We investigate the problem of maximizing the sum-rate performance of a beyond-diagonal reconfigurable intelligent surface (BD-RIS)-aided multi-user (MU)-multiple-input single-output (MISO) system using fractional programming (FP) techniques. More specifically, we leverage the Lagrangian Dual Transform (LDT) and Quadratic Transform (QT) to derive an equivalent objective function which is then solved iteratively via a manifold optimization framework. It is shown that these techniques reduce the complexity of the optimization problem for the scattering matrix solution, while also providing notable performance gains compared to state-of-the-art (SotA) methods under the same system conditions. Simulation results confirm the effectiveness of the proposed method in improving sum-rate performance.

We propose a novel low-complexity receiver design for multicarrier continuous aperture array (CAPA) systems operating over doubly-dispersive (DD) channels. The receiver leverages a Gaussian Belief Propagation (GaBP)-based framework that hinges only on element-wise scalar operations for the detection of the transmitted symbols. Simulation results for the orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM) waveforms demonstrate significant performance improvements in terms of uncoded bit error rate (BER) compared to conventional discrete antenna array systems, while maintaining very low computational complexity.

This paper presents a novel framework for enhancing the security, data rate, and sensing performance of integrated sensing and communications (ISAC) systems. We employ a random frequency and pulse repetition interval (PRI) agility (RFPA) method for the waveform design, where the necessary random sequences are governed by shared secrets. These secrets, which can be pre-shared or generated via channel reciprocity, obfuscate critical radar parameters like Doppler frequency and pulse start times, thereby significantly impeding the ability to perform reconnaissance from a passive adversary without the secret key. To further introduce enhanced data throughput, we also introduce a hybrid information embedding scheme that integrates amplitude shift keying (ASK), phase shift keying (PSK), index modulation (IM), and spatial modulation (SM), for which a low-complexity sparse-matched filter receiver is proposed for accurate decoding with practical complexity. Finally, the excellent range-velocity resolution and clutter suppression of the proposed waveform are analyzed via the ambiguity function (AF).

We introduce a comprehensive approach to enhance the security, privacy, and sensing capabilities of integrated sensing and communications (ISAC) systems by leveraging random frequency agility (RFA) and random pulse repetition interval agility (RPA) techniques. The combination of these techniques, which we collectively refer to as random frequency and pulse repetition interval agility (RFPA), with channel reciprocity-based key generation (CRKG) obfuscates both Doppler frequency and pulse repetition intervals (PRIs), significantly hindering passive adversaries’ ability to estimate radar parameters. In addition, a hybrid information embedding method integrating amplitude shift keying (ASK), phase shift keying (PSK), index modulation (IM), and spatial modulation (SM) is incorporated to significantly increase the system’s achievable bit rate. Next, a sparse-matched filter receiver design is proposed to efficiently decode the embedded information with a low bit error rate (BER). Finally, a novel RFPA-based secret generation scheme using CRKG enables secure code creation without a coordinating authority. The improved range and velocity estimation, and the reduced clutter effects achieved by the method, are demonstrated through the evaluation of the ambiguity function (AF) of the proposed waveforms.

Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) are emerging as a transformative technology in wireless communications, enabling enhanced performance and quality of service (QoS) of wireless systems in harsh urban environments due to their relatively low cost and advanced signal processing capabilities. Generally, BD-RIS systems are employed to improve robustness, increase achievable rates, and enhance energy efficiency of wireless systems in both direct and indirect ways. The direct way is to produce a favorable propagation environment via the design of optimized scattering matrices, while the indirect way is to reap additional improvements via the design of multiple-input multiple-output (MIMO) beamformers that further exploit the latter "engineered" medium. In this article, the problem of sum-rate maximization via BD-RIS is examined, with a focus on feasibility, namely low-complexity physical implementation, by enforcing reciprocity in the BD-RIS design in a manner that adheres to the geometry of the manifold of symmetric matrices. To that end, the sum-rate objective is transformed into a quadratic function via fractional programming (FP), augmented via the also quadratic reciprocity constraint in the form of a regularization term, while the unitary constraint is dealt with via a manifold optimization framework. Simulation results demonstrate the effectiveness of the proposed method in outperforming current state-of-the-art (SotA) approaches in terms of sum-rate maximization.

We propose a framework to design integrated communication and computing (ICC) receivers capable of simultaneously detecting data symbols and performing over-the-air computing (AirComp) in a manner that: a) is systematically generalizable to any nomographic function, b) scales to a massive number of user equipments (UEs) and edge devices (EDs), c) supports the computation of multiple independent functions (streams), and d) operates in a multi-access fashion whereby each transmitter can choose to transmit either data symbols, computing signals or both. For the sake of illustration, we design the proposed multi-stream and multi-access method under an uplink setting, where multiple single-antenna UEs/EDs simultaneously transmit data and computing signals to a single multiple-antenna base station (BS)/access point (AP). Under the communication functionality, the receiver aims to detect all independent communication symbols while treating the computing streams as aggregate interference which it seeks to mitigate; and conversely, under the computing functionality, to minimize the distortion over the computing streams while minimizing their mutual interference as well as the interference due to data symbols. To that end, the design leverages the Gaussian belief propagation (GaBP) framework relying only on element-wise scalar operations coupled with closed-form combiners purposebuilt for the AirComp operation, which allows for its use in massive settings, as demonstrated by simulation results incorporating up to 200 antennas and 300 UEs/EDs. The efficacy of the proposed method under different loading conditions is also evaluated, with the performance of the scheme shown to approach fundamental limiting bounds in the under/fully loaded cases.

We propose a novel doubly-dispersive (DD) multiple-input multiple-output (MIMO) channel model incorporating flexible intelligent metasurfaces (FIMs), which is suitable for integrated sensing and communications (ISAC) in high-mobility scenarios. We then discuss how the proposed FIM-parameterized DD (FPDD) channel model can be applied in a logical manner to multicarrier waveforms that are known to perform well in DD environments, namely, orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM). Leveraging the proposed model, we formulate an achievable rate maximization problem with a strong sensing constraint for all the aforementioned waveforms, which we then solve via a gradient ascent algorithm with closed-form gradients presented as a bonus. Our numerical results indicate that the achievable rate is significantly impacted by the emerging FIM technology with careful parametrization essential in obtaining strong ISAC performance across all waveforms suitable to mitigating the effects of DD channels.

We propose the affine filter bank modulation (AFBM) waveform for enhanced integrated sensing and communications (ISAC) in sixth generation (6G), designed by drawing on concepts from classical filter bank multicarrier modulation (FBMC) theory and recent advances in chirp-domain waveforms, particularly affine frequency division multiplexing (AFDM). Specifically, AFBM exhibits several desirable properties, with emphasis on its remarkably low peak-to-average power ratio (PAPR) and reduced out-of-band emission (OOBE) when bench-marked against the conventional AFDM waveform under doubly-dispersive (DD) channel conditions. In the communications setting, reliable symbol detection is achieved using a tailored low-complexity Gaussian belief propagation (GaBP)-based algorithm, while in the sensing setting, a range and velocity estimation approach is developed that integrates an expectation maximization (EM)-assisted probabilistic data association (PDA) framework to accurately identify surrounding targets. The highlighted performance and benefits of AFBM are validated through analytical and numerical evaluations, including conventional metrics such as ambiguity function (AF), bit error rate (BER), and root mean square error (RMSE), consolidating its position as a promising waveform for next-generation wireless systems.

Introduced with the advent of statistical wireless channel models for high mobility communications and having a profound role in communication-centric (CC) integrated sensing and communications (ISAC), the doubly-dispersive (DD) channel structure has long been heralded as a useful tool enabling the capture of the most important fading effects undergone by an arbitrary time-domain transmit signal propagating through some medium. However, the incorporation of this model into multiple-input multiple-output (MIMO) system setups, relying on the recent paradigm-shifting transceiver architecture based on stacked intelligent metasurfaces (SIM), in an environment with reconfigurable intelligent surfaces (RISs) remains an open problem due to the many intricate details that have to be accounted for. In this paper, we fill this gap by introducing a novel DD MIMO channel model that incorporates an arbitrary number of RISs in the ambient, as well as SIMs equipping both the transmitter and receiver. We then discuss how the proposed metasurfaces-parametrized DD (MPDD) channel model can be seamlessly applied to waveforms that are known to perform well in DD environments, namely, orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM), with each having their own inherent advantages and disadvantages. An illustrative application of the programmable functionality of the proposed model is finally presented to showcase its potential for boosting the performance of the aforementioned waveforms. Our numerical results indicate that the design of waveforms suitable to mitigating the effects of DD channels is significantly impacted by the emerging SIM technology.

We address the modeling and optimal beamforming (BF) design for multiple-input multiple-output (MIMO) continuous aperture array (CAPA) systems operating over doubly-dispersive (DD) channels. First, a comprehensive DD continuous MIMO (DDC MIMO) channel model that incorporates CAPAs at both the transmitter (TX) and receiver (RX) is derived, which is used to obtain explicit input-output (I/O) relations for various waveforms well suited to integrated sensing and communications (ISAC) and robust to DD channels, namely orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM). Then, functional optimization problems are formulated for the design of TX and RX BF matrices that maximize received power, in which novel low-complexity, closed-form solutions are obtained via the calculus of variations (CoV) method, yielding expressions closely related to the classical matched filter commonly used in conventional MIMO systems. Simulation results confirm that the proposed TX/RX BF designs with CAPAs provide significant performance and computational complexity gains over conventional MIMO systems in DD channels.

The phenomenon of mutual coupling in continuous aperture arrays (CAPAs) is studied. First, a general physical model for the phenomenon that accounts for both polarization and surface dissipation losses is developed. Then, the unipolarized coupling kernel is characterized, revealing that polarization induces anisotropic coupling and invalidates the conventional half-wavelength spacing rule for coupling elimination. Next, the beamforming design problem for CAPAs with coupling is formulated as a functional optimization problem, leading to the derivation of optimal beamforming structures via the calculus of variations. To address the challenge of inverting the coupling kernel in the optimal structure, two methods are proposed: 1) the kernel approximation method, which yields a closed-form solution via wavenumber-domain transformation and GaussLegendre quadrature, and 2) the conjugate gradient method, which addresses an equivalent quadratic functional optimization problem iteratively. Furthermore, the optimal array gain and beampattern are analyzed at the large-aperture limit. Finally, the proposed continuous mutual coupling model is extended to spatially discrete arrays (SPDAs), and comprehensive numerical results are provided, demonstrating that: 1) coupled SPDA performance correctly converges to the CAPA limit, while uncoupled models are shown to violate physics, 2) polarization results in anisotropic array gain behavior, and 3) the coupled beampattern exhibits higher directivity than the uncoupled beampattern.

We propose a quantum-assisted solution of the pilot assignment problem. In particular, we formulate a pilot assignment problem designed to minimize the interference from co-pilot user terminals as a binary optimization problem which can be solved via a quantum exhaustive search through the Grover adaptive search (GAS) algorithm. The performance of GAS in terms of query complexity is enhanced by introducing a modified initial state and an improved initial threshold using the upper bound for the minimum value of the objective function. Simulation results demonstrate that our proposed method can provide quadratic speedup compared to an exhaustive search performed by a classical computer, concretely demonstrating that quantum computers can be employed to solve optimally and efficiently the pilot assignment problem.

We propose a new quantum-assisted maximum likelihood detection (MLD) scheme for generalized spatial modulation (GSM) systems. To that end, we first present a novel MLD formulation for GSM, and then describe a corresponding novel method to prepare the initial state for an associated quantum-computing Grover adaptive search (GAS) algorithm. Simulation results are presented to show that our approach achieves optimal performance and reduces query complexity compared both to the classical MLD and to earlier quantum search methods, which do not achieve an advantage over classical MLD for some parameter settings.

