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Direct-sequence/code-division multiple-access (DS/CDMA) communication systems equipped with adaptive antenna arrays offer the opportunity for jointly effective spatial and temporal (code) multiple-access interference (MAI) and channel noise suppression. This work focuses on the development of fast joint space-time (S-T) adaptive optimization procedures that may keep up with the fluctuation rates of multipath fading channels. Along these lines, the familiar S-T RAKE processor is equipped with a single orthogonal S-T auxiliary vector (AV) selected under a maximum magnitude cross-correlation criterion. Then, blind joint spatial/temporal MAI and noise suppression with one complex S-T degree of freedom can be performed. This approach is readily extended to cover blind processing with multiple AVs and any desired number of complex degrees of freedom below the S-T product. A sequential procedure for conditional AV weight optimization is shown to lead to superior bit-error-rate (BER) performance when rapid system adaptation with limited input data is sought. Numerical studies for adaptive antenna array reception of multiuser multipath Rayleigh-faded DS/CDMA signals illustrate these theoretical developments. The studies show that the induced BER can be improved by orders of magnitude, while at the same time significantly lower computational optimization complexity is required in comparison with joint S-T minimum-variance distortionless response or equivalent minimum mean-square-error conventional filtering means.

We develop a binary signature design procedure to scale upwards overloaded minimum total-squared-correlation (TSC) binary signature sets. The quality of the design is measured against the recently published binary TSC bounds.

The conventional signature-matched RAKE processor for multipath direct-sequence code division multiple access channels is viewed as a regular linear tap-weight filter of length equal to the sum of the system processing gain and the user channel memory. In this paper, performance improvements are sought in the context of adaptive filtering under maximum signal-to-interference-plus-noise-ratio criteria. The minimum-variance-distortionless-response RAKE (RAKE-MVDR) filter and the lower complexity scalar optimized auxiliary-vector RAKE (RAKE-AUX) filter are developed. Bit error rate (BER) comparisons with the conventional RAKE signature-matched filter are carried out for training sets of reasonably small size, perfectly known, and mismatched/estimated channel coefficients, and extreme near-far system configurations.

We present a necessary and sufficient condition for any orthogonal space-time block code (STBC) to allow transceiver signal linearization (as in the well-known case of the Alamouti scheme). Then, we show that all square orthogonal STBC's that satisfy the condition have rate that goes to zero linearly with the number of transmit antennas. Hence, multiple-antenna systems with orthogonal STBC's and satisfactory rate are possible only If we abandon the linearization property or utilize non-square codes (except for the 2 times 2 Alamouti code)

A fresh look on the design of practical low-complexity direct-sequence code-division multiple-access (DS/CDMA) receivers is proposed from the Wiener reconstruction-filter point of view. The natural outcome is the emergence of a new class of linear scalar-parameterized auxiliary-vector receivers (filters). Then, the blind optimization of these receivers in the maximum signal-to-interference-plus-noise-ratio (SINR) sense becomes a straightforward procedure. The conceptual and computational simplicity of this general approach promises immediate practical utility. This new generation of receivers exhibits minimal optimization requirements and near-matched-filter (MF) operational complexity. Yet, theoretical arguments supported by numerical and simulation results included in this work suggest that the blind auxiliary-vector receiver compares favorably, both complexity-wise and performance-wise, to multiuser (MU) detectors such as the minimum output energy (MOE) and the decorrelating receiver (although the latter utilizes the assumed known spreading codes of all interfering users).

It is shown that the generalized Gaussian distribution maximizes the generalized Cramer-Rao (CR) bound for the pth absolute central moment of any classical location parameter unbiased estimator. The underlying maximization is taken over the class of distributions with fixed and finite pth-order moment and exhibits particular utility in minimax designs as well as in worst-case performance analysis. The relationship between the generalized Gaussian density and the generalized CR bound is further examined for the model of a mixture of generalized Gaussian distributions as well as for scenarios where multiple independent generalized Gaussian observations are involved.

