antenna array of M elements in a flat Rayleigh fading environment subject to additive stationary temporally-uncorrelated zero-mean Gaussian interference. The observations are complex baseband sampled signal vectors received at the array elements at discrete time instants. The pseudorandom code is periodic with period K and orthogonal to all its shifted versions. The acquisition process requires estimation of the received code lag from K consecutive M /spl times/ 1 received signal vectors. We find the maximum likelihood estimate of the code lag when the data, which is binary, is constant during the acquisition process. Using the sufficient statistic of the estimate, we analyze the performance of the acquisition system. In the case of independent and identically distributed Rayleigh fading and spatially-white Gaussian interference, we derive a closed-form expression for the probability of correct decision. The bias of the code lag estimate and the mean-square estimation error are also presented.

We evaluate the link performance of an ultra-wide band (UWB) system using reduced-complexity RAKE receivers, which are based on either partial combining (called PRAKE) or selective combining (called SRAKE). The first is suboptimum and combines the first arriving multipath components, while the second combines the strongest multipath components. We use a statistical tapped-delay-line channel model that is based on extensive measurement campaigns, and reflects both small-scale and large-scale variations of the channel. Through semi-analytical evaluations of the bit error probability, we show that the simpler PRAKE receiver is almost as good as the SRAKE even for a small number of fingers. We show how this behavior can be related to the channel characteristics.

We derive a simple closed-form upper bound on the symbol error probability for coherent detection of M-ary PSK using an array of antennas with optimum combining. We assume multiple equal-power cochannel interferers and thermal noise in a Rayleigh fading environment. The new bound applies for an arbitrary number of antenna elements as well as arbitrary number of interferers, and it is proved to be asymptotically tight. Based on the simplicity of the bound, the signal-to-noise ratio penalty due to cochannel interference is evaluated. Comparisons with simulation is also provided, showing that our bound is useful in a large number of practical interesting cases.

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.

We analyze the error performance of coherent binary keying with optimum combining in interference and noise using the joint probability density function of the eigenvalues of the interference correlation matrix. A flat Rayleigh fading environment with space diversity, equipower interferers, and additive white Gaussian noise is considered. Let L denote the diversity order and N the number of interferers. The interference correlation matrix follows a Wishart distribution when N/spl ges/L, and a pseudo-Wishart distribution when N<L. We treat both the cases of N/spl ges/L and N

We establish a statistical model for the ultra-wide bandwidth (UWB) indoor channel based on an extensive measurement campaign in a typical modern office building with 2-ns delay resolution. The approach is based on the investigation of the statistical properties of the multipath profiles measured in different rooms over a finely spaced measurement grid. The analysis leads to the formulation of a stochastic tapped-delay-line (STDL) model of the UWB indoor channel. The averaged power delay profile can be well-modeled by a single exponential decay with a statistically distributed decay constant. The small-scale statistics of path energy gains follow Gamma distributions whose parameters m are truncated Gaussian variables with mean values and standard deviations decreasing with delay. The total received energy experiences a lognormal shadowing around the mean energy given by the path-loss power law. We also find that the correlation between multipath components is negligible. Finally, we propose an implementation of the STDL model and give a comparison between the experimental data and the simulation results.

This paper characterizes the power spectral density (PSD) of various time-hopping spread-spectrum (TH-SS) signaling schemes in the presence of random timing jitter, which is characterized typically by a discrete-time stationary random process (independent of the TH sequences and data sequence) with known statistical properties. A flexible model for a general TH-SS signal is proposed and a unified spectral analysis of this generalized TH-SS signal is carried out using a systematic and tractable technique. The key idea is to express the basic baseband pulse in terms of its Fourier transform which allows flexibility in specifying different TH formats throughout the general derivation. The power spectrum of various TH-SS signaling schemes can then be obtained as a special case of the generalized PSD results. Although general PSD results are first obtained for arbitrary timing jitter statistics, specific results are then given for the cases of practical interest, namely, uniform and Gaussian distributed jitter. Applications of this unified spectral analysis includes: (1) clocked TH by a random sequence; (2) framed TH by a random sequence; and (3) framed TH by a pseudorandom periodic sequence. Detailed descriptions of these different TH techniques are given where the first two techniques employ a random sequence (stochastic model) and the third technique employs a pseudorandom sequence (deterministic model).

