In this paper, we analyze the periodic transmission masks for MASked Modulation (MASM) in half-duplex integrated sensing and communication (ISAC), and derive their closed-form expected range-Doppler response $\mathbb{E}\{r(k,l,ν)\}$. We show that range sidelobes ($k\neq l$) are Doppler-invariant, extending the range-sidelobe optimality to the 2-D setting. For the range mainlobe ($k=l$), periodic masking yields sparse Doppler sidelobes: Cyclic difference sets (CDSs) (in particular Singer CDSs) are minimax-optimal in a moderately dynamic regime, while in a highly dynamic regime the Doppler-sidelobe energy is a concave function of the mask autocorrelation, revealing an inevitable tradeoff with mainlobe fluctuation.

Integrated sensing and communication (ISAC) enables numerous innovative wireless applications. Communication-centric design is a practical choice for the construction of the sixth generation (6G) ISAC networks. Continuous-wave-based ISAC systems, with orthogonal frequency-division multiplexing (OFDM) being a representative example, suffer from the self-interference (SI) problem, and hence are less suitable for long-range sensing. On the other hand, pulse-based half-duplex ISAC systems are free of SI, but are also less favourable for high-throughput communication scenarios. In this treatise, we propose MASked Modulation (MASM), a half-duplex ISAC waveform design scheme, which minimises a range blindness metric, termed as “mainlobe fluctuation”, given a duty cycle (proportional to communication throughput) constraint. In particular, MASM is capable of supporting high-throughput communication (∼50% duty cycle) under mild mainlobe fluctuation. Moreover, MASM can be flexibly adapted to frame-level waveform designs by operating on the slow-time scale. In terms of optimal transmit mask design, a set of masks is shown to beidealin the sense of sidelobe level and mainlobe fluctuation intensity.

Integrated sensing and communications (ISAC) has emerged as a pivotal enabling technology for next-generation wireless networks. Despite the distinct signal design requirements of sensing and communication (S&C) systems, shifting the symbol-wise pulse shaping (SWiPS) framework from communication-only systems to ISAC poses significant challenges in signal design and processing This paper addresses these challenges by examining the ambiguity function (AF) of the SWiPS ISAC signal and introducing a novel pulse shaping design for single-carrier ISAC transmission. We formulate optimization problems to minimize the average integrated sidelobe level (ISL) of the AF, as well as the weighted ISL (WISL) while satisfying inter-symbol interference (ISI), out-of-band emission (OOBE), and power constraints. Our contributions include establishing the relationship between the AFs of both the random data symbols and signaling pulses, analyzing the statistical characteristics of the AF, and developing algorithmic frameworks for pulse shaping optimization using successive convex approximation (SCA) and alternating direction method of multipliers (ADMM) approaches. Numerical results are provided to validate our theoretical analysis, which demonstrate significant performance improvements in the proposed SWiPS design compared to the root-raised cosine (RRC) pulse shaping for conventional communication systems.

Integrated sensing and communications (ISAC) has been recognized as a key component in the envisioned 6G communication systems. Understanding the fundamental performance tradeoff between sensing and communication functionalities is essential for designing practical cost-efficient ISAC systems. In this paper, we aim for augmenting the current understanding of the deterministic-random tradeoff (DRT) between sensing and communication, by analyzing the sensing-optimal operating point of the fundamental capacity-distortion region. We show that the DRT exists for generic sensing performance metrics that are in general not convex/concave in the ISAC waveform. Especially, we elaborate on a representative non-convex performance metric, namely the detection probability for target detection tasks.

In this work, we consider a backscatter communication system wherein multiple asynchronous sources (tags) exploit the reverberation generated by a nearby radar transmitter as an ambient carrier to deliver a message to a common destination (reader) through a number of available subchannels. We propose a new encoding strategy wherein each tag transmits both pilot and data symbols on each subchannel and repeats some of the data symbols on multiple subchannels. We then exploit this signal structure to derive two semi-blind iterative algorithms for joint estimation of the data symbols and the subchannel responses that are also able to handle some missing measurements. The proposed encoding/decoding strategies are scalable with the number of tags and their payload and can achieve different tradeoffs in terms of transmission and error rates. Some numerical examples are provided to illustrate the merits of the proposed solutions.

In this work, we exploit the radar clutter (i.e., the ensemble of echoes generated by the terrain and/or the surrounding objects in response to the signal emitted by a radar transmitter) as a carrier signal to enable an ambient backscatter communication from a source (tag) to a destination (reader). The proposed idea relies on the fact that, since the radar excitation is periodic, the radar clutter is itself periodic over time scales shorter than the coherence time of the environment. Upon deriving a convenient signal model, we propose two encoding/decoding schemes that do not require any coordination with the radar transmitter or knowledge of the radar waveform. Different tradeoffs in terms of transmission rate and error probability can be obtained upon changing the control signal driving the tag switch or the adopted encoding rule; also, multiple tags can be accommodated with either a sourced or an unsourced multiple access strategy. Some illustrative examples are provided.

This paper considers a monostatic multiple-input multiple-output (MIMO) radar aided by a reconfigurable intelligent surface (RIS). After modeling the received target signal as the superposition of four echoes, which may involve up to two bounces off the RIS, the phases of the reflecting elements and the radar receive filter are chosen to maximize the signal-to-noise ratio (SNR). Some examples are provided to assess the performance of this novel architecture.

In this work we consider a multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system, which senses multiple spatial directions and serves multiple users. Upon resorting to an orthogonal frequency division multiplexing (OFDM) transmission format and a differential phase shift keying (DPSK) modulation, we study the design of the radiated waveforms and of the receive filters employed by the radar and the users. The approach is communication-centric, in the sense that a radar-oriented objective is optimized under constraints on the average transmit power, the power leakage towards specific directions, and the error rate of each user, thus safeguarding the communication quality of service (QoS). We adopt a unified design approach allowing a broad family of radar objectives, including both estimation- and detection-oriented merit functions. We devise a suboptimal solution based on alternating optimization of the involved variables, a convex restriction of the feasible search set, and minorization-maximization, offering a single algorithm for all of the radar merit functions in the considered family. Finally, the performance is inspected through numerical examples.