We consider a novel anchorless rigid body localization (RBL) suitable for application in autonomous driving (AD), in so far as the algorithm enables a rigid body to egoistically detect the location (relative translation) and orientation (relative rotation) of another body, without knowledge of the shape of the latter, based only on a set of measurements of the distances between sensors of one vehicle to the other. A key point of the proposed method is that the translation vector between the two-bodies is modeled using the double-centering operator from multidimensional scaling (MDS) theory, enabling the method to be used between rigid bodies regardless of their shapes, in contrast to conventional approaches which require both bodies to have the same shape. Simulation results illustrate the good performance of the proposed technique in terms of root mean square error (RMSE) of the estimates in different setups.

We propose a novel method for blind bistatic radar parameter estimation (RPE), which enables integrated sensing and communications (ISAC) by allowing passive (receive) base stations (BSs) to extract radar parameters (ranges and velocities of targets), without requiring knowledge of the information sent by an active (transmit) BS to its users. The contributed method is formulated with basis on the covariance of received signals, and under a generalized doubly-dispersive channel model compatible with most of the waveforms typically considered for ISAC, such as orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS) and affine frequency division multiplexing (AFDM). The original non-convex problem, which includes an ℓ0-norm regularization term in order to mitigate clutter, is solved not by relaxation to an ℓ1-norm, but by introducing an arbitrarily-tight approximation then relaxed via fractional programming (FP). Simulation results show that the performance of the proposed method approaches that of an ideal system with perfect knowledge of the transmit signal covariance with an increasing number of transmit frames.

We consider the problem of peak-to-average power ratio (PAPR) reduction in orthogonal frequency division mul-tiplexing (OFDM) systems via optimized sparsification of tone reservation (TR) aimed at freeing resources for the opportunistic operation of co-existing communication and sensing systems. In particular, we propose a novel TR-optimization method in which the minimum number of effectively used peak-reserved tones (PRTs) required to satisfy a prescribed PAPR level in a primary system is found, leaving the remaining resources free to be opportunistically allocated by a secondary system and other functionalities, such as joint communication and sensing (JCAS), index modulation (IM) and cognitive radio (CR). The proposed method relies on an £0 norm regularization approach to penalize the number of PRTs, leading to a problem convexized via fractional programming (FP), whose solution is shown to ensure that the prescribed PAPR is achieved with high probability with a smaller number of PRTs than state of the art (SotA) methods. The contribution can be seen as a mechanism to enable the opportunistic coexistence of systems with adjacent functionalities in presence of existing OFD M - based systems.

We consider the peak-to-average power ratio (PAPR) reduction challenge of orthogonal frequency division multiplexing (OFDM) systems utilizing tone reservation (TR) under a sensing-enabling constraint, such that the signals placed in the reserved tones (RTs) can be exploited for Integrated Sensing and Communication (ISAC). To that end, the problem is first cast as an unconstrained manifold optimization problem, and then solved via an iterative projected gradient descent algorithm assisted by an approximation of the infinity norm. Simulation results show that the proposed method, while maintaining a level of PAPR reduction similar to state of the art (SotA), not only has lower computational complexity but also outperforms the alternatives in terms of sensing performance.

We propose a novel solution to the rigid body localization (RBL) problem, in which the three-dimensional (3D) rotation and translation is estimated by only utilizing the range measurements between the wireless sensors on the rigid body and the anchor sensors. Given the prior knowledge of the absolute sensor positions, by leveraging a linearized RBL transformation model with small-angle approximations, the proposed bivariate Gaussian belief propagation (GaBP) is designed to directly estimate the 3D rotation angles and translation distances, with an interference cancellation (IC) refinement step to further improve the angle estimation performance. The effectiveness of the proposed method is verified via numerical simulations, highlighting the superior performance of the proposed method against the state-of-the-art (SotA) techniques for the rotation and translation estimation performance.

We consider the design of low complexity and highperforming mean square error (MSE) minimization combiners for over-the-air-computing (AirComp) applications operating over the uplink of a system with one multiple-antenna access point (AP) and multiple single-antenna edge devices (EDs). Within that paradigm, we offer two contributions, namely, a simple initial combiner based on a Rayleigh quotient (RQ) design, and a low-complexity refinement stage based on a convex concave procedure (CCP). The new refinement stage algorithm is further enriched with an efficient (offline) hyper-parameter tuning mechanism via Bayesian optimization (BO) and acceleration method based on a half-space constrained least square problem reformulation solved via the adaptive moment estimation (Adam) algorithm. The low complexity and good performance of the proposed method help address typical limitations of edge devices. Numerical results demonstrate that the proposed design can achieve MSE performances equivalent to those of the best stateof-the-art (SotA) alternatives currently known, at about 200-times less complexity than the highest-performing SotA, and about 4-times less complexity than its low-complexity counterpart.

We propose a new two-stage, low-complexity, and robust beamforming (BF) method for heterogeneous MIMO rate splitting multiple access (RSMA) systems. In the proposed method, the phases and powers of the BF weights are designed separately, the first based on a tensor factorization of the channels between the base station (BS) and each user, and the second based on a fractional programming (FP) formulation of the power allocation problem, which is offered in three distinct variations, aimed as sum rate maximization (SRM), minimum rate maximization (MaxMin) and the maximization of the geometric-mean (GMean) of achievable rates, respectively. Thanks to the twostage approach, the proposed method is capable of delivering robustness to both channel state information (CSI) and successive interference cancellation (SIC) errors (incorporated in the phase design), at a low complexity compared to state-of-the-art (SotA) alternatives. Also thanks to the approach, the scheme naturally handles heterogeneity in terms of the number of antennas at each user, which can be arbitrarily distinct. Direct comparisons between SotA and the proposed schemes demonstrate that the contributed method generally outperforms the best alternative at comparable complexity, while approaching the best-performing SotA method of significantly higher complexity. In fact, the computational cost advantage of the proposed technique over the latter is quantified analytically and shown to be proportional to the cube of the number of BS antennas.

We propose new schemes for joint channel and data estimation (JCDE) and radar parameter estimation (RPE) in doubly-dispersive channels, such that integrated sensing and communications (ISAC) is enabled by user equipment (UE) independently performing JCDE, and base stations (BSs) performing RPE. The contributed JCDE and RPE schemes are designed for waveforms known to perform well in doubly-dispersive channels, under a unified model that captures the features of either legacy orthogonal frequency division multiplexing (OFDM), state-of-the-art (SotA) orthogonal time frequency space (OTFS), and next-generation affine frequency division multiplexing (AFDM) systems. The proposed JCDE algorithm is based on a Bayesian parametric bilinear Gaussian belief propagation (PBiGaBP) framework first proposed for OTFS and here shown to apply to all aforementioned waveforms, while the RPE scheme is based on a new probabilistic data association (PDA) approach incorporating a Bernoulli-Gaussian denoising, optimized via expectation maximization (EM). Simulation results demonstrate that JCDE in AFDM systems utilizing a single pilot per block significantly outperforms the SotA alternative even if the latter is granted a substantial power advantage. Similarly, the AFDM-based RPE scheme is found to outperform the OTFS-based approach, as well as the sparse Bayesian learning (SBL) technique, regardless of the waveform used.

Index modulation (IM) is one of the key enabling technologies for beyond fifth generation (B5G) and sixth generation (6G) wireless systems, attracting attention for its inherent energy and spectral efficiency resulting from conveying information through the indexation of the resources utilized in during signal transmission. However, a remaining critical bottleneck for large-scale IM is the consequently infeasible detection complexity of combinatoric order. Therefore in this article, in order to maximally reap the advantages of IM in large scenarios, we propose a novel message passing (MP) decoder designed under the Gaussian belief propagation (GaBP) framework exploiting a novel unit vector decomposition (UVD) of IM signals with purpose-derived novel probability distributions. The proposed method enjoys a low decoding complexity that is independent of previously prohibitive combinatorial factors, while still approaching the performance of unfeasible state-of-the-art (SotA) search-based methods. The effectiveness of the proposed approach is demonstrated via complexity analysis and numerical results for the exemplary piloted generalized quadrature spatial modulation (GQSM) systems of truly massive sizes (up to 96 antennas).

We consider the joint design of time-domain equalization and hybrid transmit and receive beamforming schemes, so as to combat frequency-selective fading and path loss effects in single-carrier (SC) multiple-input multiple-output (MIMO) communications systems. In particular, considering the linear equalization and hybrid beamforming matrices as variables, a minimum mean square error (MMSE) problem to minimize the bit error rate (BER) of SC-MIMO systems is formulated and solved via an accelerated matrix quadratic transform (QT) and manifold optimization. The relationship between mean square error (MSE) minimization via joint MMSE equalization and beamforming, and the minimization of average BER over all data streams is expressed analytically, which together with numerical results confirm that the proposed SC scheme outperforms the SC systems whose linear equalizer and beamformers are designed separately.

We consider the estimation of three-dimensional (3D) radar parameters, namely, bearing or angle-of-arrival (AoA), delay or range, and Doppler shift velocity, under a mono-static multiple-input multiple-output (MIMO) joint communications and radar (JCR) system based on Orthogonal Time Frequency Space (OTFS) signals. In particular, we propose a novel two-step algorithm to estimate the three radar parameters sequentially, where the AoA is obtained first, followed by the estimation of range and velocity via a reduced two-dimensional (2D) grid maximum likelihood (ML) search in the delay-Doppler (DD) domain. Besides the resulting lower complexity, the decoupling of AoA and DD estimation enables the incorporation of an linear minimum mean square error (LMMSE) procedure in the ML estimation of range and velocity, which are found to significantly outperform State-of-the-Art (SotA) alternatives and approach the fundamental limits of the Cramèr-Rao lower bound (CRLB) and search grid resolution.

We propose a quantum-assisted solution for the maximum likelihood detection (MLD) of generalized spatial modulation (GSM) signals. Specifically, the MLD of GSM is first formulated as a novel polynomial optimization problem, followed by the application of a quantum algorithm, namely, the Grover adaptive search. The performance in terms of query complexity of the proposed method is evaluated and compared to the classical alternative via a numerical analysis, which reveals that under fault-tolerant quantum computation, the proposed method outperforms the classical solution if the number of data symbols and the constellation size are relatively large.

We consider an uplink integrated sensing and communications (ISAC) scenario where the detection of data symbols from multiple user equipment (UEs) occurs simultaneously with a three-dimensional (3D) estimation of the environment, extracted from the scattering features present in the channel state information (CSI) and utilizing the same physical layer communications air interface, as opposed to radar technologies. By exploiting a discrete (voxelated) representation of the environment, two novel ISAC schemes are derived with purpose-built message passing (MP) rules for the joint estimation of data symbols and status (filled/empty) of the discretized environment. The first relies on a modular feedback structure in which the data symbols and the environment are estimated alternately, whereas the second leverages a bilinear inference framework to estimate both variables concurrently. Both contributed methods are shown via simulations to outperform the state-of-the-art (SotA) in accurately recovering the transmitted data as well as the 3D image of the environment. An analysis of the computational complexities of the proposed methods reveals distinct advantages of each scheme, namely, that the bilinear solution exhibits a superior robustness to short pilots and channel blockages, while the alternating solution offers lower complexity with large number of UEs and superior performance in ideal conditions.