An ultra-wide bandwidth (UWB) signal propagation experiment is performed in a typical modern laboratory/office building. The bandwidth of the signal used in this experiment is in excess of 1 GHz, which results in a differential path delay resolution of less than a nanosecond, without special processing. Based on the experimental results, a characterization of the propagation channel from a communications theoretic view point is described, and its implications for the design of a UWB radio receiver are presented. Robustness of the UWB signal to multipath fading is quantified through histograms and cumulative distributions. The all RAKE (ARAKE) receiver and maximum-energy-capture selective RAKE (SRAKE) receiver are introduced. The ARAKE receiver serves as the best case (bench mark) for RAKE receiver design and lower bounds the performance degradation caused by multipath. Multipath components of measured waveforms are detected using a maximum-likelihood detector. Energy capture as a function of the number of single-path signal correlators used in UWB SRAKE receiver provides a complexity versus performance tradeoff. Bit-error-probability performance of a UWB SRAKE receiver, based on measured channels, is given as a function of the signal-to-noise ratio and the number of correlators implemented in the receiver.

We present an analysis of a hybrid selection/maximal-ratio combining diversity system over an evenly correlated slow frequency-nonselective Nakagami fading channel, where the correlation coefficient between any pair of the diversity branch gain amplitudes is the same, and all average branch signal-to-noise ratios (SNRs) are equal. In this system, the L branches with the largest instantaneous SNR out of N available branches are selected and combined using maximal-ratio combining. From the joint characteristic function (cf) of the instantaneous branch SNRs, we obtain an expression for the cf of the combiner output SNR as a series of elementary cfs. The expression can be conveniently used to obtain the symbol error probability of coherent detection of different M-ary modulation schemes. We illustrate our methodology using M-ary phase-shift keying as an example.

The bit error probability (BEP) for coherent detection of binary signals with dual-diversity predetection equal gain combining is derived using the Beaulieu (1991) series. In particular, we consider a correlated Rayleigh fading channel with unequal branch signal-to-noise ratios. The BEP expression is in terms of the power correlation coefficient of the branches, is easy to compute, and depicts clearly the effect of correlated fading on the error performance.

We analyze the performance for the noncoherent reception of M-ary orthogonal frequency shift keying with postdetection equal gain combining over a correlated fading channel. Two kinds of correlated fading statistics are considered: (1) Nakagami fading in which the diversity branches can have unequal signal-to-noise ratios (SNRs) as well as different m-parameters and (2) Rician fading in which the diversity branches can have unequal SNRs. Using the characteristic function of the combiner output SNR, closed-form expressions for the symbol error probability are obtained.

We derive the exact symbol error probability for coherent detection of MPSK signals with optimum combining in the presence of multiple uncorrelated equal power co-channel interferers and thermal noise in a Rayleigh fading environment. The expression is general and valid for arbitrary numbers of receiving antennas or co-channel interferers. The complexity of the analytical model depends on the smaller of the number of antennas and the number of interferers.

We consider the capacity of multiple-input-multiple-output (MIMO) systems with reduced complexity. One link end uses all available antennas, while the other chooses the "best" L out of N antennas. As "best", we use those antennas that maximize capacity. We derive an upper bound on the capacity that can be expressed as the sum of the logarithms of ordered chi-squared variables. This bound is then evaluated analytically, and compared to results from Monte Carlo simulations. As long as L is at least as large as the number of antennas at the other link end, the achieved capacity is close to the capacity of a full-complexity system. We demonstrate, for example, that for L=3, N=8 at the receiver, and 3 antennas at the transmitter, the capacity of the reduced-complexity scheme is 20 bits/s/Hz compared to 23 bits/s/Hz of a full-complexity scheme.

