Power line communication (PLC) technological advancements are currently being led by the interoperation needs in applications such as smart grids, building energy management systems, electric vehicles, and the Internet of Things. In this paper, alternative control schemes are proposed and compared for the simultaneous transmission of power and digital data through the power circuits of the modules synthesizing a single-phase cascaded H-bridge multilevel inverter. The proposed PLC method does not require any additional circuit elements in the power loop for the PLC implementation. Special focus is paid to the control schemes applied to the H-bridge driving circuits to produce a low total harmonic distortion (THD) without compromising the reliability of data communication. Various parametric simulation models are designed and tested using time-division multiplexing with two variations over the assignment technique for the PLC communication task. The results suggest that the proposed low-bandwidth PLC design is robust enough to provide the required communication fidelity and capable of successfully transmitting the mixed-signal (i.e., power and data) through the power cable under various operating conditions.

Passive Gen2 radio frequency identification (RFID) tags work thanks to the utilization of line codes that balance their operation between two opposite states: absorbing RF (for energy harvesting) and reflecting RF (for backscattering/communications). Given the current RF harvesting technology, batteryless tags need to be located very close to an active illuminator in order to harvest sufficient energy and operate. To tackle this limitation, prior art has tried to bring the illuminator closer to the tags by designing proprietary illuminating architectures. Our solution comes in two parts. First, we offer a novel, Gen2-compliant, near-optimal, noncoherent sequence detection algorithm with linear complexity (in the sequence length) for Miller line codes. We leverage the robustness of this algorithm to overcome issues inherent in multistatic setups, such as carrier frequency offset. Second, we propose a modular multistatic architecture that makes use of low-cost commodity software defined radios (SDR) and omnipresent Ethernet infrastructure. Simulations and experimental results in a monostatic, bistatic, or multistatic SDR testbed with commercial RFIDs, corroborate the low-cost, real-time and near-optimal flavor of our solution.

Scatter radio, i.e., communication by means of reflection, has been recently proposed as a promising technology for low-power wireless sensor networks (WSNs). Specifically, this paper offers noncoherent receivers in scatter radio frequency-shift keying, for either channel-coded or uncoded scatter radio reception, in order to eliminate the need for training bits of coherent schemes (for channel estimation) at the packet preamble. Noncoherent symbol-by-symbol and sequence detectors based on hybrid composite hypothesis test (HCHT) and generalized likelihood-ratio test, for the uncoded case and noncoherent decoders based on HCHT, for small block-length channel codes, are derived. Performance comparison under Rician, Rayleigh, or no fading, taking into account fixed energy budget per packet is presented. It is shown that the performance gap between coherent and noncoherent reception depends on whether channel codes are employed, the fading conditions (e.g., Rayleigh versus Rician versus no fading), as well as the utilized coding interleaving depth; the choice of one coding scheme over the other depends on the wireless fading parameters and the design choice for extra diversity versus extra power gain. Finally, experimental outdoor results at 13-dBm transmission power corroborate the practicality of the proposed noncoherent detection and decoding techniques for scatter radio WSNs.

We consider Alamouti encoding that draws symbols from phase-shift keying and develop a new differential modulation scheme that attains full rate for any constellation order. In contrast to past work, the proposed scheme guarantees that the encoded matrix maintains the characteristics of the initial codebook and, at the same time, attains full rate so that all possible sequences of space-time matrices become valid. Surprisingly, although the validity of all sequences could be thought as a drawback with respect to the cost of noncoherent sequence decoding, in fact it turns out to be an advantage. Based on recent results in the context of quadratic-form maximization over finite alphabets, we exploit the full-rate property of the proposed scheme to develop a polynomial-complexity maximum-likelihood noncoherent sequence decoder whose order is solely determined by the number of receive antennas. Numerical studies show the superiority of the proposed scheme in comparison with contemporary alternatives in terms of encoding rate, decoding complexity, bandwidth efficiency, and throughput.