In this work, we consider the target detection problem in a sensing architecture where the radar is aided by a reconfigurable intelligent surface (RIS), that can be modeled as an array of sub-wavelength small reflective elements capable of imposing a tunable phase shift to the impinging waves and, ultimately, of providing the radar with an additional echo of the target. A theoretical analysis is carried out for closely- and widely-spaced (with respect to the target) radar and RIS and for different beampattern configurations, and some examples are provided to show that large gains can be achieved by the considered detection architecture.

In this work, we study the joint detection and localization of multiple delay- and Doppler-spread targets through an opportunistic radar exploiting mmWave communication signals. The problem is formulated as the identification of an unknown number of active subspaces in a large family of subspaces, accounting for the possible positions of potential targets in the delay-Doppler domain: the resulting testing problem is composite and multi-hypothesis. At first, we derive a solution based on the generalized information criterion (GIC), whose complexity is however prohibitive; then, we propose an approximated form of the GIC-based receiver and derive two iterative data-adaptive strategies, both extracting and eliminating the superimposed back-scattered subspace signals one-by-one. Leveraging the IEEE 802.11ad standard, a short-range low-mobility application is discussed to validate the merits of the proposed procedures in terms of detection and localization capabilities, robustness to multi-target interference, and achievable resolution.

This paper considers the uplink of a massive MIMO communication system using 5G New Radio-compliant multiple access, which has to co-exist with a radar system using the same frequency band. A system model taking into account the reverberation (clutter) produced by the radar system onto the massive MIMO receiver is proposed. In this scenario, several receivers for uplink channel estimation and data detection are proposed, ranging from the simple channel-matched beamformer to the zero-forcing and linear minimum mean square error receivers for clutter disturbance rejection, under the two opposite situations of perfectly known and completely unknown clutter covariance. A theoretical analysis is also provided, deriving a lower bound on the achievable uplink spectral efficiency and the mutual information between the input Gaussian-encoded symbols and the observables available at the communication receiver of the cellular massive MIMO system: regarding the latter, in particular, it is shown that, in the large antenna number regime, and under the assumption of perfect channel state information (CSI), the effect of radar clutter at the base station is suppressed and single-user capacity may be restored. Numerical results, illustrating the performance of the proposed detection schemes, confirm the findings of the theoretical analysis, and permit quantifying the system robustness to clutter effect for increasing number of antennas at the base station.

In this work, we exploit the sector level sweep of the IEEE 802.11ad communication standard to implement an opportunistic radar at mmWaves and derive an adaptive procedure for detecting multiple echoes and estimating their parameters (namely, amplitude, delay, and Doppler shift). The proposed detector/estimator extracts the prospective echoes one-by-one from the received signal, after removing the interference caused by the previously detected (stronger) reverberations from the environment. Examples are provided to assess the system performance, also in comparison with other detectors/estimators and the Cramér-Rao bounds on the localization accuracy. Results indicate that the proposed method is robust against the interference induced by the imperfect ambiguity function and can achieve a delay resolution approximately equal to the inverse of the signal bandwidth, corresponding to a range offset of about 17 cm. Instead, the Doppler resolution is inherently limited by the short duration of the data packet and, in the most favorable case, is approximately 3.26 KHz, corresponding to a relative radial velocity off-set of about 8.16 m/s for a wavelength of 5 mm.

In this paper we consider a massive multiple-input multiple-output (MIMO) communication system using 5G New Radio-compliant multiple access, which is to co-exist with a radar system using the same frequency band. Building upon a recently proposed system model taking into account the reverberation (clutter) produced by the radar system at the massive MIMO receiver, we provide a theoretical analysis, in terms of a lower bound on the achievable uplink (UL) spectral efficiency (SE) and in terms of the mutual information of the cellular massive MIMO system, showing that for large number of antennas at the base station the radar clutter effects can be suppressed. Simulation results confirm the paper theoretical findings.

In this paper, we consider an un-cooperative spectrum sharing scenario, where a radar system is to be overlaid to a pre-existing wireless communication system. Given the order of magnitude of the transmitted powers in play, we focus on the issue of interference mitigation at the communication receiver. We explicitly account for the reverberation produced by the (typically high-power) radar transmitter whose signal hits scattering centers (whether targets or clutter) producing interference onto the communication receiver, which is assumed to operate in an un-synchronized and un-coordinated scenario. We first show that the receiver design amounts to solve a joint (non-convex) interference removal and data demodulation problem. Next, we introduce two algorithms exploiting sparsity of a proper representation of the interference and the vector containing demodulation errors of the data block. The first algorithm is basically a relaxed constrained atomic norm minimization, while the latter relies on a two-stage processing structure and is based on alternating minimization. The merits of these algorithms are demonstrated through extensive simulations; interestingly, the two-stage alternating minimization algorithm turns out to achieve satisfactory performance with moderate computational complexity.

This work develops a learning-based framework for energy-efficient power control in multi-carrier wireless networks. The problem is formulated as the maximization of the network global energy efficiency, defined as the ratio between the network sum-rate and the total consumed power, and is tackled by a novel approach which merges tools from learning, non-cooperative game theory, and fractional programming theory. The proposed algorithm is provably convergent, enjoys near-optimal performance, while requiring a much lower complexity than previous alternatives.

This paper proposes computationally efficient algorithms to maximize the energy efficiency in multicarrier wireless interference networks, by a suitable allocation of the system radio resources, namely, the transmit powers and subcarrier assignment. The problem is formulated as the maximization of the system global energy efficiency subject to both maximum power and minimum rate constraints. This leads to a challenging nonconvex fractional problem, which is tackled through an interplay of fractional programming, learning, and game theory. The proposed algorithmic framework is provably convergent and has a complexity linear in both the number of users and subcarriers, whereas other available solutions can only guarantee a polynomial complexity in the number of users and subcarriers. Numerical results show that the proposed method performs similarly as other, more complex, algorithms.