We analyze the error performance for the noncoherent reception of M-ary orthogonal frequency shift keying with postdetection equal gain combining (EGC) over a correlated fading channel. Two kinds of correlated fading statistics are considered: (1) Nakagami fading in which the diversity branches can have different m-parameters, (2) Rician fading in which the in-phase component of the complex channel gain of any branch can be correlated with the quadrature components of the gains of all other branches. Using the characteristic function of the combiner output signal-to-noise-ratio, closed-form expressions for the symbol error probability are obtained.

We study the problem of maximizing the total system throughput under a bit error rate constraint for all users in the uplink of a single-cell synchronous CDMA system. Users realize variable bit rates by using a combination of multicode transmission and adaptive QAM modulation. We assume random signature sequences for all users, and perform an asymptotic analysis. We parametrize each user's resource allocation scheme by two parameters, viz., the number of signatures the user transmits with and the number of signal points in the user's QAM constellation, and optimize the total throughput over this parameter space. We examine four different settings: single-user matched filter (SUMF) and minimum-mean-square error (MMSE) receiver at the base station, with and without maximum power constraints. For a single user system, we describe the jointly optimum number of multicodes and constellation sizes for these four different system models. When multiple users are present, we show that the total throughput is maximized when only one user transmits: with no maximum power constraints this user can be chosen arbitrarily, otherwise it should be the one with the largest SNR. This solution, although optimal in the sense of maximizing the total throughput, is unfair to all but one user: thus, we examine a scheduling mechanism that assigns equal time frames to all users, thus yielding maximum fairness, and discuss the resulting total throughput loss.

We derive analytical expressions for the symbol error probability (SEP) for a hybrid selection/maximal-ratio combining (H-S/MRC) diversity system in multipath-fading wireless environments. With H-S/MRC, L out of N diversity branches are selected and combined using maximal-ratio combining (MRC). We consider coherent detection of M-ary phase-shift keying (MPSK) and quadrature amplitude modulation (MQAM) using H-S/MRC for the case of independent Rayleigh fading with equal signal-to-noise ratio averaged over the fading. The proposed problem is made analytically tractable by transforming the ordered physical diversity branches, which are correlated, into independent and identically distributed (i.i.d.) "virtual branches," which results in a simple derivation of the SEP for arbitrary L and N. We further obtain a canonical structure for the SEP of H-S/MRC as a weighted sum of the elementary SEPs, which are the SEPs using MRC with i.i.d. diversity branches in Rayleigh fading, or equivalently the SEPs of the nondiversity (single-branch) system in Nakagami fading, whose closed-form expressions are well-known. We present numerical examples illustrating that H-S/MRC, even with L/spl Lt/N, can achieve a performance close to that of N-branch MRC.

Using a series approach, expressions for the average symbol error probability (SEP) of coherent binary signals over a correlated Rayleigh fading channel with dual predetection equal gain combining (EGC) and selection diversity (SD) are derived. For both EGC and SD cases, the SEP is in terms of the correlation coefficient of the branch amplitudes, which is easy to compute and depicts clearly the effect of correlated fading on the error performance.

We develop an analytical framework to quantify the effects of the spreading bandwidth on spread spectrum systems operating in multipath environments with arbitrary power delay profile. The focus of the paper is to characterize the symbol error probability (SEP) performance of a RAKE receiver tracking the L strongest multipath components in frequency-selective Rayleigh fading. By transforming the physical RAKE receiver with correlated ordered paths into the domain of a "virtual RAKE" receiver with conditionally independent virtual paths, analytical expressions for the SEP are derived in terms of the spreading bandwidth, multipath spread of the channel and the number of combined paths.