Orthogonal modulation, for example, frequency-shift keying (FSK) or pulse-position modulation (PPM), is primarily used in relatively-low-rate communication systems that operate in the power-limited regime. Optimal noncoherent detection of orthogonally modulated signals takes the form of sequence detection and has exponential (in the sequence length) complexity when implemented through an exhaustive search among all possible sequences. In this work, for the first time in the literature, we present an algorithm that performs generalized-likelihood-ratio-test (GLRT) optimal noncoherent sequence detection of orthogonally modulated signals in flat fading with log-linear (in the sequence length) complexity. Moreover, for Rayleigh fading channels, the proposed algorithm is equivalent to the maximum-likelihood (ML) noncoherent sequence detector. Simulation studies indicate that the optimal noncoherent FSK detector attains coherent-detection performance when the sequence length is on the order of 100, offering a 3-5 dB gain over the typical energy (single-symbol) detector. While the conventional exhaustive-search approach becomes infeasible for such sequence lengths, the proposed implementation requires a log-linear only number of operations, opening new avenues for practical deployments. Finally, we show that our algorithm also solves efficiently the optimal noncoherent sequence detection problem in contemporary radio frequency identification (RFID) systems.

Frequency-shift keying (FSK) is an orthogonal modulation technique that is primarily used in relatively low-rate communication systems that operate in the power-limited regime. Optimal noncoherent detection of FSK takes the form of sequence detection and has exponential complexity in the sequence length when implemented through an exhaustive search among all possible sequences. In this work, for the first time in the literature, we present an algorithm that performs generalized-likelihood-ratio-test (GLRT) optimal noncoherent sequence detection of orthogonally modulated signals in flat fading with log-linear complexity in the sequence length. Moreover, for Rayleigh fading channels, the proposed algorithm is equivalent to the maximum-likelihood (ML) noncoherent sequence detector. Finally, we show that our algorithm also solves efficiently the optimal noncoherent sequence detection problem in contemporary radio-frequency-identification (RFID) systems.

On-off keying (OOK) is a simple orthogonal modulation technique that is primarily used in the noncoherent mode, that is, when the propagation channel is unknown at the receiver. Although the noncoherent OOK detector is usually operated as a simple single-symbol (one-shot) energy detector, it does not take into account memory that is induced by the channel. Hence, optimal noncoherent detection of OOK takes the form of sequence detection and has exponential complexity in the sequence length when implemented through an exhaustive search among all possible sequences. In this work, we present a novel algorithm that performs generalized-likelihood-ratio-test (GLRT) optimal noncoherent sequence detection of OOK signals in flat fading with cubic (in the sequence length) complexity. Moreover, for Rayleigh fading channels, the proposed algorithm is equivalent to the maximum-a-posteriori (MAP) noncoherent sequence detector. Due to its polynomial complexity, the proposed algorithm allows implementation of the optimal sequence detector for large sequence lengths, for which the conventional exhaustive-search approach becomes infeasible. Interestingly, with a large enough sequence length, the noncoherent detector attains nearly-coherent performance, although it does not utilize any knowledge about the propagation channel.

Amplitude/phase-shift keying (APSK) is a power- and bandwidth- efficient modulation technique that is robust against high-power- amplifier nonlinear distortion effects and has been adopted in the standard DVB-S2 for digital video broadcasting and interactive broadband satellite services. Differential APSK has been studied as a simple blind alternative that avoids channel estimation and tracking at the receiver. However, if the channel is unknown at the receiver end, then the optimal blind detector takes the form of a sequence detector and has exponential (in the sequence length) complexity when implemented through a conventional exhaustive search among all possible data sequences. In this work, we develop a novel algorithm that has polynomial complexity and performs optimal blind sequence detection of APSK.

Certain optimization problems in communication systems, such as limited-feedback constant-envelope beamforming or noncoherent M-ary phase-shift keying (MPSK) sequence detection, result in the maximization of a fixed-rank positive semidefinite quadratic form over the MPSK alphabet. This form is a special case of the Rayleigh quotient of a matrix and, in general, its maximization by an MPSK sequence is \mathcal{NP}-hard. However, if the rank of the matrix is not a function of its size, then the optimal solution can be computed with polynomial complexity in the matrix size. In this work, we develop a new technique to efficiently solve this problem by utilizing auxiliary continuous-valued angles and partitioning the resulting continuous space of solutions into a polynomial-size set of regions, each of which corresponds to a distinct MPSK sequence. The sequence that maximizes the Rayleigh quotient is shown to belong to this polynomial-size set of sequences, thus efficiently reducing the size of the feasible set from exponential to polynomial. Based on this analysis, we also develop an algorithm that constructs this set in polynomial time and show that it is fully parallelizable, memory efficient, and rank scalable. The proposed algorithm compares favorably with other solvers for this problem that have appeared recently in the literature.