Wireless physical-layer security is an emerging field of research aiming at preventing eavesdropping in an open wireless medium. In this paper, we propose a novel waveform design approach to minimize the likelihood that a message transmitted between trusted single-antenna nodes is intercepted by an eavesdropper. In particular, with knowledge first of the eavesdropper's channel state information (CSI), we find the optimum waveform and transmit energy that minimize the signal-to-interference-plus-noise ratio (SINR) at the output of the eavesdropper's maximum-SINR linear filter, while at the same time provide the intended receiver with a required pre-specified SINR at the output of its own max-SINR filter. Next, if prior knowledge of the eavesdropper's CSI is unavailable, we design a waveform that maximizes the amount of energy available for generating disturbance to eavesdroppers, termed artificial noise (AN), while the SINR of the intended receiver is maintained at the pre-specified level. The extensions of the secure waveform design problem to multiple intended receivers are also investigated and semidefinite relaxation (SDR) -an approximation technique based on convex optimization- is utilized to solve the arising NP-hard design problems. Extensive simulation studies confirm our analytical performance predictions and illustrate the benefits of the designed waveforms on securing single-input single-output (SISO) transmissions and multicasting.

We develop a novel waveform design approach to minimize the likelihood that a message transmitted wirelessly between trusted single-antenna nodes is intercepted by an eavesdropper. In particular, first, with knowledge of the eavesdropper's channel state information (CSI) we find the optimal waveform and transmit energy that minimize the signal-to-interference-plus-noise ratio (SINR) at the output of the eavesdropper's maximum-SINR linear filter, while at the same time provide the intended receiver with a required pre-specified SINR at the output of its own max-SINR filter. Next, if prior knowledge of the eavesdropper's CSI is unavailable, we design a waveform that maximizes the amount of energy available for generating disturbance to eavesdroppers, termed artificial noise (AN), while the SINR of the intended receiver is maintained at the pre-specified level. Simulation studies demonstrate our analytical developments and illustrate the benefits of the designed waveforms on securing single-input single-output (SISO) transmissions.

In this paper, we develop a methodology to analyze systematically the capacity of airborne MIMO wireless communication systems with linear transmit and receive antenna arrays. Our methodology is based on a general three-dimensional airborne MIMO communication model that we develop to accommodate arbitrary alignments of the transmit and receive antenna arrays. Specifically, we determine an upper bound and a lower bound for the airborne MIMO capacity. Then, we derive a necessary and sufficient condition for airborne MIMO communication systems to achieve the capacity upper bound. The necessary and sufficient condition allows us to properly select the system parameters and design airborne MIMO communication systems that reach the best possible performance in terms of system capacity. We prove that when the distance between the transmit and receive antenna arrays is within a certain range, there exists a set of system parameter values for which the capacity of the MIMO communication system achieves the theoretical upper bound and this capacity value is larger than the average capacity of the corresponding conventional MIMO communication system with Rayleigh fading. We also show that asymptotically, as the distance between the transmit and receive antenna arrays goes to infinity, the capacity of the airborne MIMO system converges to the capacity lower bound. Extensive numerical studies included in this paper illustrate and validate our theoretical developments.

In this paper, a penalty-based distributed algorithm is proposed to design beamforming for multi-source multi-destination (MSMD) networks. The objective of the beamforming design is to minimize the transmission power of each source under the constraint that the signal-to-interference-plus-noise ratio (SINR) requirements of all source-destination pairs are satisfied. The proposed beamforming design has better power-efficiency compared to the egoism, altruism, and balancing algorithms. The convergence of the proposed algorithm is also discussed and a sufficient condition is determined for the algorithm to converge to a Pareto optimal solution. Simulation results show that the proposed algorithm has higher probability of convergence compared to the egoism, altruism, and balancing algorithms, which is close to a centralized performance benchmark.