We use a virtual branch technique to derive higher order statistics of the output signal-to-noise ratio (SNR) of hybrid selection/maximal-ratio combining (H-S/MRC) in a multipath fading environment. In particular, the cumulants, central moments, skewness and kurtosis are derived. We consider the case of independent Rayleigh fading with equal receive SNR averaged over the fading on each diversity branch.

We develop an analytical framework to quantify the effects of the spreading bandwidth (BW) on spread spectrum systems operating in dense multipath environments in terms of the receiver performance, the receiver complexity, and the multipath channel parameters. The focus of the paper is to characterize the symbol error probability (SEP) performance of a RAKE receiver tracking the L strongest multipath components in wide-sense stationary uncorrelated scattering (WSSUS) Gaussian channels with frequency-selective fading. Analytical SEP expressions of the RAKE receiver are derived in terms of the number of combined paths, the spreading BW and the multipath spread of the channel. The proposed problem is made analytically tractable by transforming the physical RAKE paths, which are correlated and ordered, into the domain of a "virtual RAKE" receiver with independent virtual paths. This results in a simple derivation of the SEP for a given spreading BW and an arbitrary number of combined paths.

For binary noncoherent orthogonal frequency-shift keying and binary differential phase-shift keying over slow nonselective Rician fading channels having arbitrarily correlated branches and unequal branch powers, a closed-form expression for the symbol-error probability in the case of postdetection equal-gain combining is obtained directly from the characteristic function of the decision variable.

Attractive features of time-hopping spread-spectrum multiple-access systems employing impulse signal technology are outlined, and emerging design issues are described. Performance of such communications systems in terms of achievable transmission rate and multiple-access capability are estimated for both analog and digital data modulation formats under ideal multiple-access channel conditions.

The performance of a hybrid selection/maximal ratio combining (H-S/MRC) diversity system in a multipath-fading environment is analyzed. With H-S/MRC, L out of N diversity branches are selected and combined using maximal-ratio combining (MRC). This technique provides improved performance over L branch MRC when additional diversity is available, without requiring additional electronics and/or power. In particular, we consider independent Rayleigh fading on each diversity branch with equal signal-to-noise ratio averaged over the fading. We analyze this system using a "virtual branch" technique which results in a simple derivation and formula for the mean and the variance of the combiner output SNR for any L and N.

One of the potential benefits of the UWB radio is its multipath resolution. The implications are multipath components which might not be resolved as distinct arrivals in more narrowband systems, may be separately identified in UWB systems. This paper explores some of the propagation characteristics associated with UWB signals, based on measured data.

We develop an analytical framework to quantify the effects of the spreading bandwidth on spread spectrum systems operating in dense multipath environments. The focus of the paper is to characterize the symbol error probability (SEP) performance of a RAKE receiver tracking the L strongest multipath components in frequency-selective fading. By transforming the physical RAKE receiver with correlated ordered paths into the domain of a "virtual RAKE" receiver with independent virtual paths, analytical expressions for the SEP are derived in terms of the spreading bandwidth, multipath spread of the channel and the number of combined paths.

Hybrid selection/maximal-ratio combining (H-S/MRC) is a reduced-complexity diversity combining scheme, where L out of N diversity branches (with the largest signal-to-noise ratio (SNR) at each instant) are selected and combined using maximal-ratio combining (MRC). We derive closed-form expressions for the symbol error probability (SEP) of a H-S/MRC diversity system with arbitrary L and N. We consider coherent detection of M-ary phase-shift keying (MPSK) for the case of independent Rayleigh fading with equal SNR averaged over the fading.