In this work, we study the fundamental tradeoff between integration time and scan rate in radar systems. The contrasting needs for a large probability of detection and a short scan time are carefully balanced by optimizing the detection rate, defined as the average number of detections from a target per unit of time. A closed-form solution for the optimum pulse train length is provided in the relevant case of Gaussian observations with unknown noise variance. Some examples are given to show the possible tradeoffs among the principal system parameters.

We consider a surveillance radar that requests a second look of a cell under inspection whenever a reliable decision cannot be made after the first observation. The available degrees of freedom are exploited to maximize the detection rate, defined as the average number of detections from a target per unit of time, under a constraint on the false alarm rate, which is the average number of false alarms per unit of time from the inspected area. A performance comparison with fixed-sample-size detection and alert-and-confirm detection is provided.

This work investigates the feasibility of opportunistic target detection in mmWaves vehicular networks employing the IEEE 802.11ad standard. The beacon signals transmitted during the network discovery phase are exploited to detect the presence of possible obstacles in the surrounding environment and to estimate their position and velocity. The detection problem is formulated as a composite hypothesis test and two detectors are derived and investigated.

In this paper we address the problem of multi-frame detection (MFD) of a Markov target observed through noisy measurements. To limit the system complexity, we gate the detector with a pre-processing stage which discards unreliable observations in each frame. A novel dynamic programming algorithm is introduced, which applies a track-before-detect (TBD) logic to declare the presence of the target and jointly estimate its position. The closed-form complexity analysis and the numerical examples show that advantageous complexity-performance tradeoffs can be obtained with respect to the single-frame detector and with respect to other strategies already present in the literature.

Channel-modeling uncertainties may affect the performance of spectrum sensing in cognitive-radio applications. Negative effects of these uncertainties may be mitigated by an appropriate choice of the sensor operation. We show how the use of a linear-quadratic detector can improve the performance of both linear and quadratic spectrum sensors. The robustness to model uncertainties of the linear-quadratic detector is studied using moment-bound theory.

We consider here the two-stage, multi-frame detection architecture proposed in in the context of radar systems, wherein the Detector and Plot Extractor provides a list of candidate detections (or plots) on a scan-by-scan basis to a Track-before-detect (TBD) Processor, which correlates data over multiple scans before taking the final decision as to the target presence. We propose a heuristic algorithm to implement the TBD processor, and we show that its computational complexity is smaller than that of the algorithm proposed in . Numerical examples demonstrate that the detection and estimation performance obtained with the new algorithm is almost coincident with that obtained with the algorithm in .

In this letter, we consider the detection architecture in , where a track-before-detect processor elaborates the plot-lists provided on a scan-by-scan basis by the detector and plot-extractor of a radar system. We derive a novel track formation procedure in order to provide improved performance in the presence of multiple, closely-spaced targets. Numerical examples are provided to assess the detection capabilities and the accuracy in the estimation of the target position.

This paper proposes a new modulation format suited to power- and band-limited, non-frequency selective channels. It is considered here for satellite broadcast and broadband applications. The proposed format uses combinations of Gold sequences transmitted through a spectral-efficient multi-carrier format with orthogonal frequency-division multiplexing (OFDM). We present the modulation design as well as an analytical approximation of the performances and a comparison with existing modulations. The focus is put on the power efficiency improvement. Moreover, the multi-carrier format alleviates the receiver synchronization and improves the spectral efficiency. The critical issue of Peak-to-Average-Power Ratio (PAPR) distribution and reduction is finally discussed.

In this work, we consider the problem of detecting the presence of a prospective moving target from a set of censored measurements produced by a generic sensor and propose a novel track-before-detect algorithm which exploits the inherent sparse nature of the measurement data to achieve complexity efficiency. The impact of the censoring stage on the system complexity and performance is investigated.

The focus of the paper is on the design of space-time codes for a general multiple-input, multiple-output detection problem, when multiple observations are available at the receiver. The figure of merit used for optimization purposes is the convex combination of the Kullback-Leibler divergences between the densities of the observations under the two hypotheses, and different system constraints are considered. This approach permits to control the average sample number (i.e., the time for taking a decision) in a sequential probability ratio test and to asymptotically minimize the probability of miss in a likelihood ratio test: the solutions offer an interesting insight in the optimal transmit policies, encapsulated in the rank of the code matrix, which rules the amount of diversity to be generated, as well as in the power allocation policy along the active eigenmodes. A study of the region of achievable divergence pairs, whose availability permits optimization of a wide range of merit figures, is also undertaken. A set of numerical results is finally given, in order to analyze and discuss the performance and validate the theoretical results.

In this paper we model and simulate a biological system describing the evolution of cancer stem cells into tumors. Starting from some basic hypotheses about the behavior of these cells, we develop a model that mimics the evolution of a system of cancer stem cells and show how random-set-theory naturally leads to a generation algorithm. Computer simulations demonstrate the potential of our approach by using simple random sampling rules and a lattice environment.

The problem of robust waveform design for multiple-input, multiple-output radars equipped with widely-spaced antennas is addressed here. Robust design is needed as a number of parameters are unknown, e.g., the target scattering covariance matrix and, possibly, the clutter covariance matrix. A min-max approach is proposed, so that the code matrix is designed to minimize the worst-case cost (or equivalently maximize the corresponding figure of merit) under all possible target (or target and clutter) covariance matrices. Surprisingly, the same min-max solution applies to many commonly adopted performance measures, such as the average signal-to-clutter-plus-noise ratio, the mutual information between the received signal echoes and the unknown target response, and the approximation of the detection probability in the high- and low-signal regimes.

This paper studies the up-link of a multi-user Multiple-Input Multiple-Output (MIMO) system under a fairly general channel model, subsuming a number of situations of relevant practical interest. Concerning the available prior information, we consider the case of Prior Channel State Information at the Transmitter (PCSIT) wherein only a statistical channel characterization is available before transmission, while we assume perfect Channel State Information at the Receiver (CSIR). Under this assumptions, we characterize the sum-capacity-achieving input covariance matrix for such a system and we also examine the low-power regime in terms of both minimum energy contrast and multiple access slope region, validating our theoretical findings through a set of numerical results.