We consider the problem of joint admission control and resource allocation in a secondary code-division network coexisting with a narrow-band primary system. Our objective is to find the maximum number of admitted secondary links and then find the optimal transmitting powers and code sequences of those secondary links such that the total energy consumption of the secondary network is minimized subject to the conditions that primary interference temperature constraints, secondary Signal-to-Interference-plus-Noise Ratio (SINR) constraints and secondary peak power constraints are all satisfied. This is an NP-hard optimization problem which motivates the development of efficient suboptimal algorithms. We propose a novel iterative algorithm to solve this problem in a computationally efficient manner. Numerical results demonstrate that the proposed algorithm provides excellent solutions that result in large admitted percentage of secondary links and high energy efficiency.

We consider the problem of joint resource allocation and admission control in a secondary code-division network coexisting with a narrowband primary system. Our objective is to find the maximum number of admitted secondary links and then find the optimal transmitting powers and code sequences of those secondary links such that the total energy consumption of the secondary network is minimized subject to the conditions that primary interference temperature constraints, secondary signal-to-interference-plus-noise ratio (SINR) constraints and secondary peak power constraints are all satisfied. This is an NP-hard optimization problem which motivates the development of suboptimal algorithms. We propose a novel iterative algorithm to solve this problem in a computationally efficient manner. Numerical results demonstrate that the proposed algorithm provides excellent solutions that result in high energy efficiency and large admitted percentage of secondary links.

In this paper, we consider the problem of designing transceiver beamforming vectors for multi-source multi-destination (MSMD) wireless networks such that the transmission power of each source is minimized while the signal-to-interference plus noise ratio (SINR) requirements of all source-destination pairs are satisfied. We propose an efficient iterative algorithm to design the transceiver beamforming vectors and address the convergence of the algorithm. We determine a necessary condition as well as a sufficient condition for the algorithm to converge to a generalized Nash equilibrium solution. Especially, if each destination has only one antenna, we obtain a necessary and sufficient condition for the algorithm to converge to a unique generalized Nash equilibrium solution. Simulation results show that the proposed iterative algorithm has higher probability of convergence compared to an iterative waterfilling (IWF) approach. For example, for a system with three source-destination pairs and SINR requirement of 2dB, the probability of convergence is 84$\%$ with the proposed algorithm while it is only 66$\%$ with the IWF algorithm.

We study a framework that captures the interdependencies between spreading channelization and routing for a secondary multi-hop cognitive radio network that operates cognitively to coexist with primary users. We consider interactions between dynamic routing and code-division channelization functionalities for cognitive radio networks. With this respect, we study the effect of joint routing, spreading code and power allocation on the network throughput. A solution for joint routing and spread-spectrum channelization is proposed as a localized approximation of the throughput-maximizing problem. Specifically, power and spreading code are allocated to guarantee the quality of service of the on-going primary and secondary transmissions, while the routing algorithm dynamically selects relays based on the network traffic dynamics and on the achievable data rates on different secondary links.

We consider the problem of passive spread-spectrum steganalysis where the objective is to decide the presence or absence of spread-spectrum hidden data in a given image (a binary hypothesis testing problem). Unlike conventional feature-based approaches, we describe an unsupervised (blind) low-complexity approach based on generalized least-squares principles that may enable rapid high-volume image processing. Extensive experiments on image sets and comparisons with existing steganalysis techniques demonstrate most satisfactory classification performance measured in probability of correct detection versus induced false alarm rate.

We cover five general areas in this report: (1) design and optimize cooperative automatic-repeat-request (ARQ) relaying protocol for airborne networks; (2) optimize power assignment for hybrid ARQ protocols in airborne networks; (3) design and optimize differential cooperative relaying protocols for airborne networks; (4) design and optimize multi-source multi destination relaying schemes for airborne networks; and (5) design and optimize cognitive code-division channelization for airborne networks.