The computation of the sparse principal component of a matrix is equivalent to the identification of its principal submatrix with the largest maximum eigenvalue. Finding this optimal submatrix is what renders the problem${\cal NP}$-hard. In this paper, we prove that, if the matrix is positive semidefinite and its rank is constant, then its sparse principal component is polynomially computable. Our proof utilizes the auxiliary unit vector technique that has been recently developed to identify problems that are polynomially solvable. In addition, we use this technique to design an algorithm which, for any sparsity value, computes the sparse principal component with complexity${\cal O}(N^{D+1})$, where$N$and$D$are the matrix size and rank, respectively. Our algorithm is fully parallelizable and memory efficient.

The recent increased interest in massive multiple-input multiple-output systems, combined with the cost of the analog RF chains, necessitates the use of efficient antenna selection (AS) schemes. Capacity or SNR optimal AS has been considered to require an exhaustive search among all possible antenna subsets. In this work, we prove that the maximum-SNR transmit AS problem with two receive antennas is polynomially solvable and develop an algorithm that solves it with quartic complexity, independently of the number of selected antennas. Our method also applies to receive AS with two transmit antennas.

In this paper, we consider the extension of the capabilities of autonomous underwater vehicles (AUV's) to operate in pairs, thus representing each pair of sonars as a bistatic sonar. We utilize principles from the emerging MIMO radar theory, considering both the sonar transmitter and the sonar receiver as systems of closely spaced antennas that transmit/receive multiple linearly independent waveforms. Our objective is to formulate a covariance matrix that allows independent direction of arrival (DOA), direction of departure (DOD), and target strength estimation with reduced complexity. Simulation results demonstrate the effectiveness of the proposed method.

We consider Alamouti encoding that draws symbols from M-ary phase-shift keying (M-PSK) and develop a new differential modulation scheme that attains full rate for any constellation order. In contrast to past work, the proposed scheme guarantees that the encoded matrix maintains the characteristics of the initial codebook and, at the same time, attains full rate so that all possible sequences of space-time matrices become valid. The latter property is exploited to develop a polynomial-complexity maximum-likelihood noncoherent sequence decoder whose order is solely determined by the number of receive antennas. We show that the proposed scheme is superior to contemporary alternatives in terms of encoding rate, decoding complexity, and performance.

Sequence detection offers improved error-rate performance over conventional symbol-by-symbol detection when channel knowledge is not available at the receiver end. However, maximum-likelihood (ML) noncoherent sequence detection is proven to be notoriously intractable in many communication settings. In this work, we develop a new ML sequence detector for pulse-amplitude modulation (PAM) or quadrature-amplitude modulation (QAM) transmissions in unknown Rayleigh fading. Our detector identifies the ML sequence with overall polynomial complexity. This is possible via an auxiliary-angle approach that unlocks a low-rank property of the ML detection problem, reduces the exponential-size set of solution sequences to a polynomial-size set of candidates, and guarantees that the ML sequence is always contained in this substantially smaller set.

Blind sequence detection offers significant gains over conventional symbol-by-symbol detection when channel knowledge is not available at the receiver. However, maximum-likelihood (ML) blind sequence detection is often intractable due to exponential complexity in the sequence length. In this work, we develop a polynomial-time ML blind sequence detector for pulse-amplitude modulation (PAM) transmissions in Rayleigh fading. Our detector follows an auxiliary-angle approach that reduces the exponential-size space of solution vectors to a polynomial-size set of candidate sequences; we prove that this significantly smaller set always contains the ML PAM sequence. Hence, with overall polynomial complexity the proposed detector solves the problem of identifying the ML PAM sequence in unknown Rayleigh fading.