We propose a novel joint channel tracking and data detection (JCTDD) scheme to combat the channel aging phenomenon typical of millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication systems in high-mobility scenarios. The contribution aims to significantly reduce the communication overhead required to estimate time-varying mmWave channels by leveraging a Bayesian message passing framework based on Gaussian approximation, to jointly perform channel tracking (CT) and data detection (DD). The proposed method can be interpreted as an extension of the Kalman filter-based two-stage tracking mechanism to a Bayesian bilinear inference (BBI)-based joint channel and data estimation (JCDE) framework, featuring the ability to predict future channel state information (CSI) from both reference and payload signals by using an auto-regressive (AR) model describing the time variability of mmWave channel as a state transition model in a bilinear inference algorithm. The resulting JCTDD scheme allows us to track the symbol-by-symbol time variation of channels without embedding additional pilots, leaving any added redundancy to be exploited for channel coding, dramatically improving system performance. The efficacy of the proposed method is confirmed by computer simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) but also approaches the performance of an idealized Genie-aided scheme.

We propose a scheme for the concomitant design of hybrid beamforming and per-carrier transmit power allocation to mitigate the effect of random path blockages in coordinated multi-point (CoMP) systems using orthogonal frequency division multiplexing (OFDM) in millimeter-wave (mmWave) channels. In order to optimize both the beamformers and power allocation while dealing simultaneously with outage minimization and sum rate maximization (SRM) requirements, a regularized sum-of-outage minimization problem is formulated. The problem is then transformed into an empirical risk minimization (ERM) problem, solved via block stochastic learning and manifold optimization, with required learning rates derived and tuned to guarantee convergence. The method, which demands only a few radio frequency (RF) chains and relies only on knowledge of blockage probabilities, is shown via simulation results not only to outperform state-of-the-art (SotA) alternatives, but to actually achieve outage probabilities comparable to those a fully digital CoMP-SRM scheme with perfect knowledge of instantaneous blockages.

We propose a wireless localization algorithm based on complex-domain super multidimensional scaling (CD-SMDS) augmented with a self-localization (SL) component, whereby each target tracks its own motion by incorporating bearing information, obtained e.g., from integrated inertial sensors. The proposed method improves localization accuracy by simultaneously using the time series information of distance and angle associated to the SL information in order to construct the SMDS rank-one edge kernel matrix, maximizing the noise reduction effect of the low-rank truncation via singular value decomposition (SVD). The efficacy of the proposed method over the original CD-SMDS is confirmed via software simulations, and compared with an SL-aware Cramér-Rao lower bound (CRLB).

This paper proposes a scalable network-assisted full-duplex (NAFD) cell-free massive multiple-input multiple-output (CF-mMIMO) system that achieves high spectral efficiency (SE) while reducing the fronthaul load by forming clusters of access points (APs) exclusively for uplink and downlink based on user equipment (UE) requirements, respectively. Specifically, we propose AP clustering techniques based on convex optimization and Hungarian algorithm, along with downlink transmit power control suitable for proposed AP clustering. Numerical results demonstrate that our proposed approach achieves higher SE than conventional scalable TDD CF-mMIMO, NAFD CF-mMIMO and small cell with dynamic time division duplex (TDD), under fronthaul capacity limitations.

We propose a hybrid beamforming for coordinated multi-point (CoMP) transmission using orthogonal frequency division multiplexing (OFDM) over millimeter-wave (mmWave) channels to combat random propagation path blockages. In particular, a sum-of-outage-probability minimization problem with manifold constraints is formulated, which designs the hybrid beamformers, the data rate allocation, and the power allocation over subcarriers jointly to meet the prescribed data rate requirement. A new block stochastic learning mechanism exploiting prior knowledge of the path blockages is also introduced to solve the problem efficiently. Numerical results confirm the effectiveness of the proposed approach in minimizing the outage probability of users according to their target rate. Furthermore, these results also show that the proposed hybrid CoMP transmission only with a few radio frequency (RF) chains and knowledge of blockage probabilities achieves comparable outage performance to a fully digital CoMP transmission alternative with perfect knowledge of instantaneous path blockages.

We propose a novel physical layer security scheme that exploits an optimization method as a one-way function. The proposed scheme builds on nonsquare differential multiple-input multiple-output (MIMO), which is capable of noncoherent detection even in massive MIMO scenarios and thus resilient against risky pilot insertion and pilot contamination attacks. In contrast to conventional nonsquare differential MIMO schemes, which require space-time projection matrices designed via highly complex, discrete, and combinatorial optimization, the proposed scheme utilizes projection matrices constructed via low-complexity continuous optimization designed to maximize the coding gain of the system. Furthermore, using a secret key generated from the true randomness nature of the wireless channel as an initial value, the proposed continuous optimization-based projection matrix construction method becomes a one-way function, making the proposed scheme a physical layer secure differential MIMO system. An attack algorithm to challenge the proposed scheme is also devised, which demonstrates that the security level achieved improves as the number of transmit antennas increases, even in an environment where the eavesdropper can perfectly estimate channel coefficients and experience asymptotically large signal-to-noise ratios.

We propose a novel joint channel and data estimation (JCDE) scheme to combat the rate limitation in fronthaul links of cell-free massive MIMO (CF-mMIMO) systems introduced by the use of analog-to-digital converters (ADCs) at access points (APs), which makes channel estimation and multi-user detection at the central AP (CAP) challenging. The latter problem is solved here via the new JCDE scheme which differs from state-of-the-art (SotA) alternatives due to two contributions. The first is the design and incorporation of de-quantization (DQ) step which relies only on scalar Gaussian approximation (SGA) assumptions in conformity with mild central limit theorem (CLT), in contrast to the much harder asymptotic conditions required by the classic bilinear generalized approximate message passing (BiGAMP) algorithm. The second is a modification of bilinear Gaussian belief propagation (BiGaBP), whereby quantized outputs are linearized via the Bussgang decomposition enabling tractable signal processing. The resulting DQ-aided JCDE method achieves both low-complexity and high-accuracy by exploiting both the spatial degrees of freedom (DoF) obtained from, and the observations at the CAP to compensate for the low-resolution distortion introduced by, the distributed APs. The efficacy of the proposed method over the SotA is confirmed via computer simulations.

In this paper, we propose a novel joint activity and channel estimation (JACE) algorithm for grant-free extra large MIMO (XL-MIMO) systems subject to spatial non-stationarity phenomena by means of a Bayesian bilinear inference framework. In XL-MIMO systems, the signal from each user is visible only by a small portion of its antenna arrays, which are typically distributed over the surface of a certain structure. The sporadic user activity due to grant-free access, as well as the spatial non-stationarity, jointly imposes a challenging JACE problem involving a nested Bernoulli-Gaussian random variable. In order to address this issue, we decompose the latter into a bilinear inference problem of two independent random quantities, deriving novel message passing rules based on Gaussian approximation and bilinear inference. Performance evaluation via software simulations is offered to demonstrate the effectiveness of the proposed algorithm, which achieves the Genie-aided ideal estimation performance.

Extra large MIMO (XL-MIMO) systems are subject to spatial non-stationarity forming visibility regions (VRs), which leads to a sub-array-wise sparse structure of the channel matrix. When XL-MIMO systems operate in grant-free access mode, in which only a fraction of the potential users are active during a given time slot, it follows that the channel matrix possesses a doubly-sparse and user-specific structure such that the activity of each user and each sub-array can be jointly modeled by a nested Bernoulli-Gaussian distribution. This article considers the joint activity and channel estimation (JACE) problem in XL-MIMO systems subject to this so-defined spatial non-stationarity, tackling this challenging inference problem. Our main contributions are 1) to introduce the novel Bernoulli-Gaussian model to simultaneously capture the aforementioned two distinct structured sparsities, and 2) a new bilinear Bayesian inference algorithm capable of jointly estimating the associated channel coefficients, user activity patterns, sub-array activity patterns ($a.k.a$. spatial non-stationarity), boosted by expectation maximization (EM)-based auto-parameterization. In addition, to shed light on a realistic modeling of VRs, we also introduce a Matérn-cluster point process (MCPP)-based approach to imitate the clustered activity pattern due to spatial non-stationarity. The efficacy of the proposed bilinear JACE algorithm is confirmed by numerical simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) but also can reach the performance of a genie-aided scheme over wide signal-to-noise-ratio (SNR) ranges, in both uniformly-random and MCPP-based sub-array activity scenarios.

We consider quadrature spatial modulation (QSM) schemes, which achieve high spectral efficiency (SE) via the dispersion of a relatively small number$P$of$M$-ary modulated symbols over a large number of combinations of$n_{T}$transmit antennas and$T$transmit instances. In particular, we design a new space-time block code (STBC)-based scalable QSM scheme combining high SE with maximum diversity and optimum coding gains. Deriving a closed-form expression for the optimum SE, we show that scaling the size$T$with$n_{T}$not only is required to achieve SE optimality, but also results in further gains in bit error rate (BER) performance. Building on the latter optimal parameterization, a fully optimized scalable QSM (OS-QSM) transmitter design is then obtained by introducing a new dispersion matrix index selection algorithm that ensures even utilization of spatial-temporal resources. Finally, a new greedy boxed iterative shrinkage thresholding algorithm (GB-ISTA) QSM receiver is proposed, which exploits the inherent sparsity of QSM signals and while detecting spatially and digitally modulated bits in a greedy fashion. The resulting low complexity of the new receiver, which is linear on$n_{T}$, enables the utilization of OS-QSM in systems of previously prohibitive dimensions.

In this work, we study a full-duplex (FD) cloud radio access network (C-RAN) from the aspects of infrastructure sharing and information secrecy, where the central unit utilizes FD remote radio units (RU)s belonging to the same operator, i.e., the trusted RUs, as well as the RUs belonging to other operators or private owners, i.e., the untrusted RUs. Furthermore, the communication takes place in the presence of untrusted external receivers, i.e., eavesdropper nodes. The communicated uplink (UL) and downlink (DL) waveforms are quantized in order to comply with the limited capacity of the fronthaul links. In order to provide information secrecy, we propose a novel utilization of the quantization noise shaping in the DL, such that it is simultaneously used to comply with the limited capacity of the fronthaul links, as well as to degrade decoding capability of the individual eavesdropper and the untrusted RUs for both the UL and DL communications. In this regard, expressions describing the achievable secrecy rates are obtained. An optimization problem for jointly designing the DL and UL quantization and precoding strategies are then formulated, with the purpose of maximizing the overall system weighted sum secrecy rate. Due to the intractability of the formulated problem, an iterative solution is proposed, following the successive inner approximation and semi-definite relaxation frameworks, with convergence to a stationary point. Numerical evaluations indicate a promising gain of the proposed approaches for providing information secrecy against the untrusted infrastructure nodes and/or external eavesdroppers in the context of FD C-RAN communications.

We propose a novel joint activity, channel and data estimation (JACDE) scheme for multiple-input multiple-output (MIMO) systems. The contribution aims to allow significant overhead reduction of MIMO systems by enabling grant-free access, while maintaining moderate throughput per user. To that end, we extend the conventional MIMO transmission framework so as to incorporate activity detection capability without resorting to spreading informative data symbols, in contrast with related work which typically relies on signal spreading. Our method leverages a Bayesian message passing scheme based on Gaussian approximation, which jointly performs active user detection (AUD), channel estimation (CE), and multi-user detection (MUD), incorporating also a well-structured low-coherence pilot design based on frame theory, which mitigates pilot contamination, and finally complemented with a detector empowered by bilinear message passing. The efficacy of the resulting JACDE-based grant-free access scheme in the cell-free MIMO system setup compliant with fifth generation (5G) new radio (NR) orthogonal frequency-division multiplexing (OFDM) signaling is demonstrated by simulation results. The results are shown to outperform the current state-of-the-art and approach the performance of an idealized (genie-aided) scheme in which user activity and channel coefficients are perfectly known.