Two feedforward neural structures intended for binary hypothesis testing are considered. The first structure, FFS1, is a tandem structure, while the second structure, FFS2, involves cumulative feedforward feedback. Both parametric and robust designs for the two structures are considered and analyzed in terms of induced false alarm and power probabilities. The inferiority of the FFS1 is rigorously proved in terms of the rate with which the induced power probability increases with respect to the number of the neural elements. Asymptotic results are presented, as well as numerical results, with emphasis on the Gaussian and location parameter nominal hypotheses model. Learning algorithms for the parameter involved in the robust network designs are discussed as well. >

The problem of minimizing the number of interstreet crossings in single-row routing, known to be intractable in the sense of NP-completeness is studied. An efficient heuristic algorithm for solving this problem is presented. The worst-case computational complexity of the algorithm is O(mn+m/sup 3/), where n is the number of nodes and m >

In this paper, the total transmission power of a multi-source multi-destination relay network is minimized under the constraint that the signal to interference plus noise ratio (SINR) requirement of each source-destination pair is satisfied. The optimization problem involves $K$ power variables, where $K$ is the number of source-destination pairs in the network, and an exhaustive search is prohibitive for large $K$. In this work, we develop an asymptotically tight approximation of the SINR that allows us to reformulate the original optimization problem to a single-variable optimization problem, which can be easily solved by numerical search of the single variable. Then, the corresponding optimal transmission power at each source and relay can be calculated directly. The proposed optimization scheme is scalable and leads to a power assignment algorithm that exhibits the same optimization complexity for any number ($K$) of source-destination pairs in the network. Moreover, for the special case of transmission over orthogonal channels, we derive analytically the solution to the optimization problem. Extensive numerical studies illustrate our theoretical developments.

We propose new deterministic low-storage constructions of compressive sampling matrices based on classical finite-geometry generalized polygons. For the noiseless measurements case, we develop a novel exact-recovery algorithm for strictly sparse signals that utilizes the geometry properties of generalized polygons and exhibits complexity linear in the sparsity value. In the presence of measurement noise, recovery of the generalized-polygon sampled signals can be carried out effectively using a belief propagation algorithm.

A cognitive radio network with decentralized control (i.e., a cognitive ad hoc network) is considered in this demonstration. The demo implements a decentralized and localized algorithm for throughputmaximization through joint routing and interference-avoiding waveform selection. The algorithm adapts to time-varying traffic demands, interference profile, and network topology to locally maximize the achievable data rate while avoiding harmful interference to co-located primary or secondary users. The prototype is based on a cross-layer protocol stack implemented in Python, which leverages GNU Radio for adaptive signal generation on a USRP2 software-defined-radio platform.

A distributed joint routing and spectrum sharing algorithm for video streaming applications over cognitive radio ad hoc networks is proposed in this article. The proposed cross-layer control scheme dynamically allocates routes, spectrum and power to maximize the network throughput under the constraints posed by delay-sensitive video applications. The algorithm evaluates the expected delay of competing flows in single-hop and two-hop networks considering the time-varying spectrum condition and occupancy, traffic characteristics, and the condition of queues at intermediate nodes. Simulation results show that the proposed algorithm significantly reduces the packet loss rate and improves the average peak signal-to-noise ratio (PSNR) of the received video streams.

In this paper, a new analytical approach is developed for the evaluation of the outage probability of decode-and-forward (DF) automatic-repeat-request (ARQ) relaying under packet-rate fading (fast fading) channels. Based on this approach, a closed-form asymptotically tight (as SNR → ∞) approximation of the outage probability is derived, and the diversity order of the DF cooperative ARQ relay scheme is shown to be equal to 2L - 1, where L is the maximum number of ARQ retransmissions. The closed-form expression clearly shows that the achieved diversity is partially due to the DF cooperative relaying and partially due to the fast fading nature of the channels (temporal diversity). Numerical and simulation studies illustrate the theoretical developments.

In this paper, a new analytical approach is developed for the evaluation of the outage probability of decode-and-forward (DF) automatic-repeat-request (ARQ) relaying under packet-rate fading (fast fading) channels. Based on this approach, a closed-form asymptotically tight (as SNR →∞ ) approximation of the outage probability is de- rived, and the diversity order of th eD F cooperative ARQ relay scheme is shown to be equal to 2L − 1, where L is the maximum number of ARQ retransmissions. The closed-form expression clearly shows that the achieved diversity is partially due to the DF cooperative relaying and par- tially due to the fast fading nature of the channels (temporal diversity). Numerical and simulation studies illustrate the theoretical developments.