In this paper, we consider a multiple-input-multiple-output (MIMO) detection problem withMwidely spaced transmit antennas andLwidely spaced receive antennas, and we study the problem of designing the signal waveforms transmitted by each source node under non-Gaussian target scattering and temporally correlated Gaussian clutter. Two figures of merit are investigated for space-time code (STC) optimization under a semidefinite rank constraint: 1) the lower Chernoff bound (LCB) to the detection probability for fixed probability of false alarm, and 2) the mutual information (MI) between the observations available at the receive nodes and the “channel response” generated by a point-like target, assumed present tout court. Both receive and transmit power constraints are discussed. If the scattering distribution possesses some suitably defined properties of unitary invariance (see Section II-B), both MI-optimal and LCB-optimal STCs have a simple canonical structure: the same set of (clutter dependant) temporal codewords are employed at the transmit nodes, the only difference among the many solutions being the amount of power radiated by each antenna. Such a spatial power allocation critically depends upon the adopted figure of merit, the specified power constraint, and the underlying scattering model. Sufficient conditions to determine the optimal power allocation for all design criteria are provided. Asymptotic power distributions are also derived in the limit of vanishingly small and increasingly large signal-to-clutter ratios, proving that assuming Gaussian scattering at the design stage is a robust choice. A case study of relevant practical interest is examined in depth so as to compare the proposed design criteria and to assess the impact of signal non-Gaussianity on the system performances.

We consider a multiple-input multiple-output (MIMO) detection problem with widely-spaced antennas at both the transmitter and the receiver, and we assume that target scattering is modeled as an exchangeable and unitarily-invariant process. We illustrate optimal signal design (i.e., space-time coding) at the transmitter for two criteria, i.e. the lower Chernoff bound (LCB) to the detection probability for fixed probability of false alarm and the mutual information (MI) between the observations and the target scattering matrix, under a semi-definite rank constraint and a transmit power constraint, showing that the Gaussian scattering assumption is robust. A by-product, of not secondary importance, of our derivation is the proof of a number of new properties concerning concavity and Schur-concavity of MI and LCB.

The problem of joint detection and state estimation of a Markov signal when a variable number of noisy measurements can be taken is here considered. In particular, the signal-observation sequence {Xi, Zi}iisinNis a hidden Markov process (HMP) while, if the signal is absent, the measurement {Zi}i2isinNis an i.i.d. process. In this framework, two coupled detection and estimation procedures are introduced for the cases of discrete and continuous state space. Bounds on the performance of the proposed procedures in terms of the thresholds are derived, similar to the classical bounds for the sequential probability ratio test (SPRT). Moreover, it is shown that, under a set of rather mild conditions, the procedures end with probability one and the stopping time is almost surely minimized in the class of tests with the same or smaller error probabilities.

The problem of jointly estimating the number, the identities, and the data of active users in a time-varying multiuser environment was examined in a companion paper (IEEE Trans. Information Theory, vol. 53, no. 9, September 2007), at whose core was the use of the theory of finite random sets on countable spaces. Here we extend that theory to encompass the more general problem of estimating unknowncontinuousparameters of the active-user signals. This problem is solved here by applying the theory of random finite sets constructed on hybrid spaces. We do so deriving Bayesian recursions that describe the evolution with time of a posteriori densities of the unknown parameters and data. Unlike in the above cited paper, wherein one could evaluate the exact multiuser set posterior density, here the continuous-parameter Bayesian recursions do not admit closed-form expressions. To circumvent this difficulty, we develop numerical approximations for the receivers that are based on Sequential Monte Carlo (SMC) methods (“particle filtering”). Simulation results, referring to a code-divisin multiple-access (CDMA) system, are presented to illustrate the theory.

In this paper, we introduce a pilot-aided multipath channel estimator for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Typical estimation algorithms assume the number of multipath components and delays to be known and constant, while their amplitudes may vary in time. In this work, we focus on the more realistic assumption that also the number of channel taps is unknown and time-varying. The estimation problem arising from this assumption is solved using random set theory (RST), which is a probability theory of finite sets. Due to the lack of a closed form of the optimal filter, a Rao-Blackwellized particle filter (RBPF) implementation of the channel estimator is derived. Simulation results demonstrate the estimator effectiveness.

We study the minimum mean square error (MMSE) and the multiuser efficiency eta of large dynamic multiple access communication systems in which optimal multiuser detection is performed at the receiver as the number and the identities of active users is allowed to change at each transmission time. The system dynamics are ruled by a Markov model describing the evolution of the channel occupancy and a large-system analysis is performed when the number of observations grow large. Starting on the equivalent scalar channel and the fixed-point equation tying multiuser efficiency and MMSE, we extend it to the case of a dynamic channel, and derive lower and upper bounds for the MMSE (and, thus, for eta as well) holding true in the limit of large signal-to-noise ratios and increasingly large observation time T.

The problem of detecting a Markov signal when a variable number of noisy measurements can be taken is here considered. In particular, the signal-observation sequence {Xi, Zi}iisinNopfis a hidden Markov model (HMM) and a sequential probability ratio test (SPRT) is used to detect {Xi, Zi}iisinNopf. It is known that the SPRT for testing simple hypotheses based on independent and identically distributed (i.i.d.) observations has a number of remarkable properties, the most appealing being the fact that it simultaneously minimizes the expected sample size under both hypotheses. These properties, however, may fail to hold as the observations {Zi}iisinNopfare not independent. In this paper sufficient conditions for the validity of these properties are stated. In particular, it is shown that under a set of rather mild conditions the test ends with probability one and its stopping time is almost surely minimized in the class of tests with the same or smaller error probabilities. Furthermore, reinforcing one of such conditions, it is also shown that any moment of the stopping time distribution is first-order asymptotically minimized in the same class of tests.