This work derives and evaluates single-antenna detection schemes for collided radio frequency identification (RFID) signals, i.e. simultaneous transmission of two RFID tags, following FM0 (biphase-space) encoding. In sharp contrast to prior art, the proposed detection algorithms take explicitly into account the FM0 encoding characteristics, including its inherent memory. The detection algorithms are derived when error at either or only one out of two tags is considered. It is shown that careful design of one-bit-memory two-tag detection can improve bit-error-rate (BER) performance by 3dB, compared to its memoryless counterpart, on par with existing art for single-tag detection. Furthermore, this work calculates the total tag population inventory delay, i.e. how much time is saved when two-tag detection is utilized, as opposed to conventional, single-tag methods. It is found that two-tag detection could lead to significant inventory time reduction (in some cases on the order of 40%) for basic framed-Aloha access schemes. Analytic calculation of inventory time is confirmed by simulation. This work could augment detection software of existing commercial RFID readers, including single-antenna portable versions, without major modification of their RF front ends.

The maximization of a positive (semi)definite complex quadratic form over a finite alphabet is NP-hard and achieved through exhaustive search when the form has full rank. However, if the form is rank-deficient, the optimal solution can be computed with only polynomial complexity in the length N of the maximizing vector. In this work, we consider the general case of a rank-D positive (semi)definite complex quadratic form and develop a method that maximizes the form with respect to a M-phase vector with polynomial complexity. The proposed method efficiently reduces the size of the feasible set from exponential to polynomial. We also develop an algorithm that constructs the polynomial-size candidate set in polynomial time and observe that it is fully parallelizable and rank-scalable.

We consider the problem of identifying the sparse principal component of a rank-deficient matrix. We introduce auxiliary spherical variables and prove that there exists a set of candidate index-sets (that is, sets of indices to the nonzero elements of the vector argument) whose size is polynomially bounded, in terms of rank, and contains the optimal index-set, i.e. the index-set of the nonzero elements of the optimal solution. Finally, we develop an algorithm that computes the optimal sparse principal component in polynomial time for any sparsity degree.

We derive new bounds on the periodic (cyclic) total squared correlation (PTSC) of binary antipodal signature sets for any number of signatures K and any signature length L. Optimal designs that achieve the new bounds are then developed for several (K,L) cases. As an example, it is seen that complete (K = L + 2) Gold sets are PTSC optimal, but not, necessarily, Gold subsets of K <; L + 2 signatures. In contrast, arguably against common expectation, the widely used Kasami sets are not PTSC optimal in general. The optimal sets provided herein are in this sense better suited for asynchronous and/or multipath code-division multiplexing applications.

We derive new bounds on the aperiodic total squared correlation (ATSC) of binary antipodal signature sets for any number of signatures K and any signature length L. We then present optimal designs that achieve the new bounds for several (K,L) cases. As interesting -arguably- side results, we show that individual maximal merit factor sequences (for example Barker sequences) are single-user ATSC-optimal, while neither the familiar Gold nor the Kasami set designs are ATSC-optimal in general. The ATSC-optimal signature set designs provided in this work are in this sense better suited for asynchronous and/or multipath code-division multiplexing applications.

The maximization of a full-rank quadratic form over the binary alphabet can be performed through exponential-complexity exhaustive search. However, if the rank of the form is not a function of the problem size, then it can be maximized in polynomial time. By introducing auxiliary spherical coordinates, we show that the rank-deficient quadratic-form maximization problem is converted into a double maximization of a linear form over a multidimensional continuous set, the multidimensional set is partitioned into a polynomial-size set of regions which are associated with distinct candidate binary vectors, and the optimal binary vector belongs to the polynomial-size set of candidate vectors. Thus, the size of the candidate set is reduced from exponential to polynomial. We also develop an algorithm that constructs the polynomial-size candidate set in polynomial time and show that it is fully parallelizable and rank-scalable. Finally, we demonstrate the efficiency of the proposed algorithm in the context of adaptive spreading code design.

