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This paper proposes the hybrid power line/wireless system (HPWS) based on the orthogonal chirp-division mul-tiplexing (OCDM) scheme. In this system, the same discrete- Fresnel domain symbols are transmitted through power line and wireless channels, enabling the application of combining techniques in the same domain and hence the signal-to-noise ratio increase at the receiver side. Furthermore, the optimal power allocation for maximizing the achievable data rate or minimizing the average bit error probability is discussed for OCDM-based HPWS. Finally, numerical analyses demonstrate that the OCDM- based HPWS is more appropriate for minimizing the average bit error probability if the transmitter is unaware of the channel state information.

This article studies optimal subcarrier permutation in hybrid power line/wireless systems to either maximize the achievable data rate or minimize the average bit error probability (BEP). In order to better exploit the frequency selectivity of power line and wireless media, subcarrier permutation is optimized under uniform or optimal power allocation over an orthogonal frequency-division multiplexing scheme using maximal-ratio combining. Different from previous works, we demonstrate that the normalized signal-to-noise (nSNR) must be considered instead of the SNR and prove that it can be extended to cases where the minimization of the average BEP and optimal power allocation are considered. Moreover, we show that subcarrier permutation and power allocation problems can be assumed to be decoupled. Numerical results show that performance gains associated with subcarrier permutation become more relevant as the frequency selectivity of the nSNRs increases. In addition, the optimal subcarrier permutation is more effective for minimizing the average BEP than maximizing the achievable data rate and yields similar improvement under the usage of uniform and optimal power allocations.

This paper investigates power allocation for minimizing the average bit error probability (BEP) in hybrid communication systems, more specifically hybrid power line/wireless systems (HPWSs). In this regard, a brief discussion of HPWSs is presented in order to point out the complementary characteristics of power line and wireless media. By considering orthogonal frequency-division multiplexing and maximal-ratio combining, optimization problems associated with the minimization of the average BEP in hybrid communication systems are formulated under two distinct transmission power constraints, called sum power and sum power-channel constraints. For both constraints, it is demonstrated that at most one medium should be used for data transmission over subchannels with the same index in order to minimize the average BEP in hybrid communication systems. Based on this finding, power allocation algorithms are derived for this purpose in HPWSs. Numerical analyses are then used to validate the proposed algorithms by comparing them with interior-point-based power allocation algorithms. In addition, performance comparisons show the advantage of the proposals over alternative algorithms from the literature, including previous algorithms proposed for maximizing the achievable data rate.

Abstract Aiming at reducing hardware complexity and power consumption of multicarrier‐based broadband transceivers, this contribution discusses and analyzes the implementation of clustered‐orthogonal frequency division multiplexing (OFDM)‐based transceiver in a field programmable gate array (FPGA) device. In this sense, several OFDM schemes covering baseband and passband data communications are implemented. The hardware resource utilization and power consumption related to each scheme, after being implemented in the FPGA device, are discussed and compared. Numerical results show that the OFDM scheme based on ‐II and ‐III, which is a modified version of the hermitian symmetric (HS)‐OFDM scheme, which performs both baseband and passband data communications, demands the lowest hardware resource utilization and power consumption, reducing to 1/3 the hardware resource utilization. As a result, ‐II and ‐III constitute an interesting candidate for implementing new generations of high data rate, low hardware resource utilization, and low‐power consumption clustered‐OFDM‐based transceivers for broadband data communication. Last but not least, numerical results show that the controllers used to integrate all components of a clustered‐OFDM‐based transceiver can demand sizeable hardware resource utilization and power consumption and, therefore, they have to be carefully designed.

This paper investigates optimal power allocation in hybrid power line/wireless systems (HPWS). By adopting maximal-ratio combining (MRC) for the signals received from different channels, power allocation problems under sum power and sum power-channel constraints are formulated. These power constraints impose distinct bounds on the transmission power and, as a consequence, constitute different optimization problems. Therefore, two power allocation algorithms, which aim to maximize the achievable data rate in HPWS, are obtained. Furthermore, mathematical analyses show that data communication must employ only one medium per subchannel as the sum power constraint and frequency-selective channels are taken into account. Also, with high probability, the sum power-channel constraint results in the same conclusion with respect to the sole use of one medium per subchannel when the channels are frequency selective. In other words, MRC and selection-combining yield the same performance in terms of achievable data rate. Numerical analyses evaluate the proposed power allocation algorithms for exploiting the diversity between power line and wireless media by comparing them to alternative algorithms reported in the literature, especially when the total transmission power belongs to a practical range, and the attained results confirm their effectiveness and validate their usage in practical scenarios.