We derive new bounds on the total squared correlation (TSC) of quaternary (quadriphase) signature/sequence sets for all lengths L and set sizes K. Then, for all K, L, we design minimum-TSC optimal sets that meet the new bounds with equality. Direct numerical comparison with the TSC value of the recently obtained optimal binary sets shows under what K, L realizations gains are materialized by moving from the binary to the quaternary code-division multiplexing alphabet. On the other hand, comparison with the Welch TSC value for real/complex-field sets shows that, arguably, not much is to be gained by raising the alphabet size above four for any K, L.

We consider the problem of simultaneous power and code-channel allocation for a secondary transmitter/receiver pair coexisting with a primary code-division multiple-access (CDMA) system. We develop a novel computationally feasible solution using semidefinite programming. Simulation studies illustrate the theoretical developments.

For any given digital host image or audio file (or group of hosts) and any (block) transform domain of interest, we find an orthogonal set of signatures that achieves maximum sum-signal-to-interference-plus-noise ratio (sum- SINR) spread-spectrum message embedding for any fixed embedding amplitude values. We also find the sumcapacity optimal amplitude allocation scheme for any given total distortion budget under the assumption of (colored) Gaussian transform-domain host data. The practical implication of the results is sum-SINR, sumcapacity optimal multiuser/multisignature spread-spectrum data hiding in the same medium. Theoretically, the findings establish optimality of the recently presented Gkizeli-Pados-Medley multisignature eigen-design algorithm.

Derived from statistical conditional optimization criteria, the auxiliary-vector (AV) detection algorithm starts from the target vector and adding non-orthogonal auxiliary vector components generates an infinite sequence of tests that converges to the ideal matched filter (MF) processor for any positive definite input autocorrelation matrix. When the input autocorrelation matrix is replaced by a conventional sample-average estimate, the algorithm effectively generates a sequence of estimators of the ideal matched filter that offer exceptional bias/covariance balance for any given finite-size observation data record. In this work, the AV algorithm is evaluated on collected airborne phased-array radar data from the MCARM program and is seen to outperform in probability of detection (for any given false alarm rate) all known and tested adaptive detectors (for example AMF, generalized likelihood ratio test, the multistage Wiener filter algorithm, etc.).

In this paper we develop a novel iterative-least-squares-type method that identifies the population size of direct-sequence CDMA signal mixtures without any knowledge about the signatures of the active spread-spectrum signals in the system or the channel state and in the absence of pilot signaling (training sequence). Simulation studies demonstrate the performance of the proposed method and offer favorable comparisons with corresponding schemes based on the conventional Akaike information criterion, the minimum description length criterion, as well as their more recent modified versions.

We consider the problem of signature waveform design for code division multiplexing in wireless collaborative relay networks. In contract to conventional randomly chosen orthogonal codes, we develop an adaptive signature design strategy under the maximum predetection signal-to-interference-plus-noise-ratio (SINR) criterion. We evaluate numerically and by simulations the bit error rate (BER) and outage probability of cooperative wireless transmissions over code division multiplexing channels. Two basic decode-and-forward relay network architectures are considered and state-of-the-art performance is demonstrated.

We consider the problem of signature waveform design for code division medium-access-control (MAC) of wireless sensor networks (WSN). In contract to conventional randomly chosen orthogonal codes, an adaptive signature design strategy is developed under the maximum pre-detection SINR (signal to interference plus noise ratio) criterion. The proposed algorithm utilizes slowest descent cords of the optimization surface to move toward the optimum solution and exhibits, upon eigenvector decomposition, linear computational complexity with respect to signature length. Numerical and simulation studies demonstrate the performance of the proposed method and offer comparisons with conventional signature code sets.