In this work, a MIMO detection problem is considered. At first, we derive the Generalized Likelihood Ratio Test (GLRT) for arbitrary transmitted signals and arbitrary time-correlation of the disturbance. Then, we investigate design criteria for the transmitted waveforms in both power-unlimited and power-limited systems and we study the interplay among the rank of the optimized code matrix, the number of transmit diversity paths and the amount of energy integrated along each path. The results show that increasing the rank of the code matrix allows generating a larger number of diversity paths at the price of reducing the average signal-to-clutter level along each path.

The problem of joint detection and estimation when a variable number of noisy measurements can be taken is here considered in the case that the signal to be detected is generated by a dynamic system with a Markov evolution and the parameter to be estimated is the trajectory of the state evolution of the system itself and/or it final state (position). Starting from previous sequential rules, different sequential strategies are proposed and assessed: they are aimed at maximizing the performance of either the detection or the track estimation or the position estimation. Bounds on the performances of the proposed procedures in terms of the system parameters are derived and computational complexity is examined. Also, numerical experiments are provided to elicit the interplay between parameters and system performances and to quantify the gain with respect to other fixed-sample-size procedures.

The problem of detection and possible estimation of a signal generated by a dynamic system when a variable number of noisy measurements can be taken is here considered. Assuming a Markov evolution of the system (in particular, the pair signal-observation forms a hidden Markov model (HMM)), a sequential procedure is proposed, wherein the detection part is a sequential probability ratio test (SPRT) and the estimation part relies upon a maximumaposterioriprobability (MAP) criterion, gated by the detection stage (the parameter to be estimated is the trajectory of the state evolution of the system itself). A thorough analysis of the asymptotic behavior of the test in this new scenario is given, and sufficient conditions for its asymptotic optimality are stated, i.e., for almost sure minimization of the stopping time and for (first-order) minimization of any moment of its distribution. An application to radar surveillance problems is also examined.

We consider the neighbor-discovery problem in a fixed wireless network where each node is identified by its signature, and the signatures are chosen so as to limit collisions. Limited-complexity constraints lead to a simple algorithm based on incoherent detection of nodes. The performance of this algorithm is evaluated by computing the probability of a false alarm and of a missed detection of a single node.

We examine the problem of determining which nodes are neighbors of a given one in a wireless network. We consider an unsupervised network operating on a frequency- flat Gaussian channel, where K + 1 nodes associate their identities to nonorthogonal signatures, transmitted at random times, synchronously, and independently. A number of neighbor- discovery algorithms, based on different optimization criteria, are introduced and analyzed. Numerical results show how reduced- complexity algorithms can achieve a satisfactory performance.

The problem of jointly estimating the number, the identities, and the data of active users in a dynamic multiuser environment was examined in (E. Biglieri and M. Lops, 2006). This paper extends the results of (E. Biglieri and M. Lops, 2006) to the more general case where some unknown continuous parameters of the active-user signals must also be estimated. This problem, which cannot be solved with traditional signal processing techniques, is solved here by applying the theory of random finite sets constructed on hybrid spaces. In particular, we derive Bayes recursions that describe the evolution with time of a posteriori densities of the unknown parameters and data. Since these recursions do not admit closed-form expressions, we use numerical approximations based on Monte Carlo methods ("particle filtering"). Simulation results, referring to a CDMA system, are presented to illustrate the theory.

High-quality channel estimation and tracking is central for coherent detection in wireless communication systems. Typical tracking algorithms assume the number of multipath components and delays to be known and constant, while their ampli- tudes may vary in time. In this work we focus on the more realistic assumption that also the number of channel taps is unknown and time-varying. The more complex estimation problem arising from this assumption is solved using Random-Set Theory, which allows one to regard the multipath-channel response as a single set-valued variable. Using a particle-filter implementation, we derive an estimator that simultaneously tracks the amplitudes and the number of channel taps for orthogonal frequency division multiplexed (OFDM) systems.

In random-access communication systems, the number of active users varies with time, and has considerable bearing on receiver's performance. Thus, techniques aimed at identifying not only the information transmitted, but also that number, play a central role in those systems. An example of application of these techniques can be found in multiuser detection (MUD). In typical MUD analyses, receivers are based on the assumption that the number of active users is constant and known at the receiver, and coincides with the maximum number of users entitled to access the system. This assumption is often overly pessimistic, since many users might be inactive at any given time, and detection under the assumption of a number of users larger than the real one may impair performance. The main goal of this paper is to introduce a general approach to the problem of identifying active users and estimating their parameters and data in a random-access system where users are continuously entering and leaving the system. The tool whose use we advocate is random-set theory (RST): applying this, we derive optimum receivers in an environment where the set of transmitters comprises an unknown number of elements. In addition, we can derive Bayesian-filter equations which describe the evolution with time of the a posteriori probability (APP) density of the unknown user parameters, and use this density to derive optimum detectors. In this paper, we restrict ourselves to interferer identification and data detection, while in a companion paper we shall examine the more complex problem of estimating users' parameters.

The problem of blind detection in a dispersive multiple-input multiple-output (MIMO) code division multiple access (CDMA) channel is considered in this paper. Unlike previous studies, each user is assigned one spreading code to be employed on all of the transmit antennas, which poses a problem of data re-association at the receiver-end in the absence of prior information as to the channel state. Focusing on the differential Alamouti scheme, we propose a two-stage receive structure. The first stage performs a linear interference-blocking transformation, which allows user separation and inter-symbol interference (ISI) suppression. The second stage is, instead, a novel differential space-time block (STB) decoder suitable for frequency-selective channels. Interestingly, the proposed detector allows decoupling of the decisions on the transmitted symbols, while its blind implementation only requires a cubic (in the processing gain) complexity. A thorough performance assessment is undertaken to investigate, on one hand, the capability of acquiring the missing information, such as the system and the encoder timings, on the other hand, the interplay between the diversity gain provided by the MIMO structure of the communication system, and the additional co-channel interference that multiple transmit antennas produce in multi-path, multiple-access channels.

We focus on the synthesis and the analysis of recursive receivers for direct-sequence code-division multiple-access systems aimed at achieving satisfactory performance at the price of a moderate computational complexity. At the analysis stage, some interesting properties shared by the proposed procedures are proven. Finally, the performance assessment shows that the new schemes are superior to the linear detectors, and some of them achieve a bit-error rate close to that of the optimum receiver.