In this paper, the localization of an emitter based on Time Difference of Arrival (TDoA) has been investigated. The classical least-squares (LS) algorithm, with a limited number of TDoA measurements, has been utilized for obtaining a closed-form solution to the source localization problem. Recently, an extension of the classical LS algorithm has been employed in an attempt to improve the precision of the localization technique by using a larger set of TDoA estimates. However, considering all TDoA values can eventually degrade the accuracy of the localization method due to the presence of heavily noisy measurements. In this work, by employing a data-selective approach, we have proposed a closed-form LS solution that disregards bad measurements. To this end, we have used two distinct objective functions, one to obtain a solution and a second one to test that particular solution among all possible ones within a subset of measurements. Simulation results indicate the superior performance of the proposed algorithm in the source localization problem.

In licensed shared access (LSA), incumbents (i.e. primary users) control the access to their spectrum bands through temporarily leasing to a third party (called LSA licensees) in a specific spatial region. Such shared access takes place based on spectrum availability information acquired from LSA repositories. In this context, this paper presents the following contributions: 1) The use of a cognitive cooperative framework based on measurement-capable devices (MCDs) in order to provide valuable information both to generate and to update radio-environment maps for LSA repositories, thus enabling more dynamic spectrum-access opportunities. 2) A new distributed and cooperative scheme among cognitive MCDs to detect and classify incumbents and LSA licensees in the LSA network. The proposal suggests a clustering strategy for cooperative spectrum sensing when neighboring MCDs observe different licensed users-whether incumbents or LSA licensees. The clustering considers the differences in underlying hidden Markov models associated with the detection of distinct licensed users. 3) Theoretical expressions for the overall probability of detection and false alarm are derived for hard cooperation strategies that may include failed MCDs in the clustering process.

Direction of Arrival (DOA) estimation of gunshots is an important asset to law-enforcement agencies and defense forces, for shooter localization is key to improving public and troop safety. Solutions vary according to scenario, application, and available resources. As the distance between the firing position and the sensor array increases, as in a typical sniper scenario, signal to noise ratio decreases and the estimation degrades. This paper proposes a gunshot DoA estimation algorithm to be used with highly noisy signals. We combine exhaustive search for selecting pairs of microphones from the array to attain the best DOA estimation results and fast response time for different shooting scenarios. We are particularly interested in highly corrupted signals for which state-of-the-art algorithms fail. Experimental results from simulated and recorded gunshot signals were used to evaluate the performance of the proposed scheme.

We propose in this work an L1-norm Linearly Constrained Normalized Least-Mean-Square (L1-CNLMS) algorithm applied to solve the beamforming problem in Standard Hexagonal Arrays (SHA) and (non-standard) Hexagonal Antenna Arrays (HAA). In addition to the linear constraints present in the CNLMS algorithm, the L1-CNLMS algorithm takes into account an L1-norm penalty on the filter coefficients which results in sparse solutions producing Thinned Hexagonal Arrays. The effectiveness of the L1-CNLMS algorithm is demonstrated by comparing, via computer simulations, its results with those of the CNLMS algorithm. When employing the L1-CNLMS algorithm to antenna array problems, the resulting effect of the L1-norm constraint is perceived as a large aperture array with few active array elements.

This paper addresses the problem of correlation due to redundancy in cooperative spectrum sensing networks and proposes an algorithm for topology design which improves significantly detection performance. In a recently proposed two-step distributed scheme, redundancy occurs when some nodes contribute more than once for the consensus decision, leading to correlation and consequently degrading performance in the same way as correlated shadowing. To eliminate this type of correlation, we employ two different topologies, primary and complementary, one for each cooperation step. Topology design is accomplished in a distributed manner by stating criteria for user selection. Results show that the proposed double-topology scheme suppresses redundancy and offers similar performance when compared to the case of independent node contributions.