To solve the problem of multi-target tracking with heavy-tailed process noise and measurement noise, a Student's t mixture δ-generalized labeled multi-Bernoulli ( δ-GLMB) filter is proposed for nonlinear systems. A third-degree Spherical-Radial rule is utilized to calculate the probability density functions of the prediction and update of target states for nonlinear multi-target models. The performance of the proposed Student's t mixture δ-GLMB filter for nonlinear systems is compared with the Sequential Monte Carlo δ-GLMB (SMC- δ-GLMB) filter through simulation experiments. Simulation results demonstrated that the proposed filter can achieve a good trade-off between efficiency and tracking accuracy.

We consider a bidirectional in-band full-duplex (FD) multiple-input multiple-output (MIMO) system subject to imperfect channel state information (CSI), hardware distortion, and limited analog cancellation capability as well as the selfinterference (SI) power requirement at the receiver analog domain so as to avoid the saturation of low noise amplifier (LNA). A novel minimum mean square error (MMSE)-based joint design of digital precoder and combiner for SI cancellation is offered, which combines the well-known gradient projection method and non-monotonicity considered in recent machine-learning literature in order to tackle the non-convexity of the optimization problem formulated in this article. Simulation results illustrate the effectiveness of the proposed SI cancellation algorithm.

Full-Duplex (FD) Amplify-and-Forward (AF) Multiple-Input Multiple-Output (MIMO) relaying has been the focus of several recent studies, due to the potential for achieving a higher spectral efficiency and lower latency, together with the inherent processing simplicity. However, when the impact of hardware distortions is considered, such relays suffer from a distortion-amplification loop, due to the inter-dependent nature of the relay transmit signal covariance and the residual self-interference covariance. The aforementioned behavior leads to a significant performance degradation for a system with a low or medium hardware accuracy. In this work, we analyse the relay transfer function as well as the Mean Squared- Error (MSE) performance of an FD-AF MIMO relay-assisted communication, under the consideration of collective sources of additive and multiplicative transmit and receive impairments. An optimization problem is then devised over the linear transmit and receive strategies to minimize the communication MSE and solved by employing the recently proposed Penalty Dual Decomposition (PDD) method. The proposed solution converges to a stationary point of the original problem via a sequence of quadratic convex programs. Numerical simulations verify the significance of the proposed distortion-aware design compared to the common simplified approaches, as the hardware accuracy degrades.

We have recently proposed a massively concurrent non-orthogonal multiple access (MC-NOMA) scheme which, unlike other non-orthogonal multiple access (NOMA) schemes such as sparse-coded multiple access (SCMA) and pattern division multiple access (PDMA) that rely on sparsity, utilizes instead dense massively multiplexing with interference optimized via Frame Theory. Thanks to the properties of unit-norm tight frames (UNTFs) employed in the MC-NOMA scheme, all users can access all available resources at once but with the resulting interference collapsed and minimized, in a manner similar to that in sparsity-based NOMA. In this article we provide analytical evidence that MC-NOMA outperforms current NOMA schemes both in terms of sum-rate and spectral efficiency. To this end, we derive expressions for the achievable sum-rate and the spectral efficiency of MC-NOMA in a general context, and use the results to numerically validate the improvement potential of MC-NOMA schemes.

We address a bi-directional full-duplex (FD) multiple-input multiple-output (MIMO) system equipped with limited capability for analog self-interference cancellation (SIC) and subjected to hardware (HW) impairments and imperfect channel state information (CSI) at the nodes. We propose an alternating algorithm to minimize transmit (TX) power subject to quality of service (QoS) guarantees in such systems, where the signal to interference-plus-noise ratio (SINR) constraint is relaxed via a Fractional Programming (FP) approach so that optimal TX beamforming vectors can be obtained using standard convex optimization tools. Simulation results show that the proposed algorithm significantly reduces the required TX power while outperforms not only a conventional zero-forcing (ZF) scheme but also the State-of-the-Art (SotA) method in terms of outage probabilities of the prescribed SINRs.

We propose a new method for the design of TX-precoders and RX-beamformers to improve the robustness of millimeter wave (mmWave) channel estimation, focusing on a frame-theoretic approach and a sparse formulation of the mmWave channel estimation problem. Concretely, the high-level design criteria of the transmit precoders and receive beamformers are modeled through a single frame, while their practical realization is achieved by distributed frames, related to the latter by the Kronecker product. We prove that the desirable frame properties are invariant to the derived Kronecker decomposition allowing for the same strategy to be applicable both in the design of a joint measurement matrix - leading to optimal but theoretical-only performance - as well as in the design of practical TX/RX precoding/beamforming matrices. Simulations outline both the improvement achieved by the proposed scheme against the state-of-the-art, as well as its trend towards optimality for systems with large number of antennas and/or subjected to high noise variances.

We present a new Beamforming-based (BB) Multiple-Input Single-Output (MISO)- Non -orthogonal Multiple Access (NOMA) scheme for Power Domain NOMA (PD-NOMA), in which the total transmit power consumption is minimized subjected to prescribed signal-to-interference-plus-noise ratio (SINR) requirements for each user, and under the assumption that only imperfect channel state information (CSI) is available at the transmitter. To this end, the fractional programming (FP)-based quadratic transform is employed to reformulate the non-convex SINR constraint of the original problem into a tractable quadratic form, which contains an estimate of the CSI error vector as a parameter. Taking advantage of the fact that the zero duality gap holds for the non-convex quadratic problems, a closed-form expression for an estimate of the CSI error vector is derived, completing the formulation. Finally, a novel iterative algorithm based on both the herein derived CSI error vector and the semidefinite relaxation (SDR) technique is contributed, which is shown to capable of efficiently solving the constrained min-power problem. Simulation results are given which illustrate the effectiveness of the proposed algorithm, which is found to sacrifice only small quantities of transmit power in return for substantial increase in robustness against CSI imperfection.

We study a multiple-input multiple-output (MIMO) full-duplex (FD) radio system, aiming to increase the feasibility of this technology in bi-directional communications. In particular, we consider that the FD radios are equipped with the State-of-the-Art (SotA) hybrid SI cancellation (SIC) capabilities, but must cope with hardware (HW) and channel state information (CSI) imperfections, contributing four new MIMO beamforming (BF) schemes for such systems. The first is a transmit (TX) beamforming scheme designed via a Fractional Programming (FP) approach, matched with a minimum mean square error (MMSE) beamformer at the receiver. In this benchmark, the FP-based method, the signal to interference-plus-noise ratio (SINR) constraints are relaxed via the quadratic transform (QT), which allows for the SINR-constrained TX-power minimization problem to be solved using interior point methods. Motivated by the high complexity of the latter, three low-complexity alternatives are then derived, in which power minimization is performed via the Perron-Frobenius (PF) approach, while the TX-BF vectors are obtained, respectively, via direct Gradient Projection (GP), QT-relaxation, and via a Double Rayleigh Quotient (DRQ) reformulation of the original optimization problem. The simulation results confirm the significant gains achieved by all four schemes over the SotA, revealing the GP and DRQ as the overall best alternatives depending on power limitation, and HW/CSI qualities.

We propose a new scheme for the robust estimation of the millimeter wave (mmWave) channel. Our approach is based on a sparse formulation of the channel estimation problem coupled with a frame theoretic representation of the sensing dictionary. To clarify, under this approach, the combined effect of transmit precoders and receive beamformers is modeled by a single frame, whose design is optimized to improve the accuracy of the sparse reconstruction problem to which the channel estimation problem is ultimately reduced. The optimized sensing dictionary frame is then decomposed via a Kronecker decomposition back into the precoding and beamforming vectors used by the transmitter and receiver. Simulation results illustrate the significant gain in estimation accuracy obtained over state of the art alternatives. As a bonus, the work offers new insights onto the sparse mmWave-multiple-input multiple-output (MIMO) channel estimation problem by casting the trade-off between correlation and variation range in terms of frame coherence and tightness.

We address the localization problem from ranging and angle information with the aim of deriving an algorithm that achieves high accuracies at lower computational demands than the state of the art. We focus in particular on the multidimensional scaling (MDS) approach, offer modified versions of the super MDS (SMDS) scheme, which has been shown to be able to robustly exploit both ranging and angle information for superior localization accuracies. The new algorithm is obtained by first recasting the SMDS formulation as a linear system in the complex domain, allowing for multiple maximum ratio combiner (MRC) variations of the SMDS to be designed, which in turn enables the adaptation to different conditions in terms of availability of information. Amongst various possible implementations of the new approach, we describe two in particular which are directly compared to the original SMDS: a singleton, and a cooperative variation. The singleton alternative is found to outperform the original in terms of accuracy at single-target scenarios, while the cooperative version is shown to exhibit significantly lower complexity in larger networks.

In the traditional model of wireless communications systems, additive white Gaussian noise (AWGN) is assumed to be linear. However, in an increasingly important class of emerging communication systems — e.g. device-to-device (D2D), full-duplex (FD) and low-cost Internet-of-things (IoT) communication systems — the nonlinearity caused by factors such as device proximity (D2D), residual self-interference (FD) and imperfect power amplification (IoT) can no longer be neglected. A possible mechanism to improve the performance of such systems is to optimize the transmit constellation utilized, which is known in the literature as constellation shaping. With that in mind, we propose a probabilistic constellation shaping scheme to maximize the achievable rates of communication systems affected by nonlinear AWGN. To this end, we derive the analytical expression of the mutual information (MI) of such nonlinear additive white Gaussian noise (AWGN) systems with arbitrary modulation, and maximize the latter by numerically optimizing the corresponding channel input distribution. The result is a semi-analytical scheme (with analytical objective optimized numerically) which are shown to outperform systems employing conventional (uniformly distributed) constellations.

In this paper we propose a novel Compressed Sensing (CS) approach for source localization in wireless sensor networks (WSN). While this is not the first work on applying CS to target localization, it is the first one (to our knowledge) to construct the sensing matrix based only on distance information and a discrete grid. Most of the CS approaches are based on received signal strength (RSS) fingerprinting methods. Moreover, we propose to use this new CS approach in conjunction with a multiple measurement vectors (MMV) problem, which we solve by the Simultaneous Orthogonal Matching Pursuit (SOMP) algorithm. Finally, we demonstrate the superiority of this new approach (even with a relatively small number of measurements) over the non-CS based and more complex Super Multidimensional Scaling (SMDS) algorithm, which is an improved version of the metric MDS. In order for the latter algorithm to be fairly compared against the MMV approach, based on the number of measurements, the noisy distances were fed to a maximum likelihood estimator (MLE) which first estimated the parameters of the Gamma distribution corresponding to the noisy measured distances. The mode of the estimated distribution was then fed to the SMDS.

To cope up with the explosive growth of mobile data demand, the fifth-generation mobile network intents to exploit the available spectrum in millimeter-wave (mmWave) band to boost the communication capacity. However, for a potential mmWave communication to happen, challenges, as propagation losses and blockages, have to be dealt with. Abundant literature illustrating techniques to circumvent these challenges and increase the cellular capacity can be found. Among the approaches used to overcome the challenges in mmWave networks, optimal transmit precoding design, spatial reuse of mmWave base stations (BSs), and mmWave-overlaid microwave (μWave) cellular networks are employed. This paper focuses on the performance analysis of mmWave-overlaid microwave cellular networks, from security perspective. We particularly developed a mathematical framework to analyze the connection outage probability, the secrecy outage probability, and the achievable average secrecy rate of the hybrid mmWave network, while taking fading and the impact of blockages into consideration. Moreover, based on the received signal strength, we formulated a scheme for a generic mobile user to be associated with either the mmWave or μWave network. The exact average secrecy rate of mmWave networks is also formulated using moment generating and Laplace functions as a tool. The derived analytic expressions are validated via simulation results; for different antenna gain, eavesdropper density, BS, and blockage density.