We develop an analytical framework to study the performance of wireless systems using maximal ratio combining (MRC) with an arbitrary number of diversity branches in correlated multipath fading. We consider the coherent detection of digital signals received over correlated Nakagami (1960) fading channels, where the instantaneous signal-to-noise ratios (SNRs) of the diversity branches are not necessarily independent or identically distributed. Specifically: (1) these SNRs can be arbitrarily correlated; (2) the SNR distributions can be from different Nakagami families, i.e., fading parameters (ms) are not necessarily equal; and (3) the average SNRs (averaged over the fading) of the branches are not necessarily equal. We derive closed-form expressions for three performance measures of a MRC diversity system: (1) probability density function (p.d.f.) of the combiner output SNR; (2) symbol error probability (SEP) for coherent detection; and (3) outage probability. We obtain a canonical structure for these performance measures as a weighted sum of the corresponding expressions for a non-diversity (single-branch) system with appropriately-defined parameters. This result is fundamental: the canonical structure depends only on the properties of the channel and diversity combiner, and not on the specific modulation technique. Calculations of the SEP for specific modulation techniques are illustrated through examples.

Spread spectrum (SS) multiple access techniques have been proposed for third generation broadband wireless access. We develop an analytical framework to quantify the effects of spreading bandwidth on SS systems operating in dense multipath environments in terms of the receiver performance, receiver complexity, and multipath channel parameters. In particular, we consider wide-sense stationary uncorrelated scattering (WSSUS) Gaussian channels with frequency-selective fading. The focus of the paper is to characterize the combined signal of the RAKE receiver fingers tracking the strongest multipath components. Closed form expressions for the mean and the variance of the total RAKE receiver output signal-to-noise ratio (SNR) are derived in terms of the number of RAKE fingers, spreading bandwidth, and multipath spread of the channel. The proposed problem is made analytically tractable by transforming the physical RAKE paths into the virtual path domain. A representative result indicates that for SS systems with 5 MHz signal bandwidth operating in a channel with constant power delay profile having 5 /spl mu/s spread, the average SNR gain from increasing the number of RAKE fingers from one to three is 3.8 dB and from three to five is 1.5 dB. Furthermore, the reduction in the variation of SNR is 1.1 dB and 0.4 dB for the same increments in the number of fingers.

Personal communications services (PCS) promise to provide a variety of information exchanges among users with any type of mobility, at any time, in any place, through any available device. To achieve this ambitious goal, two of the major challenges in the system design are: (i) to provide a high-speed wireless subsystem with large capacity and acceptable quality-of-service (QoS) and (ii) to design a network architecture capable of supporting multimedia traffic and various kinds of user mobility. A time-hopping spread-spectrum wireless communication system called ultra-wide bandwidth (UWB) radio is used to provide communications that are low power, high data rate, fade resistant, and relatively shadow free in a dense multipath environment. Receiver-signal processing of UWB radio is described, and performance of such communications systems, in terms of multiple-access capability, is estimated under ideal multiple-access channel conditions. A UWB-signal propagation experiment is performed using the bandwidth in excess of 1 GHz in a typical modern office building in order to characterize the UWB-signal propagation channel. The experimental results demonstrate the feasibility of the UWB radio and its robustness in a dense multipath environment. A ATM network is used as the backbone network due to its high bandwidth, fast switching capability, flexibility, and well-developed infrastructure. To minimize the impact caused by user mobility on the system performance, a hierarchical network-control architecture is postulated. A wireless virtual circuit (WVC) concept is proposed to improve the transmission efficiency and simplify the network control in the wireless subsystem. The key advantage of this network architecture and WVC concept is that the handoff can be done locally most of the time, due to the localized behavior of PCS users.

We use a novel virtual branch technique to succinctly derive the mean and variance of the combiner output signal-to-noise ratio for hybrid selection/maximal-ratio combining in a multipath-fading environment.

Delay-and-sum beamforming is applied to both ideal and measured ultra-wideband (UWB) signals. The results of propagation measurements are also used directly to estimate the performance of an UWB communication system and to characterize design tradeoffs.