In this paper we consider a general asynchronous frequency-selective multiuser CDMA system with transmit and receive antennae, where the information data stream is space-time encoded. Sufficient conditions for fully blind detection are established and several detection strategies are presented. The proposed detection strategy consists of three different stages. The first block exploits the deterministic structure imposed by the spreading code to jointly estimate the symbol timing. The second stage consists of a sequence of observable consisting of the two useful signals and residual interference plus white noise. The last stage is about space-time decoding of the ordered sequence.

The problem of blind multiuser detection for a DS-CDMA system employing multiple transmit and receive antennae over a fading dispersive channel is considered. Relying upon a well known signal representation, we develop a new family of linear receivers adapted to account for MIMO channels. Linear receivers share the key property of substantial immunity to co-channel interference, without requiring any prior knowledge on the signals to be decoded, except for the spreading sequence. The performance assessment, conducted through semianalytical methods - whenever possible - and validated through Monte Carlo counting techniques, shows that the newly proposed receivers perform pretty close to their non-blind counterparts, which rely on prior knowledge of the spreading codes, symbol timings and channel impulse responses for all of the active users.

Recently, a new class of linear multiuser receivers for direct-sequence code-division multiple-access (CDMA) systems employing binary phase-shift keying modulation has been introduced. Unlike classical decorrelating and minimum mean-square error linear multiuser detectors, the new receivers exploit the information contained in the pseudo-autocorrelation of the observables, and are, thus, capable of achieving much better performance. We present new results on the performance analysis of this class of new receivers. In particular, with reference to a CDMA system with deterministic spreading codes, we show that the new receivers outperform the classical ones in terms of both error probability and near-far resistance. With regard, instead, to CDMA systems with random spreading, we compute the average system near-far resistance, showing that the new receivers can accommodate twice the number of users accommodated by the classical linear multiuser receivers.

The problem of joint multiaccess interference (MAI) and intersymbol interference (ISI) suppression in DS/CDMA systems operating over fading dispersive channels is considered. A timing-free, blind and code-aided detection algorithm is proposed, i.e. the procedure requires knowledge of neither the interfering users' parameters (spreading codes, timing offsets and propagation channels), nor the timing and channel impulse response of the user of interest, but only of its spreading code. The proposed structure is a two-stage one. The first stage is aimed at suppressing the multiuser interference, while the second-stage performs channel estimation and data detection. The proposed system is shown to incur a very limited loss with respect to the non blind MMSE detector; moreover, it outperforms other previously known blind systems, and is near-far resistant. A major advantage of the new structure is that it admits an adaptive implementation with quadratic (in the processing gain) computational complexity. This adaptive algorithm, which couples a recursive-least-squares estimation of the blocking matrix and subspace tracking techniques, achieves effective steady-state performance.

The push toward more efficient and flexible use of the radio spectrum has led to the consideration of the overlay of spread-spectrum (SS) communication networks on preexisting narrow-band networks. Such systems are already in widespread and rapidly expanding use in the lightly regulated spectrum where personal networking is largely centered and they are of increasing interest in the more tightly regulated spectrum due to a dearth of spectrum for new services, the desire to incorporate multi-rate (e.g., multimedia) traffic in the same network and the survival of legacy systems. Even though SS signals are inherently robust to the effects of narrower bandwidth cochannel signals, it has been shown that the use of additional processing aimed at interference suppression can result in substantial performance improvement. Motivated by this consideration, the past quarter century has seen the development of a very large body of techniques for improving the performance of SS communications systems in the presence of narrow-band interference (NBI). Early techniques (up to late 1980s) have been reviewed in the survey by Milstein (1988). Since that time, more sophisticated strategies have been developed, making use of advances from the fields of beamforming, multiuser detection (MUD), and adaptive filtering. Also, the focus of interest has shifted from techniques aimed primarily at the suppression of NBI from single-user SS systems to systems in which the SS signaling is being used to implement a code-division multiple-access (CDMA) protocol. This paper provides a tutorial overview of the progress made in this area over the past 15 years. The focus of the paper is on direct-sequence CDMA (DS/CDMA) systems and on the so-called "code-aided" techniques for NBI suppression, a term coined to indicate those strategies in which knowledge of the spreading code of a SS signal of interest is explicitly exploited in suppressing NBI. Particular attention is devoted to the case in which the CDMA signals are subject to frequency-selective fading and to the issue of blind adaptive MUD in the presence of external NBI. In particular with regard to the former issue, the effects and implications of channel-state information on system design and performance are discussed. With regard to the latter issue, it is observed that the external NBI may introduce the need for a periodically time-varying detection rule, which has significant implications in the design of blind adaptive MUD algorithms for overlaid DS/CDMA systems. The performance of the techniques discussed is compared through analysis and simulation, as well as through considerations of their relative computational complexity and required prior information. Finally, the paper is concluded by a discussion of several challenging open problems in this area.

It has been shown that in a direct-sequence/code-division multiple-access (DS/CDMA) system employing binary phase-shift keying (BPSK) modulation the baseband equivalent of the CDMA multiplex is, under very mild assumptions, an improper complex random process, i.e., it has a nonzero pseudoautocorrelation function. The problem of linear multiuser detection for asynchronous DS/CDMA systems with improper multiaccess interference (MAI) is considered. A new mean-output-energy (MOE) cost function is introduced, whose constrained minimization leads to two new linear multiuser detectors, exploiting the information contained in the pseudoautocorrelation of the observables, and which generalize the classical decorrelating and minimum mean-square error (MMSE) receivers. The problem of blind adaptive receiver implementation based on subspace tracking is also tackled. Finally, the superiority of the new detectors with respect to the classical linear detection structures present in the literature is demonstrated through both theoretical considerations and computer simulations.