The development of adaptive filters is usually based either on a stochastic approximation of the gradient vector and the Hessian matrix, or on a deterministic minimization of quadratic a posteriori output errors. This paper investigates the design of adaptation algorithms by means of a minimum-disturbance approach together with added constraints. More than just rewriting objective functions minimized by the algorithms, the approach provides insight and extra tools for optimizing with respect to other parameters, e.g., the convergence factor μ. Designing new algorithms by adding extra costs or constraints to the objective function follows naturally, whereas the main characteristics of the algorithms remain clear. Understanding subtleties that set similar algorithms apart is also made easier. We apply the method to known algorithms, such as the LMS and RLS algorithms, and also to their variants. Rather than proposing a new algorithm, we hope this article will facilitate the development of different algorithms to meet the challenges posed by demanding applications. In addition, ensuing discussions may help understanding better the behavior of each algorithm in a particular scenario.

This paper proposes a novel two-step distributed detection scheme for cooperative spectrum-sensing networks. In the first step, individual contributions from nodes within a neighborhood are fused through an adaptive combiner, which updates the weights and makes local decisions iteratively. In the second step, local decisions are shared within a neighborhood to yield a consensus decision. Results are presented in terms of complementary receiver operating characteristic curves and show the good behavior of the proposed scheme when compared to the optimal linear fusion rule, even if correlated node contributions are considered.

Usual methods for the development of adaptive filters are based on a stochastic approximation of the gradient vector and Hessian matrix, or on a deterministic minimization of quadratic a posteriori output errors. Gradient-based algorithms are usually placed in the first group, whereas least squares (LS) based algorithms are placed in the second group. These are just how algorithms are usually presented and analyzed and alternative descriptions exit. This paper proposes to shed new light onto known adaptation algorithms by means of a minimum-disturbance approach to the cost function together with constraints added to improve their robustness. The resulting algorithms are able to perform extremely well in many demanding applications.

This paper proposes selective update and cooperation strategies for parameter estimation in distributed adaptive sensor networks. A set-membership filtering approach is employed that results in reduced complexity for updating parameter estimates at each network node, a significant reduction in information exchange between cooperating nodes, and an optimal strategy to obtain consensus estimates. The proposed strategies and the estimation algorithm offer a new way to explore cooperation in adaptive distributed sensor networks.

Speech privacy techniques are used to scramble clear speech into an unintelligible signal in order to avoid eavesdropping. Some analog speech-privacy equipments (scramblers) have been replaced by digital encryption devices (comsec), which have higher degree of security but require complex implementations and large bandwidth for transmission. However, if speech privacy is wanted in a mobile phone using a modern commercial codec, such as the AMR (adaptive multirate) codec, digital encryption may not be an option due to the fact that it requires internal hardware and software modifications. If encryption is applied before the codec, poor voice quality may result, for the vocoder would handle digitally encrypted signal resembling noise. On the other hand, analog scramblers may be placed before the voice encoder without causing much penalty to its performance. Analog scramblers are intended in applications where the degree of security is not too critical and hardware modifications are prohibitive due to its high cost. In this article we investigate the use of different techniques of voice scramblers applied to mobile communications vocoders. We present our results in terms of LPC and cepstral distances, and PESQ values.

In this paper, we present a method to obtain a set of orthogonal pulses to be used in pulse-shape modulation (PSM) for ultra-wideband communications. The pulses are built as linear combinations of Hermite functions, which are shown to have unique advantageous features. Mathematical restrictions of orthogonality and spectral eficiency are introduced as guidelines to a fully explained search procedure to find the best set of pulses. Additionally, this procedure is adapted and used to find a single FCC-compliant pulse shape. A quaternary PSM scheme is implemented with orthogonal pulses obtained by the proposed method, and the results of a simulation are shown.

Set membership adaptive filtering is known for a number of attractive features, including reduction of computational complexity due to less frequent coefficient updates. This paper addresses the problem of choosing the error bound to be used in SM adaptation algorithms. This choice has often been based on the experience of the designer, and affects directly not only algorithm performance, but also its computational complexity. We propose a time-varying error bound for a set-membership normalized least mean squares (SM-NLMS) algorithm that yields near constant average coefficient updating rate during both transient and steady state. The expressions given herein not only offer a method to calculate the error bound automatically, but also describes the behavior of a conventional SM-NLMS algorithm. The results were obtained for the particular application of linear time-invariant channel estimation, but certainly provide insightful hints to other scenarios

This paper presents a new algorithm for fast training of unbiased proximal support vector machines. PSVM was first introduced as an alternative to SVM classifiers that usually require a large amount of computation time for training. Unfortunately PSVM may present poor performance, especially for low values of a regularization parameter C, due to biased optimal hyperplanes. The proposed algorithm, named UPSVM (unbiased proximal support vector machines), uses a slightly different approach to circumvent this problem, such that an unbiased optimal hyperplane is always obtained. Simulations show that the proposed algorithm performs better than PSVM and sequential minimal optimization (SMO) with respect to training time with similar probability of correct pattern classification.