In order to estimate the performance of impulse radio communication systems, a characterization of the channel is required. In particular, knowledge of the multipath angle and time-of-arrival distributions is useful for predicting the performance of diversity reception schemes. In this paper, the CLEAN algorithm is applied to ultra-wide bandwidth (UWB) signals received on an array of sensors in order to resolve the incident signal components.

The spectral occupancy and composition of a chosen digital signaling technique when the data pulse stream is nonideal, due, for instance, to implementation imperfections, are important considerations in the design of a practical communication system. One source of imperfection is timing jitter where the rising and falling transitions do not occur at the nominal data transition time instants; nevertheless, the time instants are offset by random amounts relative to the nominal one. The amount of timing shift per transmission interval is random and is typically characterized by a discrete stationary random process (independent of the data sequence) with known statistical properties. The purpose of this paper is to characterize the power spectral density (PSD) of baseband signaling schemes in the presence of arbitrary timing jitter. Although general PSD results are first obtained for arbitrary timing jitter statistics, specific results are then given for the cases of practical interest, namely, uniform and Gaussian-distributed jitter. Examples of an uncorrelated data pulse stream, an independent identically distributed data stream, and a Markov source are given. Interesting results emerge when the generating sequence {a/sub n/} is uncorrelated. For generating sequences {a/sub n/} that are nonzero-mean, timing jitter has the effect of widening the main lobe of the spectrum and increasing the sidelobes. When the generating sequence is zero-mean and uncorrelated, a rather surprising result is that the timing jitter does not affect the PSD. Simulation results are also presented to verify the analysis.

A polyphase sequence of length n+1, A={a/sub j/}/sub j=0//sup n/, is a sequence of complex numbers, each of unit magnitude. The (unnormalized) aperiodic autocorrelation function of a sequence is denoted by C(/spl tau/). Associated with the sequence A, the sequence polynomial f/sub A/(z) of degree n and the correlation polynomial g/sub A/(z) of degree 2n are defined. For each root /spl alpha/ of f/sub A/(z), 1//spl alpha/* is a corresponding root of f*/sub A/(z/sup -1/). Transformations on the sequence A which leave |C(/spl tau/)| invariant are exhibited, and the effects of these transformations on the roots of f/sub A/(z) are described. An investigation of the set of roots A of the polynomial f/sub A/(z) has been undertaken, in an attempt to relate these roots to the behavior of C(/spl tau/). Generalized Barker (1952, 1953) sequences are considered as a special case of polyphase sequences, and examples are given to illustrate the relationship described above.

Attractive features of time-hopping spread-spectrum multiple access systems employing impulse signal technology are outlined and emerging design issues are described. Performance of such communications systems in terms of multiple-access capability is estimated for both analog and digital data modulation formats under ideal multiple access channel conditions.

An ultra-wide bandwidth (UWB) signal propagation experiment is performed in a typical modern office building in order to characterize the UWB signal propagation channel. The bandwidth of the signal used in this experiment is in excess of one GHz. The robustness of the WVB signal to fades is quantified through histogram and cumulative distribution of the received energy in various locations of the building. The results show that UWB signal does not suffer fades.

Theoretical analysis is formulated for a solid state laser based optical phase-locked loop (OPLL) disturbed by shot noise, amplitude modulated noise, and frequency noise. The frequency noise spectral density of solid state lasers is modeled to contain a white component, a 1/f component, and a strong 1/f/sup 2/ component at the laser output. This model is verified and the spectral content of each component is measured using an open-loop RF frequency discriminator. The choice of loop filter is made by considering the frequency noise components, transient effects, and the loop damping factor /spl zeta/. The total phase error variance as a function of loop bandwidth is displayed for several values of carrier signal-to-noise ratio for the measured frequency noise spectrum. Optimal loop bandwidth is also calculated as a function of carrier signal-to-noise ratio. An OPLL experiment is performed, and measured phase error variance is compared with the theoretical predictions using the measured frequency noise spectrum. The results show that the measured phase error variance closely matches the theoretical predictions.>