In this paper, the authors consider an asynchronous direct-sequence code division multiple access (DS/CDMA) system wherein users are allowed to transmit their symbols at one out of two available data rates. Three possible access schemes are considered, namely, the variable spreading length (VSL), the variable chip rate (VCR), and the variable chip rate with frequency shift (VCRFS) formats. Their performance is compared for the case that a linear one-shot multiuser receiver is employed. It is also shown that detection of the users transmitting at the higher rate requires a periodically time-varying processing of the observables. Moreover, the problem of blind adaptive receiver implementation is studied, and a cyclic blind recursive-least-squares (RLS) algorithm is provided which is capable of converging to the periodically time-varying high-rate users detection structure. Numerical results show that the proposed receivers are near-far resistant, and that the VCRFS access technique achieves the best performance. Finally as to the adaptive blind receiver implementation, computer simulations have revealed that the cyclic RLS algorithm for blind adaptive high-rate users demodulation outperforms the conventional RLS algorithm in most cases of primary importance.

In this work, the problem of joint suppression of multiple-access and narrow-band interference (NBI) for an asynchronous direct-sequence code-division multiple-access (CDMA) system operating on a frequency-selective fading channel is addressed. The receiver structure we consider can be deemed as a two-stage one: the first stage consists of a bank of minimum mean-square-error (MMSE) filters, each keyed to a given replica of the useful signal, and aimed at suppressing the overall interference; the second stage, assuming knowledge of the fading channel coefficients realizations, combines the MMSE filters outputs according to a maximal-ratio combining rule. Due to the presence of the NBI, the resulting structure is in general time-varying, and becomes periodically time-varying if the NBI bit-rate has a rational ratio to that of the CDMA system. Moreover, enlarging the observation window beyond the signaling interval and oversampling the signal space may yield a noticeable performance improvement. For the relevant case that the said ratio is rational, a new cyclic blind recursive least squares (RLS)-based algorithm is introduced, capable of tracking the periodically time-varying receiver structure, and allowing adaptive interference cancellation with a moderate complexity increase. We also come up with a closed-form expression for the conditional bit-error rate (BER), which is useful both to evaluate semi-analytical methods to assess the unconditional BER and to derive bounds on the system near-far resistance. The results indicate that the receiver achieves very satisfactory performance in comparison to previously known structures. Computer simulations also demonstrate that the cyclic blind RLS algorithm exhibits quite fast convergence dynamics.

We deal with interference suppression in asynchronous direct-sequence code-division multiple-access (CDMA) systems employing binary phase-shift keying modulation. Such an interference may arise from other users of the network, from external low-rate systems, as well as from a CDMA network coexisting with the primary network to form a dual-rate network. We derive, for all of these cases, a new family of minimum mean-square-error detectors, which differ from their conventional counterparts in that they minimize a modified cost function. Since the resulting structure is not implementable with acceptable complexity, we also propose some suboptimum systems. The statistical analysis reveals that both the optimum and the suboptimum receivers are near-far resistant, not only with respect to the other users, but also with respect to the external interference. We also present a blind and a recursive least squares-based, decision-directed implementation of the receivers wherein only the signature and the timing of the user to be decoded and the signaling time and the frequency offset of the external interferer are assumed known. Finally, computer simulations show that the proposed adaptive algorithm outperforms the classical decision-directed RLS algorithm.

In this work we consider the problem of M-ary signal detection over a single-input-multiple-output channel affected by time- and/or frequency-dispersive Rayleigh-distributed fading and in the presence of non-Gaussian noise, modeled as a spherically invariant random process (SIRP). We derive the optimum (minimum error-probability) detector, and show that its structure is canonical, i.e. it is independent of the actual noise statistics. Finally, the performance analysis of the receiver highlights that the adoption of diversity represents a suitable means to restore performance also in the presence of dispersive fading and impulsive non-Gaussian noise.

In this work, we propose an MMSE multiuser detector for asynchronous DS/CDMA systems operating over frequency-selective fading channels. It is shown that computation of a conditional MMSE estimate of the bit to be decoded may be carried out with a computational burden linear in the processing gain N. We also give a closed-form formula for the error probability and the near-far resistance of the proposed detector, and curves of such performance measures, showing that the new receiver is near-far resistant and outperforms the previously derived decorrelating detector for frequency-selective fading channels.

In this work we introduce a new multiuser detector for interference suppression in DS/CDMA systems, based on the minimization of a modified MMSE-like cost function, and which may possibly entail an independent processing of the data and of their complex conjugate. The proposed detection structure is then specialized to the relevant case that the interference consists of a secondary CDMA network whose bit-rate is slower than that of the primary network: interestingly, interference suppression entails a periodically time-varying (PTV) detection rule. We also address the issue of adaptive detection, and present a new RLS algorithm suited for the tracking of the PTV solution. As to the performance assessment, we give a closed-form formula for the system error probability, while numerical results show that the new detection structure, and its adaptive version, outperform previously known receivers.

In this paper, we handle the problem of joint suppression of multiple-access interference (MAI) and narrowband interference (NBI) in fading, dispersive channels. The detectors we consider are linear, one-shot structures, which allow for possible window enlargement and signal-space oversampling to improve performance. We focus on both zero-forcing and minimum-mean square-error design strategies, showing that the presence of NBI generally requires a time-varying processing of the observables, no matter what the optimization criterion. A thorough performance assessment of the proposed detectors is also presented, either through analytical formulas or through computer simulations. We finally deal with the problem of blind suppression of both MAI and NBI, introducing batch-estimation procedures to be implemented offline, which require very little and sometimes no prior knowledge as to the interference structure.

This paper addresses the problem of narrow-band interference (NBI) cancellation in direct-sequence spread-spectrum systems. The proposed procedure amounts to a preliminary nonlinear processing, wherein, upon projection of the received signal onto a Fourier basis, a number of samples having the largest modula are excluded from further processing. The structure of the optimum detector operating on censored observations is obtained, showing that the optimum detector performs matched filtering on the censored data. The performance assessment demonstrates that this receiver is able to suppress narrow-band interferers, no matter what their structure, provided that the censoring depth is properly chosen. A blind version of such a receiver is presented also, and a comparative performance assessment demonstrates that, unlike other suppression procedures, the proposed system allows suppression of NBI with no prior knowledge on its structure.