When data symbols modulate a signature waveform/pattern to move across a channel in the presence of disturbance, as in common spread-spectrum (SS) or code-division multiple-access (CDMA) or multiuser ultra-wideband (UWB) communication systems, the signature/spreading code that maximizes the signal-to- interference-plus-noise ratio (SINR) at the output of the maximum SINR filter is the smallest-eigenvalue eigenvector of the disturbance autocovariance matrix. In digital communication systems, however, the signature alphabet is finite and digital signature optimization is NP-hard. In this paper, we present a formal search procedure of cost linear in the signature length that returns the maximum-SINR binary signature near lines of least SINR decrease in the Euclidean vector space. The quality of the proposed adaptive binary design is compared against the theoretical upper bound of the complex/real eigenvector maximizer and other known static and adaptive binary signature designs.

In this work, we develop a new second-order statistics based multiuser multipath channel estimation algorithm for uplink wireless space-time coded CDMA systems. The estimation procedure is based on the parameterization, with respect to the multiuser channel response vectors, of the received data covariance matrix. As a side result, we also obtain an improved covariance matrix estimator. Then, we utilize both the channel and the covariance matrix estimates to obtain an estimate of the linear MMSE receiver. Simulation studies illustrate the performance improvements of the proposed estimators relative to existing methods in terms of channel estimation mean-square error as well as receiver filter output SINR and receiver BER.

We consider the problem of extraction on wireless direct-sequence code-division multiple-access (DS-CDMA) systems. We develop an iterative least-squares-type algorithm that extracts the information symbols of the multiple concurrent DS-CDMA users and assumes no knowledge of either the signature waveforms or the channel state. The proposed scheme utilizes a novel initialization step that is based on auxiliary-vector (AV) subspace decomposition. Simulation studies evaluate the performance of the proposed extraction procedure in realistic data limited environments and show that, for observation records of sufficient length, information data extraction can be achieved with probability of error rather close to what may be attained with known signatures and channel state. *Corresponding author.

When data symbols modulate a signature waveform /pattern to move across a channel in the presence of disturbance, the signature/spreading-code that maximizes the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR filter is the smallest-eigenvalue eigenvector of the disturbance autocovariance matrix. In digital communication systems, however, the signature alphabet is finite and digital signature optimization is NP-hard. In this paper, we will present a formal search procedure of cost linear in the signature code length that returns the maximum-SINR binary signature near cords of least SINR decrease in the Euclidean vector space. The quality of the proposed adaptive binary design will be compared against the theoretical upper bound of the complex/real eigenvector maximizer.

We propose an iterative generalized least squares procedure to recover unknown messages hidden in image hosts via spread-spectrum embedding. Neither the original host nor the embedding signature is assumed available. We demonstrate that for hidden messages of sufficient length (data sample support), recovery can be achieved with probability of error close to what may be attained with known embedding signature and known original host autocorrelation matrix. For small hidden messages, the signature estimate calculated by the iterative generalized least squares procedure can be fed as initial value to a (computationally costly) expectation-maximization signature identification scheme that we derive. Message recovery can again be carried out successfully by means of a linear sample-matrix-inversion minimum-mean-square-error receiver.

We consider a wireless downlink multiuser multiple-input single-output (MISO) system and we investigate the problem of jointly optimal beamforming and power allocation. In particular, under a total transmit power budget constraint and either exact or second-order statistics knowledge of the channel of all users we find the optimum set of beam vectors and associated power values such that the sum-rate between the base station and the end users is maximized. Then, we formulate and solve two variations of the above optimization problem as a low and high signal-to-interference-plus-noise ratio (SINR) approximation, respectively. To solve all optimization problems, we invoke the theory of uplink-downlink duality and derive computationally simple, coupled iterative procedures with guaranteed convergence

The objective of this work is the development of receiver designs for wireless CDMA communications with a special emphasis given to high-dimensional adaptive processing under small sample support. In this work, we propose a quadratically-constrained MVDR/MMSE filter in an effort to achieve superior filtering performance under limited sample support adaptations. The quadratically-constrained MVDR/MMSE filter estimator utilizes a regularized estimate of the input covariance matrix that is given as a convex combination of the sample-average covariance matrix and a scalar multiple of its inverse. Selection methods based on the limited sample support are proposed for both the inverse matrix multiplicative scalar as well as the regularization parameter, and they can be applied more generally to other adaptive filtering techniques such as selection of the diagonally-loaded MVDR parameter.