The paper describes the implementation of the multistage Wiener filter (MWF) through a series of nested Householder transformations applied to the input signal as a means to form the analysis filter-bank part of the structure. The input signal is successively projected onto an appropriate subspace whereby the mutual information is maximized at each step, as required by the MWF structure. The method presented is described-as a constrained optimization problem, where the constraints are imposed on the input signal, and not on the coefficients of the filter. The method is applicable to reduced-rank as well as to full-rank implementations of the MWF. The Householder transformation assures that the equivalent blocking matrices for all the stages are efficiently implemented via single reflections and that only unitary reflections are employed; robustness against deleterious finite-precision effects is, therefore, improved. Simulations of a DS-CDMA interference suppression receiver illustrate the robust behavior of the proposed scheme when implemented in finite precision as compared to the conventional MWF using non-orthogonal blocking matrices.

In this article, we show that a code division multiple access (CDMA) system employing long codes can be interpreted as a multiple input multiple output system where the transmit filters are time-varying. We derive conditions for existence of zero-forcing equalizers and verify through simulations that these conditions are useful guidelines for the design of appropriate solution in the presence of noise. Compared to similar works in the literature, the derived conditions allow a reduction in the length of the equalizer filters, while imposing no constraints on the channel order.

In this paper we test and compare different modulation strategies to be used in ultra-wide band (IR-UWB) communications. In the UWB systems of interest in this work the information is conveyed by short-duration pulses; the modulation scheme determine how the data stream is to be transmitted over those pulses. Here we test and compare several schemes based on three types of modulation. The first type is the pulse position modulation (PPM), which includes a pulse delay according to the data to be transmitted. The second is the recently proposed pulse shape modulation (PSM), that uses a different pulse shape to each data. The third one, proposed in this paper, is an M-ary modulation scheme that combines the shape and amplitude of the pulse to transmit the data, which we call pulse amplitude and shape modulation (PASM). Other M-ary schemes are tested, including the recent orthogonal M-ary PSM, based on orthogonal Hermite functions, and a new quaternary PPM scheme, also proposed here. All schemes are tested over an AWG channel.

This letter compares the transients of the constrained recursive least squares (CRLS) algorithm with the generalized sidelobe canceler (GSC) employing the recursive least squares (RLS) algorithm. We prove that the two adaptive implementations are equivalent everywhere regardless of the blocking matrix chosen. This guarantees that algorithm tuning is not affected by the blocking matrix. This result differs from the more restrictive case for transient equivalence of the constrained least mean-square (CLMS) algorithm and the GSC employing the least mean square (LMS) algorithm, for in this case the blocking matrix needs to be unitary.

In this work we derived conditions that guarantee the existence of zero-forcing (ZF) equalizers in communication systems employing block transmissions. Compared to previous works in this field, the obtained conditions allow a reduction in the length of the equalizer filters, reduce the necessary redundancy and allow transmission through channels with impulse response longer than the channel model. We also show experimental results obtained via computer simulations for evaluation of the system performance.

Partial-update adaptive filtering algorithms only update part of the filter coefficients at each time instant, leading to reduced computational complexity as compared with their conventional counterparts. In this paper, the ideas of the partial-update NLMS-type algorithms found in the literature are extended to the framework of set-membership filtering, from which data-selective NLMS type of algorithms with partial update are derived. The new algorithms combine data-selective updating from set-membership filtering with the reduced computational complexity from partial updating. Simulation results verify the good performance of the new algorithms in terms of convergence speed, final misadjustment, and reduced computational complexity.