The authors consider an asynchronous DS/CDMA system wherein users are allowed to transmit their symbols at one out of two available data-rates. Besides the variable spreading length (VSL) access technique, described in the literature, two other types of access methods are considered, namely the variable chip-rate (VCR) and the variable chip-rate frequency shifted (VCRFS), wherein users with different data-rates are accommodated by means of signature waveforms with different chip-rates. The authors derive, through an unified approach valid for each of the above access techniques, an MMSE-based multiuser detector, and show that detection of the users transmitting at the higher data-rate requires a periodically time varying processing. As to the performance analysis, results show that the VCRFS access technique presents the best performance, as well as that enlarging the processing window length and sampling at rates larger than the chip-rate yield beneficial effects on the system performance.

The authors present a new MMSE-based multiuser detector for asynchronous DS/CDMA communications over frequency-selective fading channels. The proposed detector is able to simultaneously suppress both the multiaccess interference and a narrowband interference with known second-order statistics. It is shown that narrowband interference suppression requires in general a time-varying processing of the observables. As to the performance assessment, we give formulas for the system bit error rate for Rayleigh fading and we show that the detector is near-far resistant. Results demonstrate that the proposed system is effective in combating the overall interference. Moreover, enlarging the processing window and oversampling the signal space yield a remarkable improvement in the system performance, especially for large number of users.

This paper handles the simultaneous suppression of narrow-band and multiaccess interference in code division multiple-access (CDMA) direct-sequence spread-spectrum (DSSS) systems. The basic structure we refer to is reminiscent of the decorrelating detector, but here the design strategy relies on the concept of combating jointly the two interference sources-precisely, a decision as to the bit transmitted by each user is made based on the projection of the observables onto the orthogonal complement to the subspace spanned by the other users' signatures and the narrow-band interference. We focus on several different implementations of such a strategy, assuming a different degree of prior knowledge as to the narrow-band interference. An important side result of the proposed approach is that, in general, complete suppression of data-like interference may be achieved through periodically time-varying processing. An adaptive version of such a receiver is also presented, wherein the projection direction is estimated based on suitable estimates of the covariance properties of the observables. The value of this method is also assessed by studying the rate of convergence of the estimated direction to the true projection direction.

The paper discusses the problem of detecting one out of M Gaussian correlated signals in impulsive noise, modeled as a compound-Gaussian, possibly correlated process. We first show that, for uncorrelated noise, the detection problem admits one and the same optimum-in the sense of attaining minimum error probability-solution, independent of the noise statistics: the optimum detector, in fact, amounts to an estimation block, aimed at measuring the short-time noise power spectral density (PSD), whose output is fed to a bank of estimators-correlators, each keyed to one of the M admissible waveforms and to the estimated noise PSD. We also give suggestions for realizing a suboptimum receiver, with reduced complexity, which again is canonical in its structure. As for the performance analysis, we focus on binary frequency-shift keying (BFSK) signaling: we provide numerical results for two particular channel correlations and for Cauchy-distributed noise. These results indicate that, like for the general Gauss-Gauss case, the performance depends on the energy contrast, as well as on the "time-bandwidth" product of the useful signal. Moreover, noise spikiness seems to negatively affect the performance, in the sense that heavier and heavier high-amplitude tails of the noise marginal distribution give rise to higher and higher error probabilities for fixed energy contrast and time-bandwidth signal product.

The article addresses radar detection of coherent pulse trains embedded in spherically invariant noise with unknown statistics. Starting upon a newly proposed detector, which assumes knowledge of the structure of the clutter covariance matrix, we substitute the actual matrix by a proper estimate based on a set of secondary data vectors. Interestingly, the resulting detector achieves a constant false alarm rate with respect to the texture component of the clutter, and incurs an acceptable loss with respect to the case of a known covariance matrix.

This paper deals with the synthesis and the analysis of optimum receivers to detect one out of M equally likely, equi-energy, fading signals in impulsive noise, modelled as a compound Gaussian, possibly correlated process. We show that the conventional coherent and incoherent detectors are still optimum, independent of the noise as well as the fading probability density functions. The performance analysis has been carried on with reference to the general case of arbitrarily distributed disturbance: in order to simplify the analysis, asymptotical expressions have been developed for high signal-to-noise ratios as well as high signal space dimensionality. Interestingly enough, this allows separating the effect of the noise spikyness from that of the fading law. Results indicate that, for deep fading, the noise marginal distribution does not dramatically affect the error probability, nor is it influential on the limit operating characteristics corresponding to infinite signal space dimensions. For non-fluctuating signals, instead, the noise distribution plays a primary role: spiky noise usually produces performance impairment; moreover, the limit performance in impulsive disturbance may exhibit marked deviations from the well-known stepwise shape which is typical of Gaussian channels.>

The paper deals with the detection of signals with unknown parameters in impulsive noise, modeled as a spherically symmetric random process. The proposed model subsumes several interesting families of noise amplitude distributions: generalized Cauchy, generalized Laplace, generalized Gaussian, contaminated normal. It also allows handling of the case of correlated noise by a whitening approach. The generalized maximum likelihood decision strategy is adopted, resulting in a canonical detector, which is independent of the amplitude distribution of the noise. A general method for performance evaluation is outlined, and a comprehensive performance analysis is carried out for the case of M-ary equal-energy orthogonal signals under several distributional assumptions for the noise. The performance is contrasted with that of the maximum likelihood receiver for completely known signals, so as to assess the loss due to the a-priori uncertainty as to the signal parameters.>

A new daily of constant false alarm rate (CFAR) processors is introduced. An Ll-CFAR forms its noise power estimate by linearly filtering ranked samples from the reference set: the weights of this combination, however, depend not only on the rank, but also on the relative proximity of the sample to the cell under test. From the class of Ll-CFARs may be chosen members that effectively censor spurious targets, members that exhibit impressive control of false alarm in the presence of a clutter edge, and members that are robust against both such inhomogeneities. While the design of such schemes is involved, their implementation is not significantly more burdensome than that of plain order statistic (OS)-CFAR.>