We revisit the super multidimensional scaling (SMDS) wireless localization algorithm first proposed a decade ago, recasting it onto the complex-domain. Under this new formulation, the edge kernel, which carries both angle and distance information simultaneously and plays a central role in the SMDS algorithm, becomes a complex-valued rank-one matrix, resulting in a new complex-domain SMDS framework, which yields several advantages over the original, including the elimination of redundancy, the enhancement of conditions to handle information erasure, and the possibility of designing several algorithmic variations that offer different complexity/performance improvements. To cite some concrete results, it is shown, for instance, that a distance-based localization system with 20 targets employing one of the new algorithms dubbed complex-domain SMDS (CD-SDMDS) outperforms the original SMDS at a tenth of the computational cost, and that the handling of missing information via matrix completion is superior in CDSMDS compared to SMDS. If the same network collects also anchor-to-target angle information, it is furthermore shown that localization is achieved with nearly 20× lower complexity and still higher accuracy using another new algorithm dubbed Turbo MRC-SMDS, and over 25× faster using yet another method dubbed Iterative MRC-SMDS, with only a slight degradation.

Herein, we consider the problem of cooperative multi-access in the presence of overlapped coverage areas. Assuming a frameless ALOHA transmission scheme, we derive exact analytical throughput expressions for throughput in the aforementioned scenarios as a function of the frame length of the system and for arbitrary average numbers of users transmitting in each slot (target degree). After obtaining these original expressions, we then formulate a utility function whose maximization (obtained, e.g., through genetic algorithms) yields unequal and optimum target degrees to be employed by users in each group in order to maximize the peak throughput of the whole system while satisfying a given prescribed outage. A comparison of the resulting cooperative multiple base station (BS) multi-access scheme against optimized single-BS frameless ALOHA systems-which presume the perfect isolation of users at each BS and an equal optimum target degree for all users-indicates a significant gain in overall throughput, thereby revealing that a “multi-access diversity gain” can be reaped by allowing groups of users from different BSs to overlap.

We consider the relay selection problem in large-scale energy harvesting (EH) networks. It is known that if channel state information (CSI) is available at EH relays, a diversity order equal to the number of relays can be obtained, however, at the penalty of a feedback overhead (necessary to obtain accurate CSI) which is not suitable for energy-limited devices intended, e.g., for Internet-of-Things applications. In this paper, we therefore propose a new EH relay selection scheme which is based on the residual energy at each relay's battery, and on information on the distribution of the channels between relays and the destination. The method thus minimizes both the outage probability and the feedback cost. Where previous work relay selection based on channel distribution information consider only small-scale fading distribution, we employ a stochastic geometry approach to consider jointly the geometrical distribution (i.e., large-scale fading) and small-scale fading yielding a simple relay selection criterion that furthermore utilizes only rough information on the relay's location, i.e., an ordinal number from the destination. The outage probability of the proposed relay selection scheme is analytically derived, and the achievable diversity order of the proposed approach is investigated. Computer simulations confirm our theoretical analyses and show that our approach is robust against errors in the estimation of the distances between nodes.

In this paper, the secrecy-rate characteristics of multitier downlink heterogeneous networks under generalized fading model is investigated for two types of base-station (BS)-user(s) association scenarios. Each tier within the network has a single multiantenna BS that intends to serve multiple single-antenna mobile users. These users are assumed to be distributed according to homogeneous Poisson point process (PPP) with particular density parameter. Single-antenna eavesdroppers which intend to wiretap the communication between the chosen BS and intended mobile user are also assumed to coexist within the network distributed as PPP with different intensity parameter. We have adopted the maximum received path gain, rather than distance, as a metric to describe the association scenario between a BS and typical user. Therefore, a typical user associates itself with: 1) a BS that provides the user with the maximum path gain or 2) any potential BS that provides the kth maximum path gain to the user. Using stochastic geometry as a tool, the received path gain distributions and the achievable average secrecy rate expressions from the perspective of association with the “best” and the kth best BS are analyzed analytically. Tractable numerical and simulation results are presented under various assumptions of fading scenario, path loss exponent, eavesdropper density, and antenna figures to support the lemmas and propositions stated within this paper.

We consider in this paper the mobility challenges that emerge in the context of vehicular wireless communications. Specifically, we research the problem of estimating the mobile doubly selective channels for the IEEE 802.11p orthogonal frequency-division multiplexing-based communication. To mitigate some of the major challenges imposed by the time-varying vehicular environments and to solve this estimation problem a new standard-compliant channel estimator block is proposed. The estimator structure is an innovative construct stemming from the idea of subcarrier frequencies self-organization, which leverages intersubbands connections through decision directed feedback, spectral smoothing, and time tracking. Comparisons between the new method and existing alternatives already indicate that the proposed scheme outperforms the latter. Furthermore, the bit error rate (BER) and frame error rate (FER) performance of the new estimator is close to the one with perfect channel state information for low and medium SNRs.

In this paper, we determine the optimal battery capacity for an energy harvesting wireless node. The goal is to achieve transmission performances similar to an identical system, but with infinitely large battery. In our previous work, addressing a node without any energy storage constraints, we proposed a policy to optimize instantaneous transmission rate in order to maximize the average transmission rate. Extending this idea we now minimize the capacity of the storage unit whilst maintaining the optimal instantaneous transmission rate as in the case with infinite battery capacity. A mathematical formulation to derive such optimal capacity is in turn presented and verified.

This paper addresses the problem of vehicle position estimation in dense urban environments, where traditional global positioning system (GPS)-based localisation techniques are severely affected by non line-of-sight (NLOS) signal propagation and multipaths presence. Assuming that GPS signals are fairly received only by a very small fraction of vehicles at the border of the urban environment, we propose the multihop scheme based on vehicle to X (V2X) communication to propagate this information to the whole network. As a consequence, the multihop scheme allows every vehicle to estimate its own position collecting its cumulative distances to border vehicles. Finally we introduce a new analytical framework to verify the fundamental performance of multihop scheme for location forwarding in urban dense environments in terms of position estimate error bounds, jointly considering the uncertainty introduced by the multihop process and by the GPS localization.

The current advances in the Intelligent Transportation Systems (ITS) reveal new solutions which are in need of validation in real-world deployments. The practicality, implementability, latency and robustness of such methods are key insights into their wide acceptance, and finally, absorption by standardization organizations. This paper discusses an approach to rapidly prototype newly proposed Wireless Access in Vehicular Environments (WAVE) algorithms based on the open source community and the emergence of Software Defined Radio (SDR). Concretely, this work details the practical deployment of a new adaptive channel estimation method in the context of IEEE 802.11p based vehicular communications. The development steps are illustrated and some challenges involved by real-world deployment (e.g. the phase tracking problem) are treated. Consequently, practical methods to solve the observed impairments are derived and implemented for real-time operation and validation.

We consider the problem of performing ranging measurements between a source and multiple receivers efficiently and accurately, as required by distance-based wireless localization systems. To this end, a new multipoint ranging algorithm is proposed, which is obtained by adapting superresolution techniques to the ranging problem, using for the sake of illustration the specific cases of time of arrival (ToA) and phase-difference of arrival (PDoA), unified under the same mathematical framework. The resulting nonparametric algorithm handles multipoint ranging in an efficient manner by employing an orthogonalized nonuniform sampling scheme optimized via Golomb rulers. Since the approach requires the design of mutually orthogonal sets of Golomb rulers with equivalent properties-a problem that founds no solution in current literature-a new genetic algorithm to accomplish this task is presented, which is also found to outperform the best known alternative when used to generate a single ruler. Finally, a Cramér-Rao lower bound (CRLB) analysis of the overall optimized multipoint ranging solution is performed, which together with a comparison against simulation results validates the proposed techniques.

A long-standing problem in CoMP is that existing methods either require perfect and instantaneous CSIT which implies the need of excessive piloting and backhauling, so as to acquire and distribute CSIT accurately and timely. Thus, it is required to formulate a robust beamforming method to circumvent this drawbacks and has been the focus of many research papers recently. In this paper, a solution to the problem, which follows a relaxation-restriction (RAR) approach and emerges from the observation that the crux of the robustness problem is the construction of the SINR estimates themselves, subject to partial CSIT. Specifically, it is shown considering SINR-constrained transmit-power minimization that CoMP algorithms built using the biased estimator significantly outperform the best recent alternative method, namely the outage based probabilistic approach with Bernstein-type inequality relaxation of [1]. Our proposed alternative is also less relaxed as compared to existing literature. The level of conservatism can be further reduced through incorporating bisection method on top of the robust SINR-constrained optimization problem so that the required outage probability in this case is satisfied as per the user requirement and transmit power efficient solutions is obtained.

We perform an analysis of the secrecy outage of random networks under Nakagami-m fading with multiple transmit antennas. Specifically, using a network model that accounts for uncertainties both in node locations (distances) and channel coefficients (fading), we derive the distribution of the best path gain of eavesdroppers using Probability Generating Functional property of Poisson Point Process (PPP). Using this result, the secrecy outage probability and the conditional secrecy outage probability of random networks with multiple eavesdroppers are obtained, with basic factors such as the density of eavesdropping nodes, the number of transmit antennas and the fading coefficient all accounted for by explicit parameters. The impact of transmission factors, including number of transmit antennas, fading figure, legitimate node distance and node density are studied and analysed in numerical results.

The majority of recent literature characterizes the secrecy capacity of wireless random networks by relying on Poison Point Processes which model the spatial distribution of devices. However, the realistic topologies are distinct from uniform and random assumption of the base stations or users. It has been recently demonstrated that Matérn Hard-core Point Processes (MHCPP) - rather than Poison Point Processes - are better suited to characterize such random location of users and base-stations of cellular systems. In this paper, we perform an analysis of the Secrecy Outage probability of cellular random networks under fading channel by employing the modified MHCPP model. It is also shown that the base station deployment has a significant impact on the secrecy outage.

We propose a simple solution based on Perron-Frobenius theorem and uplink-downlink duality to the longstanding problem of transmission strategies: maximize the minimum SINR or minimize the transmit power. In contrary of using perfect and instantaneous CSIT (requires higher overhead and backhauling), or using only average CSIT, e.g. channel covariance which suffers from quality fluctuations; we account for imperfect instantaneous CSIT. The solution emerges from the observation that the crux of the robustness problem is the construction of SINR estimates itself. We provide a MLE-based estimate which being sub-optimal and failing to incorporate the channel estimation error, we reinforce it by biasing the estimate, which not only achieves smaller error but is strictly conservative as well. We also consider both transmission strategy problems, and show that the biased estimates are not only robust compared to traditional unbiased estimates, but also substantially outperform the probabilistic method with Bernstein-type relaxation proposed in [1].

The authors provide compact and exact expressions for the extreme eigenvalues of finite Wishart matrices with arbitrary dimensions. Using a combination of earlier results, which they refer to as the James–Edelman–Dighe framework, not only an original expression for the cumulative distribution function (CDF) of the ‘smallest’ eigenvalue is obtained, but also the CDF of the ‘largest’ eigenvalue and the probability density functions of both are expressed in a similar and convenient matrix form. These compact expressions involve only inner products of exponential vectors, vectors of monomials and certain coefficient matrices which therefore assume a key role of carrying all the required information to build the expressions. The computation of these all‐important coefficient matrices involves the evaluation of a determinant of a Hankel matrix of incomplete gamma functions. They offer a theorem which proves that the latter matrix has ‘catalectic’ properties, such that the degree of its determinant is surprisingly small. The theorem also implies a closed‐form and numerical procedure (no symbolic calculations required) to build the coefficient matrices.