We prove that maximum-likelihood (ML) noncoherent sequence detection of orthogonal space-time block coded signals can always be performed in polynomial time with respect to the sequence length for static Rayleigh, correlated (in general) channels. Moreover, using recent results on efficient maximization of rank-deficient quadratic forms over finite alphabets, we develop an algorithm that performs ML noncoherent sequence detection with polynomial complexity. The order of the polynomial complexity of the proposed receiver equals two times the rank of the covariance matrix of the vectorized channel matrix. Therefore, the lower the Rayleigh channel covariance rank the lower the receiver complexity. Instead, for Ricean channel distribution, we prove that polynomial complexity is attained through the proposed receiver as long as the mean channel vector is in the range of the covariance matrix of the vectorized channel matrix. Hence, full-rank channel correlation is desired to guarantee polynomial ML noncoherent detection complexity for the case of static Ricean fading. Our results are presented for the general case of block-fading Rayleigh or Ricean channels where we provide conditions under which ML noncoherent sequence detection can be performed in polynomial time through our algorithm.

In amplify-and-forward single-relay systems that employ an average relay power constraint, one-shot detection at the destination terminal is not optimal when the channel between the source and relay terminals is unknown. In this work, we derive the maximum-likelihood (ML) block noncoherent detector and show that it can be expressed as a reduced-rank quadratic form maximization. We introduce an auxiliary real variable and prove that the maximization form can be closely approximated by a rank-2 positive semidefinite quadratic form through appropriate selection of the auxiliary variable. Motivated by recent developments on the polynomial-time maximization of reduced-rank quadratic forms over finite alphabets, we develop an efficient detector that has polynomial complexity in the block length and demonstrated near-ML performance.

Cooperative communications is a rapidly evolving research area. Most of the cooperative protocols that have appeared in the literature assume slow flat fading channels and Gaussian codebooks. In many cases the relays must fully decode their input. It is well known that cooperation is most effective at low SNR where binary input is optimal. Furthermore, energy and cost effectiveness make simple relays most attractive. Motivated by these two facts, we consider a half-duplex orthogonal cooperation protocol with binary input and relays that simply forward their symbol-by-symbol decisions to the destination which performs algebraic decoding; we call it demodulate-and-forward (DmF). We assume independent slow Rayleigh flat fading channels with full channel state information (CSI) at the destination and compute an upper bound for the outage capacity of the DmF protocol. For low SNR and small outage probability, we derive a simple approximation to this bound. For comparison purposes, we compute the outage capacity of direct binary transmission and a simple low-SNR small-outage-probability approximation. We observe that for very small outage probability the DmF protocol significantly outperforms direct transmission. However, for (relatively) high outage probability, the opposite may happen.

The maximization of a full-rank quadratic form over a finite alphabet is NP-hard in both a worst-case sense and an average sense. Interestingly, if the rank of the form is not a function of the problem size, then it can be maximized in polynomial time. An algorithm for the efficient computation of the binary vector that maximizes a rank-deficient quadratic form is developed based on an analytic procedure. Auxiliary spherical coordinates are introduced and the multi-dimensional space is partitioned into a polynomial-size set of regions; each region corresponds to a distinct binary vector. The binary vector that maximizes the rank-deficient quadratic form is shown to belong to the polynomial-size set of candidate vectors. Thus, the size of the feasible set is efficiently reduced from exponential to polynomial.

In wireless channels, maximum-likelihood (ML) block noncoherent detection offers significant gains over conventional symbol- by-symbol detection when the fading channel coefficients are not available and cannot be estimated at the receiver. Certainly, in general the complexity of the block detector grows exponentially with the symbol sequence length. However, it has been recently shown that for M-ary phase-shift keying (MPSK) modulation block noncoherent detection can be performed with polynomial complexity. In this work, we develop a new ML block noncoherent detector for MPSK transmission of arbitrary order and multiple-antenna reception. The proposed algorithm introduces auxiliary spherical variables and constructs with polynomial complexity a polynomial- size set which includes the ML data sequence. It is shown that the complexity of the proposed algorithm is polynomial in the sequence length and at least one order of magnitude lower than the complexity of computational-geometry based noncoherent detection algorithms that have been developed recently.