In this work we present an efficient structure based on the Wiener filter to perform channel equalization in a block communications system. We show that it is possible to derive a structure to estimate a block of transmitted symbols that use the same Wiener filter to estimate all symbols in the block. In terms of bit error rate, experimental results show that the system performs better than discrete multitone (DMT) for a wide range of signal-to-noise ratios. A multistage Wiener filter recently introduced was used to implement a reduced-rank version of the equalizer, and the performance of the system was shown to remain almost unchanged for rank values much smaller than the signal autocovariance matrix rank.

We propose an alternative technique to the implementation of discrete wavelet multitone systems based on fractional-delay filter banks. The technique yields orthogonal subband signals and allows independent equalization for each subband at the receiver end. When compared to systems employing cosine-modulated filter banks, our technique shows better performance in terms of bit-error rate, particularly for low signal-to-noise ratios and highly selective channels.

This paper introduces a constrained version of the recently proposed set-membership affine projection algorithm based on the set-membership criteria for coefficient update. The algorithm is suitable for linearly constrained minimum-variance filtering applications. The data-selective property of the proposed algorithm greatly reduces the computational burden as compared with a nonselective approach. Simulation results show the good performance in terms of convergence, final misadjustment, and reduced computational complexity.

A brief comparison of several methods to estimate the instantaneous frequency (IF) is carried out taking into consideration computational complexity, bias, and variance. A new method to estimate the IF is then proposed for high signal-to-noise ratio applications. The new method was derived based on the derivative of the phase of the analytic signal and on the assumption that the modulation varies linearly with time. When compared to other methods of similar computational complexity, the proposed method presented smaller bias for a fixed window length and smaller variance for a given acceptable bias threshold.

The composite squared-error (CSE) algorithm results from the combination of the backpropagation algorithm with the pseudo-linear algorithm of Scalero and Tepedelenlioglu (1992). It is observed that the CSE algorithm is able to avoid suboptimal solutions and associated saddle points, thus achieving lower values of the error function than the pseudo-linear algorithm, in fewer iterations than the backpropagation algorithm. In this paper, we investigate the implementation of the CSE algorithm with the concepts of momentum gain, time-varying learning rate, and time-varying combining factor. It is verified that these features can improve the overall properties of the CSE convergence process.

Based on the condition for equivalence between linearly constrained minimum-variance (LCMV) filters and their generalized sidelobe canceler (GSC) implementations, we derive the new constrained conjugate gradient (CCG) algorithm. We discuss the use of orthogonal and nonorthogonal blocking matrices for the GSC structure and how the choice of this matrix may affect the relationship with the LCMV counterpart. The newly derived algorithm was tested in a computer experiment for adaptive multiuser detection and showed excellent results.

This paper proposes a new approach to linearly-constrained adaptive filtering, where successive Householder transformations are incorporated in the algorithm update equation in order to reduce computational complexity and coefficient-error norm. We show the derivation of two new algorithms, namely the unnormalized and the normalized Householder-transform constrained LMS algorithms (HCLMS and NHCLMS, respectively). Although the derivation is carried out based on the constrained LMS (CLMS) algorithm, the technique can be applied to other constrained algorithms as well. Simulation results of a linearly-constrained minimum-variance problem show that in finite-precision implementation the coefficient-error norms obtained with the new algorithms are smaller than those obtained with the CLMS and the normalized CLMS algorithms.

Providing a quantitative mean-squared-error analysis of adaptation algorithms is of great importance for determining their usefulness and for comparison with other algorithms. However, when the algorithm reutilizes previous data, such analysis becomes very involved as the independence assumption cannot be used. In this paper, a thorough mean-squared-error analysis of the binormalized data-reusing LMS algorithm is carried out. The analysis is based on a simplified model for the input-signal vector, assuming independence between the continuous radial probability distribution and the discrete angular probability distribution. Throughout the analysis only parallel and orthogonal input-signal vectors are used in order to obtain a closed-form formula for the excess mean-squared error. The formula agrees closely with simulation results even when the input-signal vector is a delay line. Furthermore, the analysis can be readily extended to other algorithms with expected similar accuracy.

A new adaptive filtering algorithm is developed based on fairly well known off-line optimization techniques. The need for tuning parameters is eliminated through an efficient line search procedure which assures good performance in many practical situations. Simulations are included which demonstrate the algorithm's stability under conditions where other algorithms with fast convergence, e.g., RLS, LMS-Newton, diverge.>