In cooperative localization, target users take advantage of neighboring users in the network to improve their position estimates. In dense networks, the number of neighbors is high and consequently a very large amount of information is available. Using all neighbors (full cooperation) results in a considerable amount of data that is to be processed and transmitted causing high network traffic, delays and reduced battery lifetime. The goal in censoring is to limit the amount of cooperation to reduce the amount of data to be transmitted, without losing (much) in positioning accuracy compared to full cooperative localization. In this paper we propose a novel censoring technique based on the Bayesian Cramér-Rao Lower Bound (CRLB) that takes into account both the uncertainties of the neighbors and the link quality in terms of LOS/NLOS. With the use of the unscented transform and a greedy search approach, the censoring can be performed accurately and at a low computational complexity.

This paper reveals an impairment of well-known path-loss model generally assumed in conventional works with radio frequency (RF) energy harvesting. We will prove that, when RF energy harvesting is considered in a large-scale network, the summation of energy transferred to nodes in the network diverges while the radiated energy must be finite. Thus, we propose a new absorption function meeting this law and derive several expressions of average harvested energy based on the model. Furthermore, the effectiveness of RF energy harvesting with Poisson point process (PPP) network with a single transmitter is demonstrated.

Perfect channel state information at the transmitter (CSIT) is required to completely characterize the optimal degrees of freedom (DoF) of an interfering network by zero-forcing. However, it is not always realistic to know the CSIT perfectly when the channel is varying rapidly with time. In that case, a delayed CSIT is a more reasonable assumption. In this paper, we study the DoF of interfering multiple input single output (MISO) broadcast channel (BC) when the delayed CSIT is known perfectly. We propose a space-time transmission scheme based on interference alignment for two-cell interfering MISO-BC with M transmit antennas and K users in each cell. Considering the various cases, we suggest that the achievable per cell DoF converges to K/K+1 in the case when M = K and the outdated CSIT is known perfectly. The DoF per cell approaches 1 as the number of users in each cell approach to infinity.

Correlation between the legitimate receiver and the eavesdropper has been seldom considered in the literature when evaluating secrecy metrics in stochastic wireless networks. In practice, however, poor scattering conditions may lead to spatially correlated channels, which can be exploited by eavesdroppers. In this paper we investigate the connection outage and secrecy outage in Poisson-distributed random networks under Nakagami-m fading and mutually correlated legitimate and eavesdropping channels. Numerical results show that correlated fading has an important effect on security, which may be either beneficial or harmful, depending on the secrecy outage constraints.

We revisit the problem of describing optimal anchor geometries that result in the minimum achievable MSE by employing the Cramer Rao Lower bound. Our main contribution is to show that this problem can be cast onto the whelm of modern Frame Theory, which not only provides new insights, but also allows the straightforward generalization of various classical results on the anchor placement problem. For example, by employing the frame potential for single-target localization we see that the directions of the anchors, as seen from the target, should optimally be as orthogonal as possible and that the existence of an optimal geometry for an arbitrary number of anchors is governed by a fundamental inequality. Furthermore, the frame-theoretic approach allows for simple derivation of some properties on optimal anchor placement that prove to be useful in a tractable approach for the more complex, multi-target anchor placement problem. In a more general sense, the paper builds a refreshing bridge between the classical problem of wireless localization and the powerful domain of Frame Theory, with far-reaching potential.

We propose a new accurate ranging algorithm for Time of arrival (ToA)-based wireless network localization. Although many works have been done in the current literature to optimize localization and tracking algorithms, the mitigation of ranging errors is still a very challenging problem. To this end, we combine and optimize two well-known super-resolution algorithms such as Music and Root-Music, commonly used in the estimation of the direction of arrival of incoming signal sources of a linear antenna array, with the powerful mathematical notion of a Golomb Ruler. The results are proved to be tremendously efficient and to almost reach the fundamental localization limits depicted by the Cramér-Rao lower bound (CRLB).

We consider the secret key generation (SKG) problem for short-range communication (SRC) systems. Specifically, we focus on the problem that secret key generation schemes relying on the entropy of the channel are not suitable for SRC systems, which are characterized by the absence of fading. To mitigate this problem, we introduce the concept of geometric secrecy and propose a new phase-based SKG which relies only on the reciprocity of the channel in order to securely generate mutual secret keys in the presence of an eavesdropper. The method is validated via a theoretical analysis which shows that for the signal-to-noise ratio (SNR) ranges typical of SRC systems, an efficient secret key generation can be achieved.

We analyze the effect of fading onto the probability of non-zero secrecy capacity, denoted Pr{Cs:i> 0}, in stochastic wireless random networks modeled as Secrecy-Graphs. Specifically, we derive expressions to characterize the probability that the secrecy capacity of a unicast channel to a legitimate node is non-zero, in the case when the channel is affected by Nakagami-m fading, and in the presence of a random (unknown) number of eavesdroppers. The results show that fading can increase the probability of finding information-theoretic secure channels in such conditions, depending on the relative density of legitimate nodes and eavesdroppers.

We investigated the secrecy outage of unicast channels in random networks exposed to unknown numbers of randomly located eavesdroppers, obtaining original expressions which include uncertainty in terms of the location of legitimate nodes relative to eavesdroppers, the number of eavesdroppers, and fading. Under such conditions, we derive the path gain distributions of legitimate and eavesdropper nodes, as well as the corresponding secrecy non-outage. Two interesting conclusions can be drawn from our analysis. The first is that the uncertainty on the number of eavesdropper does not play a significant role in quantifying secrecy outage; and the second is that secret communication at a given rate is possible (albeit subjected to outage), with very low power. Specifically, it is found that the for a given fading figure and network density (which fundamentally determines the secrecy outage) similar secrecy outage is experience by the k-th furthest legitimate node, independent on the source's transmit power.

We consider the secrecy capacity of unicast channels of ad hoc networks exposed to randomly located eavesdroppers, as modeled by S-Graphs. Expressions that quantify the impact of fading and of the density of legitimate nodes relative to that of eavesdroppers are obtained, in terms of the probability that secrecy capacities of unicast channels are nonzero. The results indicate that depending on the relative density of eavesdroppers and the fading intensity, the secrecy capacity of unicast channels subject to fading may be higher than under additive white Gaussian noise (AWGN).

We propose a new Nakagami phase-envelope fading channel simulator that (i) allows for arbitrary real values of fading parameter, (ii) exactly matches the Nakagami first-order statistics, (iii) and closely matches the second-order statistics classically assigned to Nakagami fading. The proposed simulator is based on a cascade of two existing simulators—the random-mixture simulator and the rank-matching simulator. It combines the strenghts of these two simulators, outperforming them both.

We offer a redesigned form of the classical multidimensional scaling (C-MDS) algorithm suitable to handle the localization of multiple sources under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. To do so we propose to modify the kernel matrix used in the MDS algorithm to allow for both distance and angle information to be processed algebraically (without iteration) and simultaneously. In so doing we also show that the new formulation overcomes two well known limitations of the C-MDS approach, namely the propagation error problem and the possibility to weight the dissimilarities used as measurement information, including, for the case of binary weights, the data erasure problem. Due to the increased size of the proposed edge kernel matrix KEused in the algorithm, the Nystrom approximation is applied to reduce the overall computational complexity to few matrix multiplications. Range only scenarios are also dealt with by approximating the matrix KE. Simulations in range-angle as well as range-only scenarios demonstrate the superiority of our solution under both LOS and NLOS conditions versus semidefinite programming (SDP) formulations of the problem specifically designed to exploit the heterogeneity of the information available.

We present new lower and upper bounds on the Gaussian Q-function, unified in a single and simple algebraic expression which contains only two exponential terms with a constant and a rational coefficient, respectively. Lower- and upper-bounding properties are obtained from such unified expression by selecting the coefficients accordingly. Despite the remarkable simplicity, the bounds are found to be as tight as multi-term alternatives obtained e.g. from the Exponential [2] and Jensen-Cotes [3] families of bounds. A corollary result is that the n-th integer power of Q(x) can also be tightly bounded both below and above with only n+1 algebraic terms. In addition to offering remarkable accuracy and mathematical tractability combined, the new bounds are very consistent, in which both lower and upper counterparts are similarly tight over the entire domain.

We contribute a new solution to the problem of establishing an analytical relationship between hop-counts under a certain routing policy and Euclidean distances in random networks, both in the linear and planar cases. The contributed solution is unified, in that hop-count distributions have similar expressions both in the 1D and the 2D cases; general in terms of routing policies, in that the effect of any given policy is accounted for by means of a single parameter; closed-form, such that hop-count probability mass functions (PMF's) are given in terms of scaled versions of the closed-form PMF's of the number of nodes; and mathematically tractable, since the derived hop-count distributions are in the form of a difference of the well-known Nakagami-m cumulative density functions (CDF's). Direct and Kullback-Leibler divergence comparisons against empirical data demonstrate the high accuracy of our solution. The simplicity, accuracy and generality of the result owes partly to a self-imposed confinement to connected networks, defined formally in stochastic-geometric terms, which allows for the elimination of recursions and multivariate marginalization commonly required by existing solutions. The contributed results find application in the design and analysis of ad hoc networks, cooperative localization algorithms or latency and energy consumption analysis.

We address the Primary User (PU) detection (spectrum sensing) problem, relevant to cognitive radio, from a finite random matrix theoretical (RMT) perspective. Specifically, we employ recently-derived closed-form and exact expressions for the distribution of the standard condition number (SCN) of uncorrelated and semi-correlated random dual central Wishart matrices of finite sizes in the design Hypothesis-Testing algorithms to detect the presence of PU signals. In particular, two algorithms are designed, with basis on the SCN distribution in the absence (H_0) and in the presence (H_1) of PU signals, respectively. Due to an inherent property of the SCN's, the H_0 test requires no estimation of SNR or any other information on the PU signal, while the H_1 test requires SNR only. Further attractive advantages of the new techniques are: a) due to the accuracy of the finite SCN distributions, superior performance is achieved under a finite number of samples, compared to asymptotic RMT-based alternatives; b) since expressions to model the SCN statistics both in the absence and presence of PU signal are used, the statistics of the spectrum sensing problem in question is completely characterized; and c) as a consequence of a) and b), accurate and simple analytical expressions for the receiver operating characteristic (ROC) — both in terms of the probability of detection as a function of the probability of false alarm (P_D versus P_F) and in terms of the probability of acquisition as a function of the probability of miss detection (P_A versus P_M) — are yielded. It is also shown that the proposed finite RMT-based algorithms outperform all similar alternatives currently known in the literature, at a substantially lower complexity. In the process, several new results on the distributions of eigenvalues and SCNs of random Wishart Matrices are offered, including a closed-form of the Marchenko-Pastur's Cumulative Density Function (CDF) and extensions of the latter, as well as variations of asymptotic the distributions of extreme eigenvalues (Tracy-Widom) and their ratio (Tracy-Widom-Curtiss), which are simpler than those obtained with the "spiked population model".