We consider the maximization of a quadratic form over the binary alphabet. By introducing auxiliary spherical coordinates, we show that if the rank of the form is not a function of the problem size, then (i) the multidimensional space is partitioned into a polynomial-size set of regions which are associated with distinct binary vectors and (ii) the binary vector that maximizes the rank-deficient quadratic form belongs to the polynomial-size set of candidate vectors. Thus, the size of the feasible set of candidate vectors is efficiently reduced from exponential to polynomial. We also develop an algorithm that constructs the polynomial-size feasible set in polynomial time and show that it is fully parallelizable and rank-scalable. Finally, we examine the efficiency of the proposed algorithm in the context of multiple-input multiple-output signal detection.

We derive new bounds on the aperiodic total squared correlation (ATSC) of binary antipodal signature sets for any number of signatures K and any signature length L. We then present optimal designs that achieve the new bounds for several (K, L) cases. As an example, it is seen that the familiar Gold and (small or large) Kasami designs are not ATSC-optimal in general. The optimal signature set designs provided herein are in this sense better suited for asynchronous and/or multipath code-division multiplexing applications.

Blind channel-estimation algorithms return phase-ambiguous estimates. From a receiver design point of view, the phase-ambiguity problem can be by-passed by differential modulation and detection at the expense of a well-known performance loss, in comparison with direct modulation and coherent detection schemes. An alternative approach is followed in this paper. A theoretical minimum mean-square error phase-estimation criterion leads to a supervised phase-recovery procedure that directly corrects the phase of arbitrary linear filter receivers through a simple closed-form projection operation. Conveniently, any known blind channel-estimation algorithm can be used to provide the initial phase-ambiguous estimate. The presentation is given in the context of adaptive space-time receiver designs for binary phase-shift keying direct-sequence code-division-multiple-access antenna array systems. Numerical and simulation studies support the theoretical developments and show that effective phase correction and multiple-access interference suppression can be achieved with about 2% pilot signaling

Over the real/complex field, the spreading code that maximizes the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR linear filter is the minimum-eigenvalue eigenvector of the interference autocovariance matrix. In the context of binary spreading codes, the maximization problem is NP-hard with complexity exponential in the code length. A new method for the optimization of binary spreading codes under a rank-2 approximation of the inverse interference autocovariance matrix is presented where the rank-2-optimal binary code is obtained in lower than quadratic complexity. Significant SINR performance improvement is demonstrated over the common binary hard-limited eigenvector design which is shown to be equivalent to the rank-1-optimal solution.

The total squared correlation (TSC), maximum squared correlation (MSC), sum capacity (C/sub sum/), and total asymptotic efficiency (TAE) of underloaded signature sets, as well as the TSC and C/sub sum/ of overloaded signature sets are metrics that are optimized simultaneously over the real/complex field. In this present work, closed-form expressions are derived for the MSC, C/sub sum/, and TAE of minimum-TSC binary signature sets. The expressions disprove the general equivalence of these performance metrics over the binary field and establish conditions on the number of signatures and signature length under which simultaneous optimization can or cannot be possible. The sum-capacity loss of the recently designed minimum-TSC binary sets is found to be rather negligible in comparison with minimum-TSC real/complex-valued (Welch-bound-equality) sets.

We investigate the user capacity, throughput, and delay characteristics of a mobile slotted ALOHA direct-sequence code-division-multiple-access (DS-CDMA) link with dedicated signatures under multipath fading and packet-rate adaptive antenna array signal reception. For a given system transmission bit rate, the packet size is designed to be sufficiently small to conform with the coherence time of the channel. Then, on an individual packet-by-packet basis, a phase-ambiguous spatial-temporal channel estimate is produced by a blind (unsupervised) eigensubspace procedure. The space-time channel estimate is phase corrected via a few pilot packet mid-amble bits and used for joint spatial-temporal multiple-access-interference suppression according to the principles of auxiliary-vector filtering. Subsequently, packet success probabilities are derived in the presence or absence of forward error correction and are used to evaluate the throughput and delay characteristics of the link.

The total-squared-correlation (TSC), maximum squared correlation (MSC), total asymptotic efficiency (TAE), and sum capacity (C/sub sum/) of underloaded signature sets as well as the TSC and C/sub sum/ of overloaded signature sets are metrics that are optimized simultaneously over the real/complex field. We derive closed-form expressions for the MSC, TAE, and C/sub sum/ of minimum-TSC binary signature sets. Our expressions disprove the general equivalence of these performance metrics over the binary field and establish conditions on the number of signatures and signature length under which simultaneous optimization can or cannot be possible. We comment that the sum capacity loss of the recently designed minimum-TSC binary sets is found to be rather negligible in comparison with minimum-TSC real/complex-valued (Welch-bound-equality) sets.