In this work, we develop a new second-order statistics based multiuser multipath channel estimation algorithm for uplink wireless space-time coded CDMA systems. The estimation procedure is based on the parameterization, with respect to the multiuser channel response vector, of the received data covariance matrix. As a side result, we also obtain an improved covariance matrix estimator. Then we utilize both the channel and the covariance matrix estimates to obtain an estimate of the linear MMSE receiver. Simulation studies illustrate the performance improvements of the proposed estimators relative to existing methods in terms of channel estimation mean-square error as well as receiver filter output SINR and receiver BER.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We consider a multilayer perceptron neural network (NN) receiver architecture for the recovery of the information bits of a direct-sequence code-division-multiple-access (DS-CDMA) user. We develop a fast converging adaptive training algorithm that minimizes the bit-error rate (BER) at the output of the receiver. The adaptive algorithm has three key features: i) it incorporates the BER, i.e., the ultimate performance evaluation measure, directly into the learning process, ii) it utilizes constraints that are derived from the properties of the optimum single-user decision boundary for additive white Gaussian noise (AWGN) multiple-access channels, and iii) it embeds importance sampling (IS) principles directly into the receiver optimization process. Simulation studies illustrate the BER performance of the proposed scheme.

n this paper, we consider the transmission of video over wireless direct-sequence code-division multiple access (DS-CDMA) channels. A layered (scalable) video source codec is used and each layer is transmitted over a different CDMA channel. Spreading codes with different lengths are allowed for each CDMA channel (multirate CDMA). Thus, a different number of chips per bit can be used for the transmission of each scalable layer. For a given fixed energy value per chip and chip rate, the selection of a spreading code length affects the transmitted energy per bit and bit rate for each scalable layer. An MPEG-4 source encoder is used to provide a two-layer SNR scalable bitstream. Each of the two layers is channel-coded using Rate-Compatible Punctured Convolutional (RCPC) codes. Then, the data are interleaved, spread, carrier-modulated and transmitted over the wireless channel. A multipath Rayleigh fading channel is assumed. At the other end, we assume the presence of an antenna array receiver. After carrier demodulation, multiple-access-interference suppressing despreading is performed using space-time auxiliary vector (AV) filtering. The choice of the AV receiver is dictated by realistic channel fading rates that limit the data record available for receiver adaptation and redesign. Indeed, AV filter short-data-record estimators have been shown to exhibit superior bit-error-rate performance in comparison with LMS, RLS, SMI, or 'multistage nested Wiener' adaptive filter implementations. Our experimental results demonstrate the effectiveness of multirate DS-CDMA systems for wireless video transmission.

The value of bottom-up and robust neural network designs is demonstrated, as well as the performance superiority of recurrent neural structures over feedforward neural architectures. Along these lines, two neural structures are considered, one feedforward and one recurrent, whose objective is binary hypothesis testing. The first, FFS1, is a tandem feedforward structure, whereas the second, FFS2, is recurrent and involves cumulative forward feedback. Both parametric and robust designs for the two structures are considered and analyzed in terms of induced false alarm and power probabilities, and the inferiority of the FFS1 is rigorously proven in terms of the rate with which the induced power probability increases with the number of the neural elements. Asymptotic results are presented, as well as numerical results, with emphasis on the Gaussian and location parameter nominal hypotheses model, that exhibit the superiority of the robust designs clearly. Learning algorithms for the parameters involved in the robust network designs are also discussed. >

The conventional matched filter (MF) correlation receiver for direct sequence spread spectrum transmissions is reconsidered from the theoretical point of view of robust distributed detection (decision fusion). Separate chip-by-chip decisions on the transmitted information bit of interest are generated. The individual decisions are then optimally fused to produce the final decision on the transmitted bit. It is proven that the majority-vote strategy is the statistically optimal fusion rule for additive white Gaussian noise channels. An exact closed form expression is given for the probability of error induced by the majority-vote receiver. It is shown that the performance is independent of the identity of the active interfering user population, that is signatures and signature cross-correlations, in DS-CDMA environments. This perfect robustness with respect to the identity of the multiuser interference component is coupled by significant resistance toward occasional high-power jamming (equivalently occasional natural severe channel imperfections) or outlier-prone background noise, or both. Some numerical studies illustrate these theoretical findings.