We address the Primary User (PU) detection (spectrum sensing) problem, relevant to cognitive radio, from a finite random matrix theoretical (RMT) perspective. Specifically, we employ recently-derived closed-form and exact expressions for the distribution of the standard condition number (SCN) of uncorrelated and semi-correlated random dual Wishart matrices of finite sizes, to design Hypothesis-Testing algorithms to detect the presence of PU signals. An inherent characteristic of the SCN/RMT-based approach, is that no signal-to-noise ratio (SNR) estimation or any other information on the PU signal is required. On top of this property, other attractive advantages of the new techniques are: a) due to the accuracy of the finite SCN distributions, superior performance is achieved under a finite number of samples, compared to asymptotic RMT-based alternatives; b) since expressions to model the SCN statistics both in the absence (H0) and presence (H1) of PU signal are used, the statistics of the spectrum sensing problem in question is completely characterized; c) as a consequence of a) and b), accurate and simple analytical expressions for the receiver operating characteristic (ROC) - both in terms of PD as a function of PF and in terms of PA as a function of PM - are yielded. It is also shown that the proposed finite RMT-based algorithms outperforms all similar alternatives currently known in the literature, at a substantially lower complexity.

We study complex-valued Nakagami-m variates, establishing the moment determinance of the envelope, phase and joint envelope-phase Nakagami-m probability density functions (pdf's). Inspired by that result, we then show that Nakagami-m variates with arbitrary fading figure m can be accurately decomposed onto a mixture of Nakagami-m variates with integer or half-integer m, i.e, 2m ∈ ℕ+. The latter has the immediate theoretical implication that results currently known to hold for Nakagami-m channels under the constraint 2m ∈ ℕ+can accurately be extended to arbitrary m via simple linear decomposition, with weights given by the random mixture probabilities, for which formulas are provided. The latter is illustrated with an example of the application of the random mixture decomposition to the simplification and generalization of bit error rate (BER) expressions for PSK modulation in the Nakagami-m relay channel. For completeness, implications on possible extensions and improvements of existing methods to generate Nakagami-m variates is also briefly discussed in the form of further examples.

The transmission capacity (TmC) of an ad hoc network measures the area spectral efficiency (in bits/sec × Hz×m2) of successful transmissions as a function of the required transmission rate in single-hop links ¿, under the assumption that the average density of active links ¿atin the network is given. In reality, however, the probability that a node wishing to transmit becomes active is conditioned on the availability of a receiving peer. Consequently, ¿atis not a given parameter but rather a function of topological parameters. In this paper, we employ stochastic-geometric tools to obtain an expression of ¿atas a function of the transmission range d and network density ¿, and apply the result to evaluate the transmission capacity of autonomous interference-limited networks. We then use the TmC to study the impact of closest-neighbor, furthest-neighbor and random-neighbor hopping strategies on the performance of such networks. It is shown that amongst these alternatives, the closest-neighbor strategy always achieves the highest transmission capacity. Furthermore, it is found that the advantage of closest-neighbor hopping is more significant in networks with high densities, large transmission ranges and/or higher required rates, where interference is the dominant limiting factor. In noninterference-limited networks, however, the three strategies are equivalent.

We present a new family of tight lower and upper bounds on the Gaussian Q-function Q(x). It is first shown that, for any x, the integrand phi(thetas; x) of the Craig representation of Q(x) can be partitioned into a pair of complementary convex and concave segments. As a consequence of this property, integrals of phi(thetas; x) over arbitrary intervals within its convex region can be lower-bounded by Jensen's inequality and upper-bounded by Cotes' quadrature rule, with the opposite occurring for the concave region phi(thetas; x). The combination of these complementary bounds yield a complete family of both lower and upper bounds on Q(x), which are expressed in terms of elementary transcendental functions and can be made arbitrarily tight by finer segmentation. A by-product of the method is that various other functions, such as the squared Gaussian Q-function Q2(x), the 2D joint Gaussian Q-function Q(x, y, p), and the generalized Marcum Q-function QM(x, y), can also be both upper and lower bounded with arbitrarily tightness, which to the best of our knowledge finds no precedence in the literature. Explicit examples of the latter applications are given.

We introduce multi-hop aggregate information efficiency (MIEA), a comprehensive metric that captures several performance-affecting factors of wireless ad hoc networks in a unified formulation. This metric is then employed to analyze such networks with respect to their spectral efficiencies, network loads, and hopping strategies. The analysis reveals that the hopping strategy that achieves maximum information efficiency is that of multiple short hops with no more than a single packet retransmission allowed at each hop, as opposed to the alternative of fewer long-haul hops with multiple packet retransmissions. The implementation of that preferred strategy withstanding, it is found furthermore that the most efficient networks typically exhibit about 65 % of link outage probability, which corroborates similar findings obtained in different network settings and using different metrics. Bearing in mind that link outage is a function not only of deterministic parameters such as node density, but also of design parameters such as modulation, our analysis also shows that the modulation scheme that optimizes the aggregate information efficiency is in fact a function of node density. In that respect, our metric and method is shown to be useful to determining the modulation scheme that optimizes the performance of a network with a certain node density.

A fundamental question of interest in multi-hop networks is whether routing should be done in a smaller number of longer hops, or larger number of shorter hops. However, underneath these aspects of the routing problem itself is the number of contending nodes involved in the relay selection taking place at each hop so as to realize whichever routing strategy adopted. A larger number of nodes increases the likelihood of finding an adequate relay, whereas increasing the selection overhead, and vice-versa. In this article we investigate both the expected cost (in time consumed) incurred by the relay selection strategy, and the effectiveness of the forwarding strategy (in spatial advancements) as a function of the number of the contending nodes, when using contention-based geographic forwarding (CGF) strategies to relay packets in multi-hop scenarios. In particular, probability generating functions (PGFs) are utilized to compute the distribution of contention resolution intervals (CRIs) and quantify overhead, while stochastic geometry is utilized to model topological aspects of the network and quantify expected progress. Both a totally random and an auction-based relay selection algorithms (RSAs) are studied and compared, with the advantages of the auction-based approach established analytically. From an overhead point of view, the selection of the furthest and the nearest relays are equivalent, but our results indicate that the long-hop routing approach is less sensitive to the number of contending nodes than the short-hop alternative.

We analyze amplify-and-forward (AF) distributed space-time block coded (DSTBC) cooperative relaying systems over the random set relay channel (RSRC). A two-stage operation is considered where all terminals operate in half-duplex mode and where the source first transmits to a pool of N relays, each of which decides independently whether to relay the source's information to the destination or not, by comparing its own instantaneous received signal-to-noise ratio (SNR) to a threshold zeta. The K les N transmitting relays do so with constant transmit power per relay, and cooperate through linearly dispersed full-rate-full-diversity DSTBCs. The system concept is interesting in which no coordination amongst relays is required, and only backward channel state information (CSI) is assumed. We derive the mutual information (MI) achieved by such a scheme with Rayleigh fading at each channel branch and assuming perfect interleaving (ergodic channel). Then, we study the effect of the selected threshold onto achievableMI in the case where the source and each relay transmits with the same power. It is found that the maximal MI is not achieved under full-time-all-relay cooperation (K = N), despite the fact that the total receive power at the receiver increases with K. The maximum MI in this case can also be fitted to a simple function of N. Next, we investigate the effect of different power balances between the two transmission stages while fixing the total average transmit power. It is shown that the optimum power allocation depends on the threshold, such that the MI is maximized not with equal power allocation (and K = N), but rather with a power balance of around 2-to-3 and K < N, again, despite the fact that the total receive power at the receiver increases with K. Finally, we compare the maximum MI against the MI achieved with QPSK and QAM modulations, which reveals that the envelope behavior of the latter follows the same trend observed with the former.

In this paper, a novel distance based source-localization algorithm is proposed for location-tracking applications in home, automotive and public transportation environments. Based on the least squares (LS) formulation of the source-localization problem, we develop an optimization algorithm that proves mostly unaffected by the number of local minima of the LS-objective function. The algorithm, referred to as range global distance continuation (R-GDC), consists of an iterative procedure in which, at each k-th iteration, the original objective is smoothed by a Gaussian kernel (smoothing function) and minimized by a steepest-descent (SD) method. The results reveal that the R-GDC algorithm, not only outperforms the alternative methods, but also achieves a localization error that is close to theoretical position error bound (PEB).

In this paper we propose a low-complexity non-parametric pre-filtering/weighting technique for Time of Arrival (TOA)-based target tracking systems. The output of a wavelet transform computed on each one of the anchor-to-target links is used to estimate the noise and the dynamic for the corresponding link. Subsequently this information is used to weight and adaptively decide on the reliability of pre-filtered data. To take full advantage of the pre-filtering scheme proposed, this is used together with the optimization algorithm (SMACOF), a low-complexity solution to the multidimensional scaling (MDS) problem. Since both the pre-filtering scheme and SMACOF don't rely on any a-priori knowledge, their combination results in a completely non parametric approach to the target tracking problem. The aforementioned technique is investigated and compared against a Bayesian state variable formulation of the problem implemented through an Extended Kalman Filter (EKF). The MDS-based tracking algorithm with the wavelet pre-filtering scheme here proposed, while remaining completely non-parametric is shown to overcome the EKF algorithm under different target dynamics and noise conditions.

We consider the problem of tracking multiple targets in the presence of imperfect and incomplete ranging information using an MDS-based tracking algorithm. An advantage of this technique is that tracking accuracy is independent on target dynamics. The main feature of the aforementioned algorithm, which we proposed in an earlier work, is that tracking is performed over the eigenspace of a Nystrom-Gram kernel matrix constructed with no a-priori knowledge of the statistics of target trajectories. Consequently tracking becomes a problem of updating the eigenspace given new input data, which is achieved with an iterative Jacobian eigen-decomposition technique. In this paper it is first shown how to improve the aforementioned eigen- decomposition to fully exploit the structure of the reconstructed Gram kernel matrix, then how to use the similarity existing between subsequent Gram matrices to efficiently track the relative sub-spaces. The performance and computational complexity of two techniques, namely, the Multidimensional Scaling (MDS)- based tracking algorithm and SMACOF are investigated. As a result, the MDS-based tracking algorithm with Jacobian eigenspace updating is shown to achieve the same performance as the SMACOF algorithm, but at a significantly lower complexity.

We present new families of lower and upper bounds on Q-functions. First, we consider the Craig form of the Gaussian Q-function Q(ξ) and shown that its integrand ϕ(ϕ; ξ) can be partitioned into a pair of complementary convex and concave segments. This property is then exploited in conjunction with the Jensen inequality and the Newton-Cotes' quadrature rule to produce a complete family of upper and lower bounds on Q(ξ), which can be made arbitrarily tight by finer segmentation. The basic idea is then utilized to derive families of upper and lower bounds also for the squared Gaussian Q-function Q/2(ξ), the 2D joint Gaussian Q-function Q(x, y, p), and the generalized Marcum Q-function QM(x, y). The bounds are shown to be tighter than alternatives found in the literature, and in some cases the lower bounds provided find no equivalent in current literature. The generality of the principle is the elegant point of the method and the resulting Jensen-Cotes bounds are easy to implement and evaluate since only elementary transcendental functions are involved. As an example of application to the analysis of communication systems, we consider the bit error rates (BER's) of decode-and-forward (DF) cooperative relaying schemes with coherent and differential phase-shift keying (PSK) modulations, which have been shown to have an intricate dependence on the Gaussian Q-function, complicated by crossproducts, irrational functional arguments and multiple numerical integrations. In that example the bounds substantially reduce the complexity required to evaluate the expressions, retaining tightness despite multiple numerical integrations with infinite limits.i

We propose a robust non-parametric strategy to weight scarce and imperfect ranging information, which is shown to significantly improve the accuracy of distance-based network localization algorithms. The proposed weights have a dispersion component, which captures the effect of noise under the assumption of bias-free samples, and a penalty component, which quantifies the risk of the latter assumption and penalizes it proportionally. The dispersion weights result from the application of small-scale statistics with confidence bounds optimized under a maximum entropy criterion that mathematizes the empirical concept of reliability commonly found in related literature. In turn, the penalty weights are derived from the relationship between the risk incurred by the bias-free assumption and the geometry of 3-node cliques, established by statistical-geometry. The performance of the distance-based network localization algorithm employing the proposed dispersion-penalty weights is compared against the Cramér-Rao lower bound (CRLB) and to equivalent algorithms employing alternative weights. The comparison reveals that, amongst the alternatives, the network localization algorithm with the proposed weights performs best and closest to an unbiased estimator.