We derive lower bounds on the maximum-squared-correlation (MSC) of binary antipodal signature sets for any number of signatures K and any signature length L with K /spl les/ L (underloaded systems). We establish the tightness of the bounds for all cases except K = L /spl equiv/ 1 (mod 4) and we prove that the minimum total-squared-correlation (TSC) binary antipodal signature sets that were recently designed are, in fact doubly optimal for underloaded systems: both their TSC and MSC are minimum.

The Welch lower bound (see Welch, R.L., IEEE Trans. Inform. Theory, vol.IT-20, p.397-9, 1974) on the total squared correlation (TSC) of signature sets is known to be tight for real-valued signatures and loose for binary signatures whose number is not a multiple of four. We derive new bounds on the TSC of binary signature sets for any number of signatures K and any signature length L. Then, for almost all K, L in {1,2,...,256}, we design optimum binary signature sets that achieve the new bounds. The design procedure is based on simple transformations of Hadamard matrices.

In this paper we prove formally that the optimum (nonlinear) DS-CDMA single-user decision boundary exhibits the following properties: (i) it is symmetric with respect to the origin and (ii) as it is traversed away from the origin, it converges to a hyperplane parallel to the MF decision boundary. Then, we translate properties (i) and (ii) to a set of constraints that can be used by any optimization algorithm for the selection (training) of the parameters of a general multi-layer-perceptron neural-network receiver. Using these constraints, the number of parameters to be optimized is reduced by nearly 50% for large-size networks, which effectively doubles the speed of any training procedure. Furthermore, we utilize properties (i) and (ii) to develop a new initialization scheme that provides additional improvements on the convergence rate and can be used by any recursive optimization algorithm. As a representative case study we consider the back-propagation (BP) algorithm and develop a constrained version of it that incorporates both the proposed constraints and the proposed initialization. The convergence rate enhancement achieved fay constrained-BP is illustrated by simulations.

The Welch lower bound (see Welch, L.R., IEEE Trans. Inform. Theory, vol.20, p.397-9, 1974; Massey, J. L. and Mittelholzer, T., "Sequences II, Methods in Communication, Security, and Computer Sciences", p.63-78, Springer-Verlag, New York, 1993) on the total-squared-correlation (TSC) of signature sets is known to be tight for real-valued signatures and loose for binary signatures whose number is not a multiple of 4. We derive new bounds on the TSC of binary signature sets for any number of signatures K and any signature length L. For almost all K, L in {1, 2, ..., 200}, we develop simple algorithms for the design of optimum binary signature sets that achieve the new bound.

We show that statistical conditional optimization criteria lead to the development of a simple iterative algorithm that starts from the matched filter (or constraint vector of interest) and generates a sequence of filters that converges to the minimum-variance-distortionless-response (MVDR) solution for any positive definite input autocorrelation matrix. When the input autocorrelation matrix is replaced by a conventional sample-average (positive definite) estimate, the algorithm effectively generates a sequence of MVDR filter estimators; the bias converges rapidly to zero and the covariance trace raises slowly and asymptotically to the covariance trace of the familiar sample-matrix-inversion (SMI) estimator. For short-data-records, the early elements of the generated sequence of estimators offer favorable bias/covariance balance and are seen to outperform in mean-square estimation error (constrained-)LMS, RLS-type, and, certainly, SMI estimates.

An algorithmic procedure is developed for the random expansion of a given training set to combat overfitting and improve the generalization ability of backpropagation trained multilayer perceptrons (MLPs). The training set is K-means clustered and locally most entropic colored Gaussian joint input-output probability density function (pdf) estimates are formed per cluster. The number of clusters is chosen such that the resulting overall colored Gaussian mixture exhibits minimum differential entropy upon global cross-validated shaping. Numerical studies on real data and synthetic data examples drawn from the literature illustrate and support these theoretical developments.