A new blind FIR filter receiver is proposed for the detection of DS-CDMA signals in unknown multi-user interference (MUI) and additive white Gaussian noise. The proposed receiver is motivated by the Wiener signal reconstruction theory and it is a very low complexity alternative to the minimum-output-energy (MOE) receiver. At only a minimal increase of the computational cost, the proposed detector outperforms significantly the conventional matched filter (MF) receiver. It also compares favorably to the decorrelating detector (with similar near-far resistance), despite the fact that the latter utilizes the assumed known MUI spreading codes. The novel characteristic of the proposed receiver is the incorporation of an auxiliary vector component that allows statistically optimal, adaptive steering of the filter with respect to the incoming DS-CDMA signal.

The conventional DS/CDMA signature-matched correlation receiver is supplied with an orthonormal auxiliary vector component and the overall scheme is supported by adaptive scalar minimum BER steering. Within this class, the filter converges with probability 1 (w.p. 1) to the design that minimizes the induced probability of error.

We extend our previous work on joint source-channel coding to scalable video transmission over wireless direct-sequence code-division-multiple-access (DS-CDMA) multipath fading channels. A SNR scalable video coder is used and unequal error protection (UEP) is allowed for each scalable layer. At the receiver-end an adaptive antenna array auxiliary-vector (AV) filter is utilized that provides space-time RAKE-type processing and multiple-access interference suppression. The choice of the AV receiver is dictated by realistic channel fading rates that limit the data record available for receiver adaptation and redesign. Our problem is to allocate the available bit rate of the user of interest between source and channel coding and across scalable layers, while minimizing the end-to-end distortion of the received video sequence. The optimization algorithm that we propose utilizes universal rate-distortion characteristic curves that show the contribution of each layer to the total distortion as a function of the source rate of the layer and the residual bit error rate (the error rate after channel coding). These plots can be approximated using appropriate functions to reduce the computational complexity of the solution.

We investigate the relative output SINR performance of two linear multiuser detectors - the full decorrelator and the partial decorrelator. For each detector, we consider two implementations that are equivalent under perfectly known input statistics. The first implementation utilizes the signature matrix while the second implementation is based on the eigendecomposition of the ideal input covariance matrix. While the full decorrelator aims at decorrelating the complete multiple-access-interference, the partial decorrelator aims at decorrelating only a part of it by excluding one or more user-signatures or eigenvectors from the corresponding implementation method. We derive necessary and sufficient conditions on the signal energy and signature cross-correlation levels under which the partial decorrelator outperforms the full decorrelator in the output SINR sense. Numerical results demonstrate the validity of the above conditions and simulation studies illustrate the relative SINR and BER performance of the full and partial decorrelators under sample-average-estimated input statistics.

We consider blind adaptive linear receivers for the demodulation of DS/CDMA signals in asynchronous transmissions. The proposed structures are self-synchronized in the sense that adaptive synchronization and demodulation are viewed and treated as an integrated receiver operation. Two computationally efficient combined synchronization/demodulation schemes are proposed, developed and analyzed. The first scheme is based on the principles of minimum-variance-distortionless-response (MVDR) processing, while the second scheme follows the principles of auxiliary-vector filtering and exhibits enhanced performance in short data record scenarios. The coarse synchronization performance of combined synchronization/demodulation receivers under finite data record adaptation is also investigated. Analytic expressions are derived that approximate closely the probability of coarse synchronization error of the conventional correlator and the MVDR type combined synchronization/demodulation scheme and provide low cost highly accurate alternatives to the computationally demanding performance evaluation through simulations.