Cooperative communications via distributed space-time codes has been recently proposed as a way to form virtual multiple-antennas that provide dramatic gains in slow fading wireless environments. In this paper, we consider the design of practical distributed space-time codes for wireless relay networks using the amplify-and-forward (AF) scheme, where each relay transmits a scaled version of the linear combinations of the received symbols and their complex conjugate. We employ GABBA codes, which are systematically constructed, orthogonally decodable, full-rate, full-diversity space-time block codes, in a distributed fashion. Our scheme is valid for any number of relays with linear orthogonal decoding in the destination, which make it feasible to employ large numbers of potential relays to improve the diversity order. We generalize the distributed space-time codes in AF mode when the source-destination link contributes in both phases of the transmission. Assuming MPSK or M-QAM constellations and maximum likelihood (ML) detection, we derive an approximate formula for the symbol error probability of the investigated scheme in Rayleigh fading channels. The analytical results are confirmed by simulations, indicating both the accuracy of the analysis, and the fact that low-complexity, flexible, and high-performing distributed space-time block codes can be designed based on GABBA codes.

A cross-layer algorithm for geographic routing in wireless sensor networks (WSNs) is proposed, which is robust to dead-ends and resilient to topological variations due to network dynamics. The solution combines ideas of network tessellation (clusterization) with greedy forwarding, without suffering from the problems afflicting landmark-based alternatives. The clusterization algorithm is based on a discovered graph-spectral property and relies on connectivity information only. Cluster sizes can be varied, allowing for different trade-offs between packet delivery success ratio (PDSR) and average packet delivery latency (APDL) to be reached. Simulation results show that the technique can substantially improve the PDSR in networks where large concave holes (dead-ends) are present, with no or little impact on APDL.

A novel, simple and efficient method for the generation of Tikhonov (a.k.a. von Mises) random variates is proposed. In the proposed method, circular variates of a prescribed Tikhonov distribution pT(x;alpha,xi) are generated via the transformation of variates selected randomly, on a one-for-one basis, from a bank of K distinct Cauchy and Gaussian generators. The mutually exclusive probabilities of sampling from each of the Cauchy or Gaussian generators, as well as the variance and half-width parameters that specify the latter, are derived directly from the Cauchy, Gaussian and Tikhonov characteristic functions, all of which are either known or given in closed form. The proposed random mixture technique is extremely efficient in that a single pair of uniform random numbers is consumed in the generation of each Tikhonov (or von Mises) sample, regardless of the prescribed concentration and centrality parameters (alpha, xi), all requiring neither the rejection of samples, nor the repetitive evaluation of computationally demanding functions. Additional attractive features of the method are as follows. By construction, the first (dominant) N circular moments of Tikhonov variates generated with the proposed random mixture technique are the ones that best approximate their corresponding theoretical values, with errors measured exactly. The exact distribution of generated Tikhonov variates is determined analytically, and its (Kullback-Leibler) divergence to the exact Tikhonov PDF is shown also analytically to be negligible. Finally, the technique establishes a connection between Tikhonov and Gaussian variates which can be exploited, e.g., in the generation of piecewise-continuous pseudo-random functions with Tikhonov-distributed outcomes.

A novel, simple and efficient method for the generation of Tikhonov (a.k.a. von Mises) random variates is proposed. In the proposed method, circular variates of a prescribed Tikhonov distribution pT(chi;alpha,xi) are generated via the transformation of numbers selected randomly, on a one-for- one basis, from a bank of K distinct Cauchy and Gaussian generators. The mutually exclusive probabilities of sampling from each of the Cauchy or Gaussian generators, as well as the parameters that specify the latter, are derived directly from the Cauchy, Gaussian and Tikhonov circular moments, all of which are either known or given in closed form. The proposed technique is extremely efficient in that it requires a single pair of uniform random numbers to generate one Tikhonov (or von Mises) sample, regardless of the prescribed concentration and centrality parameters, without sample rejection or the repetitive evaluation of computationally demanding functions. Additional attractive features of the method are as follows. By construction, the first (dominant) N circular moments of Tikhonov variates generated with the proposed random mixture technique accurately approximate their corresponding theoretical values, with errors measured exactly. The exact distribution of generated Tikhonov variates is determined analytically, and its (Kullback- Leibler) divergence to the exact Tikhonov PDF is shown also analytically to be negligible. Finally, the technique establishes a connection between Tikhonov and Gaussian variates which can be exploited, e.g., in the generation of piecewise-continuous pseudo-random functions with Tikhonov-distributed outcomes.

We present an exact analysis of STBCs over uncorrelated memoryless block-fading channels with unequal and generalized fading statistics. First, the exact BER of an ideal (rate-one-full-diversity) linear STBCs with PSK or QAM modulation in the aforementioned channel is given. The formulas derive from a careful review and aggregation of results scattered in the literature, and can be applied to STBCs of various constructions. The exactness and generality of these analytical BER expressions are illustrated by comparison against corresponding simulation results presented in classic papers on STBCs, e.g. (Alamouti et al., 1998). The exact BER formulas are then utilized to analyze the effect of unequal fading statistics and uneven average transmit power distribution across diversity branches, onto the performance of a hypothetical STBC of unitary rate and full-diversity, constructible to any number of transmit antennas. The study reveals the little impact of uneven diversity branch average power distribution on the performance of such a hypothetical STBC. Finally, the formulas are employed to derive the mutual information of linear STBCs over realistic PSK and QAM modulations with block-wise hard-decision, which are then applied to reveal the concrete throughput potential of linear STBCs, in comparison to the capacity of the equivalent SIMO AWGN channel with the same transmit power.

We consider the problem of tracking multiple targets in the presence of imperfect and incomplete ranging information, focusing on the impact of target dynamics. The targets are assumed to describe independent, continuous and differentiable trajectories with non-stationary (dynamic) statistics, i.e., with variable velocities and accelerations. The impact of such dynamics onto the performance, computational complexity and memory requirements of two tracking techniques, namely, the Kalman filter (KF) and multidimensional scaling (MDS), is investigated. The main feature of the MDS-based tracking algorithm, which we proposed in an earlier work, is that tracking is performed over the eigenspace of a Nystrom-Gram kernel matrix constructed with no a-priori knowledge of the statistics of target trajectories. Consequently, tracking becomes a problem of updating the eigenspace given new input data, which is achieved with an iterative Jacobian eigen-decomposition technique. An advantage of this technique over the KF is that tracking accuracy is independent on target dynamics. Furthermore, the number of iterations required to update the eigenspace, is shown to grow only logarithmically with the target dynamics and with the number of simultaneously tracked targets. As a result, the MDS-based tracking algorithm with Jacobian eigenspace updating becomes more efficient than the KF as soon as a relatively small number of targets are simultaneously tracked, and/or target dynamics exceeds a certain threshold.

We consider the simultaneous localization of multiple sources from distance and angle information. An extension of the multidimensional scaling (MDS) technique is given, which allows for both distance and angle information to be processed algebraically (without iteration) and simultaneously. Simulations demonstrate the superiority the super MDS algorithm compared to conventional metric MDS, which relies only on Euclidean distances, and illustrate the impact that angle information may have on the accuracy of source localization. An advantage of the method is that localization under an absolute coordinate system is achievable with knowledge of the coordinates of a single node.

Accurate simulation of piecewise continuous complex-valued Nakagami-m (phasor) processes with arbitrary parameters is considered. Two solutions to this problem are discussed. The first is based on the extension of an existing decomposition technique to the general case of piecewise continuous complex-valued processes. The second is an entirely novel contribution in which a Nakagami-m process with arbitrary m is obtained from a mixture of a pair of Nakagami-m processes with positive integer and half-integer m, respectively. In deriving the foundations of the new technique, the characteristic function and entropy of Nakagami-m phase processes, the entropy of Nakagami-m envelope processes and the joint (envelope+phase) moment of Nakagami-m phasor processes are all derived in simple closed-forms, and the moment-determinance of Nakagami- m envelope processes is proved. The mixture probabilities used in the proposed random mixture technique, derived from constraints on the Nakagami-m joint-moment, are computed using a simple rational function of m and its closest integer and half integer neighbors to both sides. The remarkable accuracy achieved by the approximation is quantified analytically using the Kullback-Leibler divergence. It is shown that the proposed random mixture method is far superior to the decomposition method in terms of accuracy of envelope and phase pdfs, as well as higher order statistics, with the addition advantage of being less computationally demanding.

A non-parametric, low-complexity algorithm for accurate and simultaneous localization of multiple sensors from scarce and imperfect ranging information is proposed. The technique is based on a weighted least-squares (WLS) optimization, where the gradient and Hessian of the quadratic objective are given in closed-form. The performance of the proposed technique is studied through extensive computer simulations, with the intra-node distances randomly generated in accordance to a statistical model constructed from the results of a measurement campaign conducted with a pair of impulsive ultra-wideband (UWB) radios in an indoor scenario. The simulation results reveal that the proposed algorithm, despite its low complexity, is nearly as accurate as the known alternative of best performance, which is based on semi-definite programming and demands significantly more computational power.

Source localization from imperfect and incomplete range information is considered. The problem is formulated as a combination of the well-known Euclidean Distance Matrix (EDM) approximation/completion problem and multidimensional scaling (MDS). A powerful technique that solves the EDM approximation/completion problem by exploiting the semi-definiteness property of a corresponding Euclidean kernel has been recently proposed [1]. That technique requires, however, that the entries of the input EDM be weighted in accordance to their reliability. In this paper, a formula for such a weight function, based on confidence-bound statistics of the distance estimates and on Graph spectral properties is studied. Computer simulations show that significant improvement in localization accuracy can be achieved by utilizing the weight function derived.

The design of a class of Hermite pulses for pulse shape modulated (PSM) ultra-wideband (UWB) communications is presented. The proposed pulses offer robustness against jitter or small imperfections in synchronization between received waveforms and their matched templates. Close form expressions of the auto- and cross-correlation functions of the proposed and conventional Hermite pulses are given, which are used to model the jitter channel as a simple distortive matrix. The set of jitter-robust orthogonal pulses is then obtained by simultaneously diagonalizing a subset of selected samples of such channel matrix realizations for different jitter values. Examples of waveforms derived with the method and simulation results showing the effectiveness of the proposed pulses in combatting jitter in PSM-UWB systems are given.

A simple method to enhance the performance of simplified decoding of block space-time codes (STC) is proposed. The method is capable of removing the error floor observed when conventionally decoded STC is applied under fast fading conditions, provided that the channel is known, or accurately enough estimated, across the block. This requirement can be reached by interpolating channel estimates from pre- and post-posted pilot symbols, which allows several blocks to be framed together, enhancing spectral efficiency.

Smart antenna technology has proved to be a valuable add-in feature for communication systems. However, its application indoors, in particular to the unlicensed 2.4-GHz industrial, scientific and medical (ISM) band has not been accomplished yet, mainly due to the problem of near-field distortion that upsurges from the indoor channel characteristics at the mentioned band. We evaluate the compensation scheme to eliminate such near field distortion presented by Abren and Kohno (see Proc. of the ICT2000, vol.2, p.900-906, 2000) under the IEEE 802.11 standard